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Light Shift Detection Using a Multiple Photodetection Method Suitable for a Chip-Scale Atomic Clock

Light Shift Detection Using a Multiple Photodetection Method...

2023 Joint Conference of the European Frequency and Time Forum and IEEE International Frequency Control Symposium, EFTF/IFCS 2023

作者： Fukuoka, Masahiro Goka, Shigeyoshi Tokyo Metropolitan University Graduate School of System Design Department of Electrical Engineering and Computer Science Tokyo Japan

ISBN: (纸本)9798350311426

In this study, we demonstrated light shift detection in a running atomic clock using the multi-photodetection method. This method enables the observation of atomic resonances with different intensities using the intensity distribution of the laser beam. As it requires only two photodiodes to detect light shifts, it is easily applicable to miniature vapor-cell-based atomic clocks such as chip-scale atomic clocks and miniature atomic clocks. © 2023 IEEE.

关键词： chip-scale atomic clock coherent population trapping intensity distribution laser light shift VCSEL

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A Non-Sequential Steganography Method Using Perfect Square Quotient Differencing (NSPSQD)

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SN computer science 2025年第5期6卷 1-26页

作者： Basak, Ratan Kumar Chatterjee, Ritam Dasgupta, Kousik Dutta, Paramartha Computer Science and Engineering Institute of Engineering and Management (IEM) University of Engineering and Management Kolkata Kolkata India Computer Science and Engineering Kalyani Government Engineering College Kalyani India Computer and System Science Visva-Bharati University Bolpur India

In today’s digital world, keeping data safe is a top priority. Two common methods used to protect data are steganography and cryptography. Steganography hides secret data within everyday files (like images, GIFs or videos), while cryptography scrambles the data into an unreadable format. This paper introduces a new way to hide data using a technique called Perfect Square Quotient Differencing. Instead of embedding data in a straight sequence, the method hides information in two steps within the components of an image pixel (called the quotient and remainder). In the first step, a perfect square quantization technique is applied to the quotient part. In the second step, the Two Least Significant Bit (2LSB) method is used on the remainder part. A new range-table is also introduced to help determine how much data can be hidden in the first step. This two-step approach allows a large amount of data to be hidden (about 3 bits per pixel on average). The method was tested on many animated color images, and its performance was measured using tools like Peak-Signal-to-Noise-Ratio (PSNR), Mean Square Error (MSE), Universal Image Quality Index (UIQI), and Payload Curve. The results show that this method works better than several modern steganography techniques. Additionally, tests were conducted to ensure the method is secure against potential attacks. This new algorithm could be particularly useful for protecting digital documents stored in cloud-based platforms, offering a robust and efficient way to keep data safe.

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Cyclic Transfer Learning for Mandarin-English Code-Switching Speech Recognition

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IEEE Signal Processing Letters 2023年 30卷 1387-1391页

作者： Nga, Cao Hong Vu, Duc-Quang Luong, Huong Hoang Huang, Chien-Lin Wang, Jia-Ching National Central University Department of Computer Science and Information Engineering Taoyuan320 Taiwan Thai Nguyen University of Education Department of Computer Science and Information System Thai Nguyen7905218 Viet Nam Fpt University Department of Information Technology Can Tho900000 Viet Nam National Cheng Kung University Department of Computer Science and Information Engineering Tainan701 Taiwan

Transfer learning is a common method to improve the performance of the model on a target task via pre-training the model on pretext tasks. Different from the methods using monolingual corpora for pre-training, in this study, we propose a Cyclic Transfer Learning method (CTL) that utilizes both code-switching (CS) and monolingual speech resources as the pretext tasks. Moreover, the model in our approach is always alternately learned among these tasks. This helps our model can improve its performance via maintaining CS features during transferring knowledge. The experiment results on the standard SEAME Mandarin-English CS corpus have shown that our proposed CTL approach achieves the best performance with Mixed Error Rate (MER) of 16.3% on testman, 24.1% on testsge. In comparison to the baseline model that was pre-trained with monolingual data, our CTL method achieves 11.4% and 8.7% relative MER reduction on the testman and testsge sets, respectively. Besides, the CTL approach also outperforms compared to other state-of-the-art methods. © 1994-2012 IEEE.

关键词： Speech recognition

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Deep Learning-Based Detection and Severity Analysis of Corona and Pneumonia Using CNN with Wolf Optimization Algorithm 9

Deep Learning-Based Detection and Severity Analysis of Coron...

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9th International Conference on science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Kutti, M. Beula Karthick, T. Srmist Department of Computer Science And Engineering Chennai Kattankulathur India School of Computing Srmist Department of Datascience And Business System Chennai Kattankulathur India

ISBN: (纸本)9798350365092

Effective identification and severity analysis of two serious respiratory diseases-pneumonia and corona-are desperately needed, and this study attempts to fill that gap. Early diagnosis and treatment of many conditions depend critically on prompt intervention. Diagnoses are made slowly and subjectively by existing procedures, which frequently rely on human interpretation of medical imaging data. The existing methods for severity analysis do not have a cohesive structure. Our study presents an all-encompassing strategy to get around these restrictions. First, to improve the quality of the images, grayscale-based pre-processing is applied. Next, marker-based watershed segmentation is used to precisely locate anomalies. Feature extraction and categorization are done using a convolutional neural network, or CNN. Specifically, for severity analysis, we optimize the CNN parameters by including the Wolf Optimization Algorithm (WOA). Grayscale pre-processing guarantees the best possible picture quality;marker-based watershed segmentation improves the accuracy of lesion identification;CNN streamlines feature extraction;and WOA supports the study of disease severity. Python is used in the study's implementation, which takes advantage of its many machine learning frameworks and tools. The outcomes show how well the suggested methodology works for precisely identifying and evaluating the levels of corona and pneumonia. By integrating WOA with CNN, the shortcomings of current techniques are addressed and diagnostic accuracy and severity evaluation are improved. When compared to alternative approaches like CNN-SVM, Bayesian CNN, DCNN with BPNN, the suggested CNN with WOA (99.3%) shows an average accuracy gain of about 11.68%, demonstrating higher performance in disease diagnosis and severity analysis. This innovative method offers a methodical and effective framework for better patient treatment, which has the potential to advance the field of respiratory disease diagnostics. © 2

关键词： Extraction

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Energy Aware Planning Approach Considering Cost of Electrical Energy for Sustainable Development 1

Energy Aware Planning Approach Considering Cost of Electrica...

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1st IEEE International Conference on Smart Power Control and Renewable Energy, ICSPCRE 2024

作者： Hossain, Md. Sabbir Shovon, Sazzadul Islam Islam Shayon, Md. Siamul Rumpa, Ummay Khadiza Hasan, Mahamudul Reza, Ahmed Wasif East West University Department of Computer Science and Engineering Dhaka Bangladesh Central Michigan University Department of Information System MI United States

ISBN: (纸本)9798350377002

Given the rising cost of power and growing environmental concerns, optimizing energy utilization in manufacturing workshops has become increasingly significant. However, conventional approaches often neglect the potential for cost reduction within the power segment. This study proposes a linear programming model specifically designed for optimizing electrical costs. By strategically shifting production to off-peak hours, the model aims to minimize electricity costs while adhering to time constraints. A case study illustrates that this approach can achieve significant cost reductions and contribute to environmental sustainability by reducing greenhouse gas emissions during peak periods. Energy-aware planning is a crucial concept for achieving better utilization of resources and improving the quality of life in modern cities. This research represents a valuable step towards economical production and dependable environmental stewardship within the manufacturing industry. © 2024 IEEE.

关键词： Clean energy

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SpatialHD: Spatial Transformer Fused with Hyperdimensional Computing for AI Applications 5

SpatialHD: Spatial Transformer Fused with Hyperdimensional C...

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5th IEEE International Conference on Artificial Intelligence Circuits and systems, AICAS 2023

作者： Bettayeb, Meriem Hassan, Eman Mohammad, Baker Saleh, Hani Khalifa University of Science and Technology System on Chip Lab Electrical Engineering and Computer Science Department Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350332674

Brain-inspired computing methods have shown remarkable efficiency and robustness compared to deep neural networks (DNN). In particular, HyperDimensional Computing (HDC) and Vision Transformer (ViT) have demonstrated promising achievements in facilitating effective and reliable cognitive learning. This paper proposes SpatialHD, the first framework that combines spatial transformer networks (STN) and HDC. First, SpatialHD exploits the STN, which explicitly allows the spatial manipulation of data within the network. Then, it employs HDC to operate over STN output by mapping feature maps into high-dimensional space, learning abstracted information, and classifying data. In addition, the STN output is resized to generate a smaller input feature map. This further reduces computing complexity and memory storage compared to HDC alone. Finally, to test the model's functionality, we applied spatial HD for image classification, utilizing the MNIST and Fashion-MNIST datasets, using only 25% of the dataset for training. Our results show that SpatialHD improves accuracy by ≈ 8% and enhances efficiency by approximately 2.5x compared to base-HDC. © 2023 IEEE.

关键词： Image classification

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Smart Home Automation system controlling Through computer Vision 2

Smart Home Automation System controlling Through Computer Vi...

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2nd International Conference on Artificial Intelligence, Blockchain, and Internet of Things, AIBThings 2024

作者： Hossain, Md. Sabbir Rumpa, Ummay Khadiza Sharma, Shatabdi Rashid, Mamunur Rahman, Habibur Shovon, Sazzadul Islam East West University Department of Computer Science and Engineering Dhaka Bangladesh Central Michigan University Department of Information System MI United States

ISBN: (纸本)9798331529529

We are dwelling in the golden age of science development. Smart domestic automation has now ended up in the middle of enchantment in the lookup field. The guide machine of switching every so often motives an excessive strength loss. Keeping that in mind, we tried to make a computerized domestic which will now not require any remote, Bluetooth, or any kind of controlling device. In this paper, we have introduced a benchmark contrast of current some hardware the usage of the equal algorithm. For that, we tried to use some sensors, haar cascade documents, and some OpenCV codes for our design. After doing some processes we have chosen a layout that is convenient. In our chosen layout we tend to use a camera, laptop, ARDUINO and made human detection feasible the use of laptop vision. Through the digital camera, rooms are being monitored always, and from that actual time, the image processing presence of human beings is being determined by using the machine automatically. Finally, the microcontroller triggers in accordance with the decision, when it no longer locates any human in the room, it turns the lights, followers, off and on the opposite, it turns the lights, followers, on. © 2024 IEEE.

关键词： Microcontrollers

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Routing of Vehicular IoT Networks based on various routing Metrics, Characteristics, and Properties 1

Routing of Vehicular IoT Networks based on various routing M...

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1st International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2023

作者： Kumar, Rakesh Sinngh, Sunil K. Lobiyal, D.K. Panjab University Computer Science & Engineering Ccet Chandigarh India Jawaharlal Nehru University School of Computer & System Sciences New Delhi India

ISBN: (纸本)9798350338027

The use of the Vehicular Internet of Things (VIoT) has been rapidly increasing in recent years, which has led to the development of various routing protocols to facilitate communication among vehicles. In this paper, we provide a comprehensive review of the state-of-the-art routing metrics, characteristics, and properties used in VIoT networks. We examine different types of routing protocols, including geographic, position-based, and topology-based. We also investigate various factors affecting the performance of these protocols, such as the mobility patterns of vehicles, network topology, and energy consumption. Furthermore, we compare and contrast the advantages and disadvantages of different routing protocols in terms of their suitability for different types of VIoT applications. Finally, we identify several open research issues and challenges that need to be addressed in future VIoT routing research. This review paper provides a valuable reference for researchers and practitioners working on VIoT networks and can help them select an appropriate routing protocol for their specific applications. © 2023 IEEE.

关键词： Internet of things

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Automatic Early Detection of Tomato Blight Disease Using Deep Neural Networks 4

Automatic Early Detection of Tomato Blight Disease Using Dee...

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4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Halder, Anangsha Neogi, Subhrendu Guha Dutta, Shibdas Chandra, Soumallya Chandra Majumdar, Pritha Guha Sarkar, Akash The Neotia University Department of Computer Science and Engineering West Bengal Sarisha India Brainware University Department of Computer Science and Engineering West Bengal Kolkata India Dcg Data-Core System India Pvt. Ltd West Bengal Kolkata India

ISBN: (纸本)9798350387490

Crop disease control is crucial for food security since plant illnesses diminish agricultural production. Crop productivity and quality depend on leaf diseases like tomatoes. Early plant disease identification utilizing computer vision and artificial intelligence can lessen disease severity and overcome the limitations of continuous human monitoring. Automatic Early detection and alert systems reduce disease and boost agricultural productivity by classifying tomato diseases from tomato leaf images using Deep Neural Network Models. Our study examined leaf photos to classify tomato diseases and healthy plants using AlexNet, ResNet-9, VGG-19, and a modified CNN model for tomato plant disease diagnosis using deep neural network architectures. The proposed CNN analyses images to identify edges, colours, and textures. The suggested model can recognize healthy and diseased tomato leaves with 98% accuracy, making it a valuable crop management tool that promotes sustainable agriculture and global food security. © 2024 IEEE.

关键词： Fruits

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Autonomous Drone Control for Wireless Charging Using Power Lines 18

Autonomous Drone Control for Wireless Charging Using Power L...

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18th International Conference on Ubiquitous Information Management and Communication, IMCOM 2024

作者： Wang, Pengkai Kim, Jonghoek Mutahira, Husna Muhammad, Mannan Saeed Sungkyunkwan University Dept. of Electrical and Computer Engineering Suwon Korea Republic of Sejong University Dept. of Defense System Engineering Seoul Korea Republic of Sogang University Dept. of Computer Science and Engineering Seoul Korea Republic of

ISBN: (纸本)9798350331011

The limited autonomy of flight has long been considered a significant constraint in drone systems. In the context of drone inspections of power lines, this study focuses on a drone equipped with a coil designed for autonomous battery charging. Positioned atop the drone is a charging coil, and on the upper surface, there is an upward-facing camera with a restricted field-of-view (FOV), serving the crucial role of aligning the drone with power lines. This research introduces a wireless power transfer strategy to facilitate the self-charging process of the drone while maintaining its position beneath power lines. To enable the drone's autonomous approach to power lines, a method is devised to guide the drone from below. As the drone approaches the power lines, it monitors the current induced by these lines and gradually adjusts its altitude until a sufficiently high induced current is achieved. The proposed approach ensures safety, as the drone avoids direct contact with high-voltage power lines, setting it apart from other existing methods that require such contact. To the best of our knowledge, this study offers a novel approach to precisely controlling drone movements, and establishing reliable wireless power transfer from power lines. The effectiveness of the developed wireless transfer strategy has been verified through MATLAB simulations. © 2024 IEEE.

关键词： Drones

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