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1.
  • Karlson, Martin, et al. (författare)
  • Tree Crown Mapping in Managed Woodlands (Parklands) of Semi-Arid West Africa Using WorldView-2 Imagery and Geographic Object Based Image Analysis
  • 2014
  • Ingår i: Sensors. - Basel : MDPI AG. - 1424-8220 .- 1424-3210. ; 14:12, s. 22643-22669
  • Tidskriftsartikel (refereegranskat)abstract
    • Detailed information on tree cover structure is critical for research and monitoring programs targeting African woodlands, including agroforestry parklands. High spatial resolution satellite imagery represents a potentially effective alternative to field-based surveys, but requires the development of accurate methods to automate information extraction. This study presents a method for tree crown mapping based on Geographic Object Based Image Analysis (GEOBIA) that use spectral and geometric information to detect and delineate individual tree crowns and crown clusters. The method was implemented on a WorldView-2 image acquired over the parklands of Saponé, Burkina Faso, and rigorously evaluated against field reference data. The overall detection rate was 85.4% for individual tree crowns and crown clusters, with lower accuracies in areas with high tree density and dense understory vegetation. The overall delineation error (expressed as the difference between area of delineated object and crown area measured in the field) was 45.6% for individual tree crowns and 61.5% for crown clusters. Delineation accuracies were higher for medium (35–100 m2) and large (?100 m2) trees compared to small (
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2.
  • Piikki, Kristin, et al. (författare)
  • Performance Evaluation of Proximal Sensors for Soil Assessment in Smallholder Farms in Embu County, Kenya
  • 2016
  • Ingår i: Sensors. - : MDPI AG. - 1424-8220 .- 1424-3210. ; 16
  • Tidskriftsartikel (refereegranskat)abstract
    • Four proximal soil sensors were tested at four smallholder farms in Embu County, Kenya: a portable X-ray fluorescence sensor (PXRF), a mobile phone application for soil color determination by photography, a dual-depth electromagnetic induction (EMI) sensor, and a LED-based soil optical reflectance sensor. Measurements were made at 32-43 locations at each site. Topsoil samples were analyzed for plant-available nutrients (N, P, K, Mg, Ca, S, B, Mn, Zn, Cu, and Fe), pH, total nitrogen (TN) and total carbon (TC), soil texture, cation exchange capacity (CEC), and exchangeable aluminum (Al). Multivariate prediction models of each of the lab-analyzed soil properties were parameterized for 576 sensor-variable combinations. Prediction models for K, N, Ca and S, B, Zn, Mn, Fe, TC, Al, and CEC met the setup criteria for functional, robust, and accurate models. The PXRF sensor was the sensor most often included in successful models. We concluded that the combination of a PXRF and a portable soil reflectance sensor is a promising combination of handheld soil sensors for the development of in situ soil assessments as a field-based alternative or complement to laboratory measurements.
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3.
  • Tasin, Marco (författare)
  • Fast Direct Injection Mass-Spectrometric Characterization of Stimuli for Insect Electrophysiology by Proton Transfer Reaction-Time of Flight Mass-Spectrometry (PTR-ToF-MS)
  • 2012
  • Ingår i: Sensors. - : MDPI AG. - 1424-8220 .- 1424-3210. ; 12, s. 4091-4104
  • Tidskriftsartikel (refereegranskat)abstract
    • Electrophysiological techniques are used in insect neuroscience to measure the response of olfactory neurons to volatile odour stimuli. Widely used systems to deliver an olfactory stimulus to a test insect include airstream guided flow through glass cartridges loaded with a given volatile compound on a sorbent support. Precise measurement of the quantity of compound reaching the sensory organ of the test organism is an urgent task in insect electrophysiology. In this study we evaluated the performances of the recent realised proton transfer reaction-time of flight mass-spectrometry (PTR-ToF-MS) as a fast and selective gas sensor. In particular, we characterised the gas emission from cartridges loaded with a set of volatile compounds belonging to different chemical classes and commonly used in electrophysiological experiments. PTR-ToF-MS allowed a fast monitoring of all investigated compounds with sufficient sensitivity and time resolution. The detection and the quantification of air contaminants and solvent or synthetic standards impurities allowed a precise quantification of the stimulus exiting the cartridge. The outcome of this study was twofold: on one hand we showed that PTR-ToF-MS allows monitoring fast processes with high sensitivity by real time detection of a broad number of compounds; on the other hand we provided a tool to solve an important issue in insect electrophysiology.
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4.
  • Zamaratskaia, Galia, et al. (författare)
  • EROD and MROD as Markers of Cytochrome P450 1A Activities in Hepatic Microsomes from Entire and Castrated Male Pigs
  • 2009
  • Ingår i: Sensors. - : MDPI AG. - 1424-8220 .- 1424-3210. ; 9, s. 2134-2147
  • Tidskriftsartikel (refereegranskat)abstract
    • In the present study, we characterized the kinetic parameters of 7-ethoxyresorufin O-deethylation (EROD) and 7-methoxyresorufin O-demethylation (MROD) in hepatic microsomes from entire and castrated male pigs. Validation parameters of an HPLC-based method to analyse EROD and MROD activities are also described. Eadie-Hofstee plot analysis demonstrated a biphasic kinetic of EROD, indicating that at least two forms of cytochrome P450 are involved in this reaction. MROD followed monophasic kinetic, suggesting that a single enzyme, or enzymes with similar affinities, is responsible for the reaction. Inhibitory effects of alpha-naphthoflavone (ANF), ellipticine and furafylline were studied using microsomes from entire and castrated male pigs. ANF is a known inhibitor of both cytochrome P(450) 1A1 and 1A2 (CYP1A1 and CYP1A2); the presence of ANF in the incubations resulted in the inhibition of both EROD and MROD activities in porcine liver microsomes. EROD activities in porcine liver microsomes were also inhibited by selective CYP1A1 inhibitor ellipticine, but not by CYP1A2 inhibitor furafylline. MROD activities were strongly inhibited by ellipticine and to a much lesser extent by furafylline. Further studies are needed to evaluate substrate specificities of porcine CYP1A1 and CYP1A2.
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5.
  • Bosona, Techane, et al. (författare)
  • The role of blockchain technology in promoting traceability systems in agri-food production and supply chains
  • 2023
  • Ingår i: Sensors. - 1424-8220 .- 1424-3210. ; 23
  • Forskningsöversikt (refereegranskat)abstract
    • Due to recurring food quality and safety issues, growing segments of consumers, especially in developed markets, and regulators in agri-food supply chains (AFSCs) require a fast and trustworthy system to retrieve necessary information on their food products. With the existing centralized traceability systems used in AFSCs, it is difficult to acquire full traceability information, and there are risks of information loss and data tampering. To address these challenges, research on the application of blockchain technology (BCT) for traceability systems in the agri-food sector is increasing, and startup companies have emerged in recent years. However, there have been only a limited number of reviews on the application of BCT in the agriculture sector, especially those that focus on the BCT-based traceability of agricultural goods. To bridge this knowledge gap, we reviewed 78 studies that integrated BCT into traceability systems in AFSCs and additional relevant papers, mapping out the main types of food traceability information. The findings indicated that the existing BCT-based traceability systems focus more on fruit and vegetables, meat, dairy, and milk. A BCT-based traceability system enables one to develop and implement a decentralized, immutable, transparent, and reliable system in which process automation facilitates the monitoring of real-time data and decision-making activities. We also mapped out the main traceability information, key information providers, and challenges and benefits of the BCT-based traceability systems in AFSCs. These helped to design, develop, and implement BCT-based traceability systems, which, in turn, will contribute to the transition to smart AFSC systems. This study comprehensively illustrated that implementing BCT-based traceability systems also has important, positive implications for improving AFSC management, e.g., reductions in food loss and food recall incidents and the achievement of the United Nations SDGs (1, 3, 5, 9, 12). This will contribute to existing knowledge and be useful for academicians, managers, and practitioners in AFSCs, as well as policymakers.
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6.
  • Mcmurray, S., et al. (författare)
  • A Study on ML-Based Software Defect Detection for Security Traceability in Smart Healthcare Applications
  • 2023
  • Ingår i: Sensors. - : MDPI. - 1424-8220 .- 1424-3210. ; 23:7
  • Tidskriftsartikel (refereegranskat)abstract
    • Software Defect Prediction (SDP) is an integral aspect of the Software Development Life-Cycle (SDLC). As the prevalence of software systems increases and becomes more integrated into our daily lives, so the complexity of these systems increases the risks of widespread defects. With reliance on these systems increasing, the ability to accurately identify a defective model using Machine Learning (ML) has been overlooked and less addressed. Thus, this article contributes an investigation of various ML techniques for SDP. An investigation, comparative analysis and recommendation of appropriate Feature Extraction (FE) techniques, Principal Component Analysis (PCA), Partial Least Squares Regression (PLS), Feature Selection (FS) techniques, Fisher score, Recursive Feature Elimination (RFE), and Elastic Net are presented. Validation of the following techniques, both separately and in combination with ML algorithms, is performed: Support Vector Machine (SVM), Logistic Regression (LR), Naïve Bayes (NB), K-Nearest Neighbour (KNN), Multilayer Perceptron (MLP), Decision Tree (DT), and ensemble learning methods Bootstrap Aggregation (Bagging), Adaptive Boosting (AdaBoost), Extreme Gradient Boosting (XGBoost), Random Forest(RF), and Generalized Stacking (Stacking). Extensive experimental setup was built and the results of the experiments revealed that FE and FS can both positively and negatively affect performance over the base model or Baseline. PLS, both separately and in combination with FS techniques, provides impressive, and the most consistent, improvements, while PCA, in combination with Elastic-Net, shows acceptable improvement.
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7.
  • Ma, Christina Zong-Hao, et al. (författare)
  • Towards Wearable Comprehensive Capture and Analysis of Skeletal Muscle Activity during Human Locomotion
  • 2019
  • Ingår i: Sensors. - Basel, Switzerland : MDPI. - 1424-8220. ; 19:1
  • Tidskriftsartikel (refereegranskat)abstract
    • Background: Motion capture and analyzing systems are essential for understanding locomotion. However, the existing devices are too cumbersome and can be used indoors only. A newly-developed wearable motion capture and measurement system with multiple sensors and ultrasound imaging was introduced in this study. Methods: In ten healthy participants, the changes in muscle area and activity of gastrocnemius, plantarflexion and dorsiflexion of right leg during walking were evaluated by the developed system and the Vicon system. The existence of significant changes in a gait cycle, comparison of the ankle kinetic data captured by the developed system and the Vicon system, and test-retest reliability (evaluated by the intraclass correlation coefficient, ICC) in each channel’s data captured by the developed system were examined. Results: Moderate to good test-retest reliability of various channels of the developed system (0.512 ≤ ICC ≤ 0.988, p < 0.05), significantly high correlation between the developed system and Vicon system in ankle joint angles (0.638R ≤ 0.707, p < 0.05), and significant changes in muscle activity of gastrocnemius during a gait cycle (p < 0.05) were found. Conclusion: A newly developed wearable motion capture and measurement system with ultrasound imaging that can accurately capture the motion of one leg was evaluated in this study, which paves the way towards real-time comprehensive evaluation of muscles and joint motions during different activities in both indoor and outdoor environments.
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8.
  • Sodhro, Ali Hassan, 1986-, et al. (författare)
  • An Energy-Efficient Algorithm for Wearable Electrocardiogram Signal Processing in Ubiquitous  Healthcare Applications
  • 2018
  • Ingår i: Sensors. - : MDPI AG. - 1424-8220. ; 18:3, s. 923-923
  • Tidskriftsartikel (refereegranskat)abstract
    • Rapid progress and emerging trends in miniaturized medical devices have enabled the un-obtrusive monitoring of physiological signals and daily activities of everyone’s life in a prominent and pervasive manner. Due to the power-constrained nature of conventional wearable sensor devices during ubiquitous sensing (US), energy-efficiency has become one of the highly demanding and debatable issues in healthcare. This paper develops a single chip-based wearable wireless electrocardiogram (ECG) monitoring system by adopting analog front end (AFE) chip model ADS1292R from Texas Instruments. The developed chip collects real-time ECG data with two adopted channels for continuous monitoring of human heart activity. Then, these two channels and the AFE are built into a right leg drive right leg drive (RLD) driver circuit with lead-off detection and medical graded test signal. Human ECG data was collected at 60 beats per minute (BPM) to 120 BPM with 60 Hz noise and considered throughout the experimental set-up. Moreover, notch filter (cutoff frequency 60 Hz), high-pass filter (cutoff frequency 0.67 Hz), and low-pass filter (cutoff frequency 100 Hz) with cut-off frequencies of 60 Hz, 0.67 Hz, and 100 Hz, respectively, were designed with bilinear transformation for rectifying the power-line noise and artifacts while extracting real-time ECG signals. Finally, a transmission power control-based energy-efficient (ETPC) algorithm is proposed, implemented on the hardware and then compared with the several conventional TPC methods. Experimental results reveal that our developed chip collects real-time ECG data efficiently, and the proposed ETPC algorithm achieves higher energy savings of 35.5% with a slightly larger packet loss ratio (PLR) as compared to conventional TPC (e.g., constant TPC, Gao’s, and Xiao’s methods).
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9.
  • Abtahi, Farhad, et al. (författare)
  • Biosignal PI, an Affordable Open-Source ECG and Respiration Measurement System
  • 2014
  • Ingår i: Sensors. - : MDPI AG. - 1424-8220. ; 15:1, s. 93-109
  • Tidskriftsartikel (refereegranskat)abstract
    • Bioimedical pilot projects e.g., telemedicine, homecare, animal and human trials usually involve several physiological measurements. Technical development of these projects is time consuming and in particular costly. A versatile but affordable biosignal measurement platform can help to reduce time and risk while keeping the focus on the important goal and making an efficient use of resources. In this work, an affordable and open source platform for development of physiological signals is proposed. As a first step an 8–12 leads electrocardiogram (ECG) and respiration monitoring system is developed. Chips based on iCoupler technology have been used to achieve electrical isolation as required by IEC 60601 for patient safety. The result shows the potential of this platform as a base for prototyping compact, affordable, and medically safe measurement systems. Further work involves both hardware and software development to develop modules. These modules may require development of front-ends for other biosignals or just collect data wirelessly from different devices e.g., blood pressure, weight, bioimpedance spectrum, blood glucose, e.g., through Bluetooth. All design and development documents, files and source codes will be available for non-commercial use through project website, BiosignalPI.org.
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10.
  • Gautam, Sumit, et al. (författare)
  • Hybrid Active-and-Passive Relaying Model for 6G-IoT Greencom Networks with SWIPT
  • 2021
  • Ingår i: Sensors. - : MDPI AG. - 1424-8220. ; 21:18
  • Tidskriftsartikel (refereegranskat)abstract
    • In order to support a massive number of resource-constrained Internet-of-Things (IoT) devices and machine-type devices, it is crucial to design a future beyond 5G/6G wireless networks in an energy-efficient manner while incorporating suitable network coverage expansion methodologies. To this end, this paper proposes a novel two-hop hybrid active-and-passive relaying scheme to facilitate simultaneous wireless information and power transfer (SWIPT) considering both time-switching (TS) and power-splitting (PS) receiver architectures, while dynamically modelling the involved dual-hop time-period (TP) metric. An optimization problem is formulated to jointly optimize the throughput, harvested energy, and transmit power of a SWIPT-enabled system with the proposed hybrid scheme. In this regard, we provide two distinct ways to obtain suitable solutions based on the Lagrange dual technique and Dinkelbach method assisted convex programming, respectively, where both the approaches yield an appreciable solution within polynomial computational time. The experimental results are obtained by directly solving the primal problem using a non-linear optimizer. Our numerical results in terms of weighted utility function show the superior performance of the proposed hybrid scheme over passive repeater-only and active relay-only schemes, while also depicting their individual performance benefits over the corresponding benchmark SWIPT systems with the fixed-TP.
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11.
  • Kristoffersson, Annica, 1980-, et al. (författare)
  • A Systematic Review on the Use of Wearable Body Sensors for Health Monitoring : A Qualitative Synthesis
  • 2020
  • Ingår i: Sensors. - Basel, Switzerland : MDPI AG. - 1424-8220. ; 20:5
  • Forskningsöversikt (refereegranskat)abstract
    • The use of wearable body sensors for health monitoring is a quickly growing field with the potential of offering a reliable means for clinical and remote health management. This includes both real-time monitoring and health trend monitoring with the aim to detect/predict health deterioration and also to act as a prevention tool. The aim of this systematic review was to provide a qualitative synthesis of studies using wearable body sensors for health monitoring. The synthesis and analysis have pointed out a number of shortcomings in prior research. Major shortcomings are demonstrated by the majority of the studies adopting an observational research design, too small sample sizes, poorly presented, and/or non-representative participant demographics (i.e., age, gender, patient/healthy). These aspects need to be considered in future research work.
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12.
  • Kristoffersson, Annica, 1980-, et al. (författare)
  • Performance and Characteristics of Wearable Sensor Systems Discriminating and Classifying Older Adults According to Fall Risk: A Systematic Review
  • 2021
  • Ingår i: Sensors. - : MDPI AG. - 1424-8220. ; 21:17
  • Forskningsöversikt (refereegranskat)abstract
    • Sensor-based fall risk assessment (SFRA) utilizes wearable sensors for monitoring individuals’ motions in fall risk assessment tasks. Previous SFRA reviews recommend methodological improvements to better support the use of SFRA in clinical practice. This systematic review aimed to investigate the existing evidence of SFRA (discriminative capability, classification performance) and methodological factors (study design, samples, sensor features, and model validation) contributing to the risk of bias. The review was conducted according to recommended guidelines and 33 of 389 screened records were eligible for inclusion. Evidence of SFRA was identified: several sensor features and three classification models differed significantly between groups with different fall risk (mostly fallers/non-fallers). Moreover, classification performance corresponding the AUCs of at least 0.74 and/or accuracies of at least 84% were obtained from sensor features in six studies and from classification models in seven studies. Specificity was at least as high as sensitivity among studies reporting both values. Insufficient use of prospective design, small sample size, low in-sample inclusion of participants with elevated fall risk, high amounts and low degree of consensus in used features, and limited use of recommended model validation methods were identified in the included studies. Hence, future SFRA research should further reduce risk of bias by continuously improving methodology.
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13.
  • Lavassani, Mehrzad, et al. (författare)
  • Combining Fog Computing with Sensor Mote Machine Learning for Industrial IoT
  • 2018
  • Ingår i: Sensors. - : MDPI. - 1424-8220. ; 18:5
  • Tidskriftsartikel (refereegranskat)abstract
    • Digitalization is a global trend becoming ever more important to our connected and sustainable society. This trend also affects industry where the Industrial Internet of Things is an important part, and there is a need to conserve spectrum as well as energy when communicating data to a fog or cloud back-end system. In this paper we investigate the benefits of fog computing by proposing a novel distributed learning model on the sensor device and simulating the data stream in the fog, instead of transmitting all raw sensor values to the cloud back-end. To save energy and to communicate as few packets as possible, the updated parameters of the learned model at the sensor device are communicated in longer time intervals to a fog computing system. The proposed framework is implemented and tested in a real world testbed in order to make quantitative measurements and evaluate the system. Our results show that the proposed model can achieve a 98% decrease in the number of packets sent over the wireless link, and the fog node can still simulate the data stream with an acceptable accuracy of 97%. We also observe an end-to-end delay of 180 ms in our proposed three-layer framework. Hence, the framework shows that a combination of fog and cloud computing with a distributed data modeling at the sensor device for wireless sensor networks can be beneficial for Industrial Internet of Things applications.
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14.
  • Ourique de Morais, Wagner, 1979-, et al. (författare)
  • Active In-Database Processing to Support Ambient Assisted Living Systems
  • 2014
  • Ingår i: Sensors. - Basel : Multidisciplinary Digital Publishing Institute AG. - 1424-8220. ; 14:8, s. 14765-14785
  • Tidskriftsartikel (refereegranskat)abstract
    • As an alternative to the existing software architectures that underpin the development of smart homes and ambient assisted living (AAL) systems, this work presents a database-centric architecture that takes advantage of active databases and in-database processing. Current platforms supporting AAL systems use database management systems (DBMSs) exclusively for data storage. Active databases employ database triggers to detect and react to events taking place inside or outside of the database. DBMSs can be extended with stored procedures and functions that enable in-database processing. This means that the data processing is integrated and performed within the DBMS. The feasibility and flexibility of the proposed approach were demonstrated with the implementation of three distinct AAL services. The active database was used to detect bed-exits and to discover common room transitions and deviations during the night. In-database machine learning methods were used to model early night behaviors. Consequently, active in-database processing avoids transferring sensitive data outside the database, and this improves performance, security and privacy. Furthermore, centralizing the computation into the DBMS facilitates code reuse, adaptation and maintenance. These are important system properties that take into account the evolving heterogeneity of users, their needs and the devices that are characteristic of smart homes and AAL systems. Therefore, DBMSs can provide capabilities to address requirements for scalability, security, privacy, dependability and personalization in applications of smart environments in healthcare.
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15.
  • Seoane, Fernando, et al. (författare)
  • Wearable Biomedical Measurement Systems for Assessment of Mental Stress of Combatants in Real Time
  • 2014
  • Ingår i: Sensors. - : MDPI AG. - 1424-8220. ; 14:4, s. 7120-7141
  • Tidskriftsartikel (refereegranskat)abstract
    • The Spanish Ministry of Defense, through its Future Combatant program, has sought to develop technology aids with the aim of extending combatants' operational capabilities. Within this framework the ATREC project funded by the Coincidente program aims at analyzing diverse biometrics to assess by real time monitoring the stress levels of combatants. This project combines multidisciplinary disciplines and fields, including wearable instrumentation, textile technology, signal processing, pattern recognition and psychological analysis of the obtained information. In this work the ATREC project is described, including the different execution phases, the wearable biomedical measurement systems, the experimental setup, the biomedical signal analysis and speech processing performed. The preliminary results obtained from the data analysis collected during the first phase of the project are presented, indicating the good classification performance exhibited when using features obtained from electrocardiographic recordings and electrical bioimpedance measurements from the thorax. These results suggest that cardiac and respiration activity offer better biomarkers for assessment of stress than speech, galvanic skin response or skin temperature when recorded with wearable biomedical measurement systems.
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16.
  • Xu, Ye, et al. (författare)
  • System Implementation Trade-Offs for Low-Speed Rotational Variable Reluctance Energy Harvesters
  • 2021
  • Ingår i: Sensors. - Basel, Switzerland : MDPI. - 1424-8220. ; 21:18
  • Tidskriftsartikel (refereegranskat)abstract
    • Low-power energy harvesting has been demonstrated as a feasible alternative for the power supply of next-generation smart sensors and IoT end devices. In many cases, the output of kinetic energy harvesters is an alternating current (AC) requiring rectification in order to supply the electronic load. The rectifier design and selection can have a considerable influence on the energy harvesting system performance in terms of extracted output power and conversion losses. This paper presents a quantitative comparison of three passive rectifiers in a low-power, low-voltage electromagnetic energy harvesting sub-system, namely the full-wave bridge rectifier (FWR), the voltage doubler (VD), and the negative voltage converter rectifier (NVC). Based on a variable reluctance energy harvesting system, we investigate each of the rectifiers with respect to their performance and their effect on the overall energy extraction. We conduct experiments under the conditions of a low-speed rotational energy harvesting application with rotational speeds of 5rpm–20rpm, and verify the experiments in an end-to-end energy harvesting evaluation. Two performance metrics—power conversion efficiency (PCE) and power extraction efficiency (PEE)—are obtained from the measurements to evaluate the performance of the system implementation adopting each of the rectifiers. The results show that the FWR with PEEs of 20 % at 5 rpm to 40 % at 20 rpm has a low performance in comparison to the VD (40–60 %) and NVC (20–70 %) rectifiers. The VD-based interface circuit demonstrates the best performance under low rotational speeds, whereas the NVC outperforms the VD at higher speeds (>18 rpm). Finally, the end-to-end system evaluation is conducted with a self-powered rpm sensing system, which demonstrates an improved performance with the VD rectifier implementation reaching the system’s maximum sampling rate (40 Hz) at a rotational speed of approximately 15.5 rpm. 
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17.
  • Aghaeinezhadfirouzja, Saeid, et al. (författare)
  • Practical 3-D beam pattern based channel modeling for multi-polarized massive MIMO systems
  • 2018
  • Ingår i: Sensors. - : MDPI AG. - 1424-8220. ; 18:4
  • Tidskriftsartikel (refereegranskat)abstract
    • In this paper, a practical non-stationary three-dimensional (3-D) channel models for massive multiple-input multiple-output (MIMO) systems, considering beam patterns for different antenna elements, is proposed. The beam patterns using dipole antenna elements with different phase excitation toward the different direction of travels (DoTs) contributes various correlation weights for rays related towards/from the cluster, thus providing different elevation angle of arrivals (EAoAs) and elevation angle of departures (EAoDs) for each antenna element. These include the movements of the user that makes our channel to be a non-stationary model of clusters at the receiver (RX) on both the time and array axes. In addition, their impacts on 3-D massive MIMO channels are investigated via statistical properties including received spatial correlation. Additionally, the impact of elevation/azimuth angles of arrival on received spatial correlation is discussed. Furthermore, experimental validation of the proposed 3-D channel models on azimuth and elevation angles of the polarized antenna are specifically evaluated and compared through simulations. The proposed 3-D generic models are verified using relevant measurement data.
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18.
  • Balador, Ali, et al. (författare)
  • Supporting Beacon and Event-Driven Messages in Vehicular Platoons through Token-Based Strategies
  • 2018
  • Ingår i: Sensors. - : MDPI AG. - 1424-8220. ; 18:4
  • Tidskriftsartikel (refereegranskat)abstract
    • Timely and reliable inter-vehicle communications is a critical requirement to support traffic safety applications, such as vehicle platooning. Furthermore, low-delay communications allow the platoon to react quickly to unexpected events. In this scope, having a predictable and highly effective medium access control (MAC) method is of utmost importance. However, the currently available IEEE 802.11p technology is unable to adequately address these challenges. In this paper, we propose a MAC method especially adapted to platoons, able to transmit beacons within the required time constraints, but with a higher reliability level than IEEE 802.11p, while concurrently enabling efficient dissemination of event-driven messages. The protocol circulates the token within the platoon not in a round-robin fashion, but based on beacon data age, i.e., the time that has passed since the previous collection of status information, thereby automatically offering repeated beacon transmission opportunities for increased reliability. In addition, we propose three different methods for supporting event-driven messages co-existing with beacons. Analysis and simulation results in single and multi-hop scenarios showed that, by providing non-competitive channel access and frequent retransmission opportunities, our protocol can offer beacon delivery within one beacon generation interval while fulfilling the requirements on low-delay dissemination of event-driven messages for traffic safety applications.
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19.
  • Du, Jiaying, et al. (författare)
  • Signal quality improvement algorithms for MEMS gyroscope-based human motion analysis systems : A systematic review
  • 2018
  • Ingår i: Sensors. - : MDPI AG. - 1424-8220. ; 18:4
  • Tidskriftsartikel (refereegranskat)abstract
    • Motion sensors such as MEMS gyroscopes and accelerometers are characterized by a small size, light weight, high sensitivity, and low cost. They are used in an increasing number of applications. However, they are easily influenced by environmental effects such as temperature change, shock, and vibration. Thus, signal processing is essential for minimizing errors and improving signal quality and system stability. The aim of this work is to investigate and present a systematic review of different signal error reduction algorithms that are used for MEMS gyroscope-based motion analysis systems for human motion analysis or have the potential to be used in this area. A systematic search was performed with the search engines/databases of the ACM Digital Library, IEEE Xplore, PubMed, and Scopus. Sixteen papers that focus on MEMS gyroscope-related signal processing and were published in journals or conference proceedings in the past 10 years were found and fully reviewed. Seventeen algorithms were categorized into four main groups: Kalman-filter-based algorithms, adaptive-based algorithms, simple filter algorithms, and compensation-based algorithms. The algorithms were analyzed and presented along with their characteristics such as advantages, disadvantages, and time limitations. A user guide to the most suitable signal processing algorithms within this area is presented.
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20.
  • Olson, Nasrine, et al. (författare)
  • Biosensors-Publication Trends and Knowledge Domain Visualization
  • 2019
  • Ingår i: Sensors. - Basel, Switzerland : MDPI. - 1424-8220. ; 19:11
  • Tidskriftsartikel (refereegranskat)abstract
    • The number of scholarly publications on the topic of biosensors has increased rapidly; as a result, it is no longer easy to build an informed overview of the developments solely by manual means. Furthermore, with many new research results being continually published, it is useful to form an up-to-date understanding of the recent trends or emergent directions in the field. This paper utilizes bibliometric methods to provide an overview of the developments in the topic based on scholarly publications. The results indicate an increasing interest in the topic of biosensor(s) with newly emerging sub-topics. The US is identified as the country with highest total contribution to this area, but as a collective, EU countries top the list of total contributions. An examination of trends over the years indicates that in recent years, China-based authors have been more productive in this area. If research contribution per capita is considered, Singapore takes the top position, followed by Sweden, Switzerland and Denmark. While the number of publications on biosensors seems to have declined in recent years in the PubMed database, this is not the case in the Web of Science database. However, there remains an indication that the rate of growth in the more recent years is slowing. This paper also presents a comparison of the developments in publications on biosensors with the full set of publications in two of the main journals in the field. In more recent publications, synthetic biology, smartphone, fluorescent biosensor, and point-of-care testing are among the terms that have received more attention. The study also identifies the top authors and journals in the field, and concludes with a summary and suggestions for follow up research.
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21.
  • Wen, Wei, 1983-, et al. (författare)
  • Estimation of Image Sensor Fill Factor Using a Single Arbitrary Image
  • 2017
  • Ingår i: Sensors. - : MDPI. - 1424-8220. ; 17:3, s. 620-
  • Tidskriftsartikel (refereegranskat)abstract
    • Achieving a high fill factor is a bottleneck problem for capturing high-quality images. There are hardware and software solutions to overcome this problem. In the solutions, the fill factor is known. However, this is an industrial secrecy by most image sensor manufacturers due to its direct effect on the assessment of the sensor quality. In this paper, we propose a method to estimate the fill factor of a camera sensor from an arbitrary single image. The virtual response function of the imaging process and sensor irradiance are estimated from the generation of virtual images. Then the global intensity values of the virtual images are obtained, which are the result of fusing the virtual images into a single, high dynamic range radiance map. A non-linear function is inferred from the original and global intensity values of the virtual images. The fill factor is estimated by the conditional minimum of the inferred function. The method is verified using images of two datasets. The results show that our method estimates the fill factor correctly with significant stability and accuracy from one single arbitrary image according to the low standard deviation of the estimated fill factors from each of images and for each camera.
  •  
22.
  • Gautam, Sumit, et al. (författare)
  • Boosting Quantum Battery-Based IoT Gadgets via RF-Enabled Energy Harvesting
  • 2022
  • Ingår i: Sensors. - : MDPI AG. - 1424-8220. ; 22:14
  • Tidskriftsartikel (refereegranskat)abstract
    • The search for a highly portable and efficient supply of energy to run small-scale wireless gadgets has captivated the human race for the past few years. As a part of this quest, the idea of realizing a Quantum battery (QB) seems promising. Like any other practically tractable system, the design of QBs also involve several critical challenges. The main problem in this context is to ensure a lossless environment pertaining to the closed-system design of the QB, which is extremely difficult to realize in practice. Herein, we model and optimize various aspects of a Radio-Frequency (RF) Energy Harvesting (EH)-assisted, QB-enabled Internet-of-Things (IoT) system. Several RF-EH modules (in the form of micro- or nano-meter-sized integrated circuits (ICs)) are placed in parallel at the IoT receiver device, and the overall correspondingly harvested energy helps the involved Quantum sources achieve the so-called quasi-stable state. Concretely, the Quantum sources absorb the energy of photons that are emitted by a photon-emitting device controlled by a micro-controller, which also manages the overall harvested energy from the RF-EH ICs. To investigate the considered framework, we first minimize the total transmit power under the constraints on overall harvested energy and the number of RF-EH ICs at the QB-enabled wireless IoT device. Next, we optimize the number of RF-EH ICs, subject to the constraints on total transmit power and overall harvested energy. Correspondingly, we obtain suitable analytical solutions to the above-mentioned problems, respectively, and also cross-validate them using a non-linear program solver. The effectiveness of the proposed technique is reported in the form of numerical results, which are both theoretical and simulations based, by taking a range of operating system parameters into account.
  •  
23.
  • Peng, Cong, et al. (författare)
  • Meaningful Integration of Data from Heterogeneous Health Services and Home Environment Based on Ontology
  • 2019
  • Ingår i: Sensors. - : MDPI. - 1424-8220. ; 19:8
  • Tidskriftsartikel (refereegranskat)abstract
    • The development of electronic health records, wearable devices, health applications and Internet of Things (IoT)-empowered smart homes is promoting various applications. It also makes health self-management much more feasible, which can partially mitigate one of the challenges that the current healthcare system is facing. Effective and convenient self-management of health requires the collaborative use of health data and home environment data from different services, devices, and even open data on the Web. Although health data interoperability standards including HL7 Fast Healthcare Interoperability Resources (FHIR) and IoT ontology including Semantic Sensor Network (SSN) have been developed and promoted, it is impossible for all the different categories of services to adopt the same standard in the near future. This study presents a method that applies Semantic Web technologies to integrate the health data and home environment data from heterogeneously built services and devices. We propose a Web Ontology Language (OWL)-based integration ontology that models health data from HL7 FHIR standard implemented services, normal Web services and Web of Things (WoT) services and Linked Data together with home environment data from formal ontology-described WoT services. It works on the resource integration layer of the layered integration architecture. An example use case with a prototype implementation shows that the proposed method successfully integrates the health data and home environment data into a resource graph. The integrated data are annotated with semantics and ontological links, which make them machine-understandable and cross-system reusable.
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24.
  • Sjölander, Andreas, Ph.D, 1983-, et al. (författare)
  • Towards Automated Inspections of Tunnels: A Review of Optical Inspections and Autonomous Assessment of Concrete Tunnel Linings
  • 2023
  • Ingår i: Sensors. - : MDPI. - 1424-8220. ; 23:6, s. 3189-3189
  • Tidskriftsartikel (refereegranskat)abstract
    • In recent decades, many cities have become densely populated due to increased urbanization, and the transportation infrastructure system has been heavily used. The downtime of important parts of the infrastructure, such as tunnels and bridges, seriously affects the transportation system’s efficiency. For this reason, a safe and reliable infrastructure network is necessary for the economic growth and functionality of cities. At the same time, the infrastructure is ageing in many countries, and continuous inspection and maintenance are necessary. Nowadays, detailed inspections of large infrastructure are almost exclusively performed by inspectors on site, which is both time-consuming and subject to human errors. However, the recent technological advancements in computer vision, artificial intelligence (AI), and robotics have opened up the possibilities of automated inspections. Today, semiautomatic systems such as drones and other mobile mapping systems are available to collect data and reconstruct 3D digital models of infrastructure. This significantly decreases the downtime of the infrastructure, but both damage detection and assessments of the structural condition are still manually performed, with a high impact on the efficiency and accuracy of the procedure. Ongoing research has shown that deep-learning methods, especially convolutional neural networks (CNNs) combined with other image processing techniques, can automatically detect cracks on concrete surfaces and measure their metrics (e.g., length and width). However, these techniques are still under investigation. Additionally, to use these data for automatically assessing the structure, a clear link between the metrics of the cracks and the structural condition must be established. This paper presents a review of the damage of tunnel concrete lining that is detectable with optical instruments. Thereafter, state-of-the-art autonomous tunnel inspection methods are presented with a focus on innovative mobile mapping systems for optimizing data collection. Finally, the paper presents an in-depth review of how the risk associated with cracks is assessed today in concrete tunnel lining.
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25.
  • Abafogi, Abdurhaman Teyib, et al. (författare)
  • 3D-Printed Modular Microfluidic Device Enabling Preconcentrating Bacteria and Purifying Bacterial DNA in Blood for Improving the Sensitivity of Molecular Diagnostics
  • 2020
  • Ingår i: Sensors. - : MDPI. - 1424-8220. ; 20:4
  • Tidskriftsartikel (refereegranskat)abstract
    • Molecular diagnostics for sepsis is still a challenge due to the presence of compounds that interfere with gene amplification and bacteria at concentrations lower than the limit of detection (LOD). Here, we report on the development of a 3D printed modular microfluidic device (3Dpm mu FD) that preconcentrates bacteria of interest in whole blood and purifies their genomic DNA (gDNA). It is composed of a W-shaped microchannel and a conical microchamber. Bacteria of interest are magnetically captured from blood in the device with antibody conjugated magnetic nanoparticles (Ab-MNPs) at 5 mL/min in the W-shaped microchannel, while purified gDNA of the preconcentrated bacteria is obtained with magnetic silica beads (MSBs) at 2 mL/min in the conical microchamber. The conical microchamber was designed to be connected to the microchannel after the capturing process using a 3D-printed rotary valve to minimize the exposure of the MSBs to interfering compounds in blood. The pretreatment process of spiked blood (2.5 mL) can be effectively completed within about 50 min. With the 3Dpm mu FD, the LOD for the target microorganism Escherichia coli O157:H7 measured by both polymerase chain reaction (PCR) with electrophoresis and quantitative PCR was 10 colony forming unit (CFU) per mL of whole blood. The results suggest that our method lowers the LOD of molecular diagnostics for pathogens in blood by providing bacterial gDNA at high purity and concentration.
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26.
  • Abbas, Zaheer, et al. (författare)
  • In Situ Growth of CuWO4 Nanospheres over Graphene Oxide for Photoelectrochemical (PEC) Immunosensing of Clinical Biomarker
  • 2020
  • Ingår i: Sensors. - : MDPI. - 1424-8220. ; 20:1
  • Tidskriftsartikel (refereegranskat)abstract
    • Procalcitonin (PCT) protein has recently been identified as a clinical marker for bacterial infections based on its better sepsis sensitivity. Thus, an increased level of PCT could be linked with disease diagnosis and therapeutics. In this study, we describe the construction of the photoelectrochemical (PEC) PCT immunosensing platform based on it situ grown photo-active CuWO4 nanospheres over reduced graphene oxide layers (CuWO4@rGO). The in situ growth strategy enabled the formation of small nanospheres (diameter of 200 nm), primarily composed of tiny self-assembled CuWO4 nanoparticles (2-5 nm). The synergic coupling of CuWO4 with rGO layers constructed an excellent photo-active heterojunction for photoelectrochemical (PEC) sensing. The platform was then considered for electrocatalytic (EC) mechanism-based detection of PCT, where inhibition of the photocatalytic oxidation signal of ascorbic acid (AA), subsequent to the antibody-antigen interaction, was recorded as the primary signal response. This inhibition detection approach enabled sensitive detection of PCT in a concentration range of 10 pgmL(-1) to 50 ng.mL(-1) with signal sensitivity achievable up to 0.15 pgmL(-1). The proposed PEC hybrid (CuWO4@rGO) could further be engineered to detect other clinically important species.
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27.
  • Abbasi, Mazhar Ali, et al. (författare)
  • Potentiometric Zinc Ion Sensor Based on Honeycomb-Like NiO Nanostructures
  • 2012
  • Ingår i: Sensors. - : MDPI. - 1424-8220. ; 12:11, s. 15424-15437
  • Tidskriftsartikel (refereegranskat)abstract
    • In this study honeycomb-like NiO nanostructures were grown on nickel foam by a simple hydrothermal growth method. The NiO nanostructures were characterized by field emission electron microscopy (FESEM), high resolution transmission electron microscopy (HRTEM) and X-ray diffraction (XRD) techniques. The characterized NiO nanostructures were uniform, dense and polycrystalline in the crystal phase. In addition to this, the NiO nanostructures were used in the development of a zinc ion sensor electrode by functionalization with the highly selective zinc ion ionophore 12-crown-4. The developed zinc ion sensor electrode has shown a good linear potentiometric response for a wide range of zinc ion concentrations, ranging from 0.001 mM to 100 mM, with sensitivity of 36 mV/decade. The detection limit of the present zinc ion sensor was found to be 0.0005 mM and it also displays a fast response time of less than 10 s. The proposed zinc ion sensor electrode has also shown good reproducibility, repeatability, storage stability and selectivity. The zinc ion sensor based on the functionalized NiO nanostructures was also used as indicator electrode in potentiometric titrations and it has demonstrated an acceptable stoichiometric relationship for the determination of zinc ion in unknown samples. The NiO nanostructures-based zinc ion sensor has potential for analysing zinc ion in various industrial, clinical and other real samples.
  •  
28.
  • Abbaspour, Saadeh, et al. (författare)
  • A comparative analysis of hybrid deep learning models for human activity recognition
  • 2020
  • Ingår i: Sensors. - : MDPI AG. - 1424-8220. ; 20:19
  • Tidskriftsartikel (refereegranskat)abstract
    • Recent advances in artificial intelligence and machine learning (ML) led to effective methods and tools for analyzing the human behavior. Human Activity Recognition (HAR) is one of the fields that has seen an explosive research interest among the ML community due to its wide range of applications. HAR is one of the most helpful technology tools to support the elderly’s daily life and to help people suffering from cognitive disorders, Parkinson’s disease, dementia, etc. It is also very useful in areas such as transportation, robotics and sports. Deep learning (DL) is a branch of ML based on complex Artificial Neural Networks (ANNs) that has demonstrated a high level of accuracy and performance in HAR. Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are two types of DL models widely used in the recent years to address the HAR problem. The purpose of this paper is to investigate the effectiveness of their integration in recognizing daily activities, e.g., walking. We analyze four hybrid models that integrate CNNs with four powerful RNNs, i.e., LSTMs, BiLSTMs, GRUs and BiGRUs. The outcomes of our experiments on the PAMAP2 dataset indicate that our proposed hybrid models achieve an outstanding level of performance with respect to several indicative measures, e.g., F-score, accuracy, sensitivity, and specificity. © 2020 by the authors.
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29.
  • Abbaspour, S., et al. (författare)
  • Real-Time and Offline Evaluation of Myoelectric Pattern Recognition for the Decoding of Hand Movements
  • 2021
  • Ingår i: Sensors. - : MDPI AG. - 1424-8220. ; 21:16
  • Tidskriftsartikel (refereegranskat)abstract
    • Pattern recognition algorithms have been widely used to map surface electromyographic signals to target movements as a source for prosthetic control. However, most investigations have been conducted offline by performing the analysis on pre-recorded datasets. While real-time data analysis (i.e., classification when new data becomes available, with limits on latency under 200-300 milliseconds) plays an important role in the control of prosthetics, less knowledge has been gained with respect to real-time performance. Recent literature has underscored the differences between offline classification accuracy, the most common performance metric, and the usability of upper limb prostheses. Therefore, a comparative offline and real-time performance analysis between common algorithms had yet to be performed. In this study, we investigated the offline and real-time performance of nine different classification algorithms, decoding ten individual hand and wrist movements. Surface myoelectric signals were recorded from fifteen able-bodied subjects while performing the ten movements. The offline decoding demonstrated that linear discriminant analysis (LDA) and maximum likelihood estimation (MLE) significantly (p < 0.05) outperformed other classifiers, with an average classification accuracy of above 97%. On the other hand, the real-time investigation revealed that, in addition to the LDA and MLE, multilayer perceptron also outperformed the other algorithms and achieved a classification accuracy and completion rate of above 68% and 69%, respectively.
  •  
30.
  • Adee, Rose, et al. (författare)
  • A Dynamic Four-Step Data Security Model for Data in Cloud Computing Based on Cryptography and Steganography
  • 2022
  • Ingår i: Sensors. - : MDPI AG. - 1424-8220. ; 22:3
  • Tidskriftsartikel (refereegranskat)abstract
    • Cloud computing is a rapidly expanding field. It allows users to access computer system resources as needed, particularly data storage and computational power, without managing them directly. This paper aims to create a data security model based on cryptography and steganography for data in cloud computing that seeks to reduce existing security and privacy concerns, such as data loss, data manipulation, and data theft. To identify the problem and determine its core cause, we studied various literature on existing cloud computing security models. This study utilizes design science research methodology. The design science research approach includes problem identification, requirements elicitation, artifact design and development, demonstration, and assessment. Design thinking and the Python programming language are used to build the artifact, and discussion about its working is represented using histograms, tables, and algorithms. This paper’s output is a four-step data security model based on Rivest–Shamir–Adleman, Advanced Encryption Standard, and identity-based encryption algorithms alongside Least Significant Bit steganography. The four steps are data protection and security through encryption algorithms, steganography, data backup and recovery, and data sharing. This proposed approach ensures more cloud data redundancy, flexibility, efficiency, and security by protecting data confidentiality, privacy, and integrity from attackers.
  •  
31.
  • Adler, Karl, et al. (författare)
  • Predictions of Cu, Zn, and Cd Concentrations in Soil Using Portable X-Ray Fluorescence Measurements
  • 2020
  • Ingår i: Sensors (Basel, Switzerland). - : MDPI AG. - 1424-8220. ; 20
  • Tidskriftsartikel (refereegranskat)abstract
    • Portable X-ray fluorescence (PXRF) measurements on 1520 soil samples were used to create national prediction models for copper (Cu), zinc (Zn), and cadmium (Cd) concentrations in agricultural soil. The models were validated at both national and farm scales. Multiple linear regression (MLR), random forest (RF), and multivariate adaptive regression spline (MARS) models were created and compared. National scale cross-validation of the models gave the following R-2 values for predictions of Cu (R-2 = 0.63), Zn (R-2 = 0.92), and Cd (R-2 = 0.70) concentrations. Independent validation at the farm scale revealed that Zn predictions were relatively successful regardless of the model used (R-2 > 0.90), showing that a simple MLR model can be sufficient for certain predictions. However, predictions at the farm scale revealed that the non-linear models, especially MARS, were more accurate than MLR for Cu (R-2 = 0.94) and Cd (R-2 = 0.80). These results show that multivariate modelling can compensate for some of the shortcomings of the PXRF device (e.g., high limits of detection for certain elements and some elements not being directly measurable), making PXRF sensors capable of predicting elemental concentrations in soil at comparable levels of accuracy to conventional laboratory analyses.
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32.
  • Aghanavesi, Somayeh, 1981-, et al. (författare)
  • Verification of a Method for Measuring Parkinson's Disease Related Temporal Irregularity in Spiral Drawings
  • 2017
  • Ingår i: Sensors. - Basel : MDPI AG. - 1424-8220. ; 17:10
  • Tidskriftsartikel (refereegranskat)abstract
    • -value = 0.02). Test-retest reliability of TIS was good with Intra-class Correlation Coefficient of 0.81. When assessing changes in relation to treatment, TIS contained some information to capture changes from Off to On and wearing off effects. However, the correlations between TIS and clinical scores (UPDRS and Dyskinesia) were weak. TIS was able to differentiate spiral drawings drawn by patients in an advanced stage from those drawn by healthy subjects, and TIS had good test-retest reliability. TIS was somewhat responsive to single-dose levodopa treatment. Since TIS is an upper limb high-frequency-based measure, it cannot be detected during clinical assessment.
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33.
  • Ahmad, Jawad, 1985-, et al. (författare)
  • A Proposal of Implementation of Sitting Posture Monitoring System for Wheelchair Utilizing Machine Learning Methods
  • 2021
  • Ingår i: Sensors. - : MDPI. - 1424-8220. ; 21
  • Tidskriftsartikel (refereegranskat)abstract
    • This paper presents a posture recognition system aimed at detecting sitting postures of a wheelchair user. The main goals of the proposed system are to identify and inform irregular and improper posture to prevent sitting-related health issues such as pressure ulcers, with the potential that it could also be used for individuals without mobility issues. In the proposed monitoring system, an array of 16 screen printed pressure sensor units was employed to obtain pressure data, which are sampled and processed in real-time using read-out electronics. The posture recognition was performed for four sitting positions: right-, left-, forward- and backward leaning based on k-nearest neighbors (k-NN), support vector machines (SVM), random forest (RF), decision tree (DT) and LightGBM machine learning algorithms. As a result, a posture classification accuracy of up to 99.03 percent can be achieved. Experimental studies illustrate that the system can provide real-time pressure distribution value in the form of a pressure map on a standard PC and also on a raspberry pi system equipped with a touchscreen monitor. The stored pressure distribution data can later be shared with healthcare professionals so that abnormalities in sitting patterns can be identified by employing a post-processing unit. The proposed system could be used for risk assessments related to pressure ulcers. It may be served as a benchmark by recording and identifying individuals’ sitting patterns and the possibility of being realized as a lightweight portable health monitoring device.
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34.
  • Ahmed, Muhammad, et al. (författare)
  • Survey and Performance Analysis of Deep Learning Based Object Detection in Challenging Environments
  • 2021
  • Ingår i: Sensors. - : MDPI. - 1424-8220. ; 21:15
  • Forskningsöversikt (refereegranskat)abstract
    • Recent progress in deep learning has led to accurate and efficient generic object detection networks. Training of highly reliable models depends on large datasets with highly textured and rich images. However, in real-world scenarios, the performance of the generic object detection system decreases when (i) occlusions hide the objects, (ii) objects are present in low-light images, or (iii) they are merged with background information. In this paper, we refer to all these situations as challenging environments. With the recent rapid development in generic object detection algorithms, notable progress has been observed in the field of deep learning-based object detection in challenging environments. However, there is no consolidated reference to cover the state of the art in this domain. To the best of our knowledge, this paper presents the first comprehensive overview, covering recent approaches that have tackled the problem of object detection in challenging environments. Furthermore, we present a quantitative and qualitative performance analysis of these approaches and discuss the currently available challenging datasets. Moreover, this paper investigates the performance of current state-of-the-art generic object detection algorithms by benchmarking results on the three well-known challenging datasets. Finally, we highlight several current shortcomings and outline future directions.
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35.
  • Akalin, Neziha, 1988-, et al. (författare)
  • Reinforcement Learning Approaches in Social Robotics
  • 2021
  • Ingår i: Sensors. - : MDPI. - 1424-8220. ; 21:4
  • Forskningsöversikt (refereegranskat)abstract
    • This article surveys reinforcement learning approaches in social robotics. Reinforcement learning is a framework for decision-making problems in which an agent interacts through trial-and-error with its environment to discover an optimal behavior. Since interaction is a key component in both reinforcement learning and social robotics, it can be a well-suited approach for real-world interactions with physically embodied social robots. The scope of the paper is focused particularly on studies that include social physical robots and real-world human-robot interactions with users. We present a thorough analysis of reinforcement learning approaches in social robotics. In addition to a survey, we categorize existent reinforcement learning approaches based on the used method and the design of the reward mechanisms. Moreover, since communication capability is a prominent feature of social robots, we discuss and group the papers based on the communication medium used for reward formulation. Considering the importance of designing the reward function, we also provide a categorization of the papers based on the nature of the reward. This categorization includes three major themes: interactive reinforcement learning, intrinsically motivated methods, and task performance-driven methods. The benefits and challenges of reinforcement learning in social robotics, evaluation methods of the papers regarding whether or not they use subjective and algorithmic measures, a discussion in the view of real-world reinforcement learning challenges and proposed solutions, the points that remain to be explored, including the approaches that have thus far received less attention is also given in the paper. Thus, this paper aims to become a starting point for researchers interested in using and applying reinforcement learning methods in this particular research field.
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36.
  • Akbar, Mariam, et al. (författare)
  • Efficient Data Gathering in 3D Linear Underwater Wireless Sensor Networks Using Sink Mobility
  • 2016
  • Ingår i: Sensors. - : MDPI AG. - 1424-8220. ; 16:3
  • Tidskriftsartikel (refereegranskat)abstract
    • Due to the unpleasant and unpredictable underwater environment, designing an energy-efficient routing protocol for underwater wireless sensor networks (UWSNs) demands more accuracy and extra computations. In the proposed scheme, we introduce a mobile sink (MS), i.e., an autonomous underwater vehicle (AUV), and also courier nodes (CNs), to minimize the energy consumption of nodes. MS and CNs stop at specific stops for data gathering; later on, CNs forward the received data to the MS for further transmission. By the mobility of CNs and MS, the overall energy consumption of nodes is minimized. We perform simulations to investigate the performance of the proposed scheme and compare it to preexisting techniques. Simulation results are compared in terms of network lifetime, throughput, path loss, transmission loss and packet drop ratio. The results show that the proposed technique performs better in terms of network lifetime, throughput, path loss and scalability
  •  
37.
  • Akhoundian, Maedeh, et al. (författare)
  • Ultratrace Detection of Histamine Using a Molecularly-Imprinted Polymer-Based Voltammetric Sensor
  • 2017
  • Ingår i: Sensors. - : MDPI. - 1424-8220. ; 17:3
  • Tidskriftsartikel (refereegranskat)abstract
    • Rapid and cost-effective analysis of histamine, in food, environmental, and diagnostics research has been of interest recently. However, for certain applications, the already-existing biological receptor-based sensing methods have usage limits in terms of stability and costs. As a result, robust and cost-effective imprinted polymeric receptors can be the best alternative. In the present work, molecularly-imprinted polymers (MIPs) for histamine were synthesized using methacrylic acid in chloroform and acetonitrile as two different porogens. The binding affinity of the MIPs with histamine was evaluated in aqueous media. MIPs synthesized in chloroform displayed better imprinting properties for histamine. We demonstrate here histamine MIPs incorporated into a carbon paste (CP) electrode as a MIP-CP electrode sensor platforms for detection of histamine. This simple sensor format allows accurate determination of histamine in the sub-nanomolar range using an electrochemical method. The sensor exhibited two distinct linear response ranges of 1 x 10(-10)-7 x 10(-9) M and 7 x 10(-9)-4 x 10(-7) M. The detection limit of the sensor was calculated equal to 7.4 x 10(-11) M. The specificity of the proposed electrode for histamine is demonstrated by using the analogous molecules and other neurotransmitters such as serotonin, dopamine, etc. The MIP sensor was investigated with success on spiked serum samples. The easy preparation, simple procedure, and low production cost make the MIP sensor attractive for selective and sensitive detection of analytes, even in less-equipped laboratories with minimal training.
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38.
  • Al-Azzawi, Sana Sabah, et al. (författare)
  • HeadUp: A Low-Cost Solution for Tracking Head Movement of Children with Cerebral Palsy Using IMU
  • 2021
  • Ingår i: Sensors. - : MDPI. - 1424-8220. ; 21:23
  • Tidskriftsartikel (refereegranskat)abstract
    • Cerebral palsy (CP) is a common reason for human motor ability limitations caused before birth, through infancy or early childhood. Poor head control is one of the most important problems in children with level IV CP and level V CP, which can affect many aspects of children's lives. The current visual assessment method for measuring head control ability and cervical range of motion (CROM) lacks accuracy and reliability. In this paper, a HeadUp system that is based on a low-cost, 9-axis, inertial measurement unit (IMU) is proposed to capture and evaluate the head control ability for children with CP. The proposed system wirelessly measures CROM in frontal, sagittal, and transverse planes during ordinary life activities. The system is designed to provide real-time, bidirectional communication with an Euler-based, sensor fusion algorithm (SFA) to estimate the head orientation and its control ability tracking. The experimental results for the proposed SFA show high accuracy in noise reduction with faster system response. The system is clinically tested on five typically developing children and five children with CP (age range: 2-5 years). The proposed HeadUp system can be implemented as a head control trainer in an entertaining way to motivate the child with CP to keep their head up.
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39.
  • Al-Hilli, Safa, et al. (författare)
  • The pH Response and Sensing Mechanism of n-Type ZnO/Electrolyte Interfaces
  • 2009
  • Ingår i: Sensors. - : MDPI AG. - 1424-8220. ; 9:9, s. 7445-7480
  • Tidskriftsartikel (refereegranskat)abstract
    • Ever since the discovery of the pH-sensing properties of ZnO crystals, researchers have been exploring their potential in electrochemical applications. The recent expansion and availability of chemical modification methods has made it possible to generate a new class of electrochemically active ZnO nanorods. This reduction in size of ZnO (to a nanocrystalline form) using new growth techniques is essentially an example of the nanotechnology fabrication principle. The availability of these ZnO nanorods opens up an entire new and exciting research direction in the field of electrochemical sensing. This review covers the latest advances and mechanism of pH-sensing using ZnO nanorods, with an emphasis on the nano-interface mechanism. We discuss methods for calculating the effect of surface states on pH-sensing at a ZnO/electrolyte interface. All of these current research topics aim to explain the mechanism of pH-sensing using a ZnO bulk- or nano-scale single crystal. An important goal of these investigations is the translation of these nanotechnology-modified nanorods into potential novel applications.
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40.
  • Al-Turjman, Fadi M., et al. (författare)
  • Value-Based Caching in Information-Centric Wireless Body Area Networks
  • 2017
  • Ingår i: Sensors. - : MDPI. - 1424-8220. ; 17:1
  • Tidskriftsartikel (refereegranskat)abstract
    • We propose a resilient cache replacement approach based on a Value of sensed Information (VoI) policy. To resolve and fetch content when the origin is not available due to isolated in-network nodes (fragmentation) and harsh operational conditions, we exploit a content caching approach. Our approach depends on four functional parameters in sensory Wireless Body Area Networks (WBANs). These four parameters are: age of data based on periodic request, popularity of on-demand requests, communication interference cost, and the duration for which the sensor node is required to operate in active mode to capture the sensed readings. These parameters are considered together to assign a value to the cached data to retain the most valuable information in the cache for prolonged time periods. The higher the value, the longer the duration for which the data will be retained in the cache. This caching strategy provides significant availability for most valuable and difficult to retrieve data in the WBANs. Extensive simulations are performed to compare the proposed scheme against other significant caching schemes in the literature while varying critical aspects in WBANs (e.g., data popularity, cache size, publisher load, connectivity-degree, and severe probabilities of node failures). These simulation results indicate that the proposed VoI-based approach is a valid tool for the retrieval of cached content in disruptive and challenging scenarios, such as the one experienced in WBANs, since it allows the retrieval of content for a long period even while experiencing severe in-network node failures.
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41.
  • Alam, Mahbub Ul, et al. (författare)
  • Federated Semi-Supervised Multi-Task Learning to Detect COVID-19 and Lungs Segmentation Marking Using Chest Radiography Images and Raspberry Pi Devices : An Internet of Medical Things Application
  • 2021
  • Ingår i: Sensors. - : MDPI AG. - 1424-8220. ; 21:15
  • Tidskriftsartikel (refereegranskat)abstract
    • Internet of Medical Things (IoMT) provides an excellent opportunity to investigate better automatic medical decision support tools with the effective integration of various medical equipment and associated data. This study explores two such medical decision-making tasks, namely COVID-19 detection and lung area segmentation detection, using chest radiography images. We also explore different cutting-edge machine learning techniques, such as federated learning, semi-supervised learning, transfer learning, and multi-task learning to explore the issue. To analyze the applicability of computationally less capable edge devices in the IoMT system, we report the results using Raspberry Pi devices as accuracy, precision, recall, Fscore for COVID-19 detection, and average dice score for lung segmentation detection tasks. We also publish the results obtained through server-centric simulation for comparison. The results show that Raspberry Pi-centric devices provide better performance in lung segmentation detection, and server-centric experiments provide better results in COVID-19 detection. We also discuss the IoMT application-centric settings, utilizing medical data and decision support systems, and posit that such a system could benefit all the stakeholders in the IoMT domain.
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42.
  • Alfaras, Miquel, et al. (författare)
  • Biosensing and Actuation-Platforms Coupling Body Input-Output Modalities for Affective Technologies
  • 2020
  • Ingår i: Sensors. - : MDPI AG. - 1424-8220. ; 20:21
  • Tidskriftsartikel (refereegranskat)abstract
    • Research in the use of ubiquitous technologies, tracking systems and wearables within mental health domains is on the rise. In recent years, affective technologies have gained traction and garnered the interest of interdisciplinary fields as the research on such technologies matured. However, while the role of movement and bodily experience to affective experience is well-established, how to best address movement and engagement beyond measuring cues and signals in technology-driven interactions has been unclear. In a joint industry-academia effort, we aim to remodel how affective technologies can help address body and emotional self-awareness. We present an overview of biosignals that have become standard in low-cost physiological monitoring and show how these can be matched with methods and engagements used by interaction designers skilled in designing for bodily engagement and aesthetic experiences. Taking both strands of work together offers unprecedented design opportunities that inspire further research. Through first-person soma design, an approach that draws upon the designer's felt experience and puts the sentient body at the forefront, we outline a comprehensive work for the creation of novel interactions in the form of couplings that combine biosensing and body feedback modalities of relevance to affective health. These couplings lie within the creation of design toolkits that have the potential to render rich embodied interactions to the designer/user. As a result we introduce the concept of "orchestration". By orchestration, we refer to the design of the overall interaction: coupling sensors to actuation of relevance to the affective experience; initiating and closing the interaction; habituating; helping improve on the users' body awareness and engagement with emotional experiences; soothing, calming, or energising, depending on the affective health condition and the intentions of the designer. Through the creation of a range of prototypes and couplings we elicited requirements on broader orchestration mechanisms. First-person soma design lets researchers look afresh at biosignals that, when experienced through the body, are called to reshape affective technologies with novel ways to interpret biodata, feel it, understand it and reflect upon our bodies.
  •  
43.
  • Alhashimi, Anas, 1978-, et al. (författare)
  • Calibrating Range Measurements of Lidars Using Fixed Landmarks in Unknown Positions
  • 2021
  • Ingår i: Sensors. - : MDPI. - 1424-8220. ; 21:1
  • Tidskriftsartikel (refereegranskat)abstract
    • We consider the problem of calibrating range measurements of a Light Detection and Ranging (lidar) sensor that is dealing with the sensor nonlinearity and heteroskedastic, range-dependent, measurement error. We solved the calibration problem without using additional hardware, but rather exploiting assumptions on the environment surrounding the sensor during the calibration procedure. More specifically we consider the assumption of calibrating the sensor by placing it in an environment so that its measurements lie in a 2D plane that is parallel to the ground. Then, its measurements come from fixed objects that develop orthogonally w.r.t. the ground, so that they may be considered as fixed points in an inertial reference frame. Moreover, we consider the intuition that moving the distance sensor within this environment implies that its measurements should be such that the relative distances and angles among the fixed points above remain the same. We thus exploit this intuition to cast the sensor calibration problem as making its measurements comply with this assumption that "fixed features shall have fixed relative distances and angles". The resulting calibration procedure does thus not need to use additional (typically expensive) equipment, nor deploy special hardware. As for the proposed estimation strategies, from a mathematical perspective we consider models that lead to analytically solvable equations, so to enable deployment in embedded systems. Besides proposing the estimators we moreover analyze their statistical performance both in simulation and with field tests. We report the dependency of the MSE performance of the calibration procedure as a function of the sensor noise levels, and observe that in field tests the approach can lead to a tenfold improvement in the accuracy of the raw measurements.
  •  
44.
  • Alhashimi, Anas, 1978-, et al. (författare)
  • Joint Temperature-Lasing Mode Compensation for Time-of-Flight LiDAR Sensors
  • 2015
  • Ingår i: Sensors. - : MDPI. - 1424-8220. ; 15:12, s. 31205-31223
  • Tidskriftsartikel (refereegranskat)abstract
    • We propose an expectation maximization (EM) strategy for improving the precision of time of flight (ToF) light detection and ranging (LiDAR) scanners. The novel algorithm statistically accounts not only for the bias induced by temperature changes in the laser diode, but also for the multi-modality of the measurement noises that is induced by mode-hopping effects. Instrumental to the proposed EM algorithm, we also describe a general thermal dynamics model that can be learned either from just input-output data or from a combination of simple temperature experiments and information from the laser’s datasheet. We test the strategy on a SICK LMS 200 device and improve its average absolute error by a factor of three.
  •  
45.
  • Ali, Bako, et al. (författare)
  • Cyber and Physical Security Vulnerability Assessment for IoT-Based Smart Homes
  • 2018
  • Ingår i: Sensors. - : MDPI. - 1424-8220. ; 18:3
  • Tidskriftsartikel (refereegranskat)abstract
    • The Internet of Things (IoT) is an emerging paradigm focusing on the connection of devices, objects, or “things” to each other, to the Internet, and to users. IoT technology is anticipated to become an essential requirement in the development of smart homes, as it offers convenience and efficiency to home residents so that they can achieve better quality of life. Application of the IoT model to smart homes, by connecting objects to the Internet, poses new security and privacy challenges in terms of the confidentiality, authenticity, and integrity of the data sensed, collected, and exchanged by the IoT objects. These challenges make smart homes extremely vulnerable to different types of security attacks, resulting in IoT-based smart homes being insecure. Therefore, it is necessary to identify the possible security risks to develop a complete picture of the security status of smart homes. This article applies the operationally critical threat, asset, and vulnerability evaluation (OCTAVE) methodology, known as OCTAVE Allegro, to assess the security risks of smart homes. The OCTAVE Allegro method focuses on information assets and considers different information containers such as databases, physical papers, and humans. The key goals of this study are to highlight the various security vulnerabilities of IoT-based smart homes, to present the risks on home inhabitants, and to propose approaches to mitigating the identified risks. The research findings can be used as a foundation for improving the security requirements of IoT-based smart homes.
  •  
46.
  • Alirezaie, Marjan, 1980-, et al. (författare)
  • An ontology-based context-aware system for smart homes : E-care@home
  • 2017
  • Ingår i: Sensors. - Basel : MDPI AG. - 1424-8220. ; 17:7
  • Tidskriftsartikel (refereegranskat)abstract
    • Smart home environments have a significant potential to provide for long-term monitoring of users with special needs in order to promote the possibility to age at home. Such environments are typically equipped with a number of heterogeneous sensors that monitor both health and environmental parameters. This paper presents a framework called E-care@home, consisting of an IoT infrastructure, which provides information with an unambiguous, shared meaning across IoT devices, end-users, relatives, health and care professionals and organizations. We focus on integrating measurements gathered from heterogeneous sources by using ontologies in order to enable semantic interpretation of events and context awareness. Activities are deduced using an incremental answer set solver for stream reasoning. The paper demonstrates the proposed framework using an instantiation of a smart environment that is able to perform context recognition based on the activities and the events occurring in the home.
  •  
47.
  • Alirezaie, Marjan, 1980-, et al. (författare)
  • An Ontology-Based Reasoning Framework for Querying Satellite Images for Disaster Monitoring
  • 2017
  • Ingår i: Sensors. - : M D P I AG. - 1424-8220. ; 17:11
  • Tidskriftsartikel (refereegranskat)abstract
    • This paper presents a framework in which satellite images are classified and augmented with additional semantic information to enable queries about what can be found on the map at a particular location, but also about paths that can be taken. This is achieved by a reasoning framework based on qualitative spatial reasoning that is able to find answers to high level queries that may vary on the current situation. This framework called SemCityMap, provides the full pipeline from enriching the raw image data with rudimentary labels to the integration of a knowledge representation and reasoning methods to user interfaces for high level querying. To illustrate the utility of SemCityMap in a disaster scenario, we use an urban environment—central Stockholm—in combination with a flood simulation. We show that the system provides useful answers to high-level queries also with respect to the current flood status. Examples of such queries concern path planning for vehicles or retrieval of safe regions such as “find all regions close to schools and far from the flooded area”. The particular advantage of our approach lies in the fact that ontological information and reasoning is explicitly integrated so that queries can be formulated in a natural way using concepts on appropriate level of abstraction, including additional constraints.
  •  
48.
  •  
49.
  • Amer, Eynas, et al. (författare)
  • Evaluation of Shock Tube Retrofitted with Fast-Opening Valve for Dynamic Pressure Calibration
  • 2021
  • Ingår i: Sensors. - : MDPI. - 1424-8220. ; 21:13
  • Tidskriftsartikel (refereegranskat)abstract
    • Accurate dynamic pressure measurements are increasingly important. While traceability is lacking, several National Metrology Institutes (NMIs) and calibration laboratories are currently establishing calibration capacities. Shock tubes generating pressure steps with rise times below 1 µs are highly suitable as standards for dynamic pressures in gas. In this work, we present the results from applying a fast-opening valve (FOV) to a shock tube designed for dynamic pressure measurements. We compare the performance of the shock tube when operated with conventional single and double diaphragms and when operated using an FOV. Different aspects are addressed: shock-wave formation, repeatability in amplitude of the realized pressure steps, the assessment of the required driver pressure for realizing nominal pressure steps, and economy. The results show that using the FOV has many advantages compared to the diaphragm: better repeatability, eight times faster to operate, and enables automation of the test sequences.
  •  
50.
  • Andrei, Constantin Octavian, et al. (författare)
  • Galileo l10 satellites: Orbit, clock and signal-in-space performance analysis
  • 2021
  • Ingår i: Sensors. - : MDPI AG. - 1424-8220. ; 21:5, s. 1-20
  • Tidskriftsartikel (refereegranskat)abstract
    • The tenth launch (L10) of the European Global Navigation Satellite System Galileo filled in all orbital slots in the constellation. The launch carried four Galileo satellites and took place in July 2018. The satellites were declared operational in February 2019. In this study, we report on the performance of the Galileo L10 satellites in terms of orbital inclination and repeat period parameters, broadcast satellite clocks and signal in space (SiS) performance indicators. We used all available broadcast navigation data from the IGS consolidated navigation files. These satellites have not been reported in the previous studies. First, the orbital inclination (56.7 ± 0.15°) and repeat period (50680.7 ± 0.22 s) for all four satellites are within the nominal values. The data analysis reveals also 13.5-, 27-, 177-and 354-days periodic signals. Second, the broadcast satellite clocks show different correction magnitude due to different trends in the bias component. One clock switch and several other minor correction jumps have occurred since the satellites were declared operational. Short-term discontinuities are within ±1 ps/s, whereas clock accuracy values are constantly below 0.20 m (root-mean-square—rms). Finally, the SiS performance has been very high in terms of availability and accuracy. Monthly SiS availability has been constantly above the target value of 87% and much higher in 2020 as compared to 2019. Monthly SiS accuracy has been below 0.20 m (95th percentile) and below 0.40 m (99th percentile). The performance figures depend on the content and quality of the consolidated navigation files as well as the precise reference products. Nevertheless, these levels of accuracy are well below the 7 m threshold (95th percentile) specified in the Galileo service definition document.
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