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Pruning Binarized Neural Networks Enables Low-Latency, Low-Power FPGA-Based Handwritten Digit Classification

Pruning Binarized Neural Networks Enables Low-Latency, Low-P...

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IEEE Conference on High Performance Extreme Computing (HPEC)

作者： Syamantak Payra Gabriel Loke Yoel Fink Joseph D. Steinmeyer Dept. of Electrical Engineering Stanford University (formerly Dept. of Electrical Engineering and Computer Science Massachusetts Institute of Technology) Dept. of Materials Science and Engineering Massachusetts Institute of Technology Dept. of Materials Science and Engineering Dept. of Electrical Engineering and Computer Science and Institute for Soldier Nanotechnologies Massachusetts Institute of Technology Dept. of Electrical Enigneering and Computer Science Massachusetts Institute of Technology

As neural networks are increasingly deployed on mobile and distributed computing platforms, there is a need to lower latency and increase computational speed while decreasing power and memory usage. Rather than using FPGAs as accelerators in tandem with CPUs or GPUs, we directly encode individual neural network layers as combinational logic within FPGA hardware. Utilizing binarized neural networks minimizes the arithmetic computation required, shrinking latency to only the signal propagation delay. We evaluate size-optimization strategies and demonstrate network compression via weight quantization and weight-model unification, achieving 96% of the accuracy of baseline MNIST digit classification models while using only 3% of the memory. We further achieve 86% decrease in model footprint, 8mW dynamic power consumption, and $< 9\text{ns}$ latency, validating the versatility and capability of feature-strength-based pruning approaches for binarized neural networks to flexibly meet performance requirements amid application resource constraints.

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Deception Detection Deep Learning Comprehensive system Utilizing Explainable AI 16

Deception Detection Deep Learning Comprehensive system Utili...

引用

16th International Conference on Developments in eSystems engineering, DeSE 2023

作者： Salah, Suhaib Khater, Tarek Almajali, Eqab Khan, Wasiq Hussain, Abir University of Sharjah Dept. of Electrical Engineering Sharjah United Arab Emirates Liverpool John Moores University Department of Computer Science Liverpool United Kingdom

ISBN: (纸本)9798350381344

Deception detection plays a vital role in various domains, from security and law enforcement to human behavior analysis. In this paper, we propose a comprehensive system for deception detection that leverages S&A smart sensing device, deep transfer learning, deep learning techniques, and explainable artificial intelligence. Our approach combines visual, auditory, thermal, cardiovascular, and respiratory cues, offering enhanced accuracy and resistance to countermeasures. Deep Transfer Learning is employed to adapt pre-trained models to the deception detection task, overcoming data limitations. Incorporating Explainable AI techniques enhances transparency and interpretability, fostering trust and collaboration in human-machine interactions. Our research lays the groundwork for future advancements in deception detection technology, addressing challenges and providing promising opportunities in the realm of deception detection. © 2023 IEEE.

关键词： Deep learning

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The ORIS tool: App, library, and toolkit for quantitative evaluation of non-Markovian systems

The ORIS tool: App, library, and toolkit for quantitative ev...

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作者： Carnevali, Laura Paolieri, Marco Vicario, Enrico Information Engineering Dept. Univ. Of Florence Italy Computer Science Dept. Univ. Of Southern California United States

ORIS is a tool for quantitative modeling and evaluation of concurrent systems with non-Markovian durations. It provides a Graphical User Interface (GUI) for model specification as Stochastic Time Petri Nets (STPNs), validation by interactive simulation, and evaluation by several techniques, computing instantaneous and cumulative rewards. It also provides an open-source Java Application Programming Interface (API) to automate the workflow, and it can be used as a toolkit for derivation and evaluation of STPNs in model driven engineering. As distinguishing features, ORIS implements transient and steady-state analysis of STPNs with underlying Markov Regenerative Process (MRP), and transient analysis of STPNs with underlying Generalized Semi-Markov Process (GSMP). It also implements nondeterministic analysis of Time Petri Nets (TPNs), simulation of STPNs, and solution methods for Continuous-Time Markov Chains (CTMCs) and MRPs with at most one non-exponential timer in each state. The well-engineered software architecture of ORIS supports agile implementation of new STPN features, new modeling formalisms, and new analysis methods. © 2022 is held by the owner/author(s).

关键词： Stochastic systems

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Deep Reinforcement Learning Aided Device Profiling for Anomaly Detection in IIoT Over Zero Trust Security Network

Deep Reinforcement Learning Aided Device Profiling for Anoma...

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2023 IEEE Asia-Pacific Conference on computer science and data engineering, CSDE 2023

作者： Singh, Anamika Dhanaraj, Rajesh Kumar Hsu, Ching-Hsien Sharma, Anupam Kumar Kumar, Pardeep School of Computing Science and Engineering Galgotias University U.P Greater Noida India Pune India Asia University Dept. Computer Science and Information Engineering Taiwan Manav Rachna International Institute of Research and Studies India

ISBN: (纸本)9798350341072

The application of internet of things in industries is the fourth revolution of industries and is referred to as industry 4.0 or industrial internet of things. The shifting of manual and conventional approach to a digital platform is witnessed as a key consideration for the emergence of industrial internet of things. Industrial internet of things works around thousands of devices connected remotely and the overall monitoring, functioning and storage is performed virtually by and on devices, hence more prone to cyber threads. With the amplification in the growth and application of more complicated networks, the requirements for the security are also increased significantly. Zero trust security stands as an efficient solution for all the security questions. Zero trust security involves continuous monitoring of devices for verifying the authenticity before permitting the access to network. Continuous monitoring for detecting the honest and malicious device adversely effects the productivity of the IIoT environment. The proposed paper presents a novel approach to generate a unique profile of devices via deep reinforcement learning to avoid irrelevant authentication cycles and produce more optimized results. The proposed work surpasses the existing approaches (CRH-SRP) [1] and Cyber Security via Determinism [2] with enhanced data confidentiality rate by 3.25% and reduced false positive rate by 30.5% respectively. © 2023 IEEE.

关键词： Anomaly detection

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QPUF 2.0: Exploring Quantum Physical Unclonable Functions for Security-by-Design of Energy Cyber-Physical Systems

QPUF 2.0: Exploring Quantum Physical Unclonable Functions fo...

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IEEE International Symposium on World of Wireless Mobile and Multimedia Networks (WoWMoM)

作者： Venkata K. V. V. Bathalapalli Saraju P. Mohanty Chenyun Pan Elias Kougianos Dept. of Computer Science and Engineering University of North Texas Dept. of Electrical Engineering University of Texas at Arlington Dept. of Electrical Engineering University of North Texas

ISBN: (数字)9798331538323

ISBN: (纸本)9798331538330

The Smart Grid concept evolved from the idea of intelligent and secure management of electrical grid infrastructure components and their communication through sustainable integration with the state-of-the-art technologies. This research focuses on emerging quantum computing-assisted security and its application in the Smart Grid. The robustness of electrical grid is increasing every day through advancements in grid infrastructure management which include outage control, relay protection, reliable distribution, renewable energy resource integration, and energy trading. Quantum Computing emerges as a formidable solution for application in the smart grid due to its processing capability and scale. Its application and scope are evolving every day with the recent developments in Quantum Chips which could pave the way for emerging Quantum-Chain-of-Things (QCoT). This research focuses on providing robust security in smart grids through Quantum Physical Unclonable Functions (QPUF) primitive, a quantum-hardware assisted security approach driven by micro manufacturing quantum process variations for generating a quantum digital fingerprint driven by quantum mechanics principles. The QPUF experimental evaluation in this research was performed to uniquely fingerprint various electrical grid entities providing a sustainable and secure flow of communication. Experimental evaluation shows a robust and reliable extraction of quantum digital fingerprints from noisy IBM quantum systems. The evaluation shows an impressive 86% keys achieving 100% reliability.

关键词： Wireless communication Renewable energy sources Quantum system Quantum computing Protective relaying Fingerprint recognition Cyber-physical systems Physical unclonable function Robustness Smart grids

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Implementing a Robust and Scalable Detect-and-Avoid Algorithm for UAV Navigation in ROS 15

Implementing a Robust and Scalable Detect-and-Avoid Algorith...

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15th International Conference on Information, Intelligence, Systems and Applications, IISA 2024

作者： Meimetis, Dimitrios Daramouskas, Ioannis Lappas, Vaios Perikos, Isidoros Kapoulas, Vaggelis Paraskevas, Michael Nkua Department of Aerospace Science & Technology Athens Greece Computer Technology Institute and Press 'Diophantus' Patras Greece UoP Applied Mechanics Lab Patras Greece UoP Computer Engineering and Informatics Department Patras Greece Computer Technology Institute and Press 'Diophantus' University of Peloponnese Electrical and Computer Engineering Dept. Patras Greece

ISBN: (纸本)9798350368833

Unmanned aerial vehicles (UAVs) have garnered significant attention from the research community during the last decade, due to their diverse capabilities and potential applications. One of the most critical functions that drones must execute efficiently is navigation in real-world environments. This paper presents a decentralized approach for enabling unmanned aerial vehicles (UAVs) to navigate safely in unknown environments and avoid obstacles. Leveraging the Optimal Reciprocal Collision Avoidance (ORCA) algorithm, implemented in the Robot Operating System (ROS), our method facilitates conflict detection and resolution in 2D environments. Through simulations using ROS, Gazebo, and Iris drones, we validate the effectiveness of our approach in scenarios with initial trajectory conflicts. Our work addresses the pressing need for UAVs to autonomously plan and execute safe flights, laying the groundwork for enhanced UAV capabilities in various real-world applications. The simulation results demonstrate the efficiency and robustness of our approach. © 2024 IEEE.

关键词： Drones

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The Use of a Polarization-Agile Applicator in Hyperthermia Treatments of a Spherical Region

The Use of a Polarization-Agile Applicator in Hyperthermia T...

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2023 IEEE Conference on Antenna Measurements and Applications, CAMA 2023

作者： Dicarlofelice, Alessandro Tognolatti, Piero Bardati, Fernando University of l'Aquila Dept. of Industrial and Information Engineering and Economics L'Aquila Italy University of Rome Tor Vergata Department of Civil Engineering and Computer Science Rome Italy

ISBN: (纸本)9798350323047

A hyperthermia applicator that can independently radiate two orthogonal polarizations is proposed for a Polarization-Agile array configuration to obtain better control of power deposition. Through a numerical model of a layered sphere simulating a human head, the possibility of more efficiently limiting unwanted hot spots is demonstrated. © 2023 IEEE.

关键词： Polarization

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EMS-BERT: A Pre-Trained Language Representation Model for the Emergency Medical Services (EMS) Domain 23

EMS-BERT: A Pre-Trained Language Representation Model for th...

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Proceedings of the 8th ACM/IEEE International Conference on Connected Health: Applications, Systems and engineering Technologies

作者： M Arif Rahman Sarah Masud Preum Ronald D. Williams Homa Alemzadeh John Stankovic Dept. of Computer Science University of Virginia Charlottesville VA USA Dept. of Computer Science Dartmouth College Hanover NH USA Dept. of Electrical and Computer Engineering University of Virginia Charlottesville VA USA

ISBN: (纸本)9798400701023

Emergency Medical Services (EMS) is an important domain of healthcare. First responders save millions of lives per year. Machine learning and sensing technologies are actively being developed to support first responders in their EMS activities. However, there are significant challenges to overcome in developing these new solutions. One of the main challenges is the limitations of existing methods for EMS text mining, and developing a highly accurate language model for the EMS domain. Several important Bidirectional Encoder Representations from Transformer (BERT) models for medical domains, i.e., BioBERT and ClinicalBERT, have significantly influenced biomedical text mining tasks. But extracting information from the EMS domain is a separate challenge due to the uniqueness of the EMS domain, and the significant scarcity of a high-quality EMS corpus. In this research, we propose EMS-BERT - a BERT model specifically developed for EMS text-mining tasks. For data augmentation on our small, classified EMS corpus which consists of nearly 2.4M words, we use a simultaneous pre-training method for transfer-learning relevant information from medical, bio-medical, and clinical domains; and train a high-performance BERT model. Our thorough evaluation shows at least 2% to as much as 11% improvement of F-1 scores for EMS-BERT on different classification tasks, i.e., entity recognition, relation extraction, and inferring missing information when compared both with existing state-of-the-art clinical entity recognition tools, and with various medical BERT models.

关键词： emergency medical services (EMS) data processing and analysis

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Exhaled Breath Analysis Based Diabetes Detection with k-Nearest Neighbors Classifier 16

Exhaled Breath Analysis Based Diabetes Detection with k-Near...

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16th International Symposium on Computational Intelligence and Design, ISCID 2023

作者： Smieja, Piotr J. Lu, Jiang Sun, Qingquan Fu, Xingang Zhang, Ting Dept. of Electrical and Biomedical Engineering University of Nevada Reno Reno United States Department of Computer Engineering University of Houston Clear Lake Houston United States School of Computer Science and Engineering California State University San Bernardino San Bernardino United States Department of Computer Science University of Houston Downtown Houston United States

ISBN: (纸本)9798350317459

The diagnosis of early stages of diseases such as lung cancer or diabetes is challenging as those diseases don’t have many noticeable symptoms. The human breath, however, contains many VOCs (volatile organic compounds) that could be used as a clue for conducting the proper test. In this study, a custom breathalyzer device is developed for noninvasive detection and diagnosis of diabetes with the use of a human breath print. An array of 6 MOS sensors is used in the prototype to collect 8 VOCs of the breath print of the subjects. The k-nearest Neighbor algorithm is implemented to determine the likelihood of a disease. In the results, an accuracy of 93.6% was achieved. ©2023 IEEE.

关键词： Volatile organic compounds

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Utility Point: An User Service Assistance Based Mobile Application For the Citizens of Bangladeshi City Corporation

Utility Point: An User Service Assistance Based Mobile Appli...

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2022 International Conference on Smart Information Systems and Technologies, SIST 2022

作者： Chowdhury, Mahfuzulhoq Ahmad, Alve Saha, Arup Chowdhury, Bibhas Roy Kurmasheva, Dinara Ahmed, Forkan Uddin Chittagong University Of Engineering And Technology Dept. Of Cse Chittagong Bangladesh West Kazakhstan University Dept. Of Computer Science Uralsk Kazakhstan

ISBN: (数字)9781665467902

ISBN: (纸本)9781665467902

City-corporation is one of the important institutions that can improve the city people lifestyles by providing necessary utility services along with socioeconomic development. Currently, most of the services offered by the city corporation are handled offline mode. To keep pace with the modern world, the incorporation of information and communication technology (ICT) based digital technology is necessary. At present, the existing works related to city corporation service do not consider multiple utility features, problem reporting, and fee payment features at the same time. Thus, the existing city corporation-related service delivery works may experience huge time and cost wastage. To cope up with the existing city corporation-related issues, in this paper we have presented a user service assistance mobile application for the citizens of the Bangladeshi city corporation. Our proposed utility point mobile application offers several features for the citizens like electricity, water, gas bill payment, trash service payment, cemetery plot booking service, holding tax payment, e-trade and vehicle license apply, birth and citizen certificate, problem reporting, among others. Our user survey results indicate that almost 80% of users are satisfied with the features of the proposed city corporation mobile application. © 2022 IEEE.

关键词： Surveys

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