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Experimental study of neuromorphic node based on a multiwaveband emitting two-section quantum dot laser

Photonics Research 2021年第4期9卷 I0007-I0015页

作者： George Sarantoglou Menelaos Skontranis Adonis Bogris Charis Mesaritakis Department of Information and Communication Systems Engineering University of the AegeanPalama 2Karlovassi 83200 SamosGreece Department of Informatics and Computer Engineering University of West AtticaAghiou Spiridonos12243 EgaleoAthensGreece

In this work,we present experimental results concerning excitability in a multiband emitting quantum-dot-based photonic *** experimental investigation revealed that the same two-section quantum dot laser can be tuned through a simple bias adjustment to operate either as a leaky integrate and fire or as a resonate and fire ***,by exploiting the inherent multiband emission of quantum-dot devices revealed by the existence of multiple lasing thresholds,a significant enhancement in the neurocomputational capabilities,such as spiking duration and firing rate,is *** firing rate increased by an order of magnitude that leads to an enhancement in processing speed and,more importantly,neural spike duration was suppressed to the picosecond scale,which corresponds to a significant temporal resolution *** new regimes of operation,when combined with thermal insensitivity,silicon cointegration capability,and the fact that these multiband mechanisms are also present in miniaturized quantum-dot devices,render these neuromorphic nodes a proliferating platform for large-scale photonic spiking neural networks.

关键词： laser quantum section

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Assessment of Driver’s Stress using Multimodal Biosignals and Regularized Deep Kernel Learning

Assessment of Driver’s Stress using Multimodal Biosignals a...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Vishal Singh Roha Nagarajan Ganapathy Nicolai Spicher Sriparna Saha Thomas M. Deserno Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School Brunswick Germany Department of Computer Science Engineering Indian Institute of Technology Patna India Department of Biomedical Engineering Indian Institute of Technology Hyderabad India Institute for Medical Informatics University Medical Center Göttingen Göttingen Germany

In this work, we classify the stress state of car drivers using multimodal physiological signals and regularized deep kernel learning. Using a driving simulator in a controlled environment, we acquire electrocardiography (ECG), electrodermal activity (EDA), photoplethysmography (PPG), and respiration rate (RESP) from N = 10 healthy drivers in experiments of 25min duration with different stress states (5min resting, 10min driving, 10min driving + answering cognitive questions). We manually remove unusable segments and approximately 4h of data remain. Multimodal time and frequency features are extracted and employed to regularized deep kernel machine learning based on a fusion framework. Task-specific representations of different physiological signals are combined using intermediate fusion. Subsequently, the fused multimodal features are fed a support vector machine (SVM) and a random forest (RF) for stress classification. The experimental results show that the proposed approach can discriminate between stress states. The combination of PPG and ECG using RF as classifier yields the highest F1-score of 0.97 in the test set. PPG only and RF yield a maximum F1-score of 0.90. Furthermore, subject-specific cross-validation improves performance. ECG and PPG signals are reliable in classifying the stress state of a car driver. In summary, the proposed framework could be extended to real-time stress state assessment in driving conditions.

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Indoor Channel Characterization With Extremely Large Reconfigurable Intelligent Surfaces at 300 GHz

Indoor Channel Characterization With Extremely Large Reconfi...

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European Conference on Antennas and Propagation, EuCAP

作者： Fabio Cardoso Sérgio Matos Luís M. Pessoa George C. Alexandropoulos Department of Information Science and Technology University Institute of Lisbon Portugal Instituto de Telecomunicações Portugal INESC TEC Portugal Faculty of Engineering University of Porto Portugal Department of Informatics and Telecommunications National and Kapodistrian University of Athens Greece Department of Electrical and Computer Engineering University of Illinois Chicago IL USA

ISBN: (数字)9788831299107

ISBN: (纸本)9798350366327

The technology of Reconfigurable Intelligent Surfaces (RISs) is lately being considered as a boosting component for various indoor wireless applications, enabling wave propagation control and coverage extension. However, the incorporation of extremely large RISs, as recently being considered for ultrahigh capacity industrial environments at subTHz frequencies, imposes certain challenges for indoor channel characterization. In particular, such RISs contribute additional multipath components and their large sizes with respect to the signal wavelength lead to near-field propagation. To this end, ray tracing approaches become quite cumbersome and need to be rerun for different RIS unit cell designs. In this paper, we present a novel approach for the incorporation of RISs in indoor multipath environments towards their efficient channel characterization. An 100 × 100 RIS design with 2-bit resolution unit cells realizing a fixed anomalous reflection at 300 GHz is presented, whose radar cross section patterns are obtained via full-wave simulations. It is showcased that the RIS behavior can be conveniently approximated by a three-ray model, which can be efficiently incorporated within available ray tracing tools, and that the far-field approximation is valid for even very small distances from the RIS.

关键词： Wireless communication Indoor communication Radar cross-sections Europe Reconfigurable intelligent surfaces Ray tracing Boosting Antennas and propagation Terahertz communications

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High-fidelity Event-Radiance Recovery via Transient Event Frequency

High-fidelity Event-Radiance Recovery via Transient Event Fr...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Jin Han Yuta Asano Boxin Shi Yinqiang Zheng Imari Sato Graduate School of Information Science and Technology The University of Tokyo National Institute of Informatics National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University National Engineering Research Center of Visual Technology School of Computer Science Peking University

High-fidelity radiance recovery plays a crucial role in scene information reconstruction and understanding. Conventional cameras suffer from limited sensitivity in dynamic range, bit depth, and spectral response, etc. In this paper, we propose to use event cameras with bio-inspired silicon sensors, which are sensitive to radiance changes, to recover precise radiance values. We reveal that, under active lighting conditions, the transient frequency of event signals triggering linearly reflects the radiance value. We propose an innovative method to convert the high temporal resolution of event signals into precise radiance values. The precise radiance values yields several capabilities in image analysis. We demonstrate the feasibility of recovering radiance values solely from the transient event frequency (TEF) through multiple experiments.

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The Effect of Preprocessing on Skin Lesion Segmentation

The Effect of Preprocessing on Skin Lesion Segmentation

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International Conference on Information Technology (INCIT)

作者： Arief Kelik Nugroho Afiahayati Moh Edi Wibowo Hardyanto Soebono Department of Computer Science and Electronics Faculty of Mathematics and Natural Science Universitas Gadjah Mada Yogyakarta Indonesia Department of Informatics Engineering Faculty of Engineering Universitas Jenderal Soedirman Purwokerto Indonesia Department of Dermatology and Venereology Faculty of Medicine Public Health and Nursing Universitas Gadjah Mada Yogyakarta Indonesia

ISBN: (数字)9798350366303

ISBN: (纸本)9798350366310

Image preprocessing is a crucial step in enhancing the quality of skin lesion images before segmentation, with the primary objective of removing noise and artifacts that could interfere with the segmentation process and reduce accuracy. This process involves various techniques designed to address artifacts such as hair, black frames, skin lines, and vignettes, which may arise due to variables in image acquisition, such as lighting conditions and skin characteristics. Several preprocessing techniques used include artifact removal using Gaussian filter, bottom hat filter, and median filter, as well as hair removal methods like DullRazor and short line detector. Image augmentation techniques, such as rotation and transformation, are also employed to ensure that key features in the image are well preserved and accurately reflected in the data, helping to prevent overfitting during the classification stage. Additionally, contrast enhancement through methods like Contrast-Limited Adaptive Histogram Equalization (CLAHE) and gamma correction is used to improve lesion visibility and enhance the accuracy of lesion boundary detection. Findings from various studies indicate that appropriate preprocessing can significantly improve image quality, leading to more accurate segmentation. Descriptive statistical testing can be employed to analyze the extent to which these preprocessing steps affect segmentation outcomes, focusing on accuracy improvements and the reduction of segmentation errors caused by artifacts and noise in the images. Thus, effective preprocessing not only enhances image quality but also provides a stronger foundation for more accurate segmentation and classification in skin lesion diagnosis.

关键词： Hair Image quality Image segmentation Accuracy Statistical analysis Noise Lighting Skin Lesions Overfitting

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Minimum Mean Squared Error Holographic Beamforming for Sum-Rate Maximization

arXiv

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arXiv 2025年

作者： Sheemar, Chandan Kumar Khan, Wali Ullah Alexandropoulos, George C. Ahmed, Manzoor Chatzinotas, Symeon SnT department The University of Luxembourg Luxembourg Department of Informatics and Telecommunications National and Kapodistrian University of Athens Athens16122 Greece School of Computer and Information Science Institute for AI Industrial Technology Research Hubei Engineering University Xiaogan City432000 China

This paper studies the problem of hybrid holographic beamforming for sum-rate maximization in a communication system assisted by a reconfigurable holographic surface. Existing methodologies predominantly rely on gradient-based or approximation techniques necessitating iterative optimization for each update of the holographic response, which imposes substantial computational overhead. To address these limitations, we establish a mathematical relationship between the mean squared error (MSE) criterion and the holographic response of the RHS to enable alternating optimization based on the minimum MSE (MMSE). Our analysis demonstrates that this relationship exhibits a quadratic dependency on each element of the holographic beamformer. Exploiting this property, we derive closed-form optimal expressions for updating the holographic beamforming weights. Our complexity analysis indicates that the proposed approach exhibits only linear complexity in terms of the RHS size, thus, ensuring scalability for large-scale deployments. The presented simulation results validate the effectiveness of our MMSE-based holographic approach, providing useful insights. © 2025, CC BY.

关键词： Iterative methods

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Proposed New Framework Scheme for Path Loss in Wireless Body Area Network

Iraqi Journal for Computer Science and Mathematics

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Iraqi Journal for computer Science and Mathematics 2022年第1期3卷 11-21页

作者： Al-Barazanchi, Israa Niu, Yitong Abdulshaheed, Haider Rasheed Hashim, Wahidah Alkahtani, Ammar Ahmed Daghighi, Elika Jaaz, Zahraa A. Shawkat, Shihab A. Rauf, Hafiz Tayyab College of Computing and Informatics Universiti Tenaga Nasional Malaysia Belarusian-Russian University Mira Avenue 43 Mogilev212000 Belarus Computer Engineering Techniques Department Baghdad College of Economic Sciences University Baghdad Iraq Institute of Sustainable Energy Universiti Tenga Nasional Selangor Kajang43000 Malaysia Technical and Vocational University Tehran Iran College of Science Computer Department AlNahrain University Baghdad Iraq University of Samarra Samarra Iraq Centre for Smart Systems AI and Cybersecurity Staffordshire University Stoke-on-Trent United Kingdom

Recent technical developments in wi-fi networking, microelectronic integration and programming, sensors and the Internet have enabled us to create and enforce a range of new framework schemes to fulfil the necessities of healthcare-related wireless body area network (WBAN). WBAN sensors continually screen and measure patients' indispensable signs and symptoms, and relay them to scientific monitoring for diagnosis. WBAN has a range of applications, the most necessary of which is to help patients suffering diseases to stay alive. The quality instance is the coronary heart implant sensor, whose video display unit monitors coronary heart sign and continuously transmits it. This setup eliminates the need for patients to visit the medical doctor frequently. Instead, they can take a seat at home and acquire an analysis and prescription for the disease. Today, a sizable effort is being made to increase low-power sensors and gadgets for utility in WBAN. A new framework scheme that addresses route loss in WBAN and discusses its penalties in depth is endorsed in this paper. The new framework scheme is applied to three case scenarios to obtain parameters by measuring vital information about the human body. On-body and intrabody conversation simulations are conducted. On-body conversation findings show that the route loss between transmitter and receiver rises with growing distance and frequency. © 2022 College of Education, Al-Iraqia University. All Rights Reserved.

关键词： Heart

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Smart Traffic Light Using Raspberry Pi and Digital Image Processing

Smart Traffic Light Using Raspberry Pi and Digital Image Pro...

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International Conference on Wireless and Telematics (ICWT)

作者： Helfy Susilawati Shelmy Luthfi Pathur Rahman Ade Rukmana Iik Muhamad Malik Matin Sarbini Nanang Ismail Electrical Engineering Departement Universitas Garut Garut Indonesia Computer and Informatics Engineering Jakarta State Polytechnic Jakarta Indonesia Psychology Department UIN Sunan Gunung DJati Bandung Bandung Indonesia Departement of Electrical Engineering UIN Sunan Gunung DJati Bandung Bandung Indonesia

Congestion is one of the problems in traffic, especially at intersections with traffic lights. In this study, a traffic light management system was developed by utilizing image processing that can adjust the duration in the queue depending on road conditions. The system is developed using Raspberry Pi 4 as the controller. The system will capture the image of the traffic conditions via cameras and then perform the image processing using OpenCV and Tensorflow to calculate the number of vehicles. The number of vehicles will be sent to the webserver database to be processed as the input for traffic system settings. The testing shows that the system can set the timing of the traffic lights. When there are 3 vehicles in the queue with traffic light is red then the system set the timing for the green light to 10 seconds. Additional of 1 vehicle in the queue will increase the duration of the green light by 4 seconds.

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Grammar Error Correction in Indonesian Sports News: Comparing the Performance of Pre-Trained T5 and BART Models

Grammar Error Correction in Indonesian Sports News: Comparin...

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informatics International Conference (BTS-I2C), Beyond Technology Summit on

作者： Muh Adnan Esa Prakasa Yuyun Najirah Umar Nasrullah Adi Sadli A. Edeth Fuari Anatasya Hazriani Mashur Razak dept. of informatics engineering Handayani University of Makassar Makassar Indonesian National Research and Innovation Agency research center for data and information science Bandung Indonesian dept. of computer system Handayani University Makassar Makassar Indonesian

ISBN: (数字)9798331508579

ISBN: (纸本)9798331508586

This research compares the performance of two pre-trained models based on the transformer, namely text-to-text transfer transformer (T5) and bidirectional and auto-regressive transformer (BART), through a fine-tuned for correcting grammar in Indonesian sports news texts. We used vennify/t5-base-grammar-correction and onionLad/grammar-correction-bart-base, was trained using a dataset of 10,075 sentences from Indonesian language sports news sites. We tested the model performance using bilingual evaluation understudy (BLEU), google's language evaluation understanding (GLEU), training loss, and validation loss. The results show that BART was more efficient in learning data patterns and has better generalization ability, supported by BLEU and GLEU scores of 0.8560 and 0.9655. Meanwhile, T5 maintains simpler sentence structures, supported by BLEU and GLEU scores of 0.8120 and 0.9564. BART excels at handling more complex contexts, making it more effective at accurately correcting errors in dynamic sports news. Both models have their respective advantages, with BART being better at correcting detailed grammatical errors, while T5 offers consistent correction of more simple sentence structures.

关键词： Training Measurement Analytical models Accuracy Computational modeling computer architecture Transformers Grammar Sports Context modeling

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The Effect of Information Security Awareness, Subjective Norms and Shared Tacit Assumptions on Information Security Culture

The Effect of Information Security Awareness, Subjective Nor...

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Information and Communication Technologies (ZCICT), Zimbabwe Conference of

作者： Joseph Mupokosera Maredi Ivan Mphahlele Annelie Jordaan Osden Jokonya Department of Informatics Tshwane University of Technology Pretoria South Africa Department of Computer Engineering Tshwane University of Technology Pretoria South Africa Department of Informatics Vaal University of Technology Vanderbijlpark South Africa Department of Information Systems University of the Western Cape Cape Town South Africa

ISBN: (数字)9798350318128

ISBN: (纸本)9798350318135

Financial services institutions are one of the prime targets of information security breaches because of the potential of the attackers to reap financial rewards from launching the attacks. Information security culture which is a collection of socio-cultural processes implemented by firms, is forecast to forestall the scourge of information security breaches. Information security culture targets to safeguard the information assets of firms through influencing employees to conduct themselves in a manner that is compatible with the protection of firms’ assets. Information security awareness, subjective norms and shared tacit assumptions are proposed as some of the main determinants of information security culture in the financial services. Therefore, this paper investigates the impact that information security awareness, subjective norms and shared tacit assumptions exert on financial services firms’ information security culture. This paper is a quantitative case study of four financial services firms in Zimbabwe. The results of this study show that information security awareness and subjective norms are predictors of information security culture in the financial services. Conversely, the study established that shared tacit assumptions are not related to information security culture in the financial services.

关键词： Statistical analysis Information security Forestry Information and communication technology Protection Financial services

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