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A deep convolutional visual encoding model of neuronal responses in the LGN

Brain Informatics 2021年第1期8卷 11页

作者： Mounier, Eslam Abdullah, Bassem Mahdi, Hani Eldawlatly, Seif Computer and Systems Engineering Department Faculty of Engineering Ain Shams University 1 El-Sarayat St. Abbassia Cairo Egypt Faculty of Media Engineering and Technology German University in Cairo Cairo Egypt

The Lateral Geniculate Nucleus (LGN) represents one of the major processing sites along the visual pathway. Despite its crucial role in processing visual information and its utility as one target for recently developed visual prostheses, it is much less studied compared to the retina and the visual cortex. In this paper, we introduce a deep learning encoder to predict LGN neuronal firing in response to different visual stimulation patterns. The encoder comprises a deep Convolutional Neural Network (CNN) that incorporates visual stimulus spatiotemporal representation in addition to LGN neuronal firing history to predict the response of LGN neurons. Extracellular activity was recorded in vivo using multi-electrode arrays from single units in the LGN in 12 anesthetized rats with a total neuronal population of 150 units. Neural activity was recorded in response to single-pixel, checkerboard and geometrical shapes visual stimulation patterns. Extracted firing rates and the corresponding stimulation patterns were used to train the model. The performance of the model was assessed using different testing data sets and different firing rate windows. An overall mean correlation coefficient between the actual and the predicted firing rates of 0.57 and 0.7 was achieved for the 10 ms and the 50 ms firing rate windows, respectively. Results demonstrate that the model is robust to variability in the spatiotemporal properties of the recorded neurons outperforming other examined models including the state-of-the-art Generalized Linear Model (GLM). The results indicate the potential of deep convolutional neural networks as viable models of LGN firing. © 2021, The Author(s).

关键词： Neurons

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Comparison of Multi-Criteria Decision-Making Methods for QoS Based Web Service Selection

Comparison of Multi-Criteria Decision-Making Methods for QoS...

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International Conference on Modern Problems of Radio engineering, Telecommunications and computer Science

作者： Ravil Kudermetov Olga Polska Vadym Shkarupylo Natalia Shcherbak Department of Computer Systems and Networks Zaporizhzhia Polytechnic National University Zaporizhzhia Ukraine Department of Mathematical and Computer Modelling G.E. Pukhov Institute for Modelling in Energy Engineering Kyiv Ukraine

ISBN: (数字)9798331520564

ISBN: (纸本)9798331520571

This paper presents an approach for comparing the results of Web service rankings based on quality characteristics obtained using the multi-criteria decision-making method, Logic Scoring of Preference (LSP), with rankings obtained using several popular methods. Formulas were derived to calculate the sensitivity of the LSP composite aggregator to input criteria for the considered Web service selection problem. Sensitivity estimates were used to determine a rational range of changes in the values of input quality attributes when comparing the ranking results. Consistency measures and coefficients that consider the position of alternatives in the list of ranks were used to compare rankings. The obtained formulas and measures for comparing rankings were demonstrated through numerical experiments.

关键词： Sensitivity Web services Decision making Quality of service Position measurement Market research Telecommunications Logic

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Multi-Layer Perceptron Regression Model for Material Characterization in the Microwave Frequency Range with Cross-Dataset Validation 7

Multi-Layer Perceptron Regression Model for Material Charact...

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7th International Youth Conference on Radio Electronics, Electrical and Power engineering, REEPE 2025

作者： Hanima Kannan, C.H. Gupta, Deepa Mathur, Parul Kurup, Dhanesh G. Chaudhari, Tilesh Sanjay Mathur, Priyanka Amrita School of Engineering Amrita Vishwa Vidyapeetham Rf and Wireless Systems Laboratory Department of Electronics and Communication Engineering Bengaluru India Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bengaluru India Indian Institute of Technology Creative and Advanced Research Based on Nanomaterials Laboratory Department of Chemical Engineering Hyderabad India School of Engineering and Applied Science Bennett University U.P. Greater Noida India

ISBN: (纸本)9798331531836

This paper presents a Multi-Layer Perceptron (MLP) based regression model for characterizing materials at micrwave frequency range. A mathematical model and experimental setup of the Open-Ended Coaxial Probe (OECP) sensor interacting with the material under test (MUT) generate a cross-dataset to validate the MLP regression model, utilizing the frequency-dependent complex dielectric constant for material characterization. This paper utilizes the frequency-dependent response of the OECP sensor (scattering parameters) as input features for the MLP model, while the complex dielectric constant of the MUT serves as the output feature. The optimized MLP model has an R² of 99.991% and MSE of 6.65 × 10^-5, with predictions closely aligning with literature values for standard loads such as distilled water, acetone, and ethanol, obtained using a commercially available dielectric assessment software kit. This cost-effective model enables precise dielectric property estimation, with future work extending to unknown materials, mixtures, and electrolyte solutions. © 2025 IEEE.

关键词： Complex Permittivity Cross-Dataset Generation Material Characterization Multi-Layer Perceptron Open-Ended Coaxial Probe Stan-dard Reference Loads

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Evaluating Propagation Models for IIoT in Underground Mining: an Experimental Comparative Study in Underground Coal Mines

Evaluating Propagation Models for IIoT in Underground Mining...

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IEEE ANDESCON

作者： Fabian Medina Oscar Montañez Cristian Suancha Eduardo Avendaño Sandra Céspedes Systems and Computing Engineering school UPTC Sogamoso Colombia Electronic Engineering school UPTC Sogamoso Colombia Department of Computer Science and Software Engineering Concordia University Montreal Canada

ISBN: (数字)9798350355284

ISBN: (纸本)9798350355291

Planning the Industrial Internet of Things (IIoT) systems for underground mining is critical in guaranteeing communication between nodes (sensor/actuator) and ensuring the effectiveness of their functionalities. Therefore, it is necessary to understand how electromagnetic waves disperse in under- ground environments through models that attempt to reproduce their behavior. This understanding is critical to designing and simulating efficient communications for IIoT systems requiring reliable and continuous data transmission. This paper evaluates two propagation models for underground mining tunnels and emphasizes the analysis of the materials that make up the side walls, the floor, and the roof. The analysis of the models is compared with experimental measurements of received power in the 433 MHz band made in coal mines in Colombia. The results, which have direct practical implications, show that the True Rays model generates a behavior similar to that observed in the experimental measurements, with determination coefficients above 74%. At the same time, we assessed the impact of the standard deviation of surface roughness on the accuracy of the received power predictions in the True Rays model.

关键词： Analytical models Accuracy Predictive models Surface roughness Coal mining Rough surfaces Standards Floors Tuning Industrial Internet of Things

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Robust Stochastic Shortest-Path Planning via Risk-Sensitive Incremental Sampling*

Robust Stochastic Shortest-Path Planning via Risk-Sensitive ...

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IEEE Conference on Decision and Control

作者： Clinton Enwerem Erfaun Noorani John S. Baras Brian M. Sadler Department of Electrical & Computer Engineering Institute for Systems Research University of Maryland College Park MD University of Texas at Austin TX

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

With the pervasiveness of Stochastic Shortest-Path (SSP) problems in high-risk industries, such as last-mile autonomous delivery and supply chain management, robust planning algorithms are crucial for ensuring successful task completion while mitigating hazardous outcomes. Mainstream chance-constrained incremental sampling techniques for solving SSP problems tend to be overly conservative and typically do not consider the likelihood of undesirable tail events. We propose an alternative risk-aware approach inspired by the asymptoticallyoptimal Rapidy-Exploring Random Trees (RRT*) planning algorithm, which selects nodes along path segments with minimal Conditional Value-at-Risk (CVaR). Our motivation rests on the step-wise coherence of the CVaR risk measure and the optimal substructure of the SSP problem. Thus, optimizing with respect to the CVaR at each sampling iteration necessarily leads to an optimal path in the limit of the sample size. We validate our approach via numerical path planning experiments in a two-dimensional grid world with obstacles and stochastic pathsegment lengths. Our simulation results show that incorporating risk into the tree growth process yields paths with lengths that are significantly less sensitive to variations in the noise parameter, or equivalently, paths that are more robust to environmental uncertainty. Algorithmic analyses reveal similar query time and memory space complexity to the baseline RRT* procedure, with only a marginal increase in processing time. This increase is offset by significantly lower noise sensitivity and reduced planner failure rates.

关键词： Industries Uncertainty Supply chain management Sensitivity Simulation Noise Tail Path planning Planning Complexity theory

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Perceptual Video Summarization Using Keyframes Extraction Technique 3

Perceptual Video Summarization Using Keyframes Extraction Te...

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3rd International Conference on Innovative Practices in Technology and Management, ICIPTM 2023

作者： Rajitha Jasmine, R. Nimmagadda, Padmaja Sudhakar, K. Benitha Christinal, J. Rajasekar, P. Syed Musthafa, A. R. M. K. Engineering College Department of Information Technology Kavaraipettai India Mohan Babu University Department of Electronics and Communication Tirupati India Madanapalle Institute of Technology Department of Computer Science Madanapalle India Panimalar Engineering College Department of Information Technology Chennai India Srm Institute of Science and Technology Department of Data Science and Business Systems Chengalpattu India M. Kumarasamy College of Engineering Department of Information Technology Karur India

ISBN: (纸本)9798350336238

The growth of video content in recent years is a challenging problem due to increased memory storage and time consuming for analyzing content of the video. Therefore, there is a need to reduce the content for human usage. This paper presents a keyframes technique that uses co-occurrence matrix and permutation computation for perceptual video summarization (PVS). The Human Visual System (HVS) needs to incorporate the PVS. It helps to allow for the importance of perceptually significant contents. The proposed method uses different kinds of videos to evaluate the effectiveness of the work. The subjective evaluation scores have evaluated the proposed work. © 2023 IEEE.

关键词： Video recording

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ByteStack-ID: Integrated Stacked Model Leveraging Payload Byte Frequency for Grayscale Image-based Network Intrusion Detection

ByteStack-ID: Integrated Stacked Model Leveraging Payload By...

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IEEE International Conference on Communications (ICC)

作者： Irfan Khan Yasir Ali Farrukh Syed Wali Department of Marine Engineering Technology in joint appointment with Electrical and Computer Engineering Department and Computer Science & Engineering Department Texas A&M University Galveston TX USA Clean and Resilient Energy Systems (CARES) Lab Texas A&M University College Station TX USA

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

In the ever-evolving realm of network security, the swift and accurate identification of diverse attack classes within network traffic is of paramount importance. This paper introduces “ByteStack-ID,” a pioneering approach tailored for packet-level intrusion detection. At its core, ByteStack-ID leverages grayscale images generated from the frequency distributions of payload data, a groundbreaking technique that greatly enhances the model's ability to discern intricate data patterns. Notably, our approach is exclusively grounded in packet-level information, a departure from conventional Network Intrusion Detection systems (NIDS) that predominantly rely on flow-based data. While building upon the fundamental concept of stacking methodology, ByteStack-ID diverges from traditional stacking approaches. It seamlessly integrates additional meta learner layers into the concatenated base learners, creating a highly optimized, unified model. Empirical results unequivocally confirm the outstanding effectiveness of the ByteStack-ID framework, consistently outperforming baseline models and state-of-the-art approaches across pivotal performance metrics, including precision, recall, and F1-score. Impressively, our proposed approach achieves an exceptional 81% macro F1-score in multiclass classification tasks. In a landscape marked by the continuous evolution of network threats, ByteStack-ID emerges as a robust and versatile security solution, relying solely on packet-level information extracted from network traffic data.

关键词： Measurement Technological innovation Stacking Network intrusion detection Telecommunication traffic Gray-scale Network security

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A Hankelization-Based Neural Network-Assisted Signal Classification in Integrated Sensing and Communication systems

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IEEE Access 2025年 13卷 94648-94657页

作者： Linyi Zhang Mustafa Ozger Woong-Hee Lee School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Department of Electronic Systems Aalborg University Copenhagen Denmark Division of Electronics and Electrical Engineering Dongguk University-Seoul Seoul South Korea

In this paper, we introduce a neural network (NN)-based framework aimed at classifying sensing and communication signals at base stations, improving the efficiency of integrated sensing and communication (ISAC) systems in a bistatic configuration. The framework leverages a key mathematical insight: the Hankelized matrix formed from an equidistantly sampled signal of sparsely superimposed radio waves exhibits a low-rank property, whereas a frequency-modulated signal lacks this characteristic. It ensures that, even in practical environments, the Hankelized matrix of a sensing or communication channel statistically retains the relevant information. Hence, we use the singular values of the Hankelized matrix as the input to the neural NN, while the output is a one-hot encoded vector indicating whether the received signal is intended for sensing or communication. We investigate three scenarios where the communication and sensing signals either use the same or different waveforms in terms of the detection performance of the communication signals. The results demonstrate that the proposed method outperforms existing approaches in classification performance across all scenarios, regardless of whether the communication and sensing signals utilize the same waveform or not. The framework achieves a detection rate of over 95% even at an SNR of 0 dB. Notably, the network performs well in terms of a small number of pilot symbols, a small number of training dataset, and dynamic environments.

关键词： Sensors OFDM Robot sensing systems Symbols Wireless communication Integrated sensing and communication Wireless sensor networks Internet of Things Interference Frequency-domain analysis

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Accuracy in Wireless Communication: Improved Arrival Direction Estimation in Adaptive Antennas via Expanded MUSIC Beamforming

Accuracy in Wireless Communication: Improved Arrival Directi...

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Automation and Computation (AUTOCOM), International Conference on

作者： Kapil Kumar Priyank Pandey Manju Khari School of Computer & Systems Sciences Jawaharlal Nehru University New Delhi India Department of Computer Science & Engineering Graphic era deemed to be university Dehradun Uttarakhand India

Smart antennas are now widely expended in transmission systems due to their vital plusses. To boost the performance of smart antenna operation, it is crucial to design an efficient beamforming pattern based on specific antenna parameters. Due to their numerous benefits, smart antennas are now often utilized in communication systems. It is essential to create a successful beamforming pattern based on certain antenna parameters with the objective of improvement in the performance of smart antenna operation. Beamforming strategies utilizing a combination of soft computing techniques were proposed in earlier works. These methods, however, did not take signal Direction of Arrival (DOA) precision into account. While determining the beamforming pattern for smart antennas, this research addresses the DOA issue. The researchers use a variety of techniques and conduct a comparison analysis to determine an exact DOA estimate. The authors proposed a method that shows an optimal result after resolving the existing problems for estimating the DOA with precision. In comparison to the traditional DOA pattern, this innovation produces a more accurate DOA pattern with reduced undesired side lobes. The researchers derive the antenna beamforming pattern in accordance with the necessary direction of angles using the predicted DOA pattern. The experimental results demonstrated that the proposed beamforming technology performs better than earlier techniques. In this work, advancement in beamforming precision can lead to better communication system performance and overall efficiency of smart antennas.

关键词：

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Partial Image Generation with VQGAN for River Surveillance Video Coding

Partial Image Generation with VQGAN for River Surveillance V...

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International Technical Conference o Circuits/systems, computers and Communications (ITC-CSCC)

作者： Naoya Nakayama Takafumi Katayama Tian Song Takashi Shimamoto Department of Science and Engineering Tokushima University Tokushima City Japan Computer Systems Engineering Institute of Technology and Science Graduate School of Engineering Tokushima University Tokushima City Japan

ISBN: (数字)9798350379051

ISBN: (纸本)9798350379068

In recent years, due to typhoons and torrential rains caused by global warming river overflows have been increasing rapidly. A river monitoring systems using surveillance cameras is considered efficient. However, in mountainous areas the bandwidth is limited that restrict the video transmission with acceptable video quality. In this work, a novel river surveillance system is proposed with a semantic segmentation based important object extraction process followed by a VVC encoder on the river side which only encode the context of the most important objects to save encoding bits. On the other hand, unimportant context is generated on the decoder side in the server side with a VQGAN model. The simulation results show that the proposed system can not only significantly reduce the bitrate but also keep a very high subjective video quality.

关键词： Video coding Image synthesis Tropical cyclones Surveillance Simulation Bit rate Rivers

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