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Multispectral Organic Photodetectors and applications

Multispectral Organic Photodetectors and Applications

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IEEE International conference on Flexible and Printable sensors and systems (FLEPS)

作者： Zhuangmiao Wang Yu Tang Furong Zhu Department of Physics Research Centre of Excellence for Organic Electronics and Institute of Advanced Materials Hong Kong Baptist University Kowloon Tong Hong Kong China

ISBN: (数字)9798350383263

ISBN: (纸本)9798350383270

Spectral response of organic photodetectors (OPDs) is determined by the absorption of the active materials and optical profile in the devices. Single-band OPDs optimized for photodetection at specific spectral ranges have been reported. However, the reports on OPDs for multispectral detection are rather rare. It is a great challenge to achieve high-performance multispectral OPDs. This talk discusses a novel filter-free multispectral OPD comprising a back-to-back stacked double bulk heterojunction (BHJ) structure. The front BHJ responds to short-wavelength light, e.g., visible, whereas the rear BHJ responds to long-wavelength light, e.g., near-infrared electromagnetic waves. The back-to-back stacked double BHJ OPD allows multispectral detection that can be controlled by the polarity of a bias applied between the two contacts in the OPD. The double BHJ OPD also exhibits a broadband photoresponse when it is operated using a 50% duty-cycled square-wave supply voltage. The multispectral OPDs enable applications in areas such as health and security monitoring, artificial intelligence, high-resolution image sensing, Internet of Things, and optical communications.

关键词： Optical filters Voltage sensor systems and applications Optical imaging Photodetectors Optical materials Optical fiber communication

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19th International conference on Computer Aided systems Theory, EUROCAST 2024

19th International Conference on Computer Aided Systems Theo...

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19th International conference on Computer Aided systems Theory, EUROCAST 2024

ISBN: (纸本)9783031829512

The proceedings contain 104 papers. The special focus in this conference is on Computer Aided systems Theory. The topics include: Influence of Spike Encoding, Neuron Models and Quantization on SNN Performance;adaptive Combination in Frequency Domain: An Approach for Robust Nonlinear Acoustic Echo Cancellation;using of a Robotic Platform to Detect Acoustic Events for Indoor Environments;modeling Wildlife Accident Risk with Gaussian Mixture Models;towards a Unified Incident Detection and Response System for Autonomous Transportation;edge-Processing of Myoelectric Signals for the Control of Hand and Arm-Prostheses;AI-Driven Gesture and Action Recognition for Learning Medicine Through Virtual Reality;Medical Protocols and AI-Driven Algorithms for Enhanced Monitoring of Cardiac Implantable Electronic Devices;a Survey of Machine Learning Methods for Analyzing Synovitis Arthritis in Human Joints;motion Tracking in Augmented and Mixed Realities for Healthcare and Medicine applications;advancements and applications of Medical Human Digital Twin Technology in Cerebral Palsy Diagnosis, Therapy, and Rehabilitation;Transformation of IEC 61131-3 onto an Embedded Platform Using LLVM;machine Learning Based Parameter Estimation of Energy Models in Digital Production Environments;efficient Classification of Live sensor Data on Low-Energy IoT Devices with Simple Machine Learning Methods;machine Learning Using a Hybrid Quantum Classical Algorithm with Amplitude Data Encoding;quantitative Trend Analysis of Reinforcement Learning Algorithms in Production systems;Using AutomationML for advanced Simulation in Industrial Automation;AR Digital Twin Demonstrator for Industrial Robotics Education;Accelerating Manual Pick-and-Place Operations with AR-Projected CAD Plans and AI-Assisted Object Recognition;variety Engineering – A Cybernetic Concept with Practical Implications;using a System Archetype to Explore a Business Model for Digital Textile Microfactories;interacting with the Water Cycle -

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19th International conference on Computer Aided systems Theory, EUROCAST 2024

19th International Conference on Computer Aided Systems Theo...

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19th International conference on Computer Aided systems Theory, EUROCAST 2024

ISBN: (纸本)9783031838873

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Deployment problem of Wireless sensor Networks based on Adaptive Particle Swarm Optimization

Deployment problem of Wireless Sensor Networks based on Adap...

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International conference on Computer Engineering and applications (ICCEA)

作者： Youfa Fua Dan Liu Gao Li Haidong Huang Key Laboratory of Advanced Manufacturing Technology Ministry of Education Guizhou University Guiyang China

ISBN: (数字)9798350386776

ISBN: (纸本)9798350386783

In response to the shortcomings of particle swarm optimization (PSO) such as insufficient global search, susceptibility to local optima, and slow convergence speed, this paper proposes an improved adaptive PSO (APSO). Firstly, a superior point set is employed for particle population initialization, achieving a more uniform and extensive particle distribution. Secondly, during the particle velocity update phase, an adaptive Levy flight acceleration coefficient is introduced along with adjustments to the social learning factor, emphasizing global search. Finally, in the particle position update phase, an adaptive scaling factor is introduced to intelligently adjust particle position weights, aiding in obtaining superior solutions. The proposed APSO is applied to the wireless sensor network deployment problem, and experimental results demonstrate its outstanding performance in solving optimization problems.

关键词： Wireless sensor networks Adaptive systems Sociology Search problems Particle swarm optimization Statistics Optimization

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Real-Time Robot Gesture Control Using Online Feedback Data with Multi-tracking Capture System

Real-Time Robot Gesture Control Using Online Feedback Data w...

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International conference on advanced Engineering Theory and applications, AETA 2022

作者： Nguyen, Khang Hoang Vinh Bien Minh, Tri Le, Chi Van Do, Xuan Phu Mechatronics and Sensor Systems Technology Vietnamese-German University Binh Duong Province Ben Cat Town Viet Nam Electrical and Computer Engineering Vietnamese-German University Binh Duong Province Ben Cat Town Viet Nam

ISBN: (纸本)9789819987023

This study uses a Vicon system, a high-speed accurate marker-based motion capture system aiming to develop a wireless low-latency gesture control method that requires no hardware development and no on-board power source. A novel control is designed to track a rigid-body object’s motion in the workspace and translate it to robot control signals. The system has five main components: a host PC with a Vicon software named Tracker, a set of eight Vicon Vantage V8 infrared cameras, a client PC that receives Tracker’s broadcasted data over the Ethernet, a robot model named youBot provided by KUKA, and a rigid-body object that will act as the gesture controller. Using an Application Programming Interface (API) called DataStream, it is successfully obtained the pose (position and orientation) of the rigid-body object and processed it to remotely control the KUKA youBot robot with high accuracy and no recognizable latency. Although it is similar to a combination of accelerometer-based and gyroscope-based controllers, the gesture controller requires no specific hardware besides a set of retro-reflective markers for tracking purposes. The proposed control method proves beneficial during the robotics prototyping phase, where different control mechanisms can be replicated and tested within the motion capture system’s range before deciding the best one for the target application. Furthermore, the robot’s odometry can also be tracked during testing, making a motion capture system a valuable tool for the evaluation of motion control. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Controllers

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SENeCA: Scalable Energy-efficient Neuromorphic Computer Architecture 4

SENeCA: Scalable Energy-efficient Neuromorphic Computer Arch...

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IEEE International conference on Artificial Intelligence Circuits and systems (AICAS) - Intelligent Technology in the Post-Pandemic Era

作者： Yousefzadeh, Amirreza Van Schaik, Gert-Jan Tahghighi, Mohammad Detterer, Paul Traferro, Stefano Hijdra, Martijn Stuijt, Jan Corradi, Federico Sifalakis, Manolis Konijnenburg, Mario IMEC Eindhoven Netherlands

ISBN: (数字)9781665409964

ISBN: (纸本)9781665409964

SENeCA is our first RISC-V-based digital neuromorphic processor to accelerate bio-inspired Spiking Neural Networks for extreme edge applications inside or near sensors where ultra-low power and adaptivity features are required. SENeCA is optimized to exploit unstructured spatio-temporal sparsity in computations and data transfer. It is a digital IP, contains interconnected Neuron Cluster Cores, with RISC-V-based instruction set, an optimized Neuromorphic Co-Processor, and event-based communication infrastructure. SENeCA improves state of the art by: Addressing the flexibility issue in neuromorphic processors by allowing a fully programmable neuron model and learning/adaptivity algorithms;Improving the area efficiency by employing a 3-level memory hierarchy which allows using novel embedded memory technologies;Efficient deployment of advanced learning mechanisms and optimization algorithms by accelerating neural operations in three data types: int4, int8 and BrainFloat16;Efficient event communication by using a new Network-on-Chip with multicasting, a compression mechanism, and source-based routing. The implemented digital IP can be tuned for different applications to have a flexible number of cores and Neural Processing Elements (NPEs) per core and optional use of off-chip memory. Next to the hardware, the SENeCA platform includes an SDK and a hardware-aware simulator for close-loop synthesis/mapping optimization(1).

关键词： Multicast algorithms Neuromorphics Memory management Neurons Neural networks Network-on-chip sensor phenomena and characterization

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WaveTune: Harnessing Radar sensors to Compose Music Beats with Body Gestures

WaveTune: Harnessing Radar Sensors to Compose Music Beats wi...

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International conference on Distributed Computing in sensor systems (DCOSS)

作者： Suchdeep Singh Juneja Girish Vaidya Marco Zuniga Delft University of Technology Delft Netherlands Amsterdam Institute for Advanced Metropolitan Solutions Amsterdam Netherlands

ISBN: (数字)9798350369441

ISBN: (纸本)9798350369458

In the field of music technology, the ability to connect body movements with music generation has led to highly engaging applications. Many existing solutions use cameras or wearable devices to capture physical movements and translate them into commands for a music generation tool. While the camera-based systems are privacy-invasive and demand good lighting conditions, the wearable devices adversely impact the fluidity of movements. We propose WaveTune, a privacy-friendly and device-free interface for creating music beats through body gestures. Wavetune has the following components: (i) a millimeter-wave radar as the frontend sensor that captures body movements, (ii) a gesture recognition algorithm that performs gesture delimitation and identification in real-time, and (iii) a music generation component that maintains a seamless and pleasant experience. WaveTune also provides an option to continuously map random dynamic movements into musical parameters to encourage further interaction. We recruited 24 users to train and test WaveTune’s ability to map body gestures to musical commands. To the best of our knowledge, WaveTune is the first mmWave system that allows a layered composition of music beats.

关键词： Performance evaluation Urban areas Music Gesture recognition Millimeter wave radar Cameras Real-time systems

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Railway Switch Machine Fault Diagnosis Considering sensor Abnormality Scenarios

Railway Switch Machine Fault Diagnosis Considering Sensor Ab...

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International conference on Intelligent Transportation

作者： Xiaoxi Hu Xiaohan Zhang Zhaorong Wang Yunbo Chen Jiangnan Xia Yawen Du Yi Li State Key Laboratory of Advanced Rail Autonomous Operation Beijing Jiaotong University Beijing China Polytechnic Institute Zhejiang University Hangzhou China

Since the safe and reliable behavior of a Railway Switch Machine (RSM) is definitely pivotal for rail transportation, scholars and engineers have studied a considerable number of algorithms for RSM fault diagnosis via many kinds of sensor information. However, scholars have yet to consider the impact of the sensors' states on RSM fault classification, which cannot be ignored in practical railway applications. To fill the gap, we propose an end-to-end deep learning architecture named Fault Diagnosis Considering sensor Abnormality Network (FD-CSANet) for RSM behavior recognition under various sensor states. Our approach includes sensor Information Aggregate Module (SIAM) and Fault Diagnosis Module (FDM). The SIAM utilizes the idea of the channel attention mechanism to filter and aggregate multiple sensor features, which weights normal sensor observation and weakens anomalous sensor information. The FDM adopts the DB EM as the backbone for the RSM fault diagnosis. Ultimately, ablation experiments and comparative studies demonstrate the superiority of our algorithm.

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A CNN-based Audio sensor for Rainfall Estimation: Implementation on Embedded Board 11

A CNN-based Audio Sensor for Rainfall Estimation: Implementa...

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11th IEEE International conference on Intelligent Data Acquisition and advanced Computing systems - Technology and applications (IDAACS)

作者： Russo, Michele Puglisi, Valerio Francesco Avanzato, Roberta Beritelli, Francesco VICOSYSTEMS SRL Viale Odorico da Pordenone I-95128 Catania Italy Univ Catania DMI Viale Andrea Doria 6 I-95125 Catania Italy Univ Catania DIEEI Viale Andrea Doria 6 I-95125 Catania Italy

ISBN: (纸本)9781665426053

At present, one of the main issues of interest is climate change. It triggers calamitous phenomena (fires, hydrogeological instability and many others) that put crops, vegetation and even human beings at risk. Considering the phenomenon of hydrogeological instability, there is a wide range of smart application contexts (for example, smart city, smart road and smart agriculture), which require a real-time rainfall level monitoring, recognition and classification system. This system is supposed to provide accurate and real-time estimates of rain intensity and alert those in charge in order to reduce the risks associated with the various application contexts. Thus, the present paper proposes an audio sensor enabled real-time audio rainfall classification, based on 1-D convolutional neural network (CNN) which offers 95% classification accuracy in five rainfall intensity classes.

关键词： Audio classification Enviroment sound classification Feature extraction techniques Convolutional Neural Networks Multimodal Analysis Rainfall Estimation

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Techniques to Improve Robustness of Video-Based sensor Networks 39th

Techniques to Improve Robustness of Video-Based Sensor Netwo...

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39th IMAC, A conference and Exposition on Structural Dynamics, 2021

作者： Mantell, Sarah Sapong, Nathan Schwamb, Addison Mascareñas, David Cattaneo, Alessandro University California Polytechnic State University San Luis ObispoCA United States University of California Riverside RiversideCA United States Oklahoma Christian University EdmondOK United States Los Alamos National Laboratory Los AlamosNM United States

ISBN: (纸本)9783030760038

Recently, work has been done to combine video-based structural dynamics with structural health monitoring—rather than using contact-based sensors such as accelerometers and strain gauges. The installation and maintenance costs of contact-based sensors along with the difficulty in processing the data from malfunctioning sensors pose a significant challenge for long-term, large-scale applications. Video-based structural health monitoring provides a framework for obtaining the data relevant to structural health monitoring without the drawbacks associated with contact-based sensors. Each pixel from a video can be analyzed for structural health monitoring, resulting in a higher data resolution than that collected by contact-based sensors. As video-based structural health monitoring is a new field, there are still major challenges to overcome. Undesirable event data such as atmospheric noise, misalignment or loss of camera monitor, or objects entering the frame can obstruct measurement procedures. Additionally, videos are inherently large in size—data of this magnitude is costly to analyze, transmit, and store for long-term applications. We improve the robustness of the full-field modal analysis algorithm by increasing the algorithm’s performance under various types of digital noise, occlusions, and rotations. Additionally, we use sparse sensing techniques to reduce the size of the transmitted and stored data, working toward making video-based dynamics a low-cost and robust option for structural health monitoring applications. © 2022, The Society for Experimental Mechanics, Inc.

关键词： sensor networks

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