| 1. |
- Sisinni, Emiliano, et al.
(författare)
-
A new LoRaWAN adaptive strategy for smart metering applications
- 2020
-
Ingår i: Proceedings. - : Institute of Electrical and Electronics Engineers Inc.. - 9781728148922 ; , s. 690-695
-
Konferensbidrag (refereegranskat)abstract
- One of the most widespread applications of IoT technologies is smart metering. Despite remote metering in smart grids is not a novelty, the advent of Low Power Wide Area Network (LPWAN) wireless technologies allowed to extend the concept to gas and water distribution. However, these battery-supplied meters must ensure an operating lifetime longer than 10 years without any maintenance, thus leading to battery oversizing. This work focuses on LoRaWAN and proposes an innovative adaptive strategy for equalizing the message time duration in order to maximize the battery exploitation. A simulator has been purposely designed to evaluate the performance in a real-world scenario, considering a node density of hundreds of nodes per square km. The simulation results demonstrate that a better exploitation of the battery is possible, ensuring an increase by 10 times of the amount of the amount of useful user data bytes transferred by meters. © 2020 IEEE.
|
|
| 2. |
- Barile, G., et al.
(författare)
-
Automatic Differential Capacitive Sensing by Means of Linear Interface
- 2020
-
Ingår i: Lect. Notes Electr. Eng.. - Cham : Springer. - 9783030375577 ; , s. 131-135
-
Konferensbidrag (refereegranskat)abstract
- In this work we present the development of an integrated CMOS analog interface able to convert differential capacitive sensors variations into a DC voltage. The presented circuit is based on autobalancing bridge techniques improving its performances through the linearization of the input/output characteristic and the achievement of the full-range sensor variations capability. Comparison between theoretical and measured interface static behaviour results are reported. © 2020, Springer Nature Switzerland AG.
|
|
| 3. |
- Bonafini, F., et al.
(författare)
-
Cluster of IoT Sensors for Smart Cities : Impact of the Communication Infrastructure over Computational Performance
- 2019
-
Ingår i: Proceedings. - : Institute of Electrical and Electronics Engineers Inc.. - 9781538677131
-
Konferensbidrag (refereegranskat)abstract
- The Smart City (SC) paradigm is based on the integration of Information and Communication Technology (ICT) into the urban asset, for the optimal management of the energies and resources. The Internet of Thing (IoT) technology seems the proper solution to achieve this target, thanks to its capability to abstract the object in the real world. The deployment of IoT devices at different level in urban infrastructures is causing the presence of thousands of intelligent devices, large part of them with unused computational capabilities. Such devices could be integrated in a cluster in order to share the unused resources with other devices with limited computational resources. The use of a cluster of IoT Sensors has several benefits, including, but not limited to: high availability, sharing of computational resources, reduced response time with the respect of centralized cloud computing solution. The main bottleneck of this approach is represented by the communication infrastructure, typically based on wireless connection and, thus with a limited available bandwidth. The aim of the work related to this paper is to analyze the impact the communication infrastructure has on computational performance of a cluster of IoT sensors. An experimental set-up for the characterization of the performance of a cluster of low-cost off-the-shelf devices has been described. The experimental validation highlighted as the network infrastructure is loaded only during the data transfer and the maximum network load, with a cluster of ten IoT nodes is approximately 2 Mb/s with the considered benchmark. © 2019 IEEE.
|
|
| 4. |
- Bonafini, F., et al.
(författare)
-
Evaluating indoor and outdoor localization services for LoRaWAN in Smart City applications
- 2019
-
Ingår i: Proceedings. - : Institute of Electrical and Electronics Engineers Inc.. - 9781728104294 ; , s. 300-305
-
Konferensbidrag (refereegranskat)abstract
- Nowadays, wireless technologies penetrate all aspects of our lives. 'Internet of Things' (IoT) and 'Location- Based Services' are the pillars of Smart City concept. The IoT smart objects surrounding us are an integral part of the Internet, thanks to their computational and communication capabilities. In such applications, location information can be exploited in all the layers of the stack, from the application level (e.g., to correctly interpret measurements from sensor nodes deployed on the field), down to the physical level (e.g., for sensing coverage). One of the most viable solutions for Smart City wireless connectivity seems to be the use of long-range, low-power and low-throughput low-power wide area networks (LPWANs). In this work, the authors devise the jointly use of LPWANs with widely-diffused and well-accepted localization techniques, as the Global Positioning Systems (GPS, outdoor) and real-time location systems (RTLS, indoor), for Smart Campus applications. In particular, a LoRaWAN node equipped with both GPS and Ultra Wide Bandbased UWB-RTLS has been developed and tested in real-world scenarios. Experimental results demonstrate the feasibility of the proposed approach; in particular, location errors are in the order of few tens of meters for GPS and in the order of few tens of centimeters for UWB. © 2019 IEEE.
|
|
| 5. |
- Carvalho, D. F., et al.
(författare)
-
Architecture for the interconnection of prototypical medical instrument via cloud services
- 2019
-
Ingår i: Proceedings. - 9781538634608
-
Konferensbidrag (refereegranskat)abstract
- Medical cyber-physical systems (MCPS) have been appeared as a possible approach for detecting and mitigating human errors. In this context, a MCPS in which medical and non-medical devices are connected could promptly detect potential risky or wrong procedures that deviate from standardized approaches. In this work, such a paradigm has been expanded to prototypical diagnostic devices. A distributed platform of such instruments is proposed in order to avoid misdiagnosis: diagnostic instrument send data to a message-oriented middleware, thus allowing technicians and doctors to remotely access test-related information for further processing and for creating an historical database. A Broker has been used for collecting data and distribute them to cloud database and to users. Two of the most diffused protocols, i.e., MQTT and AMQP, have been considered. An experimental setup has been developed to verify performance; time-related metrics confirm that the proposed approach has an end-to-end delay on few hundreds of milliseconds even for geographical scale networks. © 2019 IEEE.
|
|
| 6. |
- Crema, C., et al.
(författare)
-
Characterization of a wearable system for automatic supervision of fitness exercises
- 2019
-
Ingår i: Measurement: Journal of the International Measurement Confederation. - : Elsevier BV. - 0263-2241. ; 147
-
Tidskriftsartikel (refereegranskat)abstract
- It is widely known that physical activity is an effective tool for preventing several diseases. However, unsupervised training may lead to poor execution quality, resulting in ineffective training, or even injuries in worst cases. Automatic tracking and quantification of exercise efforts by means of wearables could be a way to monitor the execution correctness. As a positive side effect, these devices help in motivating people, increasing the quantity of physical exercises of users and thus improving health conditions as well. Unfortunately, despite the availability of some commercial devices, their performance and effectiveness are not documented. This work proposes a new solution that exploits machine learning (ML) techniques (in particular Linear Discriminant Analysis, LDA) for analyzing data coming from wearable Inertial Measurement Units (IMUs). Efforts have been done in reducing the computational requirements, in order to be compatible with constraints in perspective of embedded implementation. The experimental campaign carried out to measure the performance showed an average accuracy, recall and precision on the order of 97%, 93% and 90%, respectively. © 2019 Elsevier Ltd
|
|
| 7. |
- Crema, C., et al.
(författare)
-
IMU-based solution for automatic detection and classification of exercises in the fitness scenario
- 2017
-
Ingår i: SAS 2017 - 2017 IEEE Sensors Applications Symposium, Proceedings. - : IEEE. - 9781509032020
-
Konferensbidrag (refereegranskat)abstract
- Causal relationship between physical activity and prevention of several diseases has been known for some time. Recently, attempts to quantify dose-response relationship between physical activity and health show that automatic tracking and quantification of the exercise efforts not only help in motivating people but improve health conditions as well. However, no commercial devices are available for weight training and calisthenics. This work tries to overcome this limit, exploiting machine learning technique (particularly Linear Discriminant Analysis, LDA) for analyzing data coming from wearable inertial measurement units, (IMUs) and classifying/counting such exercises. Computational requirements are compatible with embedded implementation and reported results confirm the feasibility of the proposed approach, offering an average accuracy in the detection of exercises on the order of 85%.
|
|
| 8. |
- Depari, A., et al.
(författare)
-
Exploitation of precise timing capabilities of single board computer for transcranial magnetic stimulation
- 2020
-
Ingår i: Proceedings. - : Institute of Electrical and Electronics Engineers Inc.. - 9781728148427
-
Konferensbidrag (refereegranskat)abstract
- An early Alzheimer Disease (AD) diagnosis is fundamental for maximizing the effectiveness of treatment administration. Unfortunately, distinguish AD from other neurodegenerative dementias, such as Frontotemporal Dementia (FTD) is not trivial. Transcranial Magnetic Stimulation (TMS) emerged as an effective non-invasive, easy to apply and not time-consuming solution. TMS-based techniques generally require expensive ad hoc clinical equipment that suffer from poor flexibility and user friendliness in defining the specific diagnostic protocol. In this work, a low-cost BeagleBone Black single board computer (BBB-SBC) has been used to implement all the functionalities required to manage a TMS-based instrument. Timeliness of signal generation is guaranteed by dedicated programmable real-time units hosted by the BBB-SBC System on Chip. Web-based interface, complemented by IoT-like features, provide a high degree of versatility and permit the execution of many different diagnostic protocols. In particular, the experimental validation confirms timing error in the sub-microsecond range, more than enough for the considered application. © 2020 IEEE.
|
|
| 9. |
- Depari, A., et al.
(författare)
-
Lightweight Machine Learning-Based Approach for Supervision of Fitness Workout
- 2019
-
Ingår i: Proceedings. - : Institute of Electrical and Electronics Engineers Inc.. - 9781538677131
-
Konferensbidrag (refereegranskat)abstract
- It is widely known that physical activity helps preventing several diseases. However, unsupervised training often results in low exercise quality, ineffective training, and, in worst cases, injuries. Automatic tracking and quantification of exercises by means of wearable devices could be an effective mean for the monitoring of exercise correctness. As a consequence, such devices could help motivating people, thus improving the quantity of performed physical exercise, with positive effects on users' health conditions. However, despite the availability of several commercial devices, the performance and effectiveness are not well documented. This work proposes a new solution for fitness workout supervision exploiting machine learning techniques, in particular Linear Discriminant Analysis for analyzing data coming from wearable Inertial Measurement Units. Efforts have been done in order to reduce the computational requirements, thus assuring compatibility in perspective of embedded implementation. The experimental tests carried out to assess the proposed approach performance showed an accuracy in exercise detection over 93% and error in exercise counting less than 6%. © 2019 IEEE.
|
|
| 10. |
- Depari, A., et al.
(författare)
-
Simple and power efficient interface for AC-excited differential sensors
- 2020
-
Ingår i: Proceedings. - : Institute of Electrical and Electronics Engineers Inc.. - 9781728144603
-
Konferensbidrag (refereegranskat)abstract
- Capacitive sensors are low-cost and robust devices that can be easily scaled to very small sizes, thus making them suitable for implementation as a micro electro-mechanical system (MEMS). Differential arrangements of the sensors are also available, providing improved rejection of common mode interference. Due to their nature, an AC (sinusoidal) excitation signal is usually adopted. Various types of front-end circuits have been proposed in the past, exploiting different techniques such as conversion of capacitance to current or frequency or analog-to-digital conversion and adopting different approaches, such as full analog or full digital architectures. This paper proposes a digital and microcontroller-based system for AC-excited differential sensors aiming at minimizing cost and power needs, in sight of future large volume applications as for Internet of Things (IoT) paradigm. A proof of concept implementation has been realized and experimentally validated, obtaining a relative error in the measurand estimation on the order of 1%, when the parasitic effects can be neglected. © 2020 IEEE.
|
|
