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First principle and deep learning based switching property prediction of optical bio-molecular switch

MICROSYSTEM TECHNOLOGIES-MICRO-AND NANOsystems-information STORAGE AND processing systems 2024年第7期30卷 871-882页

作者： Roy, Debarati Dey Roy, Pradipta De, Debashis BP Poddar Inst Management & Technol Dept Elect & Commun Engn 137 VIP Rd Kolkata 700052 West Bengal India Maulana Abul Kalam Azad Univ Technol Dept Comp Sci & Engn NH 12Old NH 34PO Simhat Nadia 741249 West Bengal India Dr BC Roy Acad Profess Courses Dept Comp Applicat Jemua Rd Fuljhore Durgapur 713206 West Bengal India Univ Western Australia Dept Phys M01335 Stirling Highway Perth WA 6009 Australia

Electronic characterization of bio-molecular nanoscale devices is important in the new edge of nanotechnology and the field of nanoelectronics. In this new era, the molecular transmission properties of the Adenine bio-molecular optical switch are predicted using Density Functional Theory along with Non-Equilibrium Green's Function-based First Principle approach and this phenomenon is supported with Machine Learning based algorithms. The photo-induced switching characteristics are observed for the bi-directional bio-molecular switch for both forward and reverse bias conditions. This switching behaviour converts the position of the bio-molecular switch to its straightened and 60 degrees twisted form. The electrical doping process plays an important role in generating p and n regions at the two ends of the switch. In this experiment, gold electrodes are used as the supporting anchor of the Adenine molecules. The HOMO-LUMO gap and I-V characteristics of the bio-molecular switch are analyzed using first principle formalisms and compared with the Machine learning approach. This ML approach helps design the future-generation prediction model for the nano-scale electronic device simulation process. This prediction model can be obtained without knowing the switching characteristics and also without knowing all-time sequence generating waveforms but only observing the waveform prediction for up to a certain period sequence.

关键词： Density functional theory

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Recognition and Analysis of Glass Subsurface Defects by Integrating Machine vision and Image processing Technology 5

Recognition and Analysis of Glass Subsurface Defects by Inte...

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5th International Conference on Computers, information processing and Advanced Education, CIPAE 2024

作者： Lin, Chen Ling, Xiong Luo, Guangming Wuhan University of Science and Technology Wuhan China Changli Glass Honghu Co. Ltd Wuhan China

ISBN: (纸本)9798331527662

Machine vision and computer image processing technologies are widely used in the metallurgical industry, especially in recognizing and analyzing defects in glass. High surfaces of planer surface and quality in the glass and metal manufacturing sector primarily require automated and performance visual detection, and inspection systems and algorithms are constantly improving. This article, therefore, seeks to comprehensively recognize and analyze glass subsurface defects through the integration of image processing technologies and machine vision. According to the algorithm's properties and the image features typified in the process, the defects of identified glass components are primarily classified based on determining the cause and the optical features of characteristics exhibited. Given the nature of varied and detected effects determined mainly by the classifiers, an improved accuracy level was notable, and a reduction of the computational complexity was evident following the use of machine vision and image processing technologies. © 2024 IEEE.

关键词： Machine vision

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Feed-forward Neural Network based Algorithm for Intelligent Positioning of Direction Finding Crossings 16

Feed-forward Neural Network based Algorithm for Intelligent ...

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16th International Conference on Signal processing systems, ICSPS 2024

作者： Li, Pangzhe Hu, Dexiu Zhao, Chuang Information Engineering University Signal Processing Laboratory China

ISBN: (纸本)9781510689251

This paper proposes a feed-forward neural network(FNN) based algorithm for intelligent positioning of angle of arrival(AOA) to address the problems of complex computation, slow positioning speed and low positioning accuracy of traditional cross-location direction finding methods. The algorithm obtains the position of a known target from the available data, calculates its angle of arrival at the observatory, combines them into data pairs, and inputs them into a constructed neural network to obtain the corresponding model. The network can then locate unknown targets. It is verified that the algorithm has reliable localization accuracy and higher robustness than conventional algorithms. The proposed method also shows that the training of the neural network on the basis of a large amount of data can greatly reduce the operation time and achieve real-time target localization. © 2025 SPIE.

关键词： Neural network models

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Intelligent Microbial Growth Monitoring: A Parallel OD600 Detection System Based on Artificial Intelligence 2

Intelligent Microbial Growth Monitoring: A Parallel OD600 De...

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2nd International Conference on Machine vision, Image processing and Imaging Technology, MviPIT 2024

作者： Xu, Libing Ren, Fei Doma, Bonifacio T. Nanjing SuperKong Electronics Co. Ltd. Nan Jing China School of Information Technology Mapua University Philippines School of Graduate Studies Manila Philippines

ISBN: (纸本)9798331543037

Microbial OD600 measurement is a crucial experimental technique in microbiological and molecular biological research, evaluating microbial growth by measuring optical density at 600 nm wavelength. While convenient, this method has limitations. For instance, different microbial species and growth stages may absorb light differently, potentially leading to biases when measuring multiple strains simultaneously. This paper presents an innovative parallel OD600 detection system capable of simultaneously monitoring growth curves of multiple microbial strains. Our research integrates advanced optical systems, precise mechanical control, and artificial intelligence algorithms, not only improving the accuracy and efficiency of bacterial growth measurements but also achieving parallel processing and analysis of multiple samples. We discuss in detail the hardware design, software architecture, and core algorithms, with particular emphasis on the application of machine learning in multi-strain data analysis and prediction. Experimental results demonstrate that this system significantly outperforms traditional methods in measurement accuracy, automation level, multi-sample processing capability, and data analysis. ©2024 IEEE.

关键词： Bacteria

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Error Analysis of a Multi-Sensor Maritime Targeting System 20

Error Analysis of a Multi-Sensor Maritime Targeting System

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Conference on Electro-optical and Infrared systems - Technology and Applications XX

作者： Hickman, Duncan L. Niebla, Maria Sang, Daekyu Kim, Boomin Baek, Jeonghyun Tektonex Ltd Long View Argyll Rd Kilcreggan Argyll & Bute Scotland Agcy Def Dev 160 Bugyuseong Dero 488beon Gil Daejeon South Korea

ISBN: (数字)9781510667044

ISBN: (纸本)9781510667037

Targeting systems are subject to multiple sources of error when operating in complex environments. To reduce the effect of these errors, modern targeting systems generally include both imaging and RF sensors. Data processing then provides target detection and classification information, and the detection streams are combined using a data fusion scheme to produce an optimal target location estimate with an associated latency. In this paper, the performance of a multi- sensor system in a maritime application is investigated using a mathematical simulator that has been developed to provide the system performance error analysis for different engagement scenarios and test conditions. This simulator is described together with the sources of targeting error such as image motion blur and radar glint. Additionally, the impact of flare and chaff countermeasures on the targeting performance is reviewed in terms of different types of target recognition and tracking algorithms.

关键词： Multisensor systems Error Analysis Image Blur Radar Glint Model Validation Countermeasure Model ATR processing Targeting systems

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Change Detection in optical Satellite Images Using Local Self-similarity information 11th

Change Detection in Optical Satellite Images Using Local Sel...

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11th International Conference on Intelligent Computing and Applications, ICRTC 2023

作者： Shah, Bipin Gupta, Ayushi Paul, Sourabh Tamil Nadu Chennai India Tamil Nadu Chennai India

ISBN: (纸本)9789819717231

Change detection is considered as a challenging issue in the field of satellite image processing due to the presence of significant intensity differences between the input images. Although a variety of algorithms have been proposed in recent time to detect the changes in remote sensing optical images, most of them suffer from handling large intensity variations of the images. In this paper, an unsupervised change detection method is proposed which uses image local self-similarity information to take care of large intensity variations of the input images. Initially, feature vectors are constructed for each of the pixels of the input images using local self-similarity information of images. Then, binary feature vectors are created from these feature vectors using a thresholding criterion. Finally, these binary feature vectors of the corresponding locations of the images are utilized to identify the changed and unchanged pixels. Experiments on different sets of optical satellite images show the effectiveness of the proposed method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Pixels

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Quantum Technology Fueling the Next Generation optical Communication: Challenges and Pathways 5th

Quantum Technology Fueling the Next Generation Optical Commu...

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5th EAI International Conference on Cognitive Computing and Cyber Physical systems, IC4S 2024

作者： Venkateswara Rao, Ch. Pareek, Prakash Mishra, Jitendra K. Department of Electronics and Communication Engineering Indian Institute of Information Technology Ranchi Jharkhand834010 India Department of Electronics and Communication Engineering Vishnu Institute of Technology Andhra Pradesh Bhimavaram534202 India

ISBN: (纸本)9783031770777

information transmission through light has attained significant advancements in the fields of both optical fiber communication (OFC) and optical wireless communication (OWC) systems. These advancements have been examined by numerous researchers by employing pioneering modulation schemes, adaptive optics, advanced signal processing algorithms, advancement in materials, and employing sophisticated network architectures for wide variety of applications such as: military;defense;healthcare;satellite;internet of things;and telecommunications. However, the primary factors which affecting the OFC systems are signal attenuation, dispersion, reliability, robustness, and security even though there exists a predominant development. In addition, atmospheric attenuation (due to absorption and scattering), turbulences, climate conditions (fog, rain, snow, dense fog etc,.), alignment and tracking, interference, range constraints, and security are the major challenges in OWC systems in spite of having advanced approaches from past few decades. Hence, these challenges are need to be addressed in order to achieve reliable and high-speed data transmission. This could be possible with the integration of OFC and OWC with emerging quantum communication technologies (quantum key distribution, quantum entanglement, quantum repeaters, quantum sensing, and quantum secured OWC links). Therefore, this work presents the recent advancements in the fields of OFC and OWC systems with the state-of-art-of techniques and approaches. The primary research challenges related to security, speed, range, capacity and adaptability of both OWC and OFC are outlined. In addition, the possible integration of these systems with quantum communication technologies and the recent progression have been outlined. Finally, the possibility of future research direction towards the quantum OFC, quantum OWC have been discussed for ensuring and revealing the full potential of quantum enhanced optical communication systems.

关键词： Adaptive optics

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High-Speed PAM8 Silicon Photonics IM/DD System with Artificial Neural Network Equalization 13

High-Speed PAM8 Silicon Photonics IM/DD System with Artifici...

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Optoelectronic Devices and Integration XIII 2024

作者： Lu, Junde Sun, Yu Qin, Jun Tan, Junxiong Wang, Qian Li, Yueqin Sun, Jian Li, Zhensong Key Laboratory of Information and Communication Systems Ministry of Information Industry Beijing Information Science and Technology University Beijing100192 China Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument Beijing Information Science and Technology University Beijing100192 China

ISBN: (纸本)9781510682009

The intensity modulation direct detection (IM/DD) system based on silicon photonic devices stands out as a leading contender for the next generation of short-reach optical communication due to its cost-effectiveness, low power consumption, and compact physical footprint. Nonetheless, its direct representation of digital information through amplitude variations renders them acutely susceptible to transmission impairments. To improve the signal quality at the receiver, digital signal processing (DSP) based equalization plays a pivotal role due to its programmability, flexibility and stability. Among different kinds of equalization methods, neural network (NN)-based equalization algorithms have attracted considerable attention, surpassing traditional algorithms such as feed-forward equalization (FFE), decision feedback equalization (DFE) and Volterra series-based nonlinear equalization (VNLE) et al. This increased attention is attributed to their robust capability for modeling both linear and nonlinear systems. In this paper, by employing a novel NN-based equalization with eight saturation regions activation function, we successfully transmit a 60 GBaud 8-ary pulse amplitude modulation (PAM8) signal with the bit error rate (BER) below high-definition forward error correction (HD-FEC) threshold of 3.8×10-3 and a 70 GBaud PAM8 signal with BER below soft-decision forward error correction (SD-FEC) threshold 2×10-2 using a 4-layer network architecture. Compared to the traditional activation function such as sigmoid and tanh, 1∼3 orders of magnitude of BER can be decreased. The results show that the proposed innovative NNbased equalization has the potential to significantly enhance the performance of the next generation silicon photonics based short-range optical communication systems. © 2024 SPIE. All rights reserved.

关键词： Silicon photonics

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Research on Improved TDOA Algorithm for Shallow Water Underwater Positioning 16

Research on Improved TDOA Algorithm for Shallow Water Underw...

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16th International Conference on Signal processing systems, ICSPS 2024

作者： Shen, Biyue Rong, Shaowei Ji, Lingxian Wu, Kaiyuan Li, Jiaju Deng, Xiangyu Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Faculty of Artificial Intelligence Industry Kunming University of Science and Technology Kunming650500 China

ISBN: (纸本)9781510689251

Accurate positioning in shallow water regions is crucial for various underwater applications. TDOA algorithms estimate the signal source location by measuring the time differences of signal arrival at multiple receivers. However, these methods often suffer from Non-Line-of-Sight (NLOS) errors and noise, leading to reduced positioning accuracy in shallow water environments. This research proposes an improved TDOA estimation algorithm that utilizes cross-correlation techniques to mitigate NLOS errors and combines the strengths of the Chan and Taylor algorithms to enhance positioning precision. The proposed algorithm addresses challenges such as time ambiguity and alpha-stable noise, offering advantages in TDOA modeling for underwater positioning. Implementation details are provided, including the design of matching basis functions, time delay matching procedures, algorithm flow, and computational steps. Simulation tests validate the effectiveness of the algorithm, demonstrating significant improvements in positioning accuracy. The computational complexity is also analyzed to assess the algorithm's practicality. Finally, the research findings are summarized, and directions for future work are discussed. © 2025 SPIE.

关键词： Time difference of arrival

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The application of large-scale MIMO detection algorithm in 5G and beyond 5G 4

The application of large-scale MIMO detection algorithm in 5...

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4th International Conference on Signal processing and Machine Learning, CONF-SPML 2024

作者： Tang, Qiteng Harbin Institute of Technology Shenzhen Electronics and Information Engineering Shenzhen China

ISBN: (纸本)9781510674721

In recent years, the rapid advancement of 5G technology has brought to the forefront the pivotal role of Multiple-Input Multiple-Output (MIMO) system algorithms. This paper delves into a comprehensive exploration of two distinct algorithmic approaches within the context of 5G applications for massive MIMO systems. These two approaches are matrix transformation and machine learning, and the following paragraphs will shed light on their respective attributes and intricacies. Matrix transformation is a fundamental technique in MIMO systems, which aims to optimize the transmission of signals by manipulating the channel matrices. This method, while established and reliable, exhibits certain limitations in accommodating the dynamic and complex nature of 5G environments. On the other hand, machine learning algorithms, with their adaptability and capacity for self-improvement, have gained prominence in recent years. They offer a promising avenue for addressing the challenges presented by 5G MIMO systems, such as handling interference and optimizing resource allocation. In this paper, we provide concrete examples to analyze the strengths and weaknesses of both matrix transformation and machine learning in the context of 5G applications. Furthermore, we explore potential directions for the application of these algorithms and propose areas for improvement, with the ultimate goal of enhancing the efficiency and performance of massive MIMO systems in the evolving landscape of 5G technology. © 2024 SPIE.

关键词： Machine learning

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