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arXiv 2024年

作者： He, Jiguang Vanwynsberghe, Charles Chen, Hui Huang, Chongwen Fakhreddine, Aymen School of Computing and Information Technology Great Bay University Dongguan China Technology Innovation Institute Masdar City Abu Dhabi9639 United Arab Emirates Department of Electrical Engineering Chalmers University of Technology Gothenburg Sweden College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China Institute of Networked and Embedded Systems University of Klagenfurt Klagenfurt Austria

In this paper, we investigate the potential of reconfigurable intelligent surfaces (RISs) in facilitating passive/device-free three-dimensional (3D) drone localization within existing cellular infrastructure operating at millimeter-wave (mmWave) frequencies and employing multiple antennas at the transceivers. The developed localization system operates in the bi-static mode without requiring direct communication between the drone and the base station. We analyze the theoretical performance limits via Fisher information analysis and Cramér Rao lower bounds (CRLBs). Furthermore, we develop a low-complexity yet effective drone localization algorithm based on coordinate gradient descent and examine the impact of factors such as radar cross section (RCS) of the drone and training overhead on system performance. It is demonstrated that integrating RIS yields significant benefits over its RIS-free counterpart, as evidenced by both theoretical analyses and numerical simulations. © 2024, CC BY.

关键词： Cramer-Rao bounds

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High throughput event filtering: The interpolation-based DIF algorithm hardware architecture

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Microprocessors and Microsystems 2025年

作者： Marcin Kowalczyk Tomasz Kryjak Embedded Vision Systems Group Department of Automatic Control and Robotics Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering AGH University of Krakow Poland

In recent years, there has been rapid development in the field of event vision. It manifests itself both on the technical side, as better and better event sensors are available, and on the algorithmic side, as more and more applications of this technology are proposed and scientific papers are published. However, the data stream from these sensors typically contains a significant amount of noise, which varies depending on factors such as the degree of illumination in the observed scene or the temperature of the sensor. We propose a hardware architecture of the Distance-based Interpolation with Frequency Weights(DIF) filter and implement it on an FPGA chip. To evaluate the algorithm and compare it with other solutions, we have prepared a new high-resolution event dataset, which we are also releasing to the community. Our architecture achieved a throughput of 403.39 million events per second (MEPS) for a sensor resolution of 1280 × 720 and 428.45 MEPS for a resolution of 640 × 480. The averagevalues of the Area Under the Receiver Operating Characteristic (AUROC) index ranged from 0.844 to 0.999, depending on the dataset, which is comparable to the state-of-the-art filtering solutions, but with much higher throughput and better operation over a wide range of noise levels.

关键词： Dataset Denoising DVS FPGA High-throughput

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NPU Thermal Management

NPU Thermal Management

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作者： Amrouch, Hussam Zervakis, Georgios Salamin, Sami Kattan, Hammam Anagnostopoulos, Iraklis Henkel, Jorg in the Computer Science Electrical Engineering Faculty University of Stuttgart Stuttgart Germany Chair for Embedded Systems Karlsruhe Institute of Technology Karlsruhe Germany Department of Electrical and Computer Engineering Southern Illinois University CarbondaleIL United States

Neural processing units (NPUs) are becoming an integral part in all modern computing systems due to their substantial role in accelerating neural networks (NNs). The significant improvements in cost-energy-performance stem from the massive array of multiply accumulate (MAC) units that remarkably boosts the throughput of NN inference. In this work, we are the first to investigate the thermal challenges that NPUs bring, revealing how MAC arrays, which form the heart of any NPU, impose serious thermal bottlenecks to on-chip systems due to their excessive power densities. For the first time, we explore: 1) the effectiveness of precision scaling and frequency scaling (FS) in temperature reductions and 2) how advanced on-chip cooling using superlattice thin-film thermoelectric (TE) open doors for new tradeoffs between temperature, throughput, cooling cost, and inference accuracy in NPU chips. Our work unveils that hybrid thermal management, which composes different means to reduce the NPU temperature, is a key. To achieve that, we propose and implement PFS-TE technique that couples precision and FS together with superlattice TE cooling for effective NPU thermal management. Using commercial signoff tools, we obtain accurate power and timing analysis of MAC arrays after a full-chip design is performed based on 14-nm Intel FinFET technology. Then, multiphysics simulations using finite-element methods are carried out for accurate heat simulations in the presence and absence of on-chip cooling. Afterward, comprehensive design-space exploration is presented to demonstrate the Pareto frontier and the existing tradeoffs between temperature reductions, power overheads due to cooling, throughput, and inference accuracy. Using a wide range of NNs trained for image classification, experimental results demonstrate that our novel NPU thermal management increases the inference efficiency (TOPS/Joule) by $1.33\times $ , $1.87\times $ , and $2\times $ under different temperature constrain

关键词： Temperature control

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Analysis of Photosynthetic Photon Flux Distribution Under LED Lighting for Efficient Okra Cultivation

Analysis of Photosynthetic Photon Flux Distribution Under LE...

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IEEE Industry Applications Society Annual Meeting (IAS)

作者： Yao Hervé Yao Pascal Dupuis Laurent Canale Banah Florent Degni Théodore Cissé Haba Georges Zissis Joint Research and Innovation Unit in Engineering Science and Technology (UMRI-STI) National Polytechnic Institute Houphouët-Boigny (INP-HB) Yamoussoukro Ivory Coast LAPLACE Université de Toulouse 3 CNRS INPT UPS Toulouse France CNRS LAPLACE Laboratory UMR 5213 Toulouse France Electronic Electrical and Embedded Systems Engineering Laboratory (LIEESE) National Polytechnic Institute Houphouët-Boigny (INP-HB) Yamoussoukro Ivory Coast

ISBN: (数字)9798350372717

ISBN: (纸本)9798350372724

An analytical method for evaluating the photosynthetic photon flux density (PPFD) distribution was developed using relationships between the quantities and units of electromagnetic radiation used in radiometry and photometry for horticultural lighting. We have adapted our settings specifically for growing okra under artificial lighting. We proposed a realistic analytical method to determine the number of LEDs needed, based on the desired PPFD. Firstly, the emission wavelengths and the radiant flux value of the LED modules suitable for growing Okra were determined by modeling the ideal spectrum. Then, considering each LED as an imperfect Lambertian emitter, the PPFD distribution was calculated from the photosynthetic photon intensity distribution (PPID) curves. Finally, the simulation is carried out using MATLAB software. The results show that the uniformity of the PPFD is significantly affected by the spacing D between the LED modules and the height of the luminaire relative to the work surface. The analysis method presented here can be usefully applied in the design of LED lighting for horticulture to other similar crops.

关键词： Horticulture Light emitting diodes Radiometry Artificial light Software Mathematical models Photosynthesis MATLAB Photometry Photonics

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Using LSTM and Regression Analysis for Railway Passenger Traffic Volume Prediction

Using LSTM and Regression Analysis for Railway Passenger Tra...

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IEEE Global Conference on Consumer Electronics (GCCE)

作者： Fu Jie Tey Chih-Chi Tuan Cheng-En Cai Ming-Xuan Wu Yun-Sheng Wu Tin-Yu Wu Department of Electrical Engineering National Taiwan University of Science and Technology Taipei Taiwan Master Program of E-Learning for Multimedia and Network Communications National Ilan University Yilan Taiwan Department of Management Information Systems National Pingtung University of Science and Technology Pingtung County Taiwan

ISBN: (纸本)9781665492331

The purpose of this paper is to investigate the factors affecting the passenger traffic volume on the high-capacity MRT, to analyze the traffic volume trends, and to make traffic volume predictions more accurately. This paper uses the LSTM (Long Short-term Memory) model and regression analysis for Taipei MRT passenger traffic volume prediction. The training data, including dates, temperatures and number of passengers, were collected, and a regression analysis was performed to find trends in the data.

关键词： Temperature distribution Solid modeling Government Transportation Training data Predictive models Market research

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Visual analytics methods for eye tracking data

Visual analytics methods for eye tracking data

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International KES Conference on Human Centred Intelligent systems, KES-HCIS 2020

作者： Quadar, Nordine Chehri, Abdellah Geon, Gwanggil School of Electrical Engineering and Computer Science University of Ottawa OttawaONK1N 6N5 Canada Department of Applied Sciences University of Québec in Chicoutimi ChicoutimiQCG7H 2B1 Canada Department Embedded Systems Engineering Incheon National University Incheon Korea Republic of

ISBN: (纸本)9789811557835

Nowadays, eye tracking data have become important and valuable information that help to understand the behavior of users. The gathering of these data is not an issue anymore. However, the problem is the analysis process and especially how can these raw data be converted to understandable and useful information. Visual analytics can solve this issue by combining human analytics skills and the advanced computer analytics. This leads to the novel discoveries and helps humans take control of the analytical process. These visualizations can be used to solve difficult problems by discovering new unknown patterns of available data. In this work, we discussed different methods that are used in the case of eye tracking data, and we addressed the challenges of visual analytics in this context. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020.

关键词： Big data

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Trends, Opportunities, and Challenges in Detecting Depressive Disorders Through Mobile Devices: A Review

Trends, Opportunities, and Challenges in Detecting Depressiv...

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Computer System, Information Technology, and electrical engineering (COSITE), International Conference on

作者： Gregorius Natanael Elwirehardja Mahmud Isnan Anzaludin Samsinga Perbangsa Kahlil Muchtar Bens Pardamean Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Information System Department School of Information Systems Bina Nusantara University Jakarta Indonesia Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh Indonesia Computer Science Department BINUS Graduate Program -Master of Computer Science Bina Nusantara University Jakarta Indonesia

Depressive Disorders (DD) is one of the most prevalent mental disorders in the world that may lead to suicide cases. To prevent the latter, ubiquitous early detection systems may be effective. Recent studies have since researched the development of such systems by exploiting several forms of data, including video, audio, Ecological Momentary Assessments (EMA), and passive sensing data using sensors embedded in mobile devices. To summarize the trends, opportunities, and existing challenges in this field, this study reviewed 15 papers to answer four research questions. EMA was the most popular data to be used in this task, but other approaches, such as using video, audio, and typing behaviors, may be considered due to the subjectivity of EMA. These data were typically recorded using smartphones and analyzed using Machine Learning (ML). However, most of the developed systems had yet to be implemented. Overall, it was concluded that further studies may need to explore usages of more objective data in multimodal approaches as well as consider using Mobile Cloud Computing (MCC) to deploy these systems to provide more effective and efficient diagnoses. Future studies must also take into account the existing challenges of the data and infrastructures, such as the weaknesses of several data types, limitations of mobile devices, as well as the challenges of diagnosis approaches.

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Developing a Computer-Aided Diagnostic System for Breast Cancer Ultrasound Imaging

Developing a Computer-Aided Diagnostic System for Breast Can...

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Data science and engineering in Healthcare, Medicine and Biology, IEEE EMBS Special Topic Conference on

作者： Radwa Taha Shereen Afifi Mohamed A. Abd-ElGhany Mohammed A.-M Salem Master Candidate in the Faculty of Media Engineering and Technology German University in Cairo New Cairo Egypt Assistant Professor in Computer Science Engineering Faculty of Media Engineering and Technology German University in Cairo New Cairo Egypt Professor in Electrical Engineering Faculty of Information Engineering and Technology German University in Cairo New Cairo Egypt Integrated Electronics Systems Lab TU Darmstadt Germany Digital Media Engineering Faculty of Media Engineering and Technology German University in Cairo New Cairo Egypt

In this research we aim to develop a Computer-aided diagnostic system (CAD) for breast ultrasound imaging to enhance the early detection of breast cancer. CAD tools aid physicians in the diagnostic process leading to early detection and treatment of breast cancer. The proposed CAD tool comprises three stages, the first is enhancement of breast ultrasound imaging by applying custom segmentation deep learning architectures [U-Net, SK-U-Net, RDA-U-Net] to enhance the isolation of masses in Ultrasound images, then generation of new ultrasound images by employing Generative Adversarial Networks [DAGAN, DCGAN] to overcome data limitation in available data-sets, and the last stage is the detection and classification of breast cancer masses by a CNN model. The enhancement stage shows promising results with accuracy of 98 % overall accuracy, 92 % overall precision, and 85 % overall *** Relevance: Breast cancer is one of the deadly diseases in the world. The earlier the detection the higher the chances of recovery. Thus a CAD tool that facilitates the screening process of ultrasound images will aid physicians in the early detection and diagnosis of the disease.

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A Novel Framework of Horizontal-Vertical Hybrid Federated Learning for EdgeIoT

IEEE Networking Letters

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IEEE Networking Letters 2025年

作者： Li, Kai Liang, Yilei Yuan, Xin Ni, Wei Crowcroft, Jon Yuen, Chau Akan, Ozgur B. Group Department of Engineering University of Cambridge CambridgeCB3 0FA United Kingdom Real-Time and Embedded Computing Systems Research Centre CISTER Porto4249-015 Portugal The Department of Computer Science and Technology University of Cambridge CambridgeCB3 0FA United Kingdom Commonwealth Scientific and Industrial Research Organization CSIRO SydneyNSW2122 Australia The School of Computing Science and Engineering University of New South Wales Australia Nanyang Technological University SG 639798 Singapore Group Division of Electrical Engineering Department of Engineering University of Cambridge CambridgeCB3 0FA United Kingdom The Center for neXt-generation Communications CXC Koç University Istanbul34450 Turkey

This letter puts forth a new hybrid horizontal-vertical federated learning (HoVeFL) for mobile edge computing-enabled Internet of Things (EdgeIoT). In this framework, certain EdgeIoT devices train local models using the same data samples but analyze disparate data features, while the others focus on the same features using non-independent and identically distributed (non-IID) data samples. Thus, even though the data features are consistent, the data samples vary across devices. The proposed HoVeFL formulates the training of local and global models to minimize the global loss function. Performance evaluations on CIFAR-10 and SVHN datasets reveal that the testing loss of HoVeFL with 12 horizontal FL devices and six vertical FL devices is 5.5% and 25.2% higher, respectively, compared to a setup with six horizontal FL devices and 12 vertical FL devices. © 2019 IEEE.

关键词： Federated learning

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A Genetic Algorithm to Optimize Cargo Stability in Container Transportation

A Genetic Algorithm to Optimize Cargo Stability in Container...

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IEEE Global Conference on Consumer Electronics (GCCE)

作者： Chi-Jung Hsieh Kuang-Sung Wu Tin-Yu Wu Fu Jie Tey Yi-Kai Chen Jye Yi Ho Department of Computer Science and Information Engineering National Ilan University Ilan County Taiwan Master Program of E-Learning for Multimedia and Network Communications National Ilan University Yilan Taiwan Department of Management Information Systems National Pingtung University of Science and Technology Pingtung County Taiwan Department of Electrical Engineering National Taiwan University of Science and Technology Taipei Taiwan

ISBN: (纸本)9781665492331

In the era of the global village, frequent cross-border trade in goods has made container transportation a significant part in delivery of cargo. However, rollover accidents of container trucks often occur because of tall truck height, improper cargo stowage or crooked roads. Therefore, how to find the optimal center of gravity (CG) of the packed cargo remains an issue that the transportation industry should not neglect. To optimize cargo stability and reduce the incidence of rollovers, this paper proposes a genetic algorithm that scores the cargo stacking process to find the best CG of the cargo based on the CG of the vehicle. According to the deviation angle and the point closest to the best CG, we can Figure out the optimal cargo stacking method.

关键词： Rollover Transportation industry Roads Stacking Transportation Containers Safety

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