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An enzyme-free interdigitated electrode for ketamine detection

Journal of the Chinese Institute of Engineers, Transactions of the Chinese Institute of Engineers,Series A 2023年第5期46卷 441-452页

作者： Phan, Thien Luan Van Hieu, Nguyen Lin, Jing-Jenn Wu, Hsing-Ju Chu, Pei-Yi Hsiao, Chin-Sung Yao, Fiona Yan-Dong Lin, Yung-Kai Ching, Congo Tak Shing Graduate Institute of Biomedical Engineering National Chung Hsing University Taichung Taiwan Viet Nam Department of Applied Materials and Optoelectronic Engineering National Chi Nan University Taiwan Research Assistant Center Show Chwan Memorial Hospital Taiwan Department of Medical Research Chang Bing Show Chwan Memorial Hospital Taiwan Department of Pathology Show Chwan Memorial Hospital Taiwan School of Medicine College of Medicine Fu Jen Catholic University Taiwan Computer Science and Information Engineering Asia University Taiwan Division of Science Engineering and Health Studies College of Professional and Continuing Education The Hong Kong Polytechnic University Hong Kong Department of Food Science National Taiwan Ocean University Taiwan Department of Electrical Engineering National Chi Nan University Nantou County Taiwan

Ketamine is an anesthetic, which when used wrongly would affect judgment and behavior, increasing risks to individual such as unwanted or risky sexual behavior. This study reports the development of an admittance- and capacitance-spectroscopy-based sensor to detect ketamine in solution, such as whiskey, a popular alcoholic drink. The result confirms the proposed method is capable of detecting the presence of ketamine in whiskey, in concentration as low as 6.25 ng/μL, by using admittance and capacitance measured at 6.75 MHz, as well as quantifying the measured admittance (sensitivity: 300 μΩ−1/Log (ng/μL);limit of detection (LOD): 0.16 ng/μL) or capacitance data (sensitivity: 2 nF/Log (ng/μL);LOD: 0.73 ng/μL) at 18 kHz and 7.69 MHz, respectively. © 2023 The Chinese Institute of Engineers.

关键词： Capacitance

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Occupancy-level-aware Indoor Terahertz Channel Prediction: A Robust Deep Learning Approach

Occupancy-level-aware Indoor Terahertz Channel Prediction: A...

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IEEE Conference on Vehicular Technology (VTC)

作者： Eslam Hasan Elmahedi Mahalal Muhammad Ismail Zi-Yang Wu Mostafa M. Fouda Nei Kato Department of Computer Science Tennessee Technological University Cookeville TN USA College of Information Science and Engineering Northeastern University Shenyang China Department of Electrical and Computer Engineering Idaho State University Pocatello ID USA Center for Advanced Energy Studies (CAES) Idaho Falls ID USA Graduate School of Information Sciences Tohoku University Sendai Japan

ISBN: (数字)9798331517786

ISBN: (纸本)9798331517793

Accurate channel prediction using deep learning (DL) algorithms can address the challenges of terahertz (THz) propagation, such as atmospheric absorption and object scattering, by enabling proactive handover and beamforming. However, indoor environments are inherently dynamic, with factors like occupancy level variations causing the channel characteristics to change over time. This phenomenon, known as concept drift, can severely degrade the DL model performance used in channel prediction. This paper investigates the impact of indoor occupancy level variations on the generalization ability of state-of-the-art DL models for THz channel prediction. We identify three distinct occupancy levels (low, medium, and high) within the THz indoor channel. Our results demonstrate that the state-of-the-art DL models exhibit limited generalization capabilities, with performance deterioration in prediction accuracy ranging from 4−62%. We propose a robust two-stage framework to mitigate concept drift in THz channel prediction. The first stage predicts the indoor occupancy level from the THz wireless signal, which is a multi-class classification problem. Due to the reoccurring concept of occupancy levels, the second stage contains a pool of models in a sleeping mode based on a hybrid convolutional neural network (CNN) long-short-term memory (LSTM) architecture. One of these DL expert models is activated for channel prediction based on the occupancy level predicted from the previous stage. Our framework demonstrates superior generalization by limiting the performance deterioration from 62% due to concept drift to ≤ 9%. This represents an 85% reduction in performance deterioration compared to the existing state-of-the-art DL models.

关键词： Deep learning Wireless communication Vehicular and wireless technologies Limiting Accuracy Atmospheric modeling Concept drift Scattering Predictive models Convolutional neural networks

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Memory Regressor Extended Echo State Networks for Nonlinear Dynamics Modeling

Memory Regressor Extended Echo State Networks for Nonlinear ...

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European Control Conference (ECC)

作者： Kai Hu Qian Wang Tian Shi Kohei Nakajima Yongping Pan School of Artificial Intelligence Sun Yat-sen University Zhuhai China School of Advanced Manufacturing Sun Yat-sen University Shenzhen China School of Computer Science and Engineering Sun Yat -sen University Guangzhou China Graduate School of Information Science and Technology University of Tokyo Tokyo Japan

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

Echo state network (ESN) implements an alternative paradigm called reservoir computing to train recurrent neural networks (RNNs), where internal weights are randomly generated and kept fixed, and only readout weights need to be trained, which greatly reduces the training complexity of RNNs. ESN not only facilitates the practical implementation of RNNs but also shows superior performance over fully trained RNNs across a range of applications. However, the conventional ESN suffers from the drawbacks of stringent conditions for weight convergence and slow convergence speed. This paper proposes a memory regressor extended learning method to update the readout weights of ESNs. By constructing and incorporating a generalized prediction error based on regressor extension and filtering, the capacity of ESN to utilize historical data can be greatly improved. In the discrete-time domain, it is proven that exponential convergence of readout weights is achieved under a condition termed interval excitation that is strictly weaker than the classical condition of persistent excitation. Simulation results on modeling a 10th-order nonlinear autoregressive moving-average (NARMA) system have revealed that the proposed approach accelerates weight convergence speed almost ten times higher compared to the conventional ESN.

关键词： Training Learning systems Recurrent neural networks Filtering Simulation Neurons Reservoir computing

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On Implementation of Brownian Circuits by Swarm Agents

On Implementation of Brownian Circuits by Swarm Agents

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International Symposium on Computing and Networking Workshops (CANDARW)

作者： Masahiro Kimura Teijiro Isokawa Ferdinand Peper Sho Nakade Jia Lee Naotake Kamiura Graduate School of Engineering University of Hyogo Himeji Japan Center for Information and Neural Networks National Institute of Information and Communications Technology Kobe Japan College of Computer Science Chongqing University Chongqing China

Brownian circuit is a type of delay-insensitive asynchronous circuits in which circuit modules are connected by bi-directional signal lines and computational signals can be fluctuated on signal lines. It is expected to achieve low power dissipation for conducting computation, and there are several types of implementations such as by Cellular Automata and Swarm Networks. In this paper, we propose an implementation of Brownian circuit based on swarm agents for possible applications on biomolecular computers. Swarm agents consist of several types of agents each of which has only a few functions with a fixed internal states. Operations on signals for the circuit elements are conducted by mechanical interactions between groups of agents, rather than by changing states in cells or agents through their communication. We show how to construct each of elements in Brownian circuit, such as signal, signal line, Hub, C-Join, and Ratchet, by collections of agents, thus computational universality is established by swarm agents.

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An Analysis for Pre-consultations on the Korean Government-Managed information System Projects 8th

An Analysis for Pre-consultations on the Korean Government-M...

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8th International Conference on Advances in Computing and Data sciences, ICACDS 2024

作者： Kim, Myunghee Baek, Nakhoon Department of Digital Platform Government National Information Society Agency Daegu41068 Korea Republic of Department of Information Science Kyungpook National University Daegu41566 Korea Republic of School of Computer Science and Engineering Kyungpook National University Daegu41566 Korea Republic of Graduate School of Data Science Kyungpook National University Daegu41566 Korea Republic of

ISBN: (纸本)9783031709050

To reduce duplicated information system development projects, Korean government established the pre-consultation body. The pre-consultation body performs preliminary review for various aspects of government-managed information systems. In spite of its good roles and its effectively, the pre-consultation body met over-loaded duties, and now it shows some warning signals mainly due to the increased number of submitted projects. This paper statistically analyzes the results of pre-consultation processes for 1,399 submitted projects in the field of e-Government projects in the year of 2022. Our analysis shows that it is better to exclude some small-size projects from the pre-consultation process. This paper aims to provide the theoretical and statistics-based backgrounds to the claims, and we expect that our suggestions can improve the pre-consultation process of Korean government information systems management. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Decentralized finance

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Study on material removal mechanism for Cu-CMP on a patterned silicon wafer 10

Study on material removal mechanism for Cu-CMP on a patterne...

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10th International Conference on Leading Edge Manufacturing Technologies in 21st Century, LEM 2021

作者： Bakier, Mohammed A.Y.A. Matsumoto, Ryuunosuke Fuchiwaki, Masaki Khajornrungruang, Panart Suzuki, Keisuke Department of Mechanical Information Science and Technology Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology 820-8502 Japan Department of Mathematics Faculty of Science Assiut University Egypt

Pattern effect as dishing and erosion on Cu-CMP process become a serious problem to fabricate multilevel interconnect in advanced ULSI devices. In the current study, planarization models using patterned wafer and ANSYS software are suggested to make precise predictions of the post-CMP surface height to support optimize the layout design. The slurry flow effect on the copper body has been investigated and the removed mass character is monitored through the stress distribution at the solid domain. By determination of the removal regions by the calculation, we can avoid the dishing and erosion areas in the patterned area from the post-CMP process. © 2021 The Japan Society of Mechanical Engineers.

关键词： Silicon wafers

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Deep Joint Semantic Coding and Beamforming for Near-Space Airship-Borne Massive MIMO Network

arXiv

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arXiv 2024年

作者： Wu, Minghui Gao, Zhen Wang, Zhaocheng Niyato, Dusit Karagiannidis, George K. Chen, Sheng School of Information and Electronics Beijing Institute of Technology Beijing100081 China Beijing National Research Center for Information Science and Technology Department of Electronic Engineering Tsinghua University Beijing100084 China Tsinghua Shenzhen International Graduate School Shenzhen518055 China The School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore The Department of Electrical and Computer Engineering Aristotle University of Thessaloniki Thessaloniki54124 Greece Lebanon The School of Electronics and Computer Science University of Southampton SouthamptonSO17 1BJ United Kingdom

Near-space airship-borne communication network is recognized to be an indispensable component of the future integrated ground-air-space network thanks to airships’ advantage of long-term residency at stratospheric altitudes, but it urgently needs reliable and efficient Airship-to-X link. To improve the transmission efficiency and capacity, this paper proposes to integrate semantic communication with massive multiple-input multiple-output (MIMO) technology. Specifically, we propose a deep joint semantic coding and beamforming (JSCBF) scheme for airship-based massive MIMO image transmission network in space, in which semantics from both source and channel are fused to jointly design the semantic coding and physical layer beamforming. First, we design two semantic extraction networks to extract semantics from image source and channel state information, respectively. Then, we propose a semantic fusion network that can fuse these semantics into complex-valued semantic features for subsequent physical-layer transmission. To efficiently transmit the fused semantic features at the physical layer, we then propose the hybrid data and model-driven semantic-aware beamforming networks. At the receiver, a semantic decoding network is designed to reconstruct the transmitted images. Finally, we perform end-to-end deep learning to jointly train all the modules, using the image reconstruction quality at the receivers as a metric. The proposed deep JSCBF scheme fully combines the efficient source compressibility and robust error correction capability of semantic communication with the high spectral efficiency of massive MIMO, achieving a significant performance improvement over existing approaches. Copyright © 2024, The Authors. All rights reserved.

关键词： Base stations

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Act as you wish: fine-grained control of motion diffusion model with hierarchical semantic graphs 23

Act as you wish: fine-grained control of motion diffusion mo...

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Proceedings of the 37th International Conference on Neural information Processing Systems

作者： Peng Jin Yang Wu Yanbo Fan Zhongqian Sun Yang Wei Li Yuan School of Electronic and Computer Engineering Peking University Shenzhen China and AI for Science (AI4S)-Preferred Program Peking University Shenzhen Graduate School China Tencent AI Lab China School of Electronic and Computer Engineering Peking University Shenzhen China and Peng Cheng Laboratory Shenzhen China and AI for Science (AI4S)-Preferred Program Peking University Shenzhen Graduate School China

Most text-driven human motion generation methods employ sequential modeling approaches, e.g., transformer, to extract sentence-level text representations automatically and implicitly for human motion synthesis. However, these compact text representations may overemphasize the action names at the expense of other important properties and lack fine-grained details to guide the synthesis of subtly distinct motion. In this paper, we propose hierarchical semantic graphs for fine-grained control over motion generation. Specifically, we disentangle motion descriptions into hierarchical semantic graphs including three levels of motions, actions, and specifics. Such global-to-local structures facilitate a comprehensive understanding of motion description and fine-grained control of motion generation. Correspondingly, to leverage the coarse-to-fine topology of hierarchical semantic graphs, we decompose the text-to-motion diffusion process into three semantic levels, which correspond to capturing the overall motion, local actions, and action specifics. Extensive experiments on two benchmark human motion datasets, including HumanML3D and KIT, with superior performances, justify the efficacy of our method. More encouragingly, by modifying the edge weights of hierarchical semantic graphs, our method can continuously refine the generated motion, which may have a far-reaching impact on the community. Code and pre-trained weights are available at https://***/jpthu17/GraphMotion.

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A Near-Optimal Category information Sampling in RFID Systems

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IEEE Transactions on Mobile Computing 2025年

作者： Wang, Xiujun Liu, Zhi Zhou, Xiaokang Liao, Yong Hu, Han Zheng, Xiao Li, Jie Anhui University of Technology School of Computer Science Ma'anshan243032 China Anhui Engineering Research Center for Intelligent Applications and Security of Industrial Internet Ma'anshan243032 China University of Electro-Communications Graduate School of Informatics and Engineering Tokyo182- 8585 Japan Shiga University Faculty of Data Science Hikone522-8522 Japan RIKEN Center for Advanced Intelligence Project Tokyo103-0027 Japan University of Science and Technology of China School of Cyber Science and Technology Hefei China Beijing Institute of Technology School of Information and Electronics Beijing China School of Electronic Information and Electrical Engineering Shanghai200240 China

In many RFID-enabled applications, objects are classified into different categories, and the information associated with each object's category (called category information) is written into the attached tag, allowing the reader to access it later. The category information sampling in such RFID systems, which is to randomly choose (sample) a few tags from each category and collect their category information, is fundamental for providing real-time monitoring and analysis in RFID. However, to the best of our knowledge, two technical challenges, i.e., how to guarantee a minimized execution time and reduce collection failure caused by missing tags, remain unsolved for this problem. In this paper, we address these two limitations by considering how to use the shortest possible time to sample a different number of random tags from each category and collect their category information sequentially in small batches. In particular, we first obtain a lower bound on the execution time of any protocol that can solve this problem. Subsequently, we present a near-OPTimal Category information sampling protocol (OPT-C) that solves the problem with an execution time close to the lower bound. Finally, extensive simulation results demonstrate the superiority of OPT-C over existing protocols, while real-world experiments further validate its practicality. © 2002-2012 IEEE.

关键词： category information sampling execution time lower bound near-optimal protocol RFID systems

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mmWave Enabled Connected Autonomous Vehicles: A Use Case with V2V Cooperative Perception

arXiv

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arXiv 2023年

作者： Mollah, Muhammad Baqer Wang, Honggang Karim, Mohammad Ataul Fang, Hua The Department of Electrical and Computer Engineering University of Massachusetts Dartmouth MA02747 United States The Department of Graduate Computer Science and Engineering Katz School of Science and Health Yeshiva University New York United States The Department of Computer and Information Science University of Massachusetts Dartmouth MA02747 United States

Connected and autonomous vehicles (CAVs) will revolutionize tomorrow's intelligent transportation systems, being considered promising to improve transportation safety, traffic efficiency, and mobility. In fact, envisioned use cases of CAVs demand very high throughput, lower latency, highly reliable communications, and precise positioning capabilities. The availability of a large spectrum at millimeter-wave (mmWave) band potentially promotes new specifications in spectrum technologies capable of supporting such service requirements. In this article, we specifically focus on how mmWave communications are being approached in vehicular standardization activities, CAVs use cases and deployment challenges in realizing the future fully connected settings. Finally, we also present a detailed performance assessment on mmWave-enabled vehicle-to-vehicle (V2V) cooperative perception as an example case study to show the impact of different configurations. © 2023, CC BY-SA.

关键词： Autonomous vehicles

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