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Computing and Information Technology (ICCIT-1441), 2020 International Conference on

作者： Munzir Umran Kahlil Muchtar Taufik Fuadi Abidin Fitri Arnia Dept. of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh Indonesia Dept. of Electrical and Computer Engineering Telematics Research Center (TRC) Universitas Syiah Kuala Banda Aceh Indonesia Department of Informatics Faculty of Mathematics and Natural Sciences Universitas Syiah Kuala Banda Aceh Indonesia

Since CCTV technology was widely introduced as a video surveillance system, it has become very popular due to increased security concerns. However, detecting suspicious behavior is still done manually by humans, which is inefficient in terms of accuracy and manpower (prone to distraction and fatigue). Many studies use the entire frame of a video to recognize human action, whereas in this study first localize the area of object movement using the unsupervised inflated 3D (I3D) network which produces several frames that have been determined as the region of interest (ROI) from the total frames in the video. Then the collection of ROI frames is generated into a video which will then be classified to recognize human action through the Temporal Segment Networks (TSN). For efficiency purposes, the proposed pipeline can be run separately in the edge (for localization), and cloud environment (for recognition), respectively. The results show that there are 54 classes of ROI video data sets that have an average human action recognition prediction similarity rate above 84.6% which is the average of the prediction similarity rate of the entire ROI video data. In terms of prediction accuracy, 37 classes of data (36.63%) are better than the original class, including 25 classes of data (24.75%) above the threshold, and the processing speed of classification with ROI video data is on average 5 times faster than full-frame video data.

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CEFHRI: A Communication Efficient Federated Learning Framework for Recognizing Industrial Human-Robot Interaction

arXiv

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arXiv 2023年

作者： Khalid, Umar Iqbal, Hasan Vahidian, Saeed Hua, Jing Chen, Chen Center For Research in Computer Vision University of Central Florida OrlandoFL United States Dept. of Computer Science Wayne State University DetroitMI United States Dept. of Electrical and Computer Engineering Duke University DurhamNC United States

Human-robot interaction (HRI) is a rapidly growing field that encompasses social and industrial applications. Machine learning plays a vital role in industrial HRI by enhancing the adaptability and autonomy of robots in complex environments. However, data privacy is a crucial concern in the interaction between humans and robots, as companies need to protect sensitive data while machine learning algorithms require access to large datasets. Federated Learning (FL) offers a solution by enabling the distributed training of models without sharing raw data. Despite extensive research on Federated learning (FL) for tasks such as natural language processing (NLP) and image classification, the question of how to use FL for HRI remains an open research problem. The traditional FL approach involves transmitting large neural network parameter matrices between the server and clients, which can lead to high communication costs and often becomes a bottleneck in FL. This paper proposes a communication-efficient FL framework for human-robot interaction (CEFHRI) to address the challenges of data heterogeneity and communication costs. The framework leverages pre-trained models and introduces a trainable spatiotemporal adapter for video understanding tasks in HRI. Experimental results on three human-robot interaction benchmark datasets: HRI30, InHARD, and COIN demonstrate the superiority of CEFHRI over full fine-tuning in terms of communication costs. The proposed methodology provides a secure and efficient approach to HRI federated learning, particularly in industrial environments with data privacy concerns and limited communication bandwidth. Our code is available at https://***/umarkhalidAI/ CEFHRI-Efficient-Federated-Learning. Copyright © 2023, The Authors. All rights reserved.

关键词： Human robot interaction

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Directional Metasurface Using Transistor-Loaded Circularly Polarized Antenna Elements

Directional Metasurface Using Transistor-Loaded Circularly P...

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2023 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2023

作者： Chatzichristodoulou, David Vryonides, Photos Psychogiou, Dimitra Nikolaou, Symeon Frederick Research Center Nicosia Cyprus Rf and Microwave Solutions Ltd Cyprus School of Engineering University College Cork CorkT12 K8AF Ireland Tyndall National Institute CorkT12 R5CP Ireland Frederick University Dept. Electrical Computer Engineering and Informatics Nicosia Cyprus

ISBN: (纸本)9781665442282

This paper discusses the RF design of a compact metasurface that enables directional propagation characteristics. It allows for circularly-polarized waves to penetrate through its volume in one direction of propagation whereas it absorbs them in the reverse one. Directionality is achieved by combining two transistors with two circularly-polarized patch antennas in each unit cell of the metasurface. A compact multi-layer integration scheme is proposed and is demonstrated through electromagnetic (EM) simulations at S-band. The proposed metasurface configuration exhibits directional characteristics with the following performance metrics. BW in the forward direction: 3.4-3.7 GHz, maximum Gain in the forward direction: 29 dB, isolation in the reversed direction> 50 dB. © 2023 IEEE.

关键词： Microstrip antennas

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A stochastic linearized augmented lagrangian method for decentralized bilevel optimization 22

A stochastic linearized augmented lagrangian method for dece...

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Songtao Lu Siliang Zeng Xiaodong Cui Mark S. Squillante Lior Horesh Brian Kingsbury Jia Liu Mingyi Hong IBM Research Thomas J. Watson Research Center Yorktown Heights NY Dept. of Electrical and Computer Engineering University of Minnesota Minneapolis MN Dept. of Electrical and Computer Engineering The Ohio State University Columbus OH

ISBN: (纸本)9781713871088

Bilevel optimization has been shown to be a powerful framework for formulating multi-task machine learning problems, e.g., reinforcement learning (RL) and meta-learning, where the decision variables are coupled in both levels of the minimization problems. In practice, the learning tasks would be located at different computing resource environments, and thus there is a need for deploying a decentralized training framework to implement multi-agent and multi-task learning. We develop a stochastic linearized augmented Lagrangian method (SLAM) for solving general nonconvex bilevel optimization problems over a graph, where both upper and lower optimization variables are able to achieve a consensus. We also establish that the theoretical convergence rate of the proposed SLAM to the Karush-Kuhn-Tucker (KKT) points of this class of problems is on the same order as the one achieved by the classical distributed stochastic gradient descent for only single-level nonconvex minimization problems. Numerical results tested on multi-agent RL problems showcase the superiority of SLAM compared with the benchmarks.

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On the Feasibility of RIS-enabled Cooperative Driving

On the Feasibility of RIS-enabled Cooperative Driving

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IEEE Vehicular Networking Conference (VNC)

作者： Michele Segata Paolo Casari Marios Lestas Dimitrios Tyrovolas Taqwa Saeed George Karagiannidis Christos Liaskos Department of Information Engineering and Computer Science University of Trento Italy Dept. of Electrical Engineering Frederick University Cyprus Dept. of Electrical Engineering Aristotle University of Thessaloniki Greece School of Information Technology Halmstad University Sweden Cyber Security Systems and Applied AI Research Center Lebanese American University (LAU) Lebanon Dept. of Computer Science and Engineering University of Ioannina & FORTH Greece

Future cooperative autonomous vehicles will require high-performance communication means to support functions such as cooperative maneuvering and cooperative perception. The high-bandwidth requirements of these functions can be met through mmWave communications, whose utilization is often hindered by the harsh propagation conditions of typical vehicular environments. A solution to this problem is the use of reconfigurable intelligent surfaces (RISs), which enable the reflection of signals in a configurable direction, and have recently gained attention in the vehicular domain. In this paper, we provide an initial feasibility study, highlighting the challenges ahead and the performance RISs need to deliver in order to enable this type of communications. Specifically, we utilize CoopeRIS, a simulation framework for RISs integrated into the Plexe/Veins/SUMO ecosystem that we develop as further contribution and will release to the public.

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Matrix Resolvent Eigenembeddings for Dynamic Graphs

Matrix Resolvent Eigenembeddings for Dynamic Graphs

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Vasileios Kalantzis Panagiotis A. Traganitis IBM Research Thomas J. Watson Research Center Yorktown Heights NY USA Dept. of Electrical and Computer Engineering Michigan State University East Lansing MI USA

Eigenvector embeddings have been widely used to study graph properties in signal processing, mining, and learning tasks. However, if a graph is changing dynamically, these embeddings have to be recomputed. In this work we introduce a novel matrix resolvent expansion-based projection scheme to update eigenvector embeddings of dynamic graphs. The proposed method can tackle graph updates where both new vertices and edges are added, and its potential is illustrated via numerical tests on real data.

关键词： Social networking (online) Graph neural networks Acoustics Task analysis Speech processing Signal resolution

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Prediction of Stroke Risk Within 7-Years Follow Up Using Machine Learning Models

Prediction of Stroke Risk Within 7-Years Follow Up Using Mac...

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2023 IEEE EMBS Special Topic Conference on Data Science and engineering in Healthcare, Medicine and Biology, IEEECONF 2023

作者： Tsarapatsani, Konstantina-Helen Sakellarios, Antonis Tsakanikas, Vasilis D. Rudolf, Henrik Trampisch, Hans J. Pezoulas, Vasileios C. Matsopoulos, George K. Fotiadis, Dimitrios I. University of Ioannina Unit of Medical Technology and Intelligent Information Systems Dept. of Materials Science and Engineering IoanninaGR 45110 Greece Biomedical Research Institute Greece Institute for Biostatistics and Informatics in Medicine and Ageing Research University Medical Center Rostock Germany Ruhr University Bochum Department of Medical Informatics Biometry and Epidemiology Bochum Germany National Technical University of Athens School of Electrical and Computer Engineering Athens Greece

ISBN: (纸本)9798350383386

A stroke, also known as brain attack, occurs when blood supply to your brain is interrupted. Primary prevention relies on prompt prediction of a stroke. While currently there are several clinical risk scores, machine learning (ML) models seems to be more suitable tools for accurate prediction of stroke events. Therefore, this work focuses on the prediction of stroke within 7 years follow-up in patients who have not suffered from a stroke or TIA event at baseline. LightGBM (LGBM), Extreme Grading Boosting (XGBoost), Support Vector Machine (SVM) and Decision Tree were employed in the getABI dataset, which includes 5,897 participants. The performance of models was calculated by Accuracy (ACC), Sensitivity (SENS), Specificity (SPE) and area under the receiver operating characteristic curve (AUC) of each model. According to the comparison analysis's results, LGBM has been shown to be the most trustworthy algorithm, with accuracy 68 %. Moreover, sex, age, status of peripheral artery disease (PAD), history of myocardial infarction, angina pectoris, amputation and diabetes and pulse status of different arteries can be used as a simple and cost-effective way to predict *** Relevance: A fatal medical emergency, stroke may be anticipated using artificial intelligence, and the sooner it is predicted, the more cerebrovascular disease occurrences can be avoided. © 2023 IEEE.

关键词： Forecasting

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Speech Recovery Using Adaptive Noise Canceller with Multiple Optimized Primary Channels 7

Speech Recovery Using Adaptive Noise Canceller with Multiple...

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7th International Conference on Signal and Image Processing, ICSIP 2022

作者： Zhang, L. Xiao, Y. Ma, L. Khorasani, K. Hasegawa, K. Prefectural University of Hiroshima Information and Management Systems Graduate School of Comprehensive Scientific Research Hiroshima734-8558 Japan Concordia University Dept. of Electrical & Computer Engineering Montreal Canada Western Region Industrial Research Center Hiroshima Prefectural Technology Research Institute Hiroshima737-0004 Japan

ISBN: (数字)9781665495639

ISBN: (纸本)9781665495639

In recent years, speech enhancement in the presence of very harsh noise has received a lot of attention. In this paper, an adaptive noise canceller (ANC) having a single-reference and multi-primary channels (SRMP) is introduced to recover the speech signal from its contaminated measurements. This SRMPANC takes a bone-conducted (BC) signal as its reference and adopts multiple noisy air-conducted (AC) speech measurements as its primary signals. The multiple primary channels are connected by a linear combiner (LC) which is optimized and updated by a recursive least square (RLS) algorithm. A linear or a nonlinear filter (e.g. FIR, Volterra, etc.) is equipped in the SRMP-ANC, that is updated by an LMS or LMS-like algorithm. Extensive simulations with real bone-and air-conducted speech measurements are performed to demonstrate the effectiveness of the proposed SRMP-ANC. It is revealed that the speech recovery quality is significantly improved as the number of primary channels is increased in terms of both mutual correlation and mean square error (MSE) between the AC speech signal and its recovered version. © 2022 IEEE.

关键词： Speech enhancement

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A Sensor Watermarking Design for Threat Discrimination

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IFAC-PapersOnLine 2022年第6期55卷 433-438页

作者： Kangkang Zhang Andreas Kasis Marios M. Polycarpou Thomas Parisini KIOS Research and Innovation Center of Excellence and Dept. of Electrical and Computer Engineering University of Cyprus Nicosia 1678 Cyprus Dept. of Electrical and Electronic Engineering Imperial College London London SW7 2AZ UK Dept. of Engineering and Architecture University of Trieste Trieste 34127 Italy

This paper proposes a sensor additive switching watermark methodology for detecting physical faults and replay attacks, and then identifying the occurring threat type: either the physical fault or the replay attack. The sensor watermark methodology includes a switching watermark generator in the plant side and a switching watermark remover in the control and monitoring side. The switching protocols of the generator and the remover and their common watermark seeds are specifically designed, guaranteeing the switch synchronization in both the nominal and fault cases, and allowing the switch asynchronization in the replay attack case. The latter is used for discriminating the two considered threat cases. The detectability and discrimination ability of the proposed watermark approach, characterizing the class of attacks and faults that can be detected and discriminated, is rigorously investigated. A simulation example is presented to illustrate the effectiveness of the proposed approach.

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Optimum Gain Conditions for 1-D Unidirectional Leaky-Wave Antennas

Optimum Gain Conditions for 1-D Unidirectional Leaky-Wave An...

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2021 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting, APS/URSI 2021

作者： Fuscaldo, Walter Galli, Alessandro Jackson, David R. National Research Council Institute for Microelectronics and Microsystems Rome00133 Italy Sapienza University of Rome Dept. Information Engineering Electronics and Telecommunications Rome00184 Italy University of Houston Dept. Electrical and Computer Engineering HoustonTX77204-4005 United States

ISBN: (纸本)9781728146706

The optimization of the radiating performance for finite-length 1-D unidirectional leaky-wave antennas (LWAs) is first treated in the general case, i.e., assuming that the phase and attenuation constants can be varied freely. In this general case, an approximate analytical formula is derived to calculate the gain from the beamwidth, improving the accuracy of previous existing formulas. When a particular class of 1-D LWAs is considered, design equations are required to relate the geometrical parameters to the relevant LWA wavenumber. Leaky rectangular waveguides and other LWAs based on partially reflecting sheets (PRS) are two important classes of LWAs for which approximate expressions are available. Design procedures for optimizing the gain are given here for general 1-D LWAs, including leaky rectangular waveguides and other PRS-based LWAs. © 2021 IEEE.

关键词： Traveling wave antennas

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