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2024 IEEE International conference on Intelligent systems and advanced applications, ICISAA 2024

作者： Pradhan, Rahul Prathiba, Ch. Kholmukhamedov, Timur Patil, B.N. Mary, S. Suma Christal Raj, I Infant GLA University Department of Computer Engineering and Applications Mathura India Vignana Bharathi Institute of Technology Department of CSM Ghatkesar Hyderabad India Turin Polytechnic University Automatic Control and Computer Engineering Department Tashkent Uzbekistan Yeshwantrao Chavan College of Engineering Department of Applied Physics Nagpur India Panimalar Engineering College Department of Information Technology Poonamalle Chennai India K. Ramakrishnan College of Engineering Department of CSE Tamil Nadu Trichy India

ISBN: (纸本)9798331539948

Modern urban development depends on optimizing cognitive sensor communications in smart cities, which is the focus of this investigation. In the era of expanding sensor networks, efficient data transmission is paramount for the success of smart city initiatives. The current state of sensor communication faces challenges like suboptimal adaptability, inefficient energy usage, and limited fault detection. This study proposes a solution through the application of an Adaptive Neuro-Fuzzy Inference System (ANFIS), identifying these challenges as opportunities for improvement. The novelty lies in ANFIS's introduction as an intelligent and adaptive solution. Its ability to learn from sensor data, adapt to changing conditions, and optimize communication parameters sets it apart in enhancing cognitive sensor communication. The research methodology involves a systematic approach, encompassing data collection, pre-processing, and ANFIS model development. The model is rigorously trained, validated, and tested for real-world effectiveness. Integration into the smart city communication system is explored, emphasizing applications in context-aware communication, energy-efficient protocols, and fault detection. Performance is evaluated using metrics like accuracy, reliability, and efficiency, demonstrating the model's efficacy in addressing drawbacks and enhancing overall sensor communication in smart cities. The findings contribute to advancing intelligent systems for urban infrastructure and services. © 2024 IEEE.

关键词： Fuzzy neural networks

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Research on a Bipolar Structure Voltage sensor Based on Capacitive Coupling Principle 5

Research on a Bipolar Structure Voltage Sensor Based on Capa...

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5th IEEE International conference on advanced Electrical and Energy systems, AEES 2024

作者： Luo, Bofeng Li, Shijie Hou, Tianyu Hu, Siyu Zhang, Chuyan China Southern Power Grid Company Limited Guangzhou China China University of Geosciences Beijing Beijing China

ISBN: (纸本)9798331517724

This paper investigates a bipolar structure voltage sensor based on the principle of capacitive coupling. The non-contact voltage sensor, based on electric field coupling, offers advantages such as simple structure, excellent insulation performance, and fast response. A bipolar differential electrode structure was designed for the sensor, and by reducing the internal resistance of the voltage follower circuit, the measurement stability and accuracy were improved. Simulation and experimental results indicate that the sensor can effectively detect electric field variations in a uniformly distributed field and exhibits good linear response in both low-frequency and power-frequency signal measurements, showing great potential for future applications. © 2024 IEEE.

关键词： Capacitor bank

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Strengthening the Connection and Monitoring of Dead Zones: Implementing Drone-Based Monitoring Images Using 6G IoT and Deep Learning

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WIRELESS PERSONAL COMMUNICATIONS 2024年 1-23页

作者： Cheng, Wei Yang, Zhiling Xiamen Ocean Vocat Coll Xiamen 361102 Fujian Peoples R China Xiamen Key Lab Intelligent Fishery Xiamen 361100 Peoples R China

Massive Internet of Things (IoT) with millions of devices communicating at high data rates and low latency will be made possible by 6G. advanced sensing is a major application area for 6G. Higher speeds, however, entail shifting to higher frequencies and typically call for greater transmission power. IoT sensors, however, need to be aware of their positions in many applications. Promising localization options have resulted from recent advancements in the drone industry. This study looks into the "dead zone" and coverage region for indoor flying receiver localisation (such as drones). Weighted graph structures are used to perform the drones' sensor visiting order. Additionally, Using a non-Hinge sensor removal method, the transmission's path length is shortened. Next, a machine learning model known as Random Forest is used to segment the photos that the drones took. DL DenseNet is a deep-learning network used for drone monitoring after the segmentation process. Drone energy consumption can be decreased by optimizing drone location and sensor visitation sequence. The suggested model is superior, as demonstrated by the simulation results. ML classifiers became a more sensible option for specialised applications like wetland mapping, where networks must be trained for every unique site, topography, season, and other meteorological factors. The top pixel-based machine learning classifier was Random Forest (RF), which offered good picture segmentation accuracy (91%) during testing. The enhanced accuracy of 98.3% in detecting DL-based dead zones helps rescue personnel identify dangerous areas more rapidly. Following contemporary smart city ambitions, disaster management systems will be transformed using technologies like real-time dead zone monitoring systems.

关键词： 6G Internet of Things (IoT) Deep learning Dead zone monitoring sensor positioning Machine learning

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Multi-Modal sensor for Fingertips of Anthropomorphic Grippers 20

Multi-Modal Sensor for Fingertips of Anthropomorphic Gripper...

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20th IEEE/ASME International conference on Mechatronic and Embedded systems and applications (MESA)

作者： Fazal, Tanzeel Ahmad Laudante, Gianluca Mirto, Michele Pennacchio, Olga Pirozzi, Salvatore Univ Campania Luigi Vanvitelli Engn Dept Aversa CE Italy

ISBN: (纸本)9798331516246;9798331516239

The trend of robotic manipulation in complex and unstructured environments necessitates the integration of various sensory modalities to enable accurate and adaptive grasping and control. This paper presents the design, development, and integration of a multi-modal sensor for robotic gripper, incorporating tactile sensing based on optoelectronic technology, Time of Flight (ToF) for pre-touch sensing, and Inertial Measurement Unit (IMU). Tactile sensing is fundamental for perceiving fine-grained surface textures, detecting pressure, and enabling precise object manipulation. Time-of-Flight pre-touch sensors offer proximity information with ultra-low latency, by enhancing environment interaction or anticipatory human-robot responses. IMUs provide critical motion and orientation data to track gestures and postures, as well as temperature information about grasped objects. The developed remarkably compact sensor, measuring only 12.5 x 20 mm, making it suitable for integration on the fingertip of any robotic gripper, and sensor system aims to enhance the gripper's abilities by providing real-time feedback about object properties, pose estimation and dynamic interactions during grasping and manipulation tasks. The novel multi-modal sensor has very smaller size equivalent to human fingertip and more robust design that yields the higher sensitivity, higher flexibility and less noise. Extensive tests on its measurement capabilities evaluated sampling frequency, noise, and precision. The sensor has been successfully integrated into a commercial three-fingered gripper, demonstrating its practical application in robotic manipulation tasks.

关键词： Multi-Modal sensor System Optoelectronic Based Tactile sensor Time of Flight (ToF) Inertial Measurement Unit (IMU)

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Ultrasensitive and durable borophene-based humidity sensors for advanced human-centric applications

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CHEMICAL ENGINEERING JOURNAL 2024年 500卷

作者： Wu, Zitong Liang, xinchao Zhao, Zhilin Wu, Qilong Liu, xiang Liu, Yi xu, Maoping Hou, Chuang Tai, Guoan Nanjing Univ Aeronaut & Astronaut Coll Aerosp Engn State Key Lab Mech & Control Aerosp Struct Lab Intelligent Nano MatDevices Minist Educ Nanjing 210016 Peoples R China

Borophene, a novel two-dimensional (2D) nanomaterials with high surface-to-volume ratio and abundant active sites, show great promise for humidity sensing applications, which are crucial for improving human comfort, public health, and device safety. However, traditional fabrication methods, such as drop-coating, face limitations including uncontrollable sensing layer thickness, poor water-damage resistance, and inherent variability between devices. In this work, we present a high-performance humidity sensor fabricated using borophene glass. The sensor demonstrates an impressive detection range (11-97 % RH), exceptional sensitivity (up to 1.83x105% at 97 % RH), low hysteresis (1.245 %), excellent repeatability, and relatively fast response (28.8 s)/recovery (2.6 s) times. Notably, the sensor exhibits thickness-dependent sensing performance, long-term stability, high selectivity, and water-damage resistance. First-principles calculations show that borophene exhibits an adsorption energy of 0.208 eV for H2O molecules, with the hole doping effect following adsorption driving the sensor's response to varying relative humidity levels. The sensor's sustained heightened sensitivity under high humidity conditions is attributed to the Grotthuss mechanism. Additionally, the sensor has been successfully integrated into various human-centric applications, including speech recognition, wireless monitoring of respiratory patterns, and non-contact switches. These advancements pave the way for the integration of borophene humidity sensors into smart devices and non-contact control systems, driving innovation in human-computer interaction and healthcare technologies.

关键词： Borophene Humidity sensor Thickness-dependent Speech recognition Respiratory monitoring

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Abnormal Data Detection Model Based on Autoencoder and Random Forest Algorithm: Camera sensor Data in Autonomous Driving systems

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International Journal of advanced Computer Science and applications 2025年第3期16卷 222-231页

作者： Shengwen, Geng Osman, Mohd Hafeez Faculty of Computer Science and Information Technology Universiti Putra Malaysia Serdang43400 Malaysia

This project develops an AI-based anomaly detection system. In the field of autonomous driving, abnormal data will directly affect the safety of autonomous driving systems, especially in terms of abnormal camera sensor data. sensor failure, environmental changes, or bad weather can lead to the emergence of abnormal data, which can affect the decision-making process and may have disastrous consequences. Based on the above problems, this study addresses this challenge by proposing a hybrid anomaly detection model (called CAE-RF) that combines convolutional autoencoders and random forest algorithms to achieve efficient and accurate identification of abnormal data patterns to improve the safety of autonomous driving systems. The proposed method will use convolutional autoencoders to calculate the reconstruction error and combine the hidden features extracted by the encoder as the input of the random forest to distinguish normal data from abnormal data. The key performance indicators such as accuracy, precision, recall, and F1 score are used to evaluate the model, and the robustness is guaranteed by cross-validation. Experimental results show that the CAE-RF model has an accuracy of 92% in distinguishing normal and abnormal data. Compared with traditional methods, the CAE-RF model achieves higher accuracy and reliability. The implementation of this model can timely identify and process abnormal data, reduce the risks brought by sensor failure or external environment changes, prevent potential accidents, and improve the safety and reliability of the autonomous driving system. © (2025), (Science and Information Organization). All rights reserved.

关键词： Tachometers

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Improving Efficiency in Multi-Modal Autonomous Embedded systems Through Adaptive Gating

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IEEE TRANSACTIONS ON COMPUTERS 2025年第2期74卷 691-704页

作者： Hou, xiaofeng xu, Cheng Li, Chao Liu, Jiacheng Tang, xuehan Cheng, Kwang-Ting Guo, Minyi Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai 200240 Peoples R China Chinese Univ Hong Kong Dept Comp Sci & Engn Hong Kong Peoples R China Hong Kong Univ Sci & Technol Dept Comp Sci & Engn Hong Kong Peoples R China

The parallel advancement of AI and IoT technologies has recently boosted the development of multi-modal computing ((MC)-C-2) on pervasive autonomous embedded systems (AES).(MC)-C-2 takes advantage of data from different modalities such as images, audio, and text and is able to achieve notable improvements in accuracy. However, achieving these accuracy gains often comes at the cost of increased computational complexity and energy consumption. Furthermore, the presence of numerous advanced sensors in these systems significantly contributes to power consumption, exacerbating the issue of limited power resources. Collectively, these challenges pose difficulties in deploying (MC)-C-2 on small embedded devices with scarce energy resources. In this article, we propose an Adaptive Modality Gating technique called AMG for in-situ (MC)-C-2 applications. The primary objective of AMG is to conserve energy while preserving the accuracy advantages of (MC)-C-2. To achieve this goal, AMG incorporates two first-of-its-kind designs. Firstly, it introduces a novel semi-gating architecture that enables partial modality sensor power gating. Specifically, we devise the de-centralized AMG (D-AMG) and centralized AMG (C-AMG) architecture. The former buffers raw data on sensors while the latter buffers raw data on the computing board, which are suitable for different edge scenarios respectively. Secondly, it facilitates a self-initialization/tuning process on the AES, which is supported by carefully-built analytical model. Extensive evaluations demonstrate the effectiveness of AMG. It achieves a 1.6x to 3.8x throughput higher than other power management methods and improves the lifespan of AES by 10% to 280% longer within the same energy budget, while satisfying all performance and latency requirements across various scenarios.

关键词： sensors Accuracy Computer architecture Energy consumption sensor systems sensor arrays Hardware Embedded systems Computers Pipelines Edge AI autonomous embedded systems multi-modal DNNs modality gating architecture efficiency management

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Dynamics-Based Identification of Hybrid systems using Symbolic Regression 50

Dynamics-Based Identification of Hybrid Systems using Symbol...

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50th Euromicro conference on Software Engineering and advanced applications

作者： Plambeck, Swantje Schmidt, Maximilian Fey, Goerschwin Subias, Audine Trave-Massuyes, Louise Hamburg Univ Technol Inst Embedded Syst Hamburg Germany Univ Toulouse LAAS CNRS Toulouse France Univ Toulouse INSA Toulouse France

ISBN: (纸本)9798350380279;9798350380262

Symbolic regression has shown potential in the identification of physical systems. Hybrid systems, which combine both continuous and discrete behavior, are a relevant extension of purely physical systems, used in many fields, including robotics, biological systems, and control systems. However, due to their complexity, finding an accurate model is a challenge. This paper presents a novel approach to learning models of hybrid systems using symbolic regression. Our method leverages the power of genetic programming to automatically discover accurate and interpretable mathematical models in the form of hybrid systems from observed data. Symbolic regression detects transitions between different continuous behavior of a system directly based on the dynamics, instead of pure distances of observed trajectories. Furthermore, models generated by symbolic regression can be used to predict future system behavior, detect anomalies, and identify the underlying dynamics of the system while providing a human-readable representation. Our results demonstrate that symbolic regression can effectively identify the underlying dynamics of a real system represented in a hybrid model, providing a valuable tool for system identification and diagnosis.

关键词： Hybrid systems Symbolic Regression System Identification

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Wireless Connectivity and Localization for advanced Air Mobility Services

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IEEE AEROSPACE AND ELECTRONIC systems MAGAZINE 2024年第11期39卷 4-14页

作者： Sinha, Priyanka Chowdhury, Md Moin Uddin Guvenc, Ismail Matolak, David W. Namuduri, Kamesh North Carolina State Univ Raleigh NC 27606 USA Univ South Carolina Columbia SC 29208 USA Univ North Texas Denton TX 76203 USA

By serving as an analog to traffic signal lights, communication signaling for drone-to-drone communications holds the key to the success of advanced air mobility (AAM) in both urban and rural settings. Deployment of AAM applications, such as air taxis and air ambulances, especially at large-scale, requires a reliable channel for a point-to-point and broadcast communication between two or more aircraft. Achieving such high reliability, in a highly mobile environment, requires communication systems designed for agility and efficiency. This article presents the foundations for establishing and maintaining a reliable communication channel among multiple aircraft in unique AAM settings. Subsequently, it presents concepts and results on wireless coverage and mobility for AAM services using cellular networks as a ground network infrastructure. Finally, we analyze the wireless localization performance at 3D AAM corridors when cellular networks are utilized, considering different corridor heights and base station densities. We highlight future research directions and open problems to improve wireless coverage and localization throughout this article.

关键词： Active appearance model Wireless communication Cellular networks Aircraft Drones Location awareness Wireless sensor networks Mobile communication Traffic control 5G advanced aerial mobility (AAM) air corridors drones handover localization unmanned aerial systems (UAS) urban air mobility (UAM)

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Hierarchically-Interlocked, Three-Axis soft Iontronic sensor for Omnidirectional Shear and Normal Forces

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advanced MATERIALS TECHNOLOGIES 2025年第5期10卷

作者： Shen, Zequn Ren, Jieji Zhang, Ningbin Li, Jinhao Gu, Guoying Shanghai Jiao Tong Univ Robot Inst Sch Mech Engn State Key Lab Mech Syst & Vibrat Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Meta Robot Inst Shanghai 200240 Peoples R China

Artificial tactile sensing capable of measuring shear and normal forces is crucial for the diverse human-machine interactions and dexterous robotic manipulations. However, existing soft multi-axis force sensors usually suffer from limited detectable directions and complex coupling mechanisms, limiting their applications in realistic wearable and robotic systems. Here, a hierarchically-interlocked, three-axis soft iontronic sensor is presented with an asymmetrical electrode pattern that leverages the mortise-and-tenon structure and ultracapacitive principle to detect omnidirectional shear and normal forces. The designed sensor facilitates the decoupling process and achieves enhanced sensing performances including high accuracy, fast response ability, and mechanical robustness. Prototypical integration and application of the sensor are demonstrated to perform wearable telecontrol of virtual platforms and assist robot gripper through closed-loop force feedback. A sensing array is further developed to construct a touch panel and identify handwriting based on the continuously measured directions of applied forces. This work may offer a potentially promising solution for the next-generation intelligent electronic skins requiring multimodal tactile sensing information. A hierarchically-interlocked, three-axis soft iontronic sensor is proposed to detect omnidirectional shear and normal forces. Asymmetrical electrode pattern with mortise-and-tenon sensing architecture facilitates decoupling process and achieves enhanced sensing performances. The fabricated sensor can be applied as electronic skins to provide three-axis force feedback for wearable devices and robots. image

关键词： hierarchically-interlocked structure omnidirectional shear and normal forces robotic skins three-axis soft iontronic sensors wearable electronics

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