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检索条件"任意字段=Conference on Algorithms, Devices, and Systems for Optical Information Processing"

共 3196 条记录，以下是51-60 订阅

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Leveraging Machine Learning for Predicting Molecular Refractivity in Drug Toxicology

Leveraging Machine Learning for Predicting Molecular Refract...

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International conference on Signal processing and Communication (ICSC)

作者： Saloni Joshi Reema Gabrani Ruby Beniwal Dept. of Biotechnology Jaypee Institute of Information Technology Noida UP India Dept. of Electronics and Communication Jaypee Institute of Information Technology Noida UP India

ISBN: (数字)9798331511890

ISBN: (纸本)9798331511906

The integration of electronics engineering into molecular refractivity prediction is transforming drug toxicology studies. By combining biosensors, machine learning algorithms, and high-speed data systems, scientists can better predict toxicological profiles of drugs. This interdisciplinary approach improves safety and efficacy in drug development. Electronics engineering enables real-time monitoring of drug metabolism through wearable devices and predictive models, identifying toxic metabolites early and reducing adverse reactions. These technologies enhance the assessment of metabolic processes, guiding safer drug development. Machine learning algorithms further refine predictions by analyzing large datasets, identifying toxicity patterns, and improving personalized treatment plans, ultimately reducing time and costs in toxicology testing. In this research work, machine learning classifier accuracy has been achieved 85% to identify toxic drugs and toxicity prediction accuracy by using the Bayesian Neural Network was 95.8%.

关键词： Drugs Toxicology Machine learning algorithms Accuracy Refractive index Machine learning Predictive models Real-time systems Safety Metabolism

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Dynamic Line Sag Monitoring Using Image-Based Bézier Curve Fitting

Dynamic Line Sag Monitoring Using Image-Based Bézier Curve ...

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IEEE SSD International Multi-conference on systems, Signals and devices

作者： Achref Abed Hafedh Trabelsi Faouzi Derbel Faculty Engineering Sciences University of Applied Sciences Leipzig Leipzig Germany Department of Electrical Engineering and Information Technology National Engineering School of Sfax Sfax Tunisia

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

This paper presents an automated system for detecting the sag of overhead transmission lines using image processing. A camera, mounted centrally between two towers, is repositioned using stepper motors to ensure symmetrical alignment with the transmission line. By detecting the lowest point of the line in the image, the system estimates the sag through Bezier curve fitting and converts the pixel-based measurements into real-world dimensions. The proposed system demonstrates high precision and accuracy, providing a reliable method for sag measurement in transmission line monitoring.

关键词： Power transmission lines Accuracy Image processing Heuristic algorithms Poles and towers Transmission line measurements Real-time systems Safety Curve fitting Monitoring

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Directions for Optimizing the Process of Obtaining and processing Thermograms Using a Mobile Application

Directions for Optimizing the Process of Obtaining and Proce...

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Electrical, Electronic and information Sciences (eStream), Open conference of

作者： Iryna Kyrychenko Kostiantyn Nechvolod Vitaliy Makarov Iryna Gruzdo Department of Software Engineering Kharkiv National University Of Radio Electronics Kharkiv Ukraine Department of Surgery No.4 Kharkiv National Medical University Kharkiv Ukraine

ISBN: (数字)9798331598730

ISBN: (纸本)9798331598747

Thermography is an advanced diagnostic method widely used in various fields, including medicine, engineering, and industrial applications. With the increasing use of mobile applications in diagnostics, optimising thermographic analysis for mobile platforms has become a significant area of research. This paper examines the key aspects of thermographic imaging, including the principles of infrared radiation detection, image processing techniques, and integrating thermal data into mobile applications. Special attention is given to the challenges of mobile implementation, such as hardware limitations, real-time processing, and accuracy in temperature measurement. Additionally, the study explores potential solutions, including AIbased image enhancement, cloud-based computing, and optimised algorithms for efficient thermal data analysis. The findings highlight the importance of balancing computational efficiency with diagnostic accuracy to ensure the reliability and effectiveness of mobile thermography applications.

关键词： optical filters Image segmentation Accuracy Biomedical optical imaging Infrared imaging optical variables measurement optical imaging Mobile handsets Real-time systems Mobile applications

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Adaptive Central Frequencies Locally Competitive Algorithm for Speech

Adaptive Central Frequencies Locally Competitive Algorithm f...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Soufiyan Bahadi Eric Plourde Jean Rouat NECOTIS Research Lab Université de Sherbrooke Sherbrooke Canada

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Neuromorphic computing, inspired by nervous systems, revolutionizes information processing with its focus on efficiency and low power consumption. Using sparse coding, this paradigm enhances processing efficiency, which is crucial for edge devices with power constraints. The Locally Competitive Algorithm (LCA), adapted for audio with Gammatone and Gammachirp filter banks, provides an efficient sparse coding method for neuromorphic speech processing. Adaptive LCA (ALCA) further refines this method by dynamically adjusting modulation parameters, thereby improving reconstruction quality and sparsity. This paper introduces an enhanced ALCA version, the ALCA Central Frequency (ALCA-CF), which dynamically adapts both modulation parameters and central frequencies, optimizing the speech representation. Evaluations show that this approach improves reconstruction quality and sparsity while significantly reducing the power consumption of speech classification, without compromising classification accuracy, particularly on Intel’s Loihi 2 neuromorphic chip.

关键词： Power demand Frequency modulation Accuracy Neuromorphic engineering Heuristic algorithms Signal processing algorithms Speech enhancement Encoding Classification algorithms Noise measurement

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A Study on multi-scale landscape character extraction combining GIS and graphic processing algorithms 3

A Study on multi-scale landscape character extraction combin...

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3rd International conference on Environmental Remote Sensing and Geographic information Technology, ERSGIT 2024

作者： Wang, Shuwei Liu, Yang Li, Guangqi Su, Xueying Wang, Sen Bai, Xuyong Center for Geophysical Survey China Geological Survey Langfang China Technology Innovation Center for Earth Near Surface Detection China Geological Survey China

ISBN: (纸本)9781510690219

This research presents an innovative approach to accurately extract multi-scale landscape characters by combining geographic information systems (GIS) and graphic processing algorithms. It is essential to recognize and analyze landscape characters at different scales. This study uses GIS advantages in spatial analysis and the K-Means clustering algorithm in image enhancement, feature extraction, image segmentation, and color quantification. Through image interpretation and deep learning, the distribution characteristics of buildings in Langfang City are obtained to provide guidance and suggestions for the future construction and development of the city. The proposed method covers data preparation, algorithm design, and system implementation. In addition, the future research direction of improving the method is also discussed, and the possibility of its application in other fields is explored. © 2025 SPIE.

关键词： deep learning K-Means multi-scale landscape analysis spatial analysis. GIS

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Multi-attitude based target localization method

Multi-attitude based target localization method

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2024 International conference on Mechatronic Engineering and Artificial Intelligence, MEAI 2024

作者： Yan, Suqing Xu, Xiaoyue Xiao, Jianming Ji, Yuanfa Hawari Bin Ghazali, Kamarul Guangxi Key Laboratory of Precision Navigation Technology and Application Guilin University of Electronic Technology Guilin China School of Information and Communication Guilin University of Electronic Technology Guilin China Department of Science and Engineering Guilin University Guilin China National & Local Joint Engineering Research Center of Satellite Navigation Localization and Location Service GUET-Nanning E-Tech Research Institute Co. Ltd. Nanning China Centre for Advanced Industrial Technology University of Malaysia Pahang Al Sultan Abdullah Pekan Campus Pahang Pekan Malaysia

ISBN: (纸本)9781510689176

In recent years, location-based services have gradually penetrated all aspects of people's daily life, bringing great convenience to people's work and life. Pedestrian Dead Reckoning (PDR) is a positioning method that can derive a pedestrian's movement trajectory by relying on sensors information, with a high degree of autonomy and continuity. However, existing PDR tends to restrict the target posture to remain steadily pointing in the direction of pedestrian movement throughout the localization process. In addition, although it can achieve a high degree of positioning accuracy in a short period of time, it generates a cumulative error over time. To these problems, this paper proposes a fusion localization method that combines PDR based on multiple smartphones pose recognition and acoustic localization. In this method, we first employ a GWO-ELM based target posture recognition model. Then based on the posture recognition result for PDR localization, Time Difference of Arrival (TDOA) technique is used for acoustic localization. Finally, particle filtering is utilized to combine the two methods. To demonstrate the validity of the method, we performed several experiments on a motion trajectory with a length of 97.2 meters. The results show that the method has better localization performance than the traditional method. In addition, it can realize high-precision localization under different devices with universality and stability. © 2025 SPIE.

关键词： Acoustics Target recognition Detection and tracking algorithms Particles Sensors Education and training Gesture recognition Satellite navigation systems Tunable filters Machine learning

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Pushing Wi-Fi Towards Fine-Grained Sensing Via Spectrogram Enhancement

Pushing Wi-Fi Towards Fine-Grained Sensing Via Spectrogram E...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Hongbo Jiang Yiwei Chen Jingyang Hu Siyu Chen College of Computer Science and Electronic Engineering Hunan University Changsha China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In recent years, Wi-Fi sensing has attracted much attention due to the widespread deployment of communication devices. Due to advancements in signal processing algorithms, contactless sensing technology based on Wi-Fi signals has now been widely applied. However, the limited bandwidth of Wi-Fi systems constrains the performance of Wi-Fi sensing, posing challenges for accomplishing more fine-grained tasks (distinguishing more gestures or multiple targets, etc.). To address this challenge, in this paper, we design a spectrogram enhancement network for Wi-Fi channel state information (CSI) based on the characteristics of Wi-Fi signals to improve the sensing capability of Wi-Fi signals. Specifically, we use a neural network to generate super-resolution spectrograms of CSI to distinguish different time-frequency components in the environment at a finer granularity. Through extensive evaluation, we demonstrate that our designed system can achieve finer-grained perception accuracy than the state-of-the-art systems.

关键词： Performance evaluation Time-frequency analysis Neural networks Superresolution Signal processing algorithms Bandwidth Sensors Speech processing Wireless fidelity Spectrogram

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An 88.36TOPS/W Bit-Level-Weight-Compressed Large-Language-Model Accelerator with Cluster-Aligned INT-FP-GEMM and Bi-Dimensional Workflow Reformulation 72

An 88.36TOPS/W Bit-Level-Weight-Compressed Large-Language-Mo...

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72nd IEEE International Solid-State Circuits conference, ISSCC 2025

作者： Qin, Yubin Wang, Yang Wang, Jiachen Lin, Zhiwei Zhao, Yushu Wei, Shaojun Hu, Yang Yin, Shouyi Tsinghua University Beijing China

ISBN: (纸本)9798331541019

Large language models (LLMs) have shown remarkable performance across a wide range of natural language processing (NLP) tasks, becoming an essential part of modern society [1]-[4]. This exceptional performance can be attributed to huge model size and autoregressive computation [5], [6]. However, these attributes pose challenges for the efficient deployment of LLMs from 3 aspects, as shown in Fig. 23.8.1. First, an LLM has enormous external memory access (EMA) due to its autoregressive feature. During inference, it generates output tokens one by one until all the outputs are complete. In each iteration, the processor reads all model weights and the key-value (KV) cache for computation, while also storing a newly generated KV into the cache for further use. Hence, an LLM requires over 1000x EMA compared to a non-autoregressive Transformer [7], [8]. Second, an LLM may use a unique integer-float (INT-FP) mixed-precision general matrix multiplication (MP-GEMM). Due to accuracy concerns, weights can be quantized to INT, but the activations should be retained in FP [9]-[11]. Generally, processors dequantize INT weights to FP, turning MP-GEMM to FP-GEMM. This approach does not fully exploit quantization benefits and consumes 4.6x more power. Third, apart from GEMM, LLMs frequently use non-linear functions, such as trigonometric functions and softmax, which are commonly computed by a special function unit (SFU). The GEMM, as performed by a processing element (PE), depends on the non-linear results and thus waits for the SFU. Current processors fail to provide sufficient SFU throughput, resulting in PEs left waiting for 1/3 of the time [12], [13]. These problems hinder efficient LLM deployment for both cloud and edge devices, and are largely unexplored by existing accelerators which focus on traditional Transformers [13]-[22] or are limited to specific algorithms [23], [24]. © 2025 IEEE.

关键词： Natural language processing systems

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AI Driven Rat Detection for Warehouse

AI Driven Rat Detection for Warehouse

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Emerging Smart Computing and Informatics (ESCI), conference on

作者： Ann Vincent Bhavesh Patil Aditya Murbade Hemalata Mote Shruti Mehata Dept. Elec and Telecomm. Engg. Don Bosco Institute of Technology Mumbai Mumbai India BharatGodam Solutions LLP Ahmedabad India

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

The project’s goal is to create a system for controlling insect activity and monitoring of warehouses in sizable grain storage facilities. The idea behind the system uses neural networks, deep learning algorithms, and imagery processing methods to identify, monitor, track, and notify users of the activity of pests, notably rats, in real-time. By using algorithms that use deep learning for real-time object recognition and authentication, rats may be accurately identified. In addition, the sensors augment optical detection by using non-visual methods to detect motion and activity, providing full surveillance regardless of regions that are completely concealed. The system’s network of carefully positioned imaging devices and sensors allows for constant, continuous surveillance of the warehouse ambiance. The system activates and displays an indication once it recognises a rat, and it logs the precise time and place of the event. To ensure prompt action and response, warehouse administrators get updates in real time via email, SMS, or a mobile app. Through the application of artificial intelligence (AI), this initiative seeks to improve the security of food, reduce monetary damage, and revolutionize pest control at storage facilities for grain.

关键词： Deep learning Tracking Surveillance Neural networks Real-time systems Sensor systems optical sensors Object recognition Artificial intelligence Intelligent sensors

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14th International conference on Applications and Techniques in information Security, ATIS 2024

14th International Conference on Applications and Techniques...

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14th International conference on Applications and Techniques in information Security, ATIS 2024

ISBN: (纸本)9789819797424

The proceedings contain 24 papers. The special focus in this conference is on Applications and Techniques in information Security. The topics include: optical Neural Networks – A Strategy for Secure Quantum Computing;guarding Against Quantum Threats: A Survey of Post-Quantum Cryptography Standardization, Techniques, and Current Implementations;cryptographic Distinguishers Through Deep Learning for Lightweight Block Ciphers;detection and Mitigation of Email Phishing;securing Digital Forensic Data Using Neural Networks, Elephant Herd Optimization and Complex Sequence Techniques;Design of Image Encryption Technique Using MSE Approach;Low Latency Binary Edward Curve Crypto Processor for FPGA Platforms;Augmenting Security in Edge devices: FPGA-Based Enhanced LEA Algorithm with S-Box and Chaotic Functions;machine Learning Approach for Malware Detection Using Malware Memory Analysis Data;DDOS Attack Detection in Virtual Machine Using Machine Learning algorithms;an Unsupervised Method for Intrusion Detection Using Novel Percentage Split Clustering;HATT-MLPNN: A Hybrid Approach for Cyber-Attack Detection in Industrial Control systems Using MLPNN and Attention Mechanisms;silent Threats: Monitoring Insider Risks in Healthcare Sector;Enhanced Deep Learning for IIoT Threat Intelligence: Revealing Advanced Persistent Threat Attack Patterns;Adaptive Data-Driven LSTM Model for Sensor Drift Detection in Water Utilities;Enhancing FGSM Attacks with Genetic algorithms for Robust Adversarial Examples in Remote Sensing Image Classification systems;GAN-Enhanced Multiclass Malware Classification with Deep Convolutional Networks;IOT Based Locker Access System with MFA & Remote Authentication;a Secure Authentication Scheme between Edge devices Using Hypergraph Hashing Technique in Iot Environment;enhancing Access Control and information Sharing in Cloud IoT with an Effective Blockchain-Based Authority System.

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