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19th IEEE Annual Consumer Communications and Networking Conference, CCNC 2022

作者： Gholami, Anousheh Torkzaban, Nariman Baras, John S. University of Maryland Department of Electrical and Computer Engineering Institute for Systems Research College ParkMD United States

ISBN: (纸本)9781665431620

Federated learning (FL) has received significant attention from both academia and industry, as an emerging paradigm for building machine learning models in a communication-efficient and privacy preserving manner. It enables potentially a massive number of resource constrained agents (e.g. mobile devices and IoT devices) to train a model by a repeated process of local training on agents and centralized model aggregation on a central server. To overcome the single-point-of-failure and scalability issues of the traditional FL frameworks, decentralized (server-less) FL has been proposed. In a decentralized FL setting, agents implement consensus techniques by exchanging local model updates. Despite bypassing the direct exchange of raw data between the collaborating agents, this scheme is still vulnerable to various security and privacy threats such as data poisoning *** this paper, we propose trust as a metric to measure the trustworthiness of the FL agents and thereby enhance the security of the FL training. We first elaborate on trust as a security metric by presenting a mathematical framework for trust computation and aggregation within a multi-agent system. We then discuss how this framework can be incorporated within a decentralized FL setup introducing the trusted decentralized FL algorithm. Finally, we validate our theoretical findings by means of numerical experiments. © 2022 IEEE.

关键词： Multi agent systems

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Quality of Control Based Resource Dimensioning for Collaborative Edge Robotics 22

Quality of Control Based Resource Dimensioning for Collabora...

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22nd IEEE Consumer Communications and Networking Conference, CCNC 2025

作者： Roy, Neelabhro Dhullipalla, Mani H. Sharma, Gourav Prateek Dimarogonas, Dimos V. Gross, James School of Electrical Engineering and Computer Science Digital Futures Research Center Kth Royal Institute of Technology Stockholm Sweden

ISBN: (纸本)9798331508050

With the increasing focus on flexible automation, which emphasizes systems capable of adapting to varied tasks and conditions, exploring future deployments of cloud and edge-based network infrastructures in robotic systems becomes crucial. This work, examines how wireless solutions could support the shift from rigid, wired setups toward more adaptive, flexible automation in industrial environments. We provide a quality of control (QoC) based abstraction for robotic workloads, parameterized on loop latency and reliability, and jointly optimize system performance. The setup involves collaborative robots working on distributed tasks, underscoring how wireless communication can enable more dynamic coordination in flexible automation systems. We use our abstraction to optimally maximize the QoC ensuring efficient operation even under varying network conditions. Additionally, our solution allocates the communication resources in time slots, optimizing the balance between communication and control costs. Our simulation results highlight that minimizing the delay in the system may not always ensure the best QoC but can lead to substantial gains in QoC if delays are sometimes relaxed, allowing more packets to be delivered reliably. © 2025 IEEE.

关键词： Industrial robots

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Prediabetes detection in unconstrained conditions using wearable sensors

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Clinical Nutrition Open Science 2024年 58卷 163-174页

作者： Tatli, Dimitra Papapanagiotou, Vasileios Liakos, Aris Tsapas, Apostolos Delopoulos, Anastasios Multimedia Understanding Group Dpt. of Electrical and Computer Engineering Faculty of Engineering AUTH Greece Embedded Systems Laboratory Institute of Electrical and Micro Engineering EPFL Lausanne Switzerland IMPACT Research Group Dpt. of Medicine Karolinska Institutet Huddinge Sweden Diabetes Centre Second Medical Department at Ippokratio General Hospital Thessaloniki Greece

Prediabetes is a common health condition that often goes undetected until it progresses to type 2 diabetes. Early identification of prediabetes is essential for timely intervention and prevention of complications. This research explores the feasibility of using wearable continuous glucose monitoring along with smartwatches with embedded inertial sensors to collect glucose measurements and acceleration signals respectively, for the early detection of prediabetes. We propose a methodology based on signal processing and machine learning techniques. Two feature sets are extracted from the collected signals, based both on a dynamic modeling of the human glucose-homeostasis system and on the Glucose curve, inspired by three major glucose related blood tests. Features are aggregated per individual using bootstrap. Support Vector Machines are used to classify normoglycemic vs. prediabetic individuals. We collected data from 22 participants for evaluation. The results are highly encouraging, demonstrating high sensitivity and precision. This work is a proof of concept, highlighting the potential of wearable devices in prediabetes assessment. Future directions involve expanding the study to a larger, more diverse population and exploring the integration of CGM and smartwatch functionalities into a unified device. Automated eating detecting algorithms can also be used. © 2024 The Authors

关键词： Continuous glucose monitors Machine learning Prediabetes Signal processing

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Flexible Low Noise Amplifier for IoT Applications in Biomedical Devices 3

Flexible Low Noise Amplifier for IoT Applications in Biomedi...

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3rd IEEE International Conference on Recent Advances in systems Science and engineering, RASSE 2023

作者： Krishnan, Athul Baidya, Dayarnab Bhattacharjee, Mitradip Indian Institute of Science Education and Research Bhopal Electrical Engineering and Computer Science Bhopal462066 India

ISBN: (纸本)9798350341676

This study focuses on the critical role of Low Noise Amplifiers (LNAs) in achieving low noise and high gain in wireless systems and IoT-based sensing applications. The emphasis on straightforward design is particularly relevant for wearable IoT contexts. The research centers on the creation of a single-stage LNA with notable gain, stability, and noise figure (NF) attributes. The evaluation of this LNA's performance and utility includes an in-depth analysis of its gain, noise figure, and stability characteristics. The design employs a Low Noise Gallium Arsenide FET transistor, optimizing its potential through the application of lumped components for input and output side matching. The outcomes of the study reveal impressive features of the suggested LNA design, notably a forward gain of 16.5 dB and a noise figure of 0.6 dB at a frequency of 2.4 GHz. This renders the LNA suitable for integration with sensors operating in the lower frequency ranges, aligning well with the LS frequency band. A key aspect of the design is its suitability for wearable IoT and disposable biomedical applications, achieved by minimizing component count without compromising performance. In summary, the research underscores the importance of LNAs in enhancing signal quality and presents a well-optimized single-stage LNA design. The outcomes position this LNA as a robust solution for wearable IoT scenarios, underscoring its gain, noise figure, and stability advantages while aligning with the frequency requirements of sensors. © 2023 IEEE.

关键词： Low noise amplifiers

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An Adaptive Multi-Stage Evolution Algorithm for High-Dimensional Expensive Problems

An Adaptive Multi-Stage Evolution Algorithm for High-Dimensi...

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2024 IEEE International Conference on systems, Man, and Cybernetics, SMC 2024

作者： Zhang, Boyuan Lai, Rui Gong, Guanghong Yuan, Haitao Yang, Jinhong Zhang, Jia School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Cssc Systems Engineering Research Institute Beijing China Southern Methodist University Dept. of Computer Science DallasTX75275 United States

ISBN: (纸本)9781665410205

Recently, many studies have used evolutionary algorithms (EAs) to optimize complex problems across various fields, including mechanical structure design, robotics, and cloud computing. EAs simulate the process of evolution to improve solutions to a given problem iteratively. However, EAs encounter significant challenges when dealing with high-dimensional expensive problems (HEPs). The large solution space and high computing cost of fitness evaluations (FEs) make optimization with limited FEs particularly difficult. To tackle this problem, an Adaptive Multi-stage Evolution Algorithm named AMEA is proposed. In AMEA, an adaptively enhanced teaching-learning-based optimization algorithm is adopted to explore the search space and find potential areas quickly. Then, in the next stage, the Gaussian process surrogate model and a genetic learning particle swarm optimization algorithm are adopted for further exploitation. Besides, this work proposes an adaptive stage switching criterion and an individual screening mechanism to enhance the optimization ability. AMEA demonstrates strong optimization performance when applied to HEPs. We compare AMEA with several state-of-the-art HEP optimization algorithms through seven benchmark functions, and the results show that it performs competitively with other algorithms. Finally, we also validate AMEA's effectiveness with a real-world computation offloading problem. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Deep transfer learning technique to detect white blood cell classification in regular clinical practice using histopathological images

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Multimedia Tools and Applications 2025年第9期84卷 5699-5723页

作者： Davamani, K. Anita Jawahar, Malathy Anbarasi, L. Jani Ravi, Vinayakumar Al Mazroa, Alanoud Robin, C. R. Rene Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India Leather Process Technology Division CSIR-Central Leather Research Institute Chennai India School of Computing Science and Engineering Vellore Institute of Technology Vellore India Computer Science and Engineering Sri Sairam Engineering College Sirukalathur India Center for Artificial Intelligence Prince Mohammad Bin Fahd University Khobar Saudi Arabia Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh11671 Saudi Arabia

Leukemia, a malignant disease characterized by the rapid proliferation of specific types of white blood cells (WBC), has prompted increased interest in leveraging automatic WBC classification system. This study presents the design of a robust WBC classification system, integrating deep learning with microscopic image analysis. The proposed classification model is designed to categorize blood cells into four distinct groups: eosinophil, lymphocyte, monocyte, and neutrophil. The study employs a Kaggle image dataset for comprehensive learning. The dataset comprises 12,500 microscopic images used to categorize the four blood cell groups. To enhance performance and reduce the likelihood of overfitting, transfer learning techniques are employed. Six deep transfer learning models namely VGG16, ResNet50, AlexNet, MobileNet, GoogLeNet, and EfficientNetBO are used for feature extraction and classification. The results of the experiments show that VGG16 and MobileNet demonstrated enhancements compared to other pre-trained models when evaluated on both augmented and original datasets. A diverse set of evaluation criteria was employed to thoroughly assess the effectiveness of medical image classification. Notably, the VGG16 and MobileNet models accurately predicted Lymphocytes and Monocytes with a 100% accuracy rate, and Eosinophil and Neutrophil with rates of 99.35% and 99.81%, respectively. This research work empower medical practitioners, particularly in the field of pathology and hematology by providing advanced tools for accurate blood cell classification. The proposed VGG16 and MobileNet models hold promise for simplifying WBC classification, improving decision-making processes, and ultimately advancing patient care in the medical domain. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep learning

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Data Distribution Valuation 38

Data Distribution Valuation

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38th Conference on Neural Information Processing systems, NeurIPS 2024

作者： Xu, Xinyi Wang, Shuaiqi Foo, Chuan-Sheng Low, Bryan Kian Hsiang Fanti, Giulia Department of Computer Science National University of Singapore Singapore Department of Electrical and Computer Engineering Carnegie Mellon University United States Institute for Infocomm Research Agency for Science Technology and Research Singapore

Data valuation is a class of techniques for quantitatively assessing the value of data for applications like pricing in data marketplaces. Existing data valuation methods define a value for a discrete dataset. However, in many use cases, users are interested in not only the value of the dataset, but that of the distribution from which the dataset was sampled. For example, consider a buyer trying to evaluate whether to purchase data from different vendors. The buyer may observe (and compare) only a small preview sample from each vendor, to decide which vendor's data distribution is most useful to the buyer and purchase. The core question is how should we compare the values of data distributions from their samples? Under a Huber characterization of the data heterogeneity across vendors, we propose a maximum mean discrepancy (MMD)-based valuation method which enables theoretically principled and actionable policies for comparing data distributions from samples. We empirically demonstrate that our method is sample-efficient and effective in identifying valuable data distributions against several existing baselines, on multiple real-world datasets (e.g., network intrusion detection, credit card fraud detection) and downstream applications (classification, regression). © 2024 Neural information processing systems foundation. All rights reserved.

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Spatiotemporal Traffic Analysis: Deep Hybrid Approach for Resource and Cyberattacks Prediction 19

Spatiotemporal Traffic Analysis: Deep Hybrid Approach for Re...

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19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Lee, Hyeonmin Kim, Dongchan Beom, Donggyu Byun, Gyurin Vo, Van-Vi Choo, Hyunseung Department of AI Systems Engineering Sungkyunkwan University Suwon16419 Korea Republic of Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of Convergence Research Institute Sungkyunkwan University Suwon16419 Korea Republic of Department of Computer Science and Engineering Sungkyunkwan University Suwon16419 Korea Republic of

ISBN: (纸本)9798331507817

This study proposes a hybrid deep learning approach to address the complexity and dynamic characteristics of modern network environments. The research integrates Graph Neural Networks (GNNs) and Convolutional Long and Short-Term Memory (ConvLSTM) networks to predict future network traffic and detect cyberattacks. The model was trained and evaluated on the CICIDS 2018 dataset which includes various types of normal and malicious traffic. Grid-based representations were created from the dataset. The attack type of each timestamp were visualized using distinct colors to the grid images. These images are sued as input of ConvLSTM model. Protocol and destination port are modeled as nodes in a graph, with volume of data flow represented as weighted edges. This graph-based representation is designed to capture unique traffic patterns of specific services and enhance the analysis of spatial and temporal features. Experimental results show a 13% increase in macro F1 score and a 4% increase in weighted F1 score, highlighting the effectiveness of the proposed method. This research highlights its potential to enable proactive responses through the prediction of potential cyberattack occurrences. © 2025 IEEE.

关键词： Convolutional Long Short-Term Memory Cyberattack Detection Deep Learning Graph Neural Networks Network Traffic Prediction

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A cadaveric breast cancer tissue phantom for phase-contrast X-ray imaging applications

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Animal Models and Experimental Medicine 2023年第5期6卷 427-432页

作者： Cody C.Rounds Chengyue Li Wei Zhou Kenneth M.Tichauer Jovan G.Brankov Biomedical Engineering Illinois Institute of TechnologyChicagoIllinoisUSA Medical Imaging Research Center Illinois Institute of TechnologyChicagoIllinoisUSA Electrical and Computer Engineering Illinois Institute of TechnologyChicagoIllinoisUSA

Background:As mammography X-ray imaging technologies advance and provide elevated contrast in soft tissues,a need has developed for reliable imaging phantoms for use in system design and component *** advanced imaging modalities such as refraction-based methods,it is critical that developed phantoms capture the biological details seen in clinical precancerous and cancerous cases while minimizing artifacts that may be caused due to phantom *** work presents the fabrication of a breast tissue imaging phantom from cadaveric breast tissue suitable for use in both transmission and refraction-enhanced imaging ***:Human cancer cell tumors were grown orthotopically in nude athymic mice and implanted into the fixed tissue while maintaining the native tumor/adipose tissue ***:The resulting human–murine tissue hybrid phantom was mounted on a clear acrylic housing for absorption and refraction X-ray *** breast tomosynthesis was also ***:Both attenuation-based imaging and refraction-based imaging of the phantom are presented to confirm the suitability of this phantom's use in both imaging modalities.

关键词： breast tumors digital mammography imaging phantoms orthotopic animal models phasecontrast X-ray imaging

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Investigation of Scalable Approximation of the Minimum Variance Distortionless Response Beamformer

Investigation of Scalable Approximation of the Minimum Varia...

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2024 IEEE International Symposium on Phased Array systems and Technology, ARRAY 2024

作者： Park, Jung-Jin Rodriguez-Martinez, Gumer Bolding, Mark M. Westafer, Ryan S. Sathe, Visvesh S. School of Electrical and Computer Engineering Georgia Institute of Technology United States Georgia Tech Research Institute Georgia Institute of Technology Information and Communications Laboratory United States

ISBN: (纸本)9798350392142

The minimum variance distortionless response (MVDR) beamformer is commonly used with phased arrays to perform adaptive spatial filtering - preserving array gain in a desired direction while minimizing in-band interference from other directions. This constrained optimization problem is traditionally solved by regularly constructing and inverting the array covariance matrix, resulting in computational complexity of order O(N3), where N is the number of elements in the array. This paper explores a beamspace approximation, using a combination of the spatial fast Fourier transform (FFT) and iterative search, to achieve linear-logarithmic complexity. We investigate the signal to interference-plus-noise ratio (SINR) and complexity tradeoffs associated with this approach to examine its suitability for large array systems, © 2024 IEEE.

关键词： Constrained optimization

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