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A Hierarchical BRAM/URAM Buffer for SNN

A Hierarchical BRAM/URAM Buffer for SNN

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Computing and Machine Intelligence (ICMI), International Conference on

作者： Rasha Karakchi Dept. Computer Science and Engineering University of South Carolina Columbia SC

ISBN: (数字)9798350372977

ISBN: (纸本)9798350372984

Applications such facial recognition, social networking and automated translation are well-known by their exponential growth in size and level of complexity. Such applications are the real-world examples of machine learning which are computationally modeled using neural network. Executing such operations are bounded by the needed memory to store and transfer datasets into computational units. Access rate and storage capacity are related, in somehow, to the type of memory and its structure. In domain-specific architectures, on_chip memories are usually utilized as data memory because they allow wider busses and higher operation rate, however they are less dense that off_chip memories such as DRAMs. Examples of on_chip memories are Block RAM (BRAMs) and UltraRAMs (URAMs). While both memories are equivalent in latency and speed, URAM is considered denser because of its capability to cascade and create larger memory without reversely impacting the frequency. In this work, we propose a hierarchical hybrid input buffer to store weight and spikes of spiking neural network accelerator. The buffer is designed as a combination of BRAMs and URAMs where BRAMs represent the lower layer (closer to processing unit) while URAM are the upper layer. We tested our design on Xilinx Ultrascale Zynq 104 FPGA device and we found that our design can run on a higher frequency with less hardware resources and BRAM/URAM utilization comparing with the state-of-the-art.

关键词： Power demand Social networking (online) Memory management Neural networks Memory architecture Random access memory Spiking neural networks

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Deep Learning-based Image Classification Framework for Rice Leaf Disease Detection: A Systematic Implementation Approach with Comparative Analysis

Deep Learning-based Image Classification Framework for Rice ...

引用

International conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： Joy Chakra Bortty Gouri Shankar Chakraborty Tamjid Ahmed Sourav Das Polok Tanvir Ahmed Taki Partha Saha Department of Computer Science Westcliff University Irvine California USA Dept. of Computer Science and Engineering Lovely Professional University Phagwara Punjab India

ISBN: (数字)9798350367904

ISBN: (纸本)9798350367911

Approximately half of the population of the world consumes rice every day as their daily diet. In Asia, rice is the main ingredient of the meal. However, various diseases influence the production of rice being harmed. Many factors are responsible for the issue such as climate change, pathogens and excessive usage of pesticides. Varieties of leaf diseases are there that may harm quality of rice production. To protect the crop from diseases, farmers are relying on their eyes to identify diseases. Most often human eye can possibly make mistake identifying the disease. In consequence, harvested rice gets harmed both in quality and in quantity as well. To prevent those diseases, researchers have found cutting-edge techniques such as deep neural network with image processing based frameworks to detect and classify rice plant diseases to increase crops production and to reduce losses. Recent works present all the possible techniques related to rice plant disease classification and detection. This work represents a state-of-art in disease identification approach for rice leaf that can efficiently classify several types of diseases analyzing the leaf images. By adopting the efficiency of deep learning models with hyperparameter tuning, the methodology achieved accuracy of 97.10% with ResNet50. Importantly, the experimental outcomes of the proposed framework highlight the remarkable performance comparing the results with existing state-of-the-art technique. The aim of this research is to provide a more promising future for crop management through increased productivity while addressing the urgent problem of rice plant disease detection.

关键词： Plant diseases Accuracy Systematics Computational modeling Crops computer architecture Agriculture Tuning Diseases Residual neural networks

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Survey on Online Learning Applications in Multi-UAV Networks

Survey on Online Learning Applications in Multi-UAV Networks

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International Conference on Information and Communication Technology Convergence (ICTC)

作者： Jaemin Kim Hyosu Kim Jeongyeup Paek Yongseok Son Hyung Tae Lee Sungrae Cho Dept. of Computer Science and Engineering Chung-Ang University Seoul Korea

ISBN: (数字)9798350364637

ISBN: (纸本)9798350364644

This paper provides a comprehensive review of the application of online learning techniques in multi-UAV systems, highlighting their role in enhancing autonomy and efficiency in dynamic environments. Key areas of focus include adaptive path planning, collision avoidance, and swarm behavior control, along with the associated technical challenges and future research directions.

关键词： Surveys Reviews Scalability Reinforcement learning Robustness Real-time systems Path planning Complexity theory Particle swarm optimization Testing

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TraceChainBD: Securing Supply Chains from Counterfeiting in Bangladesh with Blockchain and IoT Integration

TraceChainBD: Securing Supply Chains from Counterfeiting in ...

引用

International Conference on Innovations in science, engineering and Technology (ICISET)

作者： Sagar Karmoker Md. Adib Khan Sanjida Alam Lamia Mobinul Ahasan Tanjid Shahriar Sadman Dihan Nur Adnan Chowdhury Anik Dept. of Computer Science and Engineering East West University Dhaka Bangladesh

ISBN: (数字)9798350355499

ISBN: (纸本)9798350355505

The scheme uses blockchain technology in Bangladesh’s supply chain to eliminate counterfeit products, improve tax compliance, and build consumer trust. By ensuring product authenticity, it safeguards brand reputations and boosts government revenue. Manufacturers, distributors, and retailers can enter product data which will then generate QR codes. In case of receiving the product or checking its authenticity, they can scan these QR codes via their mobile or a dedicated IOT device and all the information will be stored in Blockchain. Since blockchain data is secure and immutable, it cannot be altered, consumers can easily check product details by scanning QR codes, and if a product is flagged as suspicious, the Mobile Court Management System can trace its origin. This blockchain-based approach ultimately helps prevent counterfeit goods from entering the market and enhances the government’s ability to collect taxes.

关键词： Technological innovation Supply chain management Manufactured products Supply chains Government Finance QR codes Blockchains Internet of Things Counterfeiting

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Quantum Anomalous Hall Effect-Based Variation Robust Binary Content Addressable Memory 66

Quantum Anomalous Hall Effect-Based Variation Robust Binary ...

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2023 IEEE 66th International Midwest Symposium on Circuits and Systems, MWSCAS 2023

作者： Islam, Md Mazharul Hutchins, Jack Alam, Shamiul Hossain, Md Shafayat Jaiswal, Akhilesh Aziz, Ahmedullah University of Tennessee Department of Electrical Engineering and Computer Science KnoxvilleTN37996 United States Princeton University Dept. of Physics PrincetonNJ08544 United States University of Southern California Deptartment of Electrical and Computer Engineering Los AngelesCA90089 United States

ISBN: (纸本)9798350302103

Electronic devices can no longer afford dimensional downscaling due to the fundamental physical limit which motivates device researchers to look for a completely new technological paradigm. Cryogenic devices are currently the most promising candidates among the alternative paradigms as they offer extremely low power and ultra-fast speed without the requirement of device miniaturization. However, the traditional von-Neumann bottleneck limits the performance throughput and power efficiency of cryogenic devices owing to the separate storage and compute elements. In this context, the concept of in-memory computing has emerged where the computations are performed inherently in the memory itself. Content addressable memory (CAM) is one such type of memory block where the memory search operation takes place inherently. We propose a Binary CAM (BCAM) based on twisted bilayer graphene moire heterostructure for cryogenic application. It provides topologically protected non-volatile quantum Hall states and exhibits variation robustness. With the appropriate circuit components and suitable bias conditions, our proposed BCAM array is capable of inherent memory search operation consuming ultra-low power of 1.2 nW/search/bit. We also perform a 1000-point Monte-Carlo variation analysis to assess the variation robustness of our proposed BCAM and find that the BCAM operates reliably even at the worst-case variation attesting to its unique variation robustness. © 2023 IEEE.

关键词： CAM Content addressable memory Cryogenic In-memory computing Moire heterostructure Quantum anomalous Hall effect Twisted bilayer graphene

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ImportChainBD: Blockchain Solutions for Transparent and Accountable Imported Cosmetic Products

ImportChainBD: Blockchain Solutions for Transparent and Acco...

引用

Blockchain and Distributed Systems Security (ICBDS), IEEE International Conference on

作者： Md. Adib Khan Sagar Karmoker Ummay Khadiza Rumpa Habibur Rahman Md. Sabbir Hossain Nur Adnan Chowdhury Anik Mahamudul Hasan Dept. of Computer Science and Engineering East West University Dhaka Bangladesh

ISBN: (数字)9798350354348

ISBN: (纸本)9798350354355

To improve the supply-chain management system of Bangladesh for imported cosmetic products this paper proposes a blockchain-based solution. One of the major goals of this project is to increase the tax compliance of the government by stopping counterfeit and illegally imported cosmetic products. Which will enhance the trust of consumers and the brand’s reputation. Using this system is quite easy and convenient, as All the information will be stored in the blockchain, so it will become a secure and immutable product tracking system for importers, distributors, and retailers to maintain their ledgers just by printing and scanning a QR code through an IoT device, and consumers can check the authenticity of these cosmetic products just by scanning them. If the product is verified to be suspicious, The Mobile Court Management System has the power to check its origin. Finally, we can say that it will avoid the sale of counterfeit items in the market and increase government revenue on tax.

关键词： Printing Supply chain management Government Finance QR codes Documentation Blockchains Stakeholders Security Internet of Things

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Securing Layers: The Synergy of Mix Networks and Shamir's Secret Sharing in Onion Routing

Securing Layers: The Synergy of Mix Networks and Shamir's Se...

引用

Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： Aniketh Vasudev Jambha Dutta Swetchana Karru Sushrutha Shinu M. Rajagopal Dept. of Computer Science and Engineering Amrita School of Computing Bengaluru India

In the contemporary digital landscape, where online privacy and security face escalating challenges due to evolving cyber threats, this investigation uncovers vulnerabilities within traditional security measures. The study presents a pioneering integrated cryptographic approach leveraging Mix Networks, Shamir's Secret Sharing, and Onion Routing, reinforcing the framework supporting secure digital communication. By seamlessly blending attributes such as anonymity, data dispersion, and layered encryption, these methods signify a monumental shift in securing online interactions. Overcoming inherent limitations and adeptly counters threats like single points of compromise and amplifies the anonymity in online communication, while enhancing resilience and offering adaptable solutions for diverse communication scenarios. Through meticulous technical analyses and empirical investigations, the study substantiates the pivotal role of the integrated cryptographic approach in reinforcing the fabric of secure digital communication. Validation of the system's functionality and precision through successful implementations of secure node-to-node connections lays the groundwork for a proactive defense against the spectrum of cyber threats.

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A Deep Learning Approach to Gun Detection in Surveillance System using YOLOv9

A Deep Learning Approach to Gun Detection in Surveillance Sy...

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Control Instrumentation System Conference (CISCON)

作者： Lucy Sumi Shouvik Dey Dept. of Computer Science & Engineering National Institute of Technology Nagaland India

ISBN: (数字)9798350375480

ISBN: (纸本)9798350375497

The escalating firearm related crimes including open firing, robbery, suicides, mass shootings, homicides, threatening at gun point, etc. has underscored the growing importance of timely detection of weapons. This paper presents a gun detection system based on YOLOv9, a state-of-the-art object detection model not previously utilized in literature for gun detection applications. We conduct a comparative analysis with its predecessor versions of YOLOv9, namely YOLOv5, YOLOv6, YOLOv7 and YOLOv8 to assess their performance in gun detection. Our experiments, conducted on a Gun Movie database, reveal that YOLOv9 consistently outperforms its predecessors, demonstrating that YOLOv9 has a good generalizability in detecting firearms in real-time. Particularly noteworthy are the precision and recall values achieved by YOLOv9, reaching 99.6% and 99%, respectively, demonstrating its superior efficacy in accurately identifying guns.

关键词： YOLO Deep learning Firing Databases Weapons Surveillance Instruments Motion pictures Control systems Real-time systems

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Phishing Website Detection Using Machine Learning 7

Phishing Website Detection Using Machine Learning

引用

7th IEEE International conference for Convergence in Technology, I2CT 2022

作者： Mandadi, Adarsh Boppana, Saikiran Ravella, Vishnu Kavitha, R. Vel Tech Rangarajan Dr Sagunthala R & D Institute of Science and Technology Dept of Computer Science and Engineering Chennai India

ISBN: (纸本)9781665421683

Phishing is an internet scam in which an attacker sends out fake messages that look to come from a trusted source. A URL or file will be included in the mail, which when clicked will steal personal information or infect a computer with a virus. Traditionally, phishing attempts were carried out through wide-scale spam campaigns that targeted broad groups of people indiscriminately. The goal was to get as many people to click on a link or open an infected file as possible. There are various approaches to detect this type of attack. One of the approaches is machine learning. The URL's received by the user will be given input to the machine learning model then the algorithm will process the input and display the output whether it is phishing or legitimate. There are various ML algorithms like SVM, Neural Networks, Random Forest, Decision Tree, XG boost etc. that can be used to classify these URLs. The proposed approach deals with the Random Forest, Decision Tree classifiers. The proposed approach effectively classified the Phishing and Legitimate URLs with an accuracy of 87.0% and 82.4% for Random Forest and decision tree classifiers respectively. © 2022 IEEE.

关键词： Decision trees

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Distinguish Between Obese and Normal Body Types Through Gait Analysis Using Classification Models

Distinguish Between Obese and Normal Body Types Through Gait...

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International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Jae-Sik Yi Ji Hun Han Min Jeong Kim Youn-Sik Hong Dept. of Computer Science and Engineering Incheon National University Incheon Korea

For gait analysis, an IMU sensor was mounted on the knee and gait related data was collected. Various gait parameters such as gait time, stance swing ratio, heel strike, and toe off can be extracted from the dataset. To explore the relationship between gait parameters and individual gait characteristics, we analyzed the gait patterns of normal and obese people were analyzed based on BMI (Body Mass Index). To apply it to a classification model of machine learning, different gait cycles between subjects were normalized. Gait data was collected from eight subjects in their 20s. Using this dataset, we applied a logistic regression model, and obtained the classification accuracy of 92%. We also investigated the correlation between BMI and gait parameters and found that, the correlation between BMI and cadence was -0.66.

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