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FrameFire: Enabling Efficient Spiking Neural Network Inference for Video Segmentation 5

FrameFire: Enabling Efficient Spiking Neural Network Inferen...

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5th IEEE international conference on Artificial Intelligence Circuits and Systems, AICAS 2023

作者： Chen, Qinyu Sun, Congyi Gao, Chang Fang, Xinyuan Luan, Haitao

ISBN: (纸本)9798350332674

Fast video recognition is essential for real-time scenarios, e.g., autonomous driving. However, applying existing Deep Neural networks (DNNs) to individual high-resolution images is expensive due to large model sizes. Spiking Neural networks (SNNs) are developed as a promising alternative to DNNs due to their more realistic brain-inspired computing models. SNNs have sparse neuron firing over time, i.e., spatio-temporal sparsity;thus they are useful to enable energy-efficient computation. However, exploiting the spatio-temporal sparsity of SNNs in hardware leads to unpredictable and unbalanced workloads, degrading energy efficiency. In this work, we, therefore, propose an SNN accelerator called FrameFire for efficient video processing. We introduce a Keyframe-dominated Workload Balance Schedule (KWBS) method. It accelerates the image recognition network with sparse keyframes, then records and analyzes the current workload distribution on hardware to facilitate scheduling workloads in subsequent frames. FrameFire is implemented on a Xilinx XC7Z035 FPGA and verified by video segmentation tasks. the results show that the throughput is improved by 1.7× with the KWBS method. FrameFire achieved 1.04 KFPS throughput and 1.15 mJ/frame recognition energy. © 2023 IEEE.

关键词： Deep neural networks

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Image Quality Assessment for Deep Learning-Enabled Saffron Adulteration Detectors 5th

Image Quality Assessment for Deep Learning-Enabled Saffron A...

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5th international conference on Computing, Communications, and Cyber Security, IC4S 2023

作者： Manzoor, Suhail Selwal, Arvind Sabha, Ambreen Department of Computer Science and IT Central University of Jammu Jammu India

ISBN: (纸本)9789819773701

Saffron, also known as the "golden spice", is one of the costly spices cultivated mainly in Iran, Greece, Spain, China, and India (mostly in Kashmir valley). However, due to its limited production and high price, it is often subjected to the issue of adulteration which leads to economic loss and several health issues. the various types of adulterants that are mixed with original saffron samples are sunflower, safflower, maize outer covering, and hibiscus stigmas to increase their quantity. Nowadays, the menace of saffron adulteration is one of the major issues that need further attention as human vision fails to discriminate between pure and adulterated saffron samples. Hence, an intelligent computer vision-enabled saffron adulteration prediction system is used by researchers to overcome this issue. In this paper, we present a novel framework for saffron adulteration prediction using intelligent data-driven approaches. Moreover, various pre-processing techniques enhance the quality of saffron images. Besides, we demonstrate the comparative analysis of recent saffron adulteration prediction techniques and identify certain open research issues that need futuristic attention. In addition, performance protocols such as performance metrics and saffron images-based data are briefly illustrated. Our analysis reveals that this study provides researchers with new directions that need to be addressed in the future and can potentially prevent economic loss and ensure consumer safety. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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A Smart Monitoring Approach Based on Decentralized Digital Twins 20th

A Smart Monitoring Approach Based on Decentralized Digital T...

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20th international conference on Computing in Civil and Building Engineering-ICCCBE

作者： Dragos, Kosmas Smarsly, Kay Hamburg Univ Technol Inst Digital & Autonomous Construct Blohmstr 15 D-21079 Hamburg Germany

ISBN: (纸本)9783031842108;9783031842085;9783031842078

the abundance of data, collected by sensors installed in civil engineering structures, has been fostering smart monitoring approaches that leverage digital twins, i.e. virtual representations that "live" and "evolve" in tandem with the physical structures. Conventional digital twins usually reside in centralized servers, which also serve as data collection hubs of sensor networks, installed in the structures. therefore, the centralized servers facilitate the data exchange between the structures and the respective digital twins, essentially constituting the "single source of truth" regarding the structural condition. However, centralized digital twin (DT) approaches are inherently vulnerable to malfunctions of the server or data loss in wireless smart monitoring systems, which may end up being a "single point of failure". this paper aims to circumvent this problem by decentralizing digital twins into the microcontrollers of smart wireless sensor nodes. In particular, building on previous studies that utilize the embedded processing capabilities of smart wireless sensor nodes, a decentralized DT approach is proposed, in which the DT is segmented into partial digital twins that are embedded in the microcontrollers of the sensor nodes. By defining interfaces for data exchange, the wireless sensor nodes use the partial digital twins to collaboratively analyze the structural condition. the proposed approach is validated through experiments using a laboratory test structure, in which the decentralized DT is represented by a finite element model. the results of the tests demonstrate the capability of the decentralized DT approach to evolve with the physical structure, thus serving as an attractive solution for modern smart monitoring systems.

关键词： Digital twins smart monitoring wireless sensor nodes embedded computing finite element modeling

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Internet of Lighting for Smart Cities 5th

Internet of Lighting for Smart Cities

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5th international conference on Optical and Wireless Technologies (OWT)

作者： thirupathi, Lingala Rajesh, A. Sandeep, R. Methodist Coll Engn & Technol CSE Dept Hyderabad India

ISBN: (纸本)9789811916458;9789811916441

the term smart is an acronym for the five components, which are unique, observable, practicable, appropriate and time-based. the Internet of things (IoT) identifies the widespread and increasing number of wireless devices as the trillions of networks of a potentially global scale running today. When the planet expands quicker, people are drawn to this clever term. India is one of the world's fastest-growing economies. the IoT offers innovative services and stimulates market shifts. the lighting industry often welcomes this transition by developing an Internet of Lighting (IoL). the term smart lighting describes three important design attributes: advanced control, solid state, and broad network connectivity in accordance with international standards. It provides an optical networking gateway as an additional function that enables coexistence with traditional Wi-Fi. In this context, various IoT-compatible wireless communication interfaces are being used to meet the lighting's needs. Related IoT systems are forecasted to expand dramatically in the entire smart housing and industrial sectors in the coming years. this paper presents the numerous options for creating and implementing IoT-based smart lighting solutions, issues related to interoperability and on-line services are addressed.

关键词： IoT IoL sensor Smart cities

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NyxVigil: FPGA based Night Vision Spy Bot

NyxVigil: FPGA based Night Vision Spy Bot

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Image processing and Capsule networks (ICIPCN), international conference on

作者： S Keerthi D Pratheesha Sanjana Bharadvaj Santhosh Yadav K Shashi Raj Department of Electronics and Communication Engineering Dayananda Sagar College of Engineering Bengaluru India

ISBN: (数字)9798350367171

ISBN: (纸本)9798350367188

this research study intends to provide an innovative approach for image processing on Field Programmable Gate Array (FPGA) platforms to enable remote monitoring of robotic systems. Leveraging the capabilities of FPGA technology, this study proposes a solution to efficiently handle complex image processing tasks, including obstacle detection and environmental monitoring. By utilizing the FPGA-based hardware acceleration, the processing speed and resource utilization are significantly increased, guaranteeing prompt and precise image analysis. Additionally, the proposed system integrates with robotic vehicles equipped with sensors and cameras to enable continuous monitoring of remote or hazardous environments. Experimental results demonstrate the effectiveness and scalability of the FPGA-based approach, highlighting its potential for various applications in robotics, surveillance, and environmental monitoring. Overall, a useful solution for improving robotic systems’ remote monitoring capabilities is provided, along with a contribution to the field of image processing on FPGA platforms.

关键词： Navigation Image processing Surveillance thermal sensors Chatbots Real-time systems sensor systems

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Secure Images Transmission Using a three-Dimensional S-Box-Based Encryption Algorithm 5

Secure Images Transmission Using a Three-Dimensional S-Box-B...

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5th IEEE international conference on Advanced Systems and Emergent Technologies (IC_ASET)

作者： Boussif, Mohamed Mnassri, Aymen Univ Tunis El Manar Fac Sci Tunis Tunis Tunisia

ISBN: (纸本)9781665427357

It is known that information security and privacy revolve around three principal keys: confidentiality, integrity, and availability. Depending on the environment, application, context, or use case, one of these principles may prevail over the others. For a medical application, for example, confidentiality is essential. therefore, any imaging transferred electronically must be encrypted to prevent unauthorized persons from seeing its contents. this paper exhibits a new efficient ciphering algorithm for the security of transmitted images in public networks such as the Internet or archived in cloud computing based on a three-dimensional S-box. the suggested method encrypts and decrypts the image block by block. Each block is 8x8 pixels. the encrypted data are obtained by replacing the original data and the key by its value in S-box. Substitution boxes (S-box) are generated using a proposed algorithm that produces a 3D S-box using the random function and permutation. the security analyses show that the presented algorithm has an important security level. the runtime of the proposed algorithms is fast compared to others image encryption algorithms.

关键词： Image processing Securing Communication Images Encryption three-Dimensional (3D) S-box

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Motor Imagery Classification Combining Riemannian Geometry and Artificial Neural networks 5th

Motor Imagery Classification Combining Riemannian Geometry a...

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5th international conference on Recent Trends in Image processing and Pattern Recognition (RTIP2R)

作者： Cecotti, Hubert Tiwale, Girish Calif State Univ Dept Comp Sci Fresno CA 93740 USA

ISBN: (纸本)9783031235986;9783031235993

Brain-computer interfaces (BCIs) based on non-invasive electroencephalography provide a means of communication for people with severe disabilities. BCI based on the detection of motor imagery can be used for both communication and rehabilitation purposes [8]. For transferring BCIs outside of the lab to clinical settings, it is necessary to have a high accuracy. the current state of the art techniques includes the use of distance based on the Riemannian geometry. In this paper, we propose a new pattern recognition system for the multiclass classification of brain evoked responses corresponding to motor imagery. the method is based on the combination of features based on Riemannian geometry obtained from 15 frequency bands from 8 24Hz to cover the mu and beta bands, and a feedforward neural network for the classification. We compare the performance of the multi-layer perceptron (MLP) and the extreme learning machine (ELM) classifiers. the system has been assessed on two publicly available datasets. the kappa value for 4-class is 0.53. the average binary classification across the six pairwise tasks is 80.83%. the results support the conclusion that multi-band classification can be successfully achieved using artificial neural networks and MLPs provide substantially better performance than ELMs approaches.

关键词： Brain-computer interface Motor imagery Classification

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Early Identification of Cancer Blood Disorder Using Deep Convolutional Neural networks 5

Early Identification of Cancer Blood Disorder Using Deep Con...

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5th international conference on Information Management and Machine Intelligence, ICIMMI 2023

作者： Sujarani, Pulla Yogeshwari, M. Kalaiselvi, K. Chennai600117 India Chennai600117 India Department of Computer Applications Saveetha College of Liberal Arts and Sciences Thandalam Chennai602105 India

ISBN: (纸本)9798400709418

A cancer blood disorder can be dangerous if not detected in time. It causes aberrant white blood cell production in the blood by the bone marrow. Using image processing of microscopic images of the blood, it may be quickly diagnosed. Deep learning techniques are a practical approach to cancer blood disorders in early diagnosis. In this study, we have proposed a novel method to identify cancer blood disorders in the early stage using the deep convolutional neural network (DCNN). Using filtering techniques, the microscopic images are first preprocessed. the 2D Adaptive Anisotropic Diffusion Filter (2DAADF) technique is used for image filtering to eliminate noise from the input images. Feature extraction is carried out utilizing the Grey Level Co-Occurrence Matrix (GLCM) from the filtered images to increase the identification accuracy. Finally, the proposed DCNN classifier is used to detect the cancer blood disorder. In comparison to the current approaches, the proposed methodology attained the maximum accuracy of 97%. According to the results, the proposed method can identify blood cancer with high accuracy and may help with its diagnosis and treatment in the early stages. © 2023 ACM.

关键词： Convolutional neural networks

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Abstractive Text Summarization for Tamil Language Using m-T5 7th

Abstractive Text Summarization for Tamil Language Using m-T5

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7th international conference on Inventive Systems and Control, ICISC 2023

作者： Saraswathi, C. Prinitha, V. Briskilal, J. Department of Computing Technologies SRM Institute of Science and Technology Tamil Nadu Kattankulathur Chengalpattu 603203 India

ISBN: (纸本)9789819916238

Summarization is the act of reducing a long form of text to a shorter version by lowering the length of the original text while retaining and keeping the summary’s core and the informational parts which conveys the content’s meaning. Summarizing lengthy papers by hand is difficult and prone to inaccuracy. the exponential rise of information and data has made automatic text summarization crucial. It leads to a summary generated by a summarizing system that enables users to understand the document’s contents. Abstractive and extractive summarization are the two categories into which summarization falls. We can see that there are not many automatic text summarizers available for Indian languages. Our main goal in this direction is to create an automatic abstractive text summarizer for Tamil using the m-T5 transformer model. We have also used manual dataset of Tamil Kid’s stories for training and testing. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Text processing

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2022 5th international conference on Circuits, Systems and Simulation, ICCSS 2022

2022 5th International Conference on Circuits, Systems and S...

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5th international conference on Circuits, Systems and Simulation, ICCSS 2022

ISBN: (纸本)9781665481199

the proceedings contain 38 papers. the topics discussed include: a high linearity and low load regulation LDO with SATEC and TIR compensation;a cross-shaped vertical vacuum channel transistor;TCAD simulation research of the single event burnout and hardening in power LDMOS transistors;fault injection model of SRAM memory circuit based on hybrid modeling;a simplified flop MTBF extraction methodology;research on battery state identification algorithm based on equivalent model;a new approach to increase the operating frequency and the duty-cycle range of high-speed level shifter in 600V gate driver;fuzzy inference model for recognition of single-phase-to-ground fault caused by insulation deterioration in medium-voltage distribution networks;research on comprehensive evaluation and prediction of power quality in low voltage area;a wide-input-range multi-phase clock generator design for CMOS image sensors;a pseudo-differential high-speed sensing scheme for phase-change memory;and hand gesture recognition system using the dynamic vision sensor.

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