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Empirical Wavelet Transform Grounded Poignant Ground Target Recognition and Classification by Seismic signal processing 1

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3rd international conference on Machine Intelligence and Smart Systems, MISS 2023

作者： Mittal, Aman Centre for Interdisciplinary Research in Business and Technology Chitkara University Punjab Rajpura140401 India

ISBN: (数字)9783031317231

ISBN: (纸本)9783031317224

Humans have had the ability to recognise items for hundreds of years, possibly even since they first appeared on Earth. Humans use their senses of sight, smell, hearing, taste, and touch to determine the identities of things in their immediate surroundings. Transmission of sensory organ impulses to the brain allows for the latter's processing and interpretation of the data. That's why repetition is so important for learning. The learned knowledge is put to use in a wide variety of ways, from the mundane to the crucial, such as in the areas of security, surveillance, traffic monitoring, etc. Due to the short range of the human senses and the potential dangers of working in some environments, this approach has spatial and temporal constraints. Seismic signal processing employs the wavelet transform for detection and classification. However, picking the mother wavelet is tricky because it depends on how well it fits the original signal. As a result, the empirical wavelet transform (EWT) is investigated in this research paper as a potential adaptive wavelet transform for detection and classification. In the same way as EWT does, the mother wavelet is chosen mechanically with reference to the input signal. This study shows that EWT-based detection and classification methods perform better than STFT-based methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Seismic response

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FPGA Implementation of Floating-Point Multiplier Employing Carry-Lookahead Adders

FPGA Implementation of Floating-Point Multiplier Employing C...

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Device Intelligence, Computing and Communication Technologies, (DICCT), international conference on

作者： Abhay Sharma Md. Irfanul Hasan Devesh Tiwari Graphic Era (Deemed to be University) Dehradun India Graphic Era Hill University Dehradun India

ISBN: (数字)9798331543358

ISBN: (纸本)9798331543365

For hardware implementation of digital signal processing algorithms, the key components are multipliers. The design of multipliers plays an important role in overall system architecture. For algorithms requiring a dynamic range of data, floating-point representation is favoured over fixed-point. Nevertheless, floating-point multipliers provide difficulties for designers because of the large latency and space that they need. In this work, we suggest a more efficient method of floatingpoint multiplication with respect to both time and space. The bottleneck in the floating-point multiplier design is the Mantissa multiplication which is implemented through fast tree multipliers. Instead of using half-adders and full-adders as in conventional Wallace or Dadda tree multipliers, Carry-lookahead adders are used as compressors in the proposed structure, which resulted in a better partial product reduction in mantissa multiplication. The proposed design is validated through FPGA implementation using Verilog HDL description. It was observed that an improvement of 8.5% with respect to Wallace and an improvement of 9.9% with respect to Dadda is obtained in terms of latency.

关键词： Heuristic algorithms signal processing algorithms Systems architecture Digital signal processing Dynamic range Hardware Compressors Hardware design languages Field programmable gate arrays Adders

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Simulative Analysis of DSP-PDM-256-QAM-Enabled Free Space Optics Transmission: Impact of Rain and Fog Weather conditions

Simulative Analysis of DSP-PDM-256-QAM-Enabled Free Space Op...

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Device Intelligence, Computing and Communication Technologies, (DICCT), international conference on

作者： Ritu Sharma Neetu Harmanpreet Kaur ECE Department Chandigarh University Mohali India Department of Physics Chandigarh University Mohali India

ISBN: (数字)9798331543358

ISBN: (纸本)9798331543365

This research work presents a complete simulation-based analysis of a free-space optics (FSO) communication system that combines digital signal processing (DSP) with polarization-division multiplexing (PDM) and 256-QAM modulation. The performance of the system is assessed under different weather conditions, such as rain and fog. The design and evaluation are done using a simulation framework, with bit error rate (BER) and error vector magnitude (EVM) acting as the main performance parameters. Significant findings indicate that the system is able to transmit the data over a maximum distance of 1200 m in light fog, 700 m in moderate fog, and 550 m in heavy fog. Similarly, in rainfall conditions, the system provides data transmission over distances of 1600 m during light rain, 1250 m in moderate rain, and 750 m during heavy rain, all considering the EVM value below 3% and the BER value at or below 10– 4 . These results explain the reliability and adaptability of the DSP-PDM-256-QAM-enabled FSO system throughout the challenging weather conditions, highlighting its suitability for implementation in difficult environments. The study gives a key understanding of optimizing FSO systems to combat harsh weather, laying the foundation for robust, high-performance communication networks.

关键词： Multiplexing Rain Free-space optical communication Atmospheric modeling Bit error rate Weather forecasting Digital signal processing Attenuation data communication Meteorology

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Accuracy verification of sulfur dioxide data based on ultraviolet hyperspectral solar radiometer observations 3

Accuracy verification of sulfur dioxide data based on ultrav...

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3rd international conference on Environmental Remote Sensing and Geographic Information Technology, ERSGIT 2024

作者： Zeng, Hao Wang, Shun Xun, Lina The Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Electrical Engineering and Automation Anhui University Hefei China

ISBN: (纸本)9781510690219

One of the primary air pollutants that has a detrimental effect on both the environment and human health is sulfur dioxide. The ecosystem and climate greatly depend on the high-precision monitoring of sulfur dioxide. In this study, SO2 ground-based data in Xi'an and Hangzhou were calculated using the UV continuous spectral inversion method, and the accuracy was confirmed using OMI satellite data that had undergone bilinear spatial interpolation. Although there are still some discrepancies in the comparative analysis results, the results indicate a good correlation between the ground-based measurement results of the UV hyperspectral solar radiometer and OMI data products;the correlation numbers are 0.938 and 0.941, respectively, and the relative errors are roughly 10%. The ultraviolet hyperspectral solar radiometer can complement this study's findings with accuracy and is a valuable tool for environmental monitoring. © 2025 SPIE.

关键词： accuracy verification correlation sulfur dioxide distribution ultraviolet hyperspectral solar radiometer

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EpiCNN-LSTMDetect: A Combined Deep Learning Method for Identifying Epileptic Seizures

EpiCNN-LSTMDetect: A Combined Deep Learning Method for Ident...

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international Symposium on Advanced Computing and Communication (ISACC)

作者： Priyaranjan Kumar Prabhat Kumar Upadhyay Department of Electrical & Electronics Engineering Birla Institute of Technology Mesra Ranchi India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Epilepsy, characterized by recurrent seizures, necessitates timely and accurate detection for effective management. This study presents EpiCNN-LSTMDetect, a hybrid deep learning framework combining Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks to analyze EEG data for seizure identification. Leveraging the CHBMIT EEG dataset, the model extracts features such as mean, skewness, kurtosis, and STFT. These features effectively capture spatial and temporal brain dynamics. EpiCNN-LSTMDetect achieved exceptional performance. It recorded an average accuracy of 99.61%, sensitivity of 99.83%, specificity of 99.23%, and an F1 score of 99.64%. The results surpass existing methods. These findings highlight the potential of advanced deep learning techniques in improving epilepsy care.

关键词： Deep learning Accuracy Sensitivity Epilepsy signal processing Brain modeling Feature extraction Electroencephalography Convolutional neural networks Long short term memory

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Analysis of Bit Rate vs SNR in Coded FSO Communication Systems

Analysis of Bit Rate vs SNR in Coded FSO Communication Syste...

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Industrial Electronics: Developments & Applications (ICIDeA), IEEE international conference on-

作者： Ritu Gupta Lalit Kumar Department of ECE Chandigarh University Gharuan Punjab India Department of CSE MMEC Maharishi Markandeshwar (Deemed to be University) Mullana-Ambala India

ISBN: (数字)9798331533205

ISBN: (纸本)9798331533212

The main requirements for a future communication system are low complexity, stronger performance, data processing, and higher data rate. These requirements have been fulfilled by Free Space Optical (FSO) communication systems. FSO is a visible or wireless light communication (WLC) system. Performance analysis itself is a critical stage in the field of FSO/ wireless communications due to the nature of multi-path fading that is encountered due to scatters. The BER performance depends on the employed detection scheme, signal-to-noise ratio (SNR), and diversity order. The main basic components for selecting channel coding techniques are Bit Error Rate (BER) and SNR. This paper examined the performance of different channel coding schemes including convolutional coding, LDPC (low-density parity check) coding, TCM (trellis coded modulation) coding, and concatenated coding schemes during diverse turbulence conditions using the log-normal and gamma-gamma distribution model in MATLAB. Simulation results and performance evaluation in terms of bit rate indicated that the concatenated coding scheme outperforms the other single channel coding schemes under various turbulence conditions.

关键词： Wireless communication Convolutional codes Simulation Bit rate Bit error rate Parity check codes Mathematical models Channel coding MATLAB signal to noise ratio

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32×40 Gbps WDM Based FSO Transmission System for 5G Communication Services

32×40 Gbps WDM Based FSO Transmission System for 5G Communi...

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Device Intelligence, Computing and Communication Technologies, (DICCT), international conference on

作者： Gargi Verma Prabhleen Kaur Riya Rattanpal Anju Academic Unit - 1 Chandigarh University Mohali India CSE Department Chandigarh University Mohali India

ISBN: (数字)9798331543358

ISBN: (纸本)9798331543365

Designed to reach a total transmission capacity of 1.28 Tbps with each channel running at 40 Gbps, this work proposes a 32-channel Wavelength Division Multiplexing (WDM) based Free Space Optical (FSO) communication system. Under a variety of meteorological conditions-including clear weather and fog-the performance of the proposed system is assessed using the Q-factor and bit error rate (BER). Reliable data transmission across a 50 km FSO link in clear conditions is shown by simulation results; performance declines to transmission distances of 3000 m, 1700 m, and 1260 m in light, medium, and heavy fog, respectively. These results underline the system's dependability and viability for high-volume communication, thereby stressing its capacity to help next-generation 5G wireless networks to be implemented.

关键词： Q-factor 5G mobile communication Simulation Wireless networks Bit error rate signal processing algorithms Receivers Wavelength division multiplexing Reliability Meteorology

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Hybrid Beamforming Optimization in Millimeter Wave MIMO Using Deep Deterministic Policy Gradient Based Deep Reinforcement Learning

Hybrid Beamforming Optimization in Millimeter Wave MIMO Usin...

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Integrated Circuits and Communication Systems (ICICACS), IEEE international conference on

作者： Rame Riadhusin Sudhakar K D. Bhanu Prakash S. Senthil Kumar Rajasubha J Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Artificial Intelligence and Data Science Nitte Meenakshi Institute of Technology Bengaluru India Department of Electronics and Communication Engineering CVR College of Engineering Hyderabad India Department of Electrical and Electronics Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai India Department of Computer Science and Engineering Dhanalakshmi Srinivasan College of Engineering Technology Mamallapuram India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

In recent years, the rapid growth of Hybrid Beamforming (HB) technology has developed rapidly to improve high spectrum efficiency and data transmission in wireless communication systems. Massive Multiple Input Multiple Output (MIMO) systems are integrated with HB to encourage wireless network performance by improving signal quality. However, combination of digital and analog beamforming increases hardware complexity and quantization errors in multiple streams. Hence, this research proposes Deep Reinforcement Learning (DRL)-based hybrid beamforming optimization framework to learn relevant Channel State Information (CSI) by learning complex non-linear relationships and beam forming weights dynamically. Initially, Deep Multiple Input Multiple Output (Deep MIMO) dataset which consists of channel measurements and capacity to receive and transmit signal processing is used. After that, preprocessing is performed using Amplitude Scaling (AS) for signal compression and Phase Normalization (PN) to reduce phase noise in multiple frequencies. Finally, Deep Deterministic Policy Gradient (DDPG)-based DRL model is used to perform HB optimization through adaptive learning policy by improving sum rate and spectral efficiency while reducing quantization errors. Proposed method results with the Sum-rate (5.35 bps/HZ), Accuracy (99.26%) and Inference time (0.03 sec) when compared with existing CNN-Bi-directional Long Short-Term Memory (CNNBiLSTM).

关键词： Quantization (signal) Array signal processing Spectral efficiency Transmitting antennas Receiving antennas Deep reinforcement learning MIMO Millimeter wave communication Integrated circuit modeling Optimization

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Natural Language processing: Innovations, Recent Trends and Challenges 3rd

Natural Language Processing: Innovations, Recent Trends and ...

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3rd international conference on Machine Intelligence and Smart Systems, MISS 2023

作者： Prasada, Padma Rao, M. V. Panduranga Suryawanshi, Ujwala Vishwanatharao Kanakapura Road Bengaluru India Department of Artificial Intelligence & Data Science Sri Dharmasthala Manjunatheshwara Institute of Technology Karnataka Ujire India Maharashtra Latur India

ISBN: (纸本)9783031691140

Natural Language processing (NLP) is a rising technique for developing various sorts of Artificial Intelligence (AI), and it will continue to be a major emphasis for more cognitive application. This article covered NLP's most beneficial uses. Our objective of this study is to provide a conceptual understanding of the many different areas where NLP has the potential to make a significant impact and change the entire landscape using its automating methods. It's a popular topic right now, and everybody wants to get in on the action by investing there. The completion of these implementations is made possible by detailed and in-depth research on NLP as well as the field it covers. The review begins with an explanation of the tendencies of NLP and the elements of NLP, and afterwards moves forward to the application of NLP as well as its development and issues. Text to speech generation, liver cancer diagnosis, sentence generator, graph-based technique, and postoperative complications prediction for patients with chronic conditions are some of the features that we have included in our discussion. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Natural language processing systems

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Deep Learning for Automated Image Captioning: A CNN and Transformer Model Analysis

Deep Learning for Automated Image Captioning: A CNN and Tran...

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Intelligent data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： Kathula Saarika Vasaraju Varsha P.Sri Harsha Anjum Nabi Sheikh Dept. Artificial Intelligence and Data Science Chaitanya Bharathi Institute of Technology

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Automatic image captioning is a multidisciplinary effort that combines machine learning, natural language processing, and computer vision in creating meaningful and context-rich captions for images. Within a short time, this research problem has garnered a lot of attention and new techniques are continuously being proposed. However, there are many dismaying factors as well. For instance, the current models still have a long way to go in attaining human accuracy. This paper seeks to conduct a systematic review of the recent trends and developments in the field of image captioning techniques and models with a focus on 2023 and 2024 by systematically analyzing the 13 publications located in Springer and IEEEXplore libraries. According to our research, Convolutional Neural Networks allow the identification of image content at an early stage, and Recurrent Neural Networks or Long Short-Term Memory networks usually appear at the text generation stage. The most common image datasets are MS COCO and Flickr8k. Almost all studies utilized the BLEU scores (1, 2, 3, and 4) mechanism. It is alleviated that CNN with LSTM models tends to perform better when compared to RNN-modeled networks. Attention mechanisms and Encoder-Decoder architecture are aggressively becoming the two major solutions to strengthening image captioning. This review will help and provide direction to those who are interested in practicing research that aims to promote the techniques and processes of automatic image captioning.

关键词： Deep learning Analytical models Recurrent neural networks Transformers Market research Natural language processing Convolutional neural networks Particle swarm optimization Long short term memory Systematic literature review

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