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11th EAI International Conference on Advancement of science and Technology, ICAST 2023

作者： Ayalew, Hailu Dessalegn Talabattula, Srinivas Annamalai, Pushparaghavan Electrical and Computer Engineering Faculty Bahir Dar University-Institute of Technology Bahir Dar Ethiopia Department of Electrical Communication Engineering Indian Institute of Science Bangalore India

ISBN: (纸本)9783031641503

One of the key components of wavelength division multiplexing (WDM) networks is tunable optical filters (TOFs). This paper focused on the theoretical analysis and design of a new dual-channel micro-electromechanical system (MEMS) actuated tunable optical filter based on polarization insensitive compound ring resonators (PICRRs) for dense wavelength division multiplexing (DWDM) optical networking applications. It is basically structured on an optimally designed capacitive type microcantilever beam, which is fixed at both ends, with built-in PICRRs, which are placed at both fixed ends of the beam to gain linearly varying longitudinal strain and stress on the resonators. Also, the dimensions of the rings are properly chosen to achieve a full width at half maximum (FWHM) of 0.4078 nm with a flat pass band at 1 dB of 0.3626 nm and a good shape factor for both channels. It also shows a free spectral range of about 20.05 nm for the first channel and 20.046 nm for the second channel, with a high extinction ratio that allows filtering of adjacent channels of 50 GHz DWDM systems. Thus, when the electrostatic attraction force (EAF) is applied, the beam will bend, which induces strain and stress in the rings, which brings a change in effective refractive index of the rings and also affects the perimeter and coupling coefficients of the rings that leads to a change in the output spectrum shift, providing the tunability as high as 4.779 nm/μN, and it is capable of tuning up to 355 GHz with negligible variations of FWHM and channel spacing within the tunability range of the two channels. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Dense wavelength division multiplexing

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Lasso-Based Fast Residual Recovery For Modulo Sampling 48

Lasso-Based Fast Residual Recovery For Modulo Sampling

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Shah, Shaik Basheeruddin Mulleti, Satish Eldar, Yonina C. Weizmann Institute of Science Faculty of Mathematics and Computer Science Israel Bombay Department of Electrical Engineering India

ISBN: (纸本)9781728163277

In practice, Analog-to-Digital Converter (ADC) is used to perform sampling. A practical bottleneck of ADC is its lower dynamic range, leading to loss of information. To address this issue, researchers suggested folding operation on the signal using a modulo operator before passing it as an input to ADC. Though this process preserves the signal information, an unfolding algorithm is required to get the true samples from the folded samples. Noise robustness and computational time are two key parameters of an unfolding algorithm. In this paper, we propose a fast and robust algorithm for unfolding. Specifically, we first show that the first-order difference of the residual samples (the difference between the folded and true samples) is sparse by deriving an upper bound on its sparsity, and can be recovered from its partial Fourier measurements by formulating a sparse recovery problem. We demonstrate that the proposed algorithm is robust to noise and computationally efficient compared to the existing methods. © 2023 IEEE.

关键词： Recovery

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High-Dynamic Range ADC for Finite-Rate-of-Innovation Signals 48

High-Dynamic Range ADC for Finite-Rate-of-Innovation Signals

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Mulleti, Satish Eldar, Yonina C. Bombay Department of Electrical Engineering India Weizmann Institute of Science Faculty of Math and Computer Science Israel

ISBN: (纸本)9781728163277

Modulo folding can be used to sample high-dynamic range signals without increasing the dynamic range of the sampler. Specifically, folding is used prior to sampling and then the folded signal is sampled. After sampling, unfolding algorithms are used to compute the true samples (up to a constant factor) from the folded ones. In this work, we consider a modulo sampling framework for finite rate of innovation (FRI) signals which are used to model signals in time of flight imaging. We suggest compactly supported sum-of-sincs filter as a sampling kernel prior to modulo folding and sampling. We derive conditions on the sampling rate and filter coefficients which lead to unfolding of the samples up to an unknown constant. We then propose a modified annihilating filter approach that can uniquely determine the FRI parameters from the unfolded samples with a constant unknown offset. We show that the proposed framework outperforms existing techniques in the presence of noise. © 2023 IEEE.

关键词： Signal sampling

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Direction of Arrival Estimation Using a Transmitarray and a Single Antenna Element

Direction of Arrival Estimation Using a Transmitarray and a ...

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2024 International Symposium on Antennas and Propagation, ISAP 2024

作者： Sunagawa, Ryusuke Shiiba, Hibiki Sano, Makoto Kuse, Ryuji Fukusako, Takeshi Department of Computer Science and Electrical Engineering Kumamoto University Kumamoto Japan Faculty of Advanced Science and Technology Kumamoto University Kumamoto Japan

ISBN: (纸本)9798350364774

This paper deals with a direction-of-arrival estimation (DOA) using an optimized transmitarray and a single receiving *** transmitarray consists of 1-bit elements with 0° and 180° transmission *** transmission phase distribution was optimized using a genetic *** accuracy of the DOA estimation is almost the same as that of the DOA estimation system using a 3-element linear array antenna. © 2024 IEEE.

关键词： Genetic algorithms

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Monocular 3D Object Detection for Robotics

Monocular 3D Object Detection for Robotics

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2024 Beyond Technology Summit on Informatics International Conference, BTS-I2C 2024

作者： Nurdin, Farid Rahman Sadida, Qaulan Ul Haq, Qazi Mazhar Ul Islam, Naeem Haa, Muhamad Amirul Universitas Muhammadiyah Surabaya Faculty of Engineering Surabaya Indonesia Universitas Gadjah Mada Faculty of Psychology Yogyakarta Indonesia Yuan Ze University Department of Computer Science and Engineering Taoyuan Taiwan

ISBN: (纸本)9798331508579

3D object detection is considered as computationally expensive problem as it heavily relies on processing extensive sensory data making it unsuitable for the real-time application. To address this problem, we propose a monocular 3D object detection method for robotics, particularly the autonomous vehicles, using the YOLOv3 algorithm, known for its efficiency and speed. To evaluate the performance of the proposed approach, we used KITTI dataset, one of the most robust 3D object detection datasets. The proposed approach is computationally less expensive, suitable for real-time applications, and highly accurate. Our approach utilizes the Darknet53 backbone network with 74 convolutional layers along with the YOLOv3. We have extensively evaluated the proposed approach where each object class has a custom mAP evaluation script 0.5 IoU for the evaluation process and mAP results. The best outcome, namely a mAP value of 0.60, was obtained after several training processes. The result showed that YOLOv3 is one of the key candidates for the monocular 3D object detection. © 2024 IEEE.

关键词： 3D object detection KITTI dataset monocular YOLOv3

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Smart Drive Safe: Harnessing CNN for Enhanced Traffic Sign Recognition and Voice Alerts 5

Smart Drive Safe: Harnessing CNN for Enhanced Traffic Sign R...

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Priya, D. Divya Chinnasamy, P. Ayyasamy, Ramesh Kumar Kiran, Ajmeera Jalil, Norazira Binti A. Sangodiah, Anbuselvan Mlr Institue of Technology Department of Computer Science Engineering Hyderabad India School of Computing Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamil Nadu Srivilliputtur India Faculty of Information and Communication Technology Perak Kampar Malaysia Quest International University Faculty of Computing and Engineering Perak Ipoh Malaysia

ISBN: (纸本)9798350379945

Traffic signs secure their safety on the roads and the protection of others around them;everyone must be aware of traffic signs and other aspects of road safety. Traffic sign detection is a highly relevant computer vision problem that forms the basis of numerous applications in sectors like the automotive industry. Between classes, traffic signs differ widely in terms of colour, shape, and the use of pictograms or text. The project's classification of traffic signs in an image into various categories is based on a deep neural network model. IoT devices that capture traffic signs and notify users of them are used to build a model. © 2024 IEEE.

关键词： Automotive industry

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Spatiotemporal crowds features extraction of infrared images using neural network

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Journal of Ambient Intelligence and Humanized Computing 2024年第4期15卷 2543-2556页

作者： Al-Oraiqat, Anas M. Drieiev, Oleksandr Drieieva, Hanna Meleshko, Yelyzaveta AlRawashdeh, Hazim Al-Oraiqat, Karim A. Hasan, Yassin M. Y. Maricar, Noor Khan, Sheroz Department of Computer Science College of Engineering and Information Technology Onaizah Colleges Qassim Saudi Arabia Department of Cybersecurity and Software Central Ukrainian National Technical University Kropyvnytskyi Ukraine Department of Artificial Intelligence Faculty of Information Technology Zarqa University Zarqa Jordan Department of Electrical Engineering College of Engineering and Information Technology Onaizah Colleges Qassim Saudi Arabia

Crowds can lead up to severe disasterous consequences resulting in fatalities. Videos obtained through public cameras or captured by drones flying overhead can be processed with artificial intelligence-based crowd analysis systems. Being a hot area of research over the past few years, the goal is not only to identify the presence of crowds but also to predict the probability of crowd-formation in order to issue timely warnings and preventive measures. Such systems will significantly reduce the probablity of the potential disasters. Developing effective systems is a challenging task, especially due to factors such as naturally occuring diverse conditions, variations in people or background pixel areas, noise, behaviors of individuals, relative amounts/distributions/directions of crowd movements, and crowd building reasons. This paper proposes an infrared video processing system based on U-Net convolutional neural network for crowd monitoring in infrared video frames to help estimate the people crowd with normal or abnormal trends. The proposed U-Net architecture aims to efficiently extract crowd features, achieve sufficient people marking-up accuracy, competitively with optimal network configurations in terms of the depth and number of filters to consequently minimise the number of coefficients. For further faster processing, hardware resources/implementation area savings, and lower power, the optimized network coefficients measured are represented in Canonic-Signed Digit with minimal number of nonzero (± 1) digits, minimizing the number of underlying shift-add/subtract operations of all multipliers. The achieved significantly reduced computational cost makes the proposed U-Net effectively suitable for resource-constrained and low power applications. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Convolutional neural networks

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Ear-based Person Recognition using Pix2Pix GAN Augmentation 23

Ear-based Person Recognition using Pix2Pix GAN Augmentation

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23rd International Conference of the Biometrics Special Interest Group, BIOSIG 2024

作者： Alomari, Eman Abdullah M. Yang, Su Hoque, Sanaul Deravi, Farzin Swansea University Faculty of Science Department of Computer Science Swansea United Kingdom University of Kent School of Engineering Canterbury United Kingdom

ISBN: (纸本)9798350373714

This study presents a robust framework that leverages advanced deep-learning techniques for ear-based human recognition. Faced with the challenge of dataset sizes, our approach is developed based on a generative adversarial network (GAN) method namely Pix2Pix to augment the dataset. It is demonstrated that this approach offers the ability to produce complementary images for ear recognition. To be more specific, Pix2Pix GAN is employed to generate missing sides in ear image pairs (i.e., creating corresponding left ear images for right ear images and vice versa). As such, this augmentation could substantially increase the dataset size, making it more diverse and of significantly greater use for training purposes. The employed dataset consisted of several images of the right ear and only one left ear for each individual. A series of corresponding synthetic left-ear images is generated using Pix2Pix GAN as a tool for augmenting the available data and mitigate the dataset's lack of left ear images. The experiment framework used the EarNet model and conducted comparative evaluations before and after Pix2Pix GAN augmentation using the AMI Ear dataset. By employing the Pix2Pix GAN, the proposed approach can effectively double the size of a dataset and, in the process, provide significantly greater utility regarding how that data can be utilised in real-world applications scenarios. The resulting accuracy reaches 98% on the AMI dataset, demonstrating that this technique can improve model performance for ear-based human recognition. © 2024 IEEE.

关键词： Generative adversarial networks

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GS-FFO Based Meta-Heuristic Clustering for Discovering Colossal Patterns in High-Dimensional Data 2nd

GS-FFO Based Meta-Heuristic Clustering for Discovering Colos...

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2nd Congress on Control, Robotics, and Mechatronics, CRM 2024

作者： Reddy, T. Sreenivasula Sathya, R. Nuka, Mallikharjuna Rao Research Scholar Department of Computer Science and Engineering Faculty of Engineering and Technology Annamalai University Annamalai Nagar Tamil Nadu Chidambaram608002 India Assistant Professor Department of Computer Science and Engineering Faculty of Engineering and Technology Annamalai University Annamalai Nagar Tamil Nadu Chidambaram608002 India Professor Department of Computer Applications Annamacharya Institute of Technology & Sciences YSR Kadapa Andhra Pradesh Rajampet516115 India

ISBN: (纸本)9789819768097

Colossal pattern mining, which involves deriving important relationships and insights from large and complex datasets, is an essential part of data analysis. Extracting association rules that satisfy preset minimum support and confidence criteria from a given database is the aim of association rule mining (ARM). Nevertheless, ARM often generates an unusually large number of association rules that are hard for end users to comprehend or validate, which lessens the significance of data mining discoveries. Based on the clustering methodology, this research study presents a new mining strategy. The process begins with a method called the Expressed Data Matrix (EDM), which transforms unstructured data into a structured format and enhances the quality and organisation of the information. Primary data cleaning processes are used to eliminate noise and inconsistencies from the data in order to further improve it. To address high-dimensionality issues and boost mining productivity, dimensionality reduction—which lowers the dataset to its most valuable features—is accomplished using Eigen-Principal Component Analysis (PCA). The core of this study is the hybridised clustering approach, or GS-FFO, which combines the Fruit Fly Optimisation technique (FOA) with Glow Swarm Optimisation (GSO). This technique improves the quality of clustering and reduces the probability of local optima convergence by integrating the benefits of both approaches. The suggested GS-FFO model outperforms with four datasets, including accident, pumbs*, retail, and yeast, with an accuracy of up to 98.5%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Dimensionality reduction

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Integrating Innovative Pedagogies in engineering: The Impact of Temperature on PV Cells 10

Integrating Innovative Pedagogies in Engineering: The Impact...

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10th International Conference on Control, Instrumentation and Automation, ICCIA 2024

作者： Samadani, Mehraneh Moradi, Elahe Gavagsaz-Ghoachani, Roghayeh Islamic Azad University Science and Research Branch Environmental Science and Engineering Department Tehran Iran Shahre Rey Branch Department of Electrical and Computer Engineering Tehran Iran Shahid Beheshti University Faculty of Mechanical and Energy Engineering Renewable Energy Engineering Department Tehran Iran

ISBN: (纸本)9798331509972

This paper explores the impact of temperature on photovoltaic (PV) cell performance, emphasizing the critical role of innovative teaching methods in engineering education. By integrating illustrative representations and analogies, such as the popcorn analogy, complex concepts related to solar energy, and PV cell efficiency are made accessible. The study examines key factors, like the Shockley five-parameter model and the effects of environmental conditions on PV cell efficiency, highlighting how higher temperatures reduce open-circuit voltage (VOC) and overall power output. Using visual aids and storytelling techniques, aims to enhance student understanding and retention of these intricate topics. The findings underscore the importance of educational strategies that foster deeper comprehension and critical thinking, ultimately preparing future engineers to contribute effectively to sustainable energy solutions. This approach not only aids in better grasping the technical aspects but also cultivates a sense of environmental responsibility among students. © 2024 IEEE.

关键词： Cell engineering

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