| 1. |
- Alqaysi, Hiba, et al.
(författare)
-
A temporal boosted yolo-based model for birds detection around wind farms
- 2021
-
Ingår i: Journal of Imaging. - : MDPI AG. - 2313-433X. ; 7:11
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Tidskriftsartikel (refereegranskat)abstract
- Object detection for sky surveillance is a challenging problem due to having small objects in a large volume and a constantly changing background which requires high resolution frames. For example, detecting flying birds in wind farms to prevent their collision with the wind turbines. This paper proposes a YOLOv4-based ensemble model for bird detection in grayscale videos captured around wind turbines in wind farms. In order to tackle this problem, we introduce two datasets—(1) Klim and (2) Skagen—collected at two locations in Denmark. We use Klim training set to train three increasingly capable YOLOv4 based models. Model 1 uses YOLOv4 trained on the Klim dataset, Model 2 introduces tiling to improve small bird detection, and the last model uses tiling and temporal stacking and achieves the best mAP values on both Klim and Skagen datasets. We used this model to set up an ensemble detector, which further improves mAP values on both datasets. The three models achieve testing mAP values of 82%, 88%, and 90% on the Klim dataset. mAP values for Model 1 and Model 3 on the Skagen dataset are 60% and 92%. Improving object detection accuracy could mitigate birds’ mortality rate by choosing the locations for such establishment and the turbines location. It can also be used to improve the collision avoidance systems used in wind energy facilities.
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| 2. |
- Alqaysi, Hiba
(författare)
-
Cost Optimization of Volumetric Surveillance for Sky Monitoring : Towards Flying Object Detection and Positioning
- 2022
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Unlike surface surveillance, volumetric monitoring deals with three-dimensional target space and moving objects within it. In sky monitoring, objects fly within outdoor and often remote volumes, such as wind farms and airport runways. Therefore, multiple cameras should be implemented to monitor these volumes and analyze flying activities.Due to that, challenges in designing and deploying volumetric surveillance systems for these applications arise. These include configuring the multi-camera node placement, coverage, cost, and the system's ability to detect and position flying objects.The research in this dissertation focuses on three aspects to optimize volumetric surveillance systems in sky monitoring applications. First, the node placement and coverage should be considered in accordance with the monitoring constraints. Also, the node architecture should be configured to minimize the design cost and maximize the coverage. Last, the system should detect small flying objects with good accuracy.Placing the multi-camera nodes in a hexagonal pattern while allowing overlap between adjacent nodes optimizes the placement. The inclusion of monitoring constraints like monitoring altitude and detection pixel resolution influences the node design. Furthermore, presented results show that modeling the multi-camera nodes as a cylinder rather than a hemisphere minimizes the cost of each node. The design exploration in this thesis provides a method to minimize the node cost based on defined design constraints. It also maximizes the coverage in terms of the number of square meters per dollar. Surveillance systems for sky monitoring should be able to detect and position flying objects. Therefore, two new annotated datasets were introduced that can be used for developing in-flight birds detection methods. The datasets were collected by Mid Sweden University at two locations in Denmark. A YOLOv4-based model for birds detection in 4k grayscale videos captured in wind farms is developed. The model overcomes the problem of detecting small objects in dynamic background, and it improves detection accuracy through tiling and temporal information incorporation, compared to the standard YOLOv4 and background subtraction.
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| 3. |
- Alqaysi, Hiba, et al.
(författare)
-
Cost Optimized Design of Multi-Camera Domefor Volumetric Surveillance
- 2021
-
Ingår i: IEEE Sensors Journal. - 1530-437X .- 1558-1748. ; 21:3, s. 3730-3737
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Tidskriftsartikel (refereegranskat)abstract
- A multi-camera dome consists of number ofcameras arranged in layers to monitor a hemisphere aroundits center. In volumetric surveillance,a 3D space is required tobemonitoredwhich can be achievedby implementing numberof multi-camera domes. A monitoring height is consideredas a constraint to ensure full coverage of the space belowit. Accordingly, the multi-camera dome can be redesignedinto a cylinder such that each of its multiple layers hasdifferent coverage radius. Minimum monitoring constraintsshould be met at all layers. This work is presenting a costoptimized design for the multi-camera dome that maximizesits coverage. The cost per node and number of squaremetersper dollar of multiple configurations are calculated using asearch space of cameras and considering a set of monitoring and coverage constraints. The proposed design is costoptimized per node and provides more coverage as compared to the hemispherical multi-camera dome.
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| 4. |
- Alqaysi, Hiba, et al.
(författare)
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Design Exploration of Multi-Camera Dome
- 2019
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Ingår i: ICDSC 2019 Proceedings of the 13th International Conference on Distributed Smart Cameras. - New York, NY : ACM Digital Library. - 9781450371896
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Konferensbidrag (refereegranskat)abstract
- Visual monitoring systems employ distributed smart cameras toeffectively cover a given area satisfying specific objectives. Thechoice of camera sensors and lenses and their deployment affectsdesign cost, accuracy of the monitoring system and the ability toposition objects within the monitored area. Design cost can bereduced by investigating deployment topology such as groupingcameras together to form a dome at a node and optimize it formonitoring constraints. The constraints may include coverage area,number of cameras that can be integrated in a node and pixelresolution at a given distance. This paper presents a method foroptimizing the design cost of multi-camera dome by analyzing tradeoffsbetween monitoring constraints. The proposed method can beused to reduce monitoring cost while fulfilling design objectives.Results show how to increase coverage area for a given cost byrelaxing requirements on design constraints. Multi-camera domescan be used in sky monitoring applications such as monitoring windparks and remote air-traffic control of airports where all-round fieldof view about a point is required to monitor.
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| 5. |
- Alqaysi, Hiba, et al.
(författare)
-
Evaluating Coverage Effectiveness of Multi-Camera Domes Placement for Volumetric Surveillance
- 2017
-
Ingår i: ICDSC 2017 Proceedings of the 11th International Conference on Distributed Smart Cameras. - New York, NY, USA : Association for Computing Machinery (ACM). - 9781450354875 ; , s. 49-54
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Konferensbidrag (refereegranskat)abstract
- Multi-camera dome is composed of a number of cameras arranged to monitor a half sphere of the sky. Designing a network of multi-camera domes can be used to monitor flying activities in open large area, such as birds' activities in wind parks. In this paper, we present a method for evaluating the coverage effectiveness of the multi-camera domes placement in such areas. We used GPS trajectories of free flying birds over an area of 9 km2 to analyze coverage effectiveness of randomly placed domes. The analysis is based on three criteria namely, detection, positioning and the maximum resolution captured. The developed method can be used to evaluate results of designing and optimizing dome placement algorithms for volumetric monitoring systems in order to achieve maximum coverage.
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| 6. |
- Alqaysi, Hiba, et al.
(författare)
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Full Coverage Optimization for Multi Camera Dome Placement in Volumetric Monitoring
- 2018
-
Ingår i: ACM International Conference Proceeding Series. - New York, NY, USA : ACM Digital Library. - 9781450365116
-
Konferensbidrag (refereegranskat)abstract
- Volumetric monitoring can be challenging due to having a 3D target space and moving objects within it. Multi camera dome is proposed to provide a hemispherical coverage of the 3D space around it. This paper introduces a method that optimizes multi camera placement for full coverage in volumetric monitoring system. Camera dome placement is modeled in a volume by adapting the hexagonal packing of circles to provide full coverage at a given height, and 100% detection of flying objects within it. The coverage effectiveness of different placement configurations was assessed using an evaluation environment. The proposed placement is applicable in designing and deploying surveillance systems for remote outdoor areas, such as sky monitoring in wind farms and airport runways in order to record and analyze flying activities.
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| 7. |
- Anwar, Qaiser, et al.
(författare)
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Intelligence Partitioning as a Method for Architectural Exploration of Wireless Sensor Node
- 2016
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Ingår i: Proceedings of the International Conference on Computational Science and Computational Intelligence (CSCI), 2016.. - : IEEE Press. - 9781509055104 ; , s. 935-940
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Konferensbidrag (refereegranskat)abstract
- Embedded systems with integrated sensing, processing and wireless communication are driving future connectivity concepts such as Wireless Sensor Networks (WSNs) and Internet of Things (IoTs). Because of resource limitations, there still exists a number of challenges such as low latency and energy consumption to realize these concepts to full potential. To address and understand these challenges, we have developed and employed an intelligence partitioning method which generates different implementation alternatives by distributing processing load across multiple nodes. The task-to-node mapping has exponential complexity which is hard to compute for a large scale system. Regarding this, our method provides recommendation to handle and minimize such complexity for a large system. Experiments on a use-case concludes that the proposed method is able to identify unfavourable architecture solutions in which forward and backword communication paths exists in task-to-node mapping. These solution can be avoided for further architectural exploration, thus limiting the space for architecture exploration of a sensor node.
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| 8. |
- Aurangzeb, Khursheed, et al.
(författare)
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Analysis of Binary Image Coding Methods for Outdoor Applications of Wireless Vision sensor Networks
- 2018
-
Ingår i: IEEE Access. - 2169-3536. ; 6, s. 16932-16941
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Tidskriftsartikel (refereegranskat)abstract
- The processing of images at the vision sensor nodes (VSN) requires a high computation power and their transmission requires a large communication bandwidth. The energy budget is limited in outdoor applications of wireless vision sensor networks (WVSN). This means that both the processing of images at the VSN and the communication to server must be energy efficient. The wireless communication of uncompressed data consumes huge amounts of energy. Data compression methods are efficient in reducing data in images and can be used for the reduction in transmission energy. We have evaluated seven binary image coding techniques. Our evaluation is based on the processing complexity and energy consumption of the compression methods on the embedded platforms. The focus is to come up with a binary image coding method, which has good compression efficiency and short processing time. An image coding method with such attributes will result in reduced total energy requirement of the node. We have used both statistically generated images and real captured images, in our experiments. Based on our results, we conclude that International Telegraph and Telephone Consultative Committee Group 4, gzip_pack and JPEG-LS are suitable coding methods for the outdoor applications of WVSNs.
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| 9. |
- Aurangzeb, Khursheed, et al.
(författare)
-
Data Reduction Using Change Coding for Remote Applications of wireless Visual Sensor Networks
- 2018
-
Ingår i: IEEE Access. - : IEEE. - 2169-3536. ; 6, s. 37738-37747
-
Tidskriftsartikel (refereegranskat)abstract
- The data reduction capability of image compression schemes is limited by the underlying compression technique. For applications with minor changes between consecutive frames, change coding can be used to further reduce the data. We explored the efficiency of change coding for data reduction in a wireless visual sensor network (WVSN). This paper presents an analysis of the compression efficiency of change coding for a variety of changes, such as different shapes, sizes, and locations of white objects in adjacent sets of frames. Compressing change frame provides a better performance compared with compressing the original frames for up to 95% changes in the number of objects in adjacent frames. Due to illumination noise, the size of the objects increases at its boundaries, which negatively affects the performance of change coding. We experimentally proved that the negative impact of illumination noise could be reduced by applying morphology on the change frame. Communication energy consumption of the VSN is dependent on the data that are transmitted to the server. Our results show that the communication energy consumption of the VSN can be reduced by 27%, 29%, and 46% by applying change coding in combination with JBIG2, Group4, and Gzip_pack, respectively. The findings presented in this paper will aid researchers in enhancing the compression potential of image coding schemes in the energy-constrained applications of WVSNs.
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| 10. |
- Carratu, M., et al.
(författare)
-
A CNN-based approach to measure wood quality in timber bundle images
- 2021
-
Ingår i: 2021 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). - : IEEE. - 9781728195391
-
Konferensbidrag (refereegranskat)abstract
- At present, the Smart Industry is becoming a field of great interest for many worldwide researchers since it allows to experiment and research new advanced techniques. One of the most common explored approaches in operations where image processing has already been a milestone is the use of Convolutional Neural Networks (CNN). Those networks have enhanced the current image processing algorithms, achieving an improvement in decision processes usually based on human experience, where an analytical model is not always available. This paper proposes a novel approach for measuring the number of rotted logs in timber bundles using a CNN trained on thousands of timber log images extracted from bundles. Today, the Swedish forest industry bases the selling price of timber bundles on the evaluation of a visual inspection. This operation is based on human experience to evaluate and measure timber bundles' features, which is necessary to categorize them. The proposed approach has shown promising results compared to the actual visual inspection made by operators showing an F1 score with the best CNN architecture of 0.89.
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| 11. |
- Carratú, Marco, et al.
(författare)
-
A novel IVS procedure for handling Big Data with Artificial Neural Networks
- 2020
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Ingår i: 2020 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). - : IEEE. - 9781728144603
-
Konferensbidrag (refereegranskat)abstract
- In recent times, thanks to the availability of a large quantity of data coming from the industrial process, several techniques based on a data-driven approach could be developed. Between all the data-driven techniques, as Principle Component Regression, Support Vector Machines, Artificial Neural Networks, Neuro-Fuzzy Systems, and many others, the data on which they rely should be analyzed to find correlations and dependencies that could improve their design. For this reason, the Input variable Selection (IVS) process has become of great interest in the recent period. The classical IVS relies on classical statistics, as Pearson coefficients, able to discover linear dependencies among data; today, due to the significant amount of data available, the challenge of also discovering non-linear dependencies appears to be a necessary skill, mainly for the design and development of a neural network. This paper proposes the use of a novel statistical tool named Maximal Information Coefficient (MIC) for developing an IVS procedure able to discover dependencies in a considerable dataset and guide the IVS designer to the selection of input variables in a data-driven application. As a case study, the procedure will be applied to a real application developed in the context of the Swedish forest industry, in order to choose the input variables of a neural network able to estimate the timber bundles volume, which represents an expensive parameter to measure in this context.
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| 12. |
- Carratu, M., et al.
(författare)
-
An innovative method for log diameter measurements based on deep learning
- 2023
-
Ingår i: 2023 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). - : IEEE. - 9781665453837
-
Konferensbidrag (refereegranskat)abstract
- The widespread adoption of Deep Learning techniques for Computer Vision in recent years has brought major changes to the world of industry, contributing greatly to this sector's transition to Industry 4.0, also referred to as Smart Industry. This involves an increasingly predominant role of machines and automation within industrial processes. In this context, the Swedish forest industry is an excellent context for applying these techniques. In particular, this work will deal with automating the measurement of log diameters to date carried out manually by operators in the industry. The proposed methodology will use two object detection neural networks, one deputed to detect logs in the scene and the other for the calibrated target. The latter thus allows the camera calibration to be fully automated, enabling each diameter to be measured without any further operations by the operator. The results obtained are satisfactory and open the way for the industrial application of the proposed methodology.
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| 13. |
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| 14. |
- Carratù, Marco, et al.
(författare)
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Design and Evaluation of a Soft Sensor for Snow Weight Measurement
- 2024
-
Ingår i: Conference Record - IEEE Instrumentation and Measurement Technology Conference. - : IEEE conference proceedings. - 9798350380903
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Konferensbidrag (refereegranskat)abstract
- Snow accumulations, especially if of great intensity, as is the case in northern countries, for example, can be very damaging, especially if they occur in urban environments. The damage provoked by snow is not only related to the weight of the accumulations, causing damage to structures but also to the pollution retained by the structure of the snowflakes. However, snow weight monitoring is a complex task, both because of the placement of the sensors and the specific operating ranges they must have in terms of operating temperature. These complications can be overcome by the design and use of a soft sensor, that is, a sensor capable of making indirect measurements from other parameters related to the measurement under consideration. This paper presents the design and metrological validation of a soft sensor for indirect weight measurement of snow accumulations. The designed soft sensor has been based on Artificial Neural Network and achieved, as a result, a Root-Mean-Square Error (RMSE) of 114g and a maximum extended uncertainty, evaluated by Monte Carlo simulation, of 300g in a measurement range from 150g to 5200g.
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| 15. |
- Carratù, M., et al.
(författare)
-
Vision-Based System for Measuring the Diameter of Wood Logs
- 2023
-
Ingår i: IEEE Open Journal of Instrumentation and Measurement. - : IEEE. - 2768-7236. ; 2, s. 1-12
-
Tidskriftsartikel (refereegranskat)abstract
- Detecting and measuring objects with vision-based systems in uncontrolled environments is a difficult task that today, thanks to the development of increasingly advanced artificial intelligence-based techniques, can be solved with greater ease. In this context, this article proposes a novel approach for the vision-based measurement of objects in uncontrolled environments using a specific type of convolutional neural network (CNN) named you only look once (YOLO) and a direct linear transformation (DLT) process. The case study concerned designing a novel vision-based system for measuring the diameter of wood logs cut and loaded onto trucks. This problem has been occurring in the Swedish forestry industry. In fact, this operation is not carried out with computer vision algorithms because of the high variability of environmental conditions caused by the changing position of the sun, weather conditions, and the variability of truck positioning. To solve this problem, the YOLO network is proposed to locate logs while attempting to maintain a high Intersection over Union (IoU) value for the correct estimation of log size. Furthermore, in order to obtain accurate measurements, the DLT is used to convert into world coordinates the dimensions of the logs themselves. The proposed CNN-based solution is described after briefly introducing today’s methodologies adopted for wood bundle analysis. Particular attention is paid to both the training and the calibration steps. Results report that for 80% of cases, the error reported has been smaller than 4 cm, representing only 8% of the measurement, considering a mean log diameter for the application of 50 cm.
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| 16. |
- Carratù, Marco, et al.
(författare)
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Wireless Sensor Network Calibration for PM10 Measurement
- 2020
-
Ingår i: 2020 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA). - : IEEE. - 9781728144337
-
Konferensbidrag (refereegranskat)abstract
- The proposal of an Advanced Metering Infrastructure based on short-range communication is suggested for the continuous monitoring of Particulate Matter. A prototype of Automatic Measurement System (AMS), including a low-cost off-the-shelf PM sensor, has been developed as a remote node to be adopted in the radio Local Area Network. The results of the system calibration and comparison with the data quality requirements of the PM measurement according to European regulations, as well as the simulation of a typical Smart City scenario in terms of communication performance, confirm the feasibility of the proposed distributed AMS for an effective adoption within an urban area.
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| 17. |
- Fedorov, Igor, et al.
(författare)
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A two-layer 3D reconstruction method and calibration for multi-camera-based volumetric positioning and characterization
- 2021
-
Ingår i: IEEE Transactions on Instrumentation and Measurement. - 0018-9456 .- 1557-9662. ; 70
-
Tidskriftsartikel (refereegranskat)abstract
- A three-dimensional (3D) reconstruction method and multi-camera calibration using multiple artificial reference markers have been used for precise volumetric surveillance of fast-flying objects. The method uses a two-layer 3D reconstruction that integrates two multi-camera stereo-nodes. The fields of view of stereo nodes are directed at an acute angles to each other to provide greater coverage with the given constraints and to determine the flight characteristics of objects in 3D. The object’s flight reconstruction includes a “rough” estimation of its positions relative to selected artificial reference points in both stereo nodes separately and subsequent “refinement” of calculated positions. In this paper, we describe the proposed method and calibration technique, using a multi-camera system to measure object characteristics in 3D. The proposed method applies to volumetric surveillance in situations where it is necessary to count, track, and analyze the activities of flying objects, especially birds, using high spatial resolution.
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| 18. |
- Fedorov, Igor, et al.
(författare)
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Placement Strategy of Multi-Camera Volumetric Surveillance System for Activities Monitoring
- 2017
-
Ingår i: ICDSC 2017 Proceedings of the 11th International Conference on Distributed Smart Cameras. - New York, NY, USA : Association for Computing Machinery (ACM). - 9781450354875 ; , s. 113-118
-
Konferensbidrag (refereegranskat)abstract
- The design of multi-camera surveillance system comes with many advantages, for example it facilitates as understanding how flying objects act in a given volume. One possible application is for the observation interaction of birds and calculate their trajectories around wind turbines to create promising systems for preventing bird collisions with turbine blades. However, there are also challenges, such as finding the optimal node placement and camera calibration. To address these challenges we investigated a trade-off between calibration accuracy and node requirements, including resolution, modulation transfer function, field of view and angle baseline. We developed a strategy for camera placement to achieve improved coverage for golden eagle monitoring and tracking. This strategy based on the modified resolution criterion taking into account the contrast function of the camera and the estimation of the base angle between the cameras.
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| 19. |
- Fedorov, Igor, 1980-, et al.
(författare)
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Towards calibration of outdoor multi-camera visual monitoring system
- 2018
-
Ingår i: ACM International Conference Proceeding Series. - New York, NY, US : ACM Digital Library. - 9781450365116
-
Konferensbidrag (refereegranskat)abstract
- This paper proposes a method for calibrating of multi-camera systems where no natural reference points exist in the surrounding environment. Monitoring the air space at wind farms is our test case. The goal is to monitor the trajectories of flying birds to prevent them from colliding with rotor blades. Our camera calibration method is based on the observation of a portable artificial reference marker made out of a pulsed light source and a navigation satellite sensor module. The reference marker can determine and communicate its position in the world coordinate system at centimeter precision using navigartion sensors. Our results showed that simultaneous detection of the same marker in several cameras having overlapping field of views allowed us to determine the markers position in 3D world coordinate space with an accuracy of 3-4 cm. These experiments were made in the volume around a wind turbine at distances from cameras to marker within a range of 70 to 90 m.
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| 20. |
- Forsström, Stefan, 1984-, et al.
(författare)
-
Specialanpassade kurser för yrkesverksamma ingenjörer : Erfarenheter och upplevelser
- 2023
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Ingår i: Bidrag från den 9:e utvecklingskonferensen för Sveriges ingenjörsutbildningar. - : Mälardalens universitet. - 9789174856200 ; , s. 348-353
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- I dagens samhälle blir det allt viktigare att fortbilda sig under hela sitt yrkesverksamma liv. För att möta efterfrågan på det livslånga lärandet har Mittuniversitetet utvecklat och genomfört ett antal kurser som riktar sig mot yrkesverksamma ingenjörer. Detta arbete presenterar våra erfarenheter av att ge dessa kurser, med en tyngdpunkt på studenternas upplevelser. Syftet med detta är att bygga upp en vetenskaplig bas för vad vi gör som är bra, men även vad som kan förbättras och förändras. Målsättningen är att göra dessa specialanpassade kurser riktade mot yrkesverksamma ingenjörer så givande och flexibla som möjligt. Våra initiala resultat visar bland annat att studenternas negativa upplevelser ofta var kopplade till antagningsförfarandet och det praktiska genomförandet av kurserna. Man hade svårigheter med att hitta hur man skulle registrera sig på kursen och att tidsramen för registrering kunde vara ett problem. Läroplattformen uppfattades som svår att överblicka och det förekom även viss otydlighet gällande var undervisningen skulle äga rum. Den positiva responsen i utvärderingarna gällde främst det faktiska kursinnehållet, då man ansåg att uppgifter och kursmaterial var givande. Vidare uppskattades kursupplägget, att man kunde kombinera studierna med arbete. Framledes kommer vi att fortsätta med dessa utvärderingar i takt med att kurserna ges, och därefter anpassa vårt mottagande och kommunikationen med studenterna. Även kursupplägget ses över kontinuerligt via den återkoppling vi mottar.
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| 21. |
- Gallo, Vincenzo, et al.
(författare)
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Metrological Characterization of a Clip Fastener assembly fault detection system based on Deep Learning
- 2023
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Ingår i: 2023 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). - : IEEE. - 9781665453837
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Konferensbidrag (refereegranskat)abstract
- In a time when Artificial Intelligence (AI) technologies are nearly ubiquitous, railway construction and maintenance systems have not fully grasped the capabilities of such technologies. Traditional railway inspection methods rely on inspection from experienced workers, making such tasks costly from both, the monetary and the time perspective. From an overview of the state-of-the-art research in this area regarding AI-based systems, we observed that their main focus was solely on detection accuracy of different railway components. However, if we consider the critical importance of railway fastening in the overall safety of the railway, there is a need for a thorough analysis of these AI-based methodologies, to define their uncertainty also from a metrological perspective. In this article we address this issue, proposing an image-based system that detects the rotational displacement of the fastened railway clips. Furthermore, we provide an uncertainty analysis of the measurement system, where the resulting uncertainty is of 0.42°, within the 3° error margin defined by the clip manufacturer.
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| 22. |
- Gatner, Ola, et al.
(författare)
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Method for Capturing Measured LiDAR Data with Ground Truth for Generation of Big Real LiDAR Data Sets
- 2024
-
Ingår i: Conference Record - IEEE Instrumentation and Measurement Technology Conference. - : IEEE conference proceedings. - 9798350380903
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Konferensbidrag (refereegranskat)abstract
- The development of machine learning has resulted in data gaining a pivotal role in the technological advancement, especially data where the ground truth of targeted parameters can be efficiently captured. This requires the development of methods that facilitate accurate data collection with ground truth. Under this perspective, Time of Flight sensors pose a high complexity due to the multifaceted nature of noise in the captured data. To enable the use of such sensors in a wide range of applications including Artificial Intelligence, we need to provide also accurate ground truth data. In this article, we present a method for automated data capturing from a LiDAR sensor together with ground truth data generation. This method will facilitate generating big datasets from LiDAR sensors with high accuracy ground truth data. In addition, we provide a dataset that aside from depth sensor data contains also RGB, confidence and infrared data captured from the LiDAR sensor. As a result, the proposed method not only facilitates data capturing but it enables to generate accurate ground truth data, with RMSE of only 0.04 m at 1.3 m distance.
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| 23. |
- Hussain, Mazhar, 1980-, et al.
(författare)
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A Deep Learning Approach for Classification and Measurement of Hazardous Gases Using Multi-Sensor Data Fusion
- 2023
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Ingår i: 2023 IEEE Sensors Applications Symposium (SAS). - : IEEE conference proceedings.
-
Konferensbidrag (refereegranskat)abstract
- Significant risks to public health and the environment are posed by the release of hazardous gases from industries such as pulp and paper. In this study, the aim was to develop a multi-sensor system with a minimal number of sensors to detect and identify hazardous gases. Training and test data for two gases, hydrogen sulfide and methyl mercaptan, which are known to contribute significantly to odors, were generated in a controlled laboratory environment. The performance of two deep learning models, a 1d-CNN and a stacked LSTM, for data fusion with different sensor configurations was evaluated. The performance of these models was compared with a baseline machine learning model. It was observed that the baseline model was outperformed by the deep learning models and achieved good accuracy with a four-sensor configuration. The potential of a cost-effective multi-sensor system and deep learning models in detecting and identifying hazardous gases is demonstrated by this study, which can be used to collect data from multiple locations and help guide the development of in-situ measurement systems for real-time detection and identification of hazardous gases at industrial sites. The proposed system has important implications for reducing pollution and protecting public health.
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| 24. |
- Hussain, Mazhar, 1980-, et al.
(författare)
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A Study on the Correlation between Change in the Geometrical Dimension of a Free-Falling Molten Glass Gob and Its Viscosity
- 2022
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Ingår i: Sensors. - : MDPI. - 1424-8220. ; 22:2, s. 661-661
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- To produce flawless glass containers, continuous monitoring of the glass gob is required. It is essential to ensure production of molten glass gobs with the right shape, temperature, viscosity and weight. At present, manual monitoring is common practice in the glass container industry, which heavily depends on previous experience, operator knowledge and trial and error. This results in inconsistent measurements and consequently loss of production. In this article, a multi-camera based setup is used as a non-invasive real-time monitoring system. We have shown that under certain conditions, such as keeping the glass composition constant, it is possible to do in-line measurement of viscosity using sensor fusion to correlate the rate of geometrical change in the gob and its temperature. The correlation models presented in this article show that there is a strong correlation, i.e., 0.65, between our measurements and the projected viscosity.
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| 25. |
- Hussain, Mazhar, 1980-, et al.
(författare)
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Multi-Camera Based Setup for Geometrical Measurement of Free-Falling Molten Glass Gob
- 2021
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Ingår i: Sensors. - : MDPI AG. - 1424-8220. ; 21:4
-
Tidskriftsartikel (refereegranskat)abstract
- High temperatures complicate the direct measurements needed for continuous characterization of the properties of molten materials such as glass. However, the assumption that geometrical changes when the molten material is in free-fall can be correlated with material characteristics such as viscosity opens the door to a highly accurate contactless method characterizing small dynamic changes. This paper proposes multi-camera setup to achieve accuracy close to the segmentation error associated with the resolution of the images. The experimental setup presented shows that the geometrical parameters can be characterized dynamically through the whole free-fall process at a frame rate of 600 frames per second. The results achieved show the proposed multi-camera setup is suitable for estimating the length of free-falling molten objects.
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| 26. |
- Hussain, Mazhar, 1980-, et al.
(författare)
-
Selection of optimal parameters to predict fuel consumption of city buses using data fusion
- 2022
-
Ingår i: 2022 IEEE Sensors Applications Symposium (SAS). - : IEEE. - 9781665409810
-
Konferensbidrag (refereegranskat)abstract
- The study aims to explore the fuel consumption of city buses with data fusion using a dataset with multiple parameters such as travelled distance, weekday, hour of the day, drivers, buses, and routes, that influence the trip fuel consumption. In this study, manipulated parameters such as modified driver, bus and route identification numbers are used together with original parameters to identify the optimal combination of parameters that can be used to enhance the accuracy of the prediction model. Two regression methods, i.e. cubic SVM and artificial neural networks (ANN), are used to demonstrate the performance of the proposed approach. Results shows that a combination of original parameters and processed parameters increases the performance.
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| 27. |
- Khursheed, Khursheed, 1983-, et al.
(författare)
-
Detecting and Coding Region of Interests in Bi-Level Images for Data Reduction in Wireless Visual Sensor Network
- 2012
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Ingår i: Wireless and Mobile Computing, Networking and Communications (WiMob), 2012 IEEE 8th International Conference on. - : IEEE conference proceedings. - 9781467314299 ; , s. 705-712
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Konferensbidrag (refereegranskat)abstract
- Wireless Visual Sensor Network (WVSN) is formed by deploying many Visual Sensor Nodes (VSNs) in the field. The VSNs acquire images of the area of interest in the field, perform some local processing on these images and transmit the results using an embedded wireless transceiver. The energy consumption on transmitting the results wirelessly is correlated with the information amount that is being transmitted. The images acquired by the VSNs contain huge amount of data due to many kinds of redundancies in the images. Suitable bi-level image compression standards can efficiently reduce the information amount in images and will thus be effective in reducing the communication energy consumption in the WVSN. But compression capability of the bi-level image compression standards is limited to the underline compression algorithm. Further data reduction can be achieved by detecting Region of Interest (ROI) in the bi-level images and then coding these ROIs using bi-level image compression method. We explored the compression performance of the lossless ROI detection and coding method for various kinds of changes such as different shapes, locations and number of objects in the continuous set of frames. The CCITT Group 4, JBIG2 and Gzip are used for coding the detected ROIs. We concluded that CCITT Group 4 is a better choice for coding the ROIs in the Bi-level images because of its comparatively good compression performance and less computational complexity. This paper is intended to be a resource for the researchers interested in reducing the amount of data in the bi-level images for energy constrained WVSNs.
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| 28. |
- Khursheed, Khursheed, et al.
(författare)
-
Efficient Data Reduction Techniques for Remote Applications of a Wireless Visual Sensor Network
- 2013
-
Ingår i: International Journal of Advanced Robotic Systems. - : SAGE Publications. - 1729-8806 .- 1729-8814. ; 10, s. Art. no. 240-
-
Tidskriftsartikel (refereegranskat)abstract
- A Wireless Visual Sensor Network (WVSN) is formed by deploying many Visual Sensor Nodes (VSNs) in the field. After acquiring an image of the area of interest, the VSN performs local processing on it and transmits the result using an embedded wireless transceiver. Wireless data transmission consumes a great deal of energy, where energy consumption is mainly dependent on the amount of information being transmitted. The image captured by the VSN contains a huge amount of data. For certain applications, segmentation can be performed on the captured images. The amount of information in the segmented images can be reduced by applying efficient bi-level image compression methods. In this way, the communication energy consumption of each of the VSNs can be reduced. However, the data reduction capability of bi-level image compression standards is fixed and is limited by the used compression algorithm. For applications attributing few changes in adjacent frames, change coding can be applied for further data reduction. Detecting and compressing only the Regions of Interest (ROIs) in the change frame is another possibility for further data reduction. In a communication system, where both the sender and the receiver know the employed compression standard, there is a possibility for further data reduction by not including the header information in the compressed bit stream of the sender. This paper summarizes different information reduction techniques such as image coding, change coding and ROI coding. The main contribution is the investigation of the combined effect of all these coding methods and their application to a few representative real life applications. This paper is intended to be a resource for researchers interested in techniques for information reduction in energy constrained embedded applications.
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| 29. |
- Khursheed, Khursheed, 1983-, et al.
(författare)
-
Selection of bi-level image compression method for reduction of communication energy in wireless visual sensor networks
- 2012
-
Ingår i: SPIE. - : SPIE. - 9780819491299
-
Konferensbidrag (refereegranskat)abstract
- Wireless Visual Sensor Network (WVSN) is an emerging field which combines image sensor, on board computation unit, communication component and energy source. Compared to the traditional wireless sensor network, which operates on one dimensional data, such as temperature, pressure values etc., WVSN operates on two dimensional data (images) which requires higher processing power and communication bandwidth. Normally, WVSNs are deployed in areas where installation of wired solutions is not feasible. The energy budget in these networks is limited to the batteries, because of the wireless nature of the application. Due to the limited availability of energy, the processing at Visual Sensor Nodes (VSN) and communication from VSN to server should consume as low energy as possible. Transmission of raw images wirelessly consumes a lot of energy and requires higher communication bandwidth. Data compression methods reduce data efficiently and hence will be effective in reducing communication cost in WVSN. In this paper, we have compared the compression efficiency and complexity of six well known bi-level image compression methods. The focus is to determine the compression algorithms which can efficiently compress bi-level images and their computational complexity is suitable for computational platform used in WVSNs. These results can be used as a road map for selection of compression methods for different sets of constraints in WVSN.
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| 30. |
- Krug, Silvia, et al.
(författare)
-
A Case Study on Energy Overhead of Different IoT Network Stacks
- 2019
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Ingår i: 2019 IEEE 5th World Forum on Internet of Things (WF-IoT). - : IEEE. - 9781538649817 - 9781538649800 ; , s. 528-529
-
Konferensbidrag (refereegranskat)abstract
- Due to the limited energy budget for sensor nodes in the Internet of Things (IoT), it is crucial to develop energy efficient communications amongst others. This need leads to the development of various energy-efficient protocols that consider different aspects of the energy status of a node. However, a single protocol covers only one part of the whole stack and savings on one level might not be as efficient for the overall system, if other levels are considered as well. In this paper, we analyze the energy required for an end device to maintain connectivity to the network as well as perform application specific tasks. By integrating the complete stack perspective, we build a more holistic view on the energy consumption and overhead for a wireless sensor node. For better understanding, we compare three different stack variants in a base scenario and add an extended study to evaluate the impact of retransmissions as a robustness mechanism. Our results show, that the overhead introduced by the complete stack has an significant impact on the nodes energy consumption especially if retransmissions are required.
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| 31. |
- Krug, Silvia, et al.
(författare)
-
IoT Communication Introduced Limitations for High Sampling Rate Applications
- 2018
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Ingår i: GI/ITG KuVS Fachgespräch Sensornetze 13. & 14. September 2018, Braunschweig : Technical Report.
-
Konferensbidrag (refereegranskat)abstract
- Networking solutions for the Internet of Things aretypically designed for applications that require low data rates andfeature rare transmission events. The initial assumption leads to asystem design towards minimal data transfers and packet sizes.However, this can become a challenge, if applications requiredifferent traffic patterns or cooperative interaction betweendevices. Applications requiring a high sampling rate to capturethe desired phenomenon produce larger amounts of data thatneed to be transported. In this paper, we present a studyhighlighting some of the challenging aspects for such applicationsand how the choice of communication technology can limit bothapplication behavior and network structure.
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| 32. |
- Krug, Silvia, et al.
(författare)
-
Modeling and Comparison of Delay and Energy Cost of IoT Data Transfers
- 2019
-
Ingår i: IEEE Access. - 2169-3536. ; 7, s. 58654-58675
-
Tidskriftsartikel (refereegranskat)abstract
- Communication is often considered as the most costly component of a wireless sensor node. As a result, a variety of technologies and protocols aim to reduce the energy consumption for the communication especially in the Internet of Things context. In order to select the best suitable technology for a given use case, a tool that allows the comparison of these options is needed. The goal of this paper is to introduce a new modular modeling framework that enables a comparison of various technologies based on analytical calculations. We chose to model the cost for a single data transfer of arbitrary application data amounts in order to provide flexibility regarding the data amount and traffic patterns. The modeling approach covers the stack traversal of application data and thus in comparison to other approaches includes the required protocol overhead directly. By applying our models to different data amounts, we are able to show tradeoffs between various technologies and enable comparisons for different scenarios. In addition, our results reveal the impact of design decisions that can help to identify future development challenges.
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| 33. |
- Krug, Silvia, et al.
(författare)
-
Suitability of Communication Technologies for Harvester-Powered IoT-Nodes
- 2019
-
Ingår i: IEEE International Workshop on Factory Communication Systems - Proceedings, WFCS. - : Institute of Electrical and Electronics Engineers (IEEE). - 9781728112688
-
Konferensbidrag (refereegranskat)abstract
- The Internet of Things introduces Internet connectivity to things and objects in the physical world and thus enables them to communicate with other nodes via the Internet directly. This enables new applications that for example allow seamless process monitoring and control in industrial environments. One core requirement is that the nodes involved in the network have a long system lifetime, despite limited access to the power grid and potentially difficult propagation conditions. Energy harvesting can provide the required energy for this long lifetime if the node is able to send the data based on the available energy budget. In this paper, we therefore analyze and evaluate which common IoT communication technologies are suitable for nodes powered by energy harvesters. The comparison includes three different constraints from different energy sources and harvesting technologies besides several communication technologies. Besides identifying possible technologies in general, we evaluate the impact of duty-cycling and different data sizes. The results in this paper give a road map for combining energy harvesting technology with IoT communication technology to design industrial sensor nodes.
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| 34. |
- Lundström, Adam, et al.
(författare)
-
An interactive threshold-setting procedure for improved multivariate anomaly detection in time series
- 2023
-
Ingår i: IEEE Access. - : IEEE. - 2169-3536. ; 11, s. 93898-93907
-
Tidskriftsartikel (refereegranskat)abstract
- Anomaly detection in multivariate time series is valuable for many applications. In this context, unsupervised and semi-supervised deep learning methods that estimate how normal a new observation is have shown promising results on benchmark datasets. These methods are dependent on a threshold that determines which points should be regarded as anomalous and not be anomalous. However, finding the optimal threshold is not easy since no information about the ground truth is known in advance, which implies that there are limitations to automatic threshold-setting methods available today. An alternative is to utilize the expertise of users that can interact in a threshold-setting procedure, but for this to be practically feasible, the method needs to be both accurate and efficient in relation to the state-of-the-art automatic methods. Therefore, this study develops an interactive threshold-setting schema and examines to what extent it can outperform the current state-of-the-art automatic threshold-setting methods. The result of the study strongly indicates that the suggested method with little effort can provide higher accuracy than the automatic threshold-setting methods on a general basis.
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| 35. |
- Lundström, Adam, et al.
(författare)
-
Factory-Based Vibration Data for Bearing-Fault Detection
- 2023
-
Ingår i: DATA. - : MDPI. - 2306-5729. ; 8:7
-
Tidskriftsartikel (refereegranskat)abstract
- The importance of preventing failures in bearings has led to a large amount of research being conducted to find methods for fault diagnostics and prognostics. Many of these solutions, such as deep learning methods, require a significant amount of data to perform well. This is a reason why publicly available data are important, and there currently exist several open datasets that contain different conditions and faults. However, one challenge is that almost all of these data come from a laboratory setting, where conditions might differ from those found in an industrial environment where the methods are intended to be used. This also means that there may be characteristics of the industrial data that are important to take into account. Therefore, this study describes a completely new dataset for bearing faults from a pulp mill. The analysis of the data shows that the faults vary significantly in terms of fault development, rotation speed, and the amplitude of the vibration signal. It also suggests that methods built for this environment need to consider that no historical examples of faults in the target domain exist and that external events can occur that are not related to any condition of the bearing.
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| 36. |
- Lundström, Adam, et al.
(författare)
-
Improving deep learning based anomaly detection on multivariate time series through separated anomaly scoring
- 2022
-
Ingår i: IEEE Access. - 2169-3536. ; 10, s. 108194-108204
-
Tidskriftsartikel (refereegranskat)abstract
- The importance of anomaly detection in multivariate time series has led to the development of several prominent deep learning solutions. As a part of the anomaly detection method, the scoring method has shown to be of significant importance when separating non-anomalous points from anomalous ones. At this time, most of the solutions utilize an aggregated score which means that relevant information created by the anomaly detection model might be lost. Therefore, this study has set out to examine to what extent anomaly detection in multivariate time series based on deep learning can be improved if all the residuals from each individual channel is considered in the anomaly score. To achieve this, an aggregated and separated scoring method has been applied with a simple denoising convulutional autoencoder (DCAE). In addition, the performance has been compared with other state-of-the-art methods. The result showed that the separated approach has the potential to generate a significantly higher performance than the aggregated one. At the same time, there were some indications suggesting that an aggregated scoring is better at generalizing when no labels to base the anomaly thresholds on, are available. Therefore, the result should serve as an encouragement to use a separated scoring approach together with a small sample of labeled anomalies to optimise the thresholds. Lastly, due to the impact of the anomaly score, the result suggests that future research within this field should consider applying the same anomaly scoring method when comparing the performance of deep learning algorithms.
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| 37. |
- Mahmood, Aamir, 1980-, et al.
(författare)
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Remote-Timber : An Outlook for Teleoperated Forestry With First 5G Measurements
- 2023
-
Ingår i: IEEE Industrial Electronics Magazine. - : IEEE. - 1932-4529 .- 1941-0115. ; 17:3, s. 42-53
-
Tidskriftsartikel (refereegranskat)abstract
- Across all industries, digitalization and automation are on the rise under the Industry 4.0 vision, and the forest industry is no exception. The forest industry depends on distributed flows of raw materials to the industry through various phases, wherein the typical workflow of timber loading and offloading is finding traction in using automation and 5G wireless networking technologies to enhance efficiency and reduce cost. This article presents one such ongoing effort in Sweden, Remote-Timber—demonstrating a 5G-connected teleoperation use-case within a workflow of timber terminal—and disseminates its business attractiveness as well as first measurement results on network performance. Also, it outlines the future needs of the 5G network design/optimization from teleoperation perspective. Overall, the motivation of this article is to disseminate our early-stage findings and reflections to the industrial and academic communities for furthering the research and development activities in enhancing 5G networks for verticals.
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| 38. |
- Nie, Yali, et al.
(författare)
-
A Deep CNN Transformer Hybrid Model for Skin Lesion Classification of Dermoscopic Images Using Focal Loss
- 2023
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Ingår i: Diagnostics. - : MDPI AG. - 2075-4418. ; 13:1
-
Tidskriftsartikel (refereegranskat)abstract
- Skin cancers are the most cancers diagnosed worldwide, with an estimated > 1.5 million new cases in 2020. Use of computer-aided diagnosis (CAD) systems for early detection and classification of skin lesions helps reduce skin cancer mortality rates. Inspired by the success of the transformer network in natural language processing (NLP) and the deep convolutional neural network (DCNN) in computer vision, we propose an end-to-end CNN transformer hybrid model with a focal loss (FL) function to classify skin lesion images. First, the CNN extracts low-level, local feature maps from the dermoscopic images. In the second stage, the vision transformer (ViT) globally models these features, then extracts abstract and high-level semantic information, and finally sends this to the multi-layer perceptron (MLP) head for classification. Based on an evaluation of three different loss functions, the FL-based algorithm is aimed to improve the extreme class imbalance that exists in the International Skin Imaging Collaboration (ISIC) 2018 dataset. The experimental analysis demonstrates that impressive results of skin lesion classification are achieved by employing the hybrid model and FL strategy, which shows significantly high performance and outperforms the existing work.
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| 39. |
- Nie, Yali, et al.
(författare)
-
Automatic Detection of Melanoma with Yolo Deep Convolutional Neural Networks
- 2019
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Ingår i: 2019 E-Health and Bioengineering Conference (EHB). - : IEEE. - 9781728126036
-
Konferensbidrag (refereegranskat)abstract
- In the past three years, deep convolutional neural networks (DCNNs) have achieved promising performance in detecting skin cancer. However, improving the accuracy and efficiency of the automatic detection of melanoma is still urgent due to the visual similarity of benign and malignant dermoscopy. There is also a need for fast and computationally effective systems for mobile applications targeting caregivers and homes. This paper presents the You Only Look Once (Yolo) algorithms, which are based on DCNNs applied to the detection of melanoma. The Yolo algorithms comprise YoloV1, YoloV2, and YoloV3, whose methodology first resets the input image size and then divides the image into several cells. According to the position of the detected object in the cell, the network will try to predict the bounding box of the object and the class confidence score. Our test results indicate that the mean average precision (mAP) of Yolo can exceed 0.82 with a training set of only 200 images, proving that this method has great advantages for detecting melanoma in lightweight system applications.
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| 40. |
- Nie, Yali
(författare)
-
Automatic Melanoma Diagnosis in Dermoscopic Imaging Base on Deep Learning System
- 2021
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Melanoma is one of the deadliest forms of cancer. Unfortunately, its incidence rates have been increasing all over the world. One of the techniques used by dermatologists to diagnose melanomas is an imaging modality called dermoscopy. The skin lesion is inspected using a magnification device and a light source. This technique makes it possible for the dermatologist to observe subcutaneous structures that would be invisible otherwise. However, the use of dermoscopy is not straightforward, requiring years of practice. Moreover, the diagnosis is many times subjective and challenging to reproduce. Therefore, it is necessary to develop automatic methods that will help dermatologists provide more reliable diagnoses. Since this cancer is visible on the skin, it is potentially detectable at a very early stage when it is curable. Recent developments have converged to make fully automatic early melanoma detection a real possibility. First, the advent of dermoscopy has enabled a dramatic boost in the clinical diagnostic ability to the point that it can detect melanoma in the clinic at the earliest stages. This technology’s global adoption has allowed the accumulation of extensive collections of dermoscopy images. The development of advanced technologies in image processing and machine learning has given us the ability to distinguish malignant melanoma from the many benign mimics that require no biopsy. These new technologies should allow earlier detection of melanoma and reduce a large number of unnecessary and costly biopsy procedures. Although some of the new systems reported for these technologies have shown promise in preliminary trials, a widespread implementation must await further technical progress in accuracy and reproducibility. This thesis provides an overview of our deep learning (DL) based methods used in the diagnosis of melanoma in dermoscopy images. First, we introduce the background. Then, this paper gives a brief overview of the state-of-art article on melanoma interpret. After that, a review is provided on the deep learning models for melanoma image analysis and the main popular techniques to improve the diagnose performance. We also made a summary of our research results. Finally, we discuss the challenges and opportunities for automating melanocytic skin lesions’ diagnostic procedures. We end with an overview of a conclusion and directions for the following research plan.
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| 41. |
- Nie, Yali
(författare)
-
Deep Learning Approaches towards Skin Lesion Classification with Dermoscopic Images
- 2023
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Melanoma is a skin cancer that tends to be deadly. The incidence of melanoma is currently at the highest level ever recorded in Europe, North America and Oceania. The survival rate can be significantly increased if skin lesions are identified in dermoscopic images at an early stage. In the other hand, the classification of skin lesions is incredibly challenging. Skin lesion classification using deep learning approaches has provided better results in classifying skin diseases than those of dermatologist, which is lifesaving in terms of diagnosis.This thesis presents a review of our research articles on classifying skin lesions using deep learning. Regarding the research, I have four goals concerning research frontier work, small datasets, data imbalance, and improving accuracy. In this thesis, I discuss how deep learning can classify skin diseases, summarizing the problems that remain at this stage and the outlook for the future.For the above goals, I first studied and summarized more than 200 highguality articles published over five years. I then used three versions of You only look once (Yolo) to detect skin lesions. Although there were only 200 pictures, the test was very effective for detection. I applied the five-fold algorithm to Vgg_16, trained five models, and fused them so solve the small data problem. To improve the accuracy, I also tried to combine the traditional machine learning method, i.e., the seven-point checklist, with three different backbones. Since the learning rate. Then, I also tried to use the hybrid model, combining convolutional neural networks (CNN) and transformer to train the dataset, and applied focal loss to balance the extremely unbalanced weight of the data.In addition to high-quality data sets and high-performance computers being extremely important in the research and application of deep learning, the optimization of machine learning algorithms for skin lesions can be endless
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| 42. |
- Nie, Yali, et al.
(författare)
-
Deep Melanoma classification with K-Fold Cross-Validation for Process optimization
- 2020
-
Ingår i: 2020 IEEE International Symposium on Medical Measurements and Applications (MeMeA). - : IEEE. - 9781728153865
-
Konferensbidrag (refereegranskat)abstract
- Deep convolution neural networks (DCNNs) enable effective methods to predict the melanoma classes otherwise found with ultrasonic extraction. However, gathering large datasets in local hospitals in Sweden can take years. Small datasets will result in models with poor accuracy and insufficient generalization ability, which has a great impact on the result. This paper proposes to use a K-Fold cross validation approach based on a DCNN algorithm working on a small sample dataset. The performance of the model is verified via a Vgg16 extracting the features. The experimental results reveal that the model built by the approach proposed in this paper can effectively achieve a better prediction and enhance the accuracy of the model, which proves that K-Fold can achieve better performance on a small skin cancer dataset.
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| 43. |
- Nie, Yali, et al.
(författare)
-
Multi-Path Interference Denoising of LiDAR Data Using a Deep Learning Based on U-Net Model
- 2024
-
Ingår i: 2024 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). - : IEEE conference proceedings. - 9798350380903
-
Konferensbidrag (refereegranskat)abstract
- Eliminating Multi-Path Interference (MPI) stands as a significant unresolved challenge in the domain of depth estimation using Time-of-Flight (ToF) cameras. ToF data is typically influenced by significant noise and artifacts stemming from MPI. Although a variety of conventional methods have been suggested to enhance ToF data quality, the application of machine learning techniques has been infrequent, primarily due to the scarcity of authentic training data with accurate depth information. This paper introduces an approach that eliminates the dependency on labeled real-world data within the learning framework. We employ a U-Net trained on the data with ground truth in a supervised manner, enabling it to leverage multi-frequency ToF data for MPI correction. Concurrently, we compare three channels as input with one channel and two channels. Our experimental results convincingly showcase the effectiveness of this approach in reducing noise in real-world data.
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| 44. |
- Nie, Yali, et al.
(författare)
-
Recent Advances in Diagnosis of Skin Lesions using Dermoscopic Images based on Deep Learning
- 2022
-
Ingår i: IEEE Access. - 2169-3536. ; 10, s. 95716-95747
-
Tidskriftsartikel (refereegranskat)abstract
- Skin cancer is one of the most threatening cancers, which spreads to the other parts of the body if not caught and treated early. During the last few years, the integration of deep learning into skin cancer has been a milestone in health care, and dermoscopic images are right at the center of this revolution. This review study focuses on the state-of-the-art automatic diagnosis of skin cancer from dermoscopic images based on deep learning. This work thoroughly explores the existing deep learning and its application in diagnosing dermoscopic images. This study aims to present and summarize the latest methodology in melanoma classification and the techniques to improve this. We discuss advancements in deep learning-based solutions to diagnose skin cancer, along with some challenges and future opportunities to strengthen these automatic systems to support dermatologists and enhance their ability to diagnose skin cancer. Author
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| 45. |
- Nie, Yali, et al.
(författare)
-
Skin Cancer Classification based on Cosine Cyclical Learning Rate with Deep Learning
- 2022
-
Ingår i: Conference Record - IEEE Instrumentation and Measurement Technology Conference. - : IEEE. - 9781665483605
-
Konferensbidrag (refereegranskat)abstract
- Since early-stage skin cancer identification can improve melanoma prognosis and significantly reduce treatment costs, AI-based diagnosis systems might greatly benefit patients suffering from suspicious skin lesions. The study proposes a cosine cyclical learning rate with a skin cancer classification model to improve melanoma prediction. The contributions of models involve three critical CNNs, which are standard deep feature extraction modules for the skin cancer classification in this study (Vgg19, ResNet101 and InceptionV3). Each CNN model applies three different learning rates: fixed learning rate(LR), Cosine Annealing LR, and Cosine Annealing with WarmRestarts. HAM10000 is a large collection of publicly available dermoscopic images dataset used for our experiments. The performance of the proposed approach was appraised through comparative experiments. The outcome has indicated that the proposed method has high efficiency in diagnosing skin lesions with a cosine cyclical learning rate.
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| 46. |
- Nordin, Lisa, 1981-, et al.
(författare)
-
Analysis of the quality of optical fibre and fines measurement for prediction of dewatering characteristics for mechanical pulps
-
Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- The quality of the optical fibre and fines measurement has been investigated. Fibres and fines of different quality were mixed in defined proportions and the mixtures were characterized by means of optical fibre measurements and dewatering behaviour. The results show that the same measured fines amounts show different dewatering behaviour, depending on the quality of the fines used. The difference in fines quality was, however, not reflected in the optical measurement. We conclude that this is caused by too low resolution in the optical measurement, so there is a large need for higher resolution of the measurement equipments in order to make it possible to measure the shape of the fines.
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| 47. |
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| 48. |
- Nordin, Lisa, 1981-, et al.
(författare)
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Measurement and prediction of dewatering characteristics for mechanical pulps using optical fibre analyzers
- 2009
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Ingår i: Proceedings - 2009 International Mechanical Pulping Conference, IMPC 2009. ; , s. 309-316
-
Konferensbidrag (refereegranskat)abstract
- The aim of this work was to obtain an on-line measurement for dewatering behaviour in the wire section based on fibre and fines characteristics. Four laboratory dewatering equipments were compared and the fibre characteristics were measured by means of optical fibre analyzers. The results show that rough correlations do appear to exist between the dewatering equipments; however they rank the pulps differently depending on the raw wood material used and whether the refining conditions are mild or harsh. The prediction models based on fibre characteristics showed a high degree of statistical accuracy. The descriptions, however, proved not to be sufficiently good with regards to the dewatering behaviour for them to be used in relation to on-line applications. This might have been because consideration was not given to some important variables which do, in fact, have a significant impact on the drainability. These variables could include physical fibre properties or others that are not measured, or properties that, at present, are unable to be measured at a sufficient resolution.
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| 49. |
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| 50. |
- O'Nils, Mattias, 1969-, et al.
(författare)
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Threshold Modulation for Continuous Energy Resolution with Two Channels per Pixel in a Photon Counting X-ray Image Detector
- 2009
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Ingår i: Nuclear Instruments and Methods in Physics Research Section A. - : Elsevier. - 0168-9002 .- 1872-9576. ; 607:1, s. 236-239
-
Tidskriftsartikel (refereegranskat)abstract
- The introduction of energy resolution in X-ray image detectors will lead to tradeoffs between circuit complexity and spatial/energy resolution in the pixel design. The proposed method provides continuous energy resolution with only two energy channels per pixel, which is a comparable complexity to that of a window discriminator pixel like Medipix2. The paper illustrates the method and validates the method through analytical analysis and through simulation of real and synthetic data.
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