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Software Defined Small Cell Networking under Dynamic Traffic Patterns

Software Defined Small Cell Networking under Dynamic Traffic...

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ieee international symposium on Dependable, Autonomic and Secure Computing (DASC)

作者： Li Zhou Jiao Zhang Boon-Chong Seet Lei Zuo Xiping Hu Jiaxun Li Shan Wang Jibo Wei College of Electronic Science and Engineering National University of Defense Technology Changsha China Department of Electrical and Electronic Engineering Auckland University of Technology Auckland New Zealand College of Mechatronic Engineering and Automation National University of Defense Technology Changsha China Bravolol Limited Hong Kong

Software defined networking (SDN) has the potential to meet the requirements of the next generation traffic and service requirements. It is especially feasible and flexible when combining with small cell networks, which emerges into a software defined small cell networking (SDSCN) framework. SDSCN stands a chance to play a fundamental role in developing future 5G networks. It is particularly a challenging task to deploy dense small cell networks in the presence of dynamic traffic patterns and severe co-channel interference. Based on the SDSCN framework, in this paper, we propose a traffic clustering method to obtain all traffic patterns in a given area and an energy-efficient scheme to deploy and switch on/off small cell base stations (s-BSs) according to the prevailing traffic pattern. The simulation results indicate that our scheme can meet dynamic traffic demands with optimized deployment of small cells and enhance the energy efficiency of the system without compromising on the spectrum efficiency and quality-of-service (QoS) requirements. In addition, our scheme can achieve very close performance compared with the leading optimization solver CPLEX and find solutions in much less computational times than CPLEX.

关键词： Microprocessors Computer architecture Planning Interference Switches Feature extraction Energy consumption

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RelationGrasp: Object-Oriented Prompt Learning for Simultaneously Grasp Detection and Manipulation Relationship in Open Vocabulary

RelationGrasp: Object-Oriented Prompt Learning for Simultane...

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ieee/RSJ international Conference on Intelligent Robots and Systems (IROS)

作者： Songting Liu Tat Joo Teo Zhiping Lin Haiyue Zhu Singapore Institute of Manufacturing Technology (SIMTech) Agency for ScienceTechnology and Research (A*STAR) School of Electrical & Electronic Engineering Nanyang Technological University (NTU) Singapore Robotics Automation & Unmanned SystemsCentre of Expertise Home Team Science and Technology Agency (HTX) Singapore Singapore Institute of Manufacturing Technology (SIMTech) Agency for Science Technology and Research (A*STAR) Singapore Singapore

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Autonomous robotic grasping under complex, clustered, and unstructured environments is a fundamental but challenging task. To achieve human-like rationality in dealing with the grasping task, the agent requires hybrid intelligence from multilateral aspects. This paper introduces RelationGrasp, a unified framework employing a transformer encoder-decoder structure to simultaneously achieve open-vocabulary object detection, manipulation relationship inference, and grasp pose detection. A unique object-oriented prompt learning mechanism is designed to seamlessly bridge the grasp pose and manipulation relationship branches, delivering high fidelity of object-grasp affiliation for object-aware grasping and grasp sequence planning. By formulating the relationship detection as an adjacency matrix regression task under multi-task learning, our framework significantly increases the relationship accuracy with reduced computational overhead. Moreover, to facilitate the robust and adaptive deployment of the proposed RelationGrasp to novel environments, we propose a consistency-based self-supervised adaptation strategy to adapt the pre-trained network to new scenarios and improve grasp accuracy on unseen objects. Our proposed network achieved state-of-the-art performance on various public dataset such as VMRD, OCID, etc., in both grasp detection and manipulation relationship classification, and real-world robot experiments has also been conducted to show the practical usages.

关键词： Learning systems Bridges Vocabulary Accuracy Grasping Object detection Transformers Multitasking Planning Intelligent robots

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MeLo: Low-Rank Adaptation is Better than Fine-Tuning for Medical Image Diagnosis

MeLo: Low-Rank Adaptation is Better than Fine-Tuning for Med...

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ieee international symposium on Biomedical Imaging

作者： Yitao Zhu Zhenrong Shen Zihao Zhao Sheng Wang Xin Wang Xiangyu Zhao Dinggang Shen Qian Wang School of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices Shanghai Tech University Shanghai China School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China Shanghai United Imaging Intelligence Co. Ltd. Shanghai China Shanghai Clinical Research and Trial Center Shanghai China

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

The common practice in developing computer-aided diagnosis (CAD) models based on transformer architectures usually involves fine-tuning from ImageNet pre-trained weights. However, with recent advances in large-scale pre-training and the practice of scaling laws, Vision Transformers (ViT) have become much larger and less accessible to medical imaging communities. Additionally, in real-world scenarios, the deployments of multiple CAD models can be troublesome due to problems such as limited storage space and time-consuming model switching. To address these challenges, we propose a new method MeLo (Medical image Low-rank adaptation), which enables the development of a single CAD model for multiple clinical tasks in a lightweight manner. It adopts low-rank adaptation instead of resource-demanding fine-tuning. By fixing the weight of ViT models and only adding small low-rank plug-ins, we achieve competitive results on various diagnosis tasks across different imaging modalities using only a few trainable parameters. Specifically, our proposed method achieves comparable performance to fully fine-tuned ViT models on four distinct medical imaging datasets using about 0.17% trainable parameters. Moreover, MeLo adds only about 0.5MB of storage space and allows for extremely fast model switching in deployment and inference. Our source code and pre-trained weights are available here.

关键词： Solid modeling Adaptation models Design automation computational modeling Biological system modeling Source coding Switches

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Numerical analysis of Goldschmied geometry with boundary-layer suction

Numerical analysis of Goldschmied geometry with boundary-lay...

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international symposium on Underwater Technology

作者： Sudipta Ray Soumen Sen Dipankar Chatterjee Robotics & Automation CSIR-Central Mechanical Engineering Research Institute Durgapur India Simulation & Modelling Laboratory CSIR-Central Mechanical Engineering Research Institute Durgapur India

An effort is made to implement the phenomenon called the Aerodynamic Pressure Thrust (APT) for the purpose of effective propulsion of underwater vehicle. The two-dimensional Goldschmied body with boundary-layer ingestion near the stern section is considered for this purpose. This particular shape, which up to this point is being considered mainly for aerodynamic propulsion, can be preferred for autonomous underwater vehicles over conventional streamlined bodies for their large volume-to-length ratio. The wind-tunnel experiments of 1960's by Fabio R. Goldschmied paved a way for development of energy efficient propulsion through proper interaction of aerodynamic design and engine power. Decades later, this eventually led to the development of several futuristic crafts which exploit the Pressure Thrust technique. Similar idea is attempted here to obtain improved pressure recovery behind the aft of a blunt underwater vehicle to generate additional thrust. In the present study, a simplified version of the Goldschmied geometry is considered with a single slot for suction between the fore-body and the stern. The computations are carried out utilizing commercial CFD solver Ansys Fluent. The axi-symmetric shape of the Goldschmied body is employed to generate a fully-structured mesh throughout the entire domain. The simulations are carried out for a range of Reynolds number and the suction pressure. The distribution of pressure at different radial locations is examined and plotted, similar to the original work by Goldschmied, to illustrate the alternate zones of drag and thrust produced along the radius. A sizeable portion of the thrust region in the plot is required to overcome the drag force and to achieve self-propulsion solely by means of boundary layer suction. The results of the computational study indicate a trend in that direction.

关键词： Geometry Vehicles

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A Model of the Contribution of Interneuron Diversity to Recurrent Network Oscillation Generation and Information Coding 16th

A Model of the Contribution of Interneuron Diversity to ...

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16th international Conference on Brain Informatics, BI 2023

作者： Lorenz, Gabriel Matías Martínez-Cañada, Pablo Panzeri, Stefano Istituto Italiano di Tecnologia Rovereto Italy Department of Pharmacy and Biotechnology University of Bologna Bologna Italy Hamburg Germany Department of Computer Engineering Automation and Robotics University of Granada Granada Spain

ISBN: (纸本)9783031430749

Spiking neural network models that have studied how oscillations are generated by recurrent cortical circuits and how they encode information have been focused on describing the encoding of information about external sensory stimuli carried by feed-forward inputs in a two-population circuit configuration that includes excitatory cells and fast-spiking interneurons. Here we extend these models to explore the contribution of different classes of cortical interneurons to cortical oscillations. We found that in our extended model, the feed-forward stimulus is still mainly encoded in the gamma frequency range, consistent with earlier works using a single interneuron type. However, we also found that the information carried by different regions of the gamma frequency range was larger than the sum of the information carried by the two individual frequencies. This shows that the power values at different frequencies carried information about the feedforward input in a synergistic way. This is in contrast to previous models with only one interneuron type, which mainly led to redundant information between frequencies in the gamma range. These results suggest that interneuron diversity has useful properties for enriching the encoding of information in the gamma frequency range. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Neural network models

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Ultra-Fast Edge Computing Approach for Hand Gesture Classification Based on EIT Measurements

Ultra-Fast Edge Computing Approach for Hand Gesture Classifi...

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international Workshop on Robot Sensing (ROSE)

作者： Mahdi Mnif Salwa Sahnoun Marouene Kaaniche Bilel Ben Atitallah Ahmed Fakhfakh Olfa Kanoun National School of Electronics and Telecommunications of Sfax University of Sfax Tunisia Laboratory of Signals Systems Artificial Intelligence and Networks (SM@RTS) Digital Research Center of Sfax (CRNS) Tunisia Measurements and Sensor Technology Faculty of Electrical Engineering and Information Technology Chemnitz University of Technology Germany National Institute of Applied Sciences and Technology University of Carthage Tunisia

ISBN: (数字)9798350362367

ISBN: (纸本)9798350362374

Gesture-based robot control offers intuitive interaction between humans and robots, with applications ranging from industrial automation to assistive robotics. However, existing solutions face challenges in achieving real-time requirements while ensuring accurate gesture recognition. This paper presents a new edge computing-based approach for real-time control of robots using Electrical Impedance Tomography (EIT) measurements to classify hand gesture numbers in American Sign Language (ASL). Existing solutions for gesture recognition struggle to achieve real-time performance while maintaining accuracy and energy efficiency. This challenge becomes higher in the case of EIT because of its relative complexity. We focus therefore on leveraging the capabilities of the edge device to implement effectively the Convolutional Neural Network (CNN) acceleration. The proposed solution combines hardware-aware optimization techniques to achieve fast and accurate gesture recognition by enabling rapid inference while minimizing energy consumption on a low-power resource-constrained device with Tiny Machine Learning (TinyML) capabilities. The lightweight CNN model required only 10.2 s to train using the Keras library of TensorFlow and achieved an accuracy of 89.37% for 10 sign language classes, with only 66 μs taken to run inference on the hardware-accelerated microcontroller-based device.

关键词： Performance evaluation Electrical impedance tomography Sign language Accuracy Service robots Tiny machine learning Robot control

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LEECAU-Net: A Latent Entropy-Quantized and Efficient Channel Attention-Aided U-Net Model for Microscopic Medical Image Segmentation

LEECAU-Net: A Latent Entropy-Quantized and Efficient Channel...

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symposium of Image, Signal Processing, and Artificial Vision (STSIVA)

作者： Sanchita Das Gouranga Maity Diptarka Mandal Dmitrii Kaplun Alexander Voznesensky Ram Sarkar Dept. of CSE Jadavpur University Kolkata India Department of Applied Mathematics Defence Instituite of Advanced Technology Pune India Artificial Intelligence Research Institute China University of Mining and Technology Xuzhou China Dept. of Automation and Control Process St. Petersburg Electrotechnical University “LETI” St. Petersburg Russian Federation

ISBN: (数字)9798350363203

ISBN: (纸本)9798350363210

Microscopic medical image segmentation is essential for detailed cellular-level analysis in computational pathology, offering insights into disease mechanisms. However, this task is particularly challenging due to the high similarity between different cellular classes and variability within each class, especially in densely packed or overlapping cell regions. To this end, we present LEECAU-Net (Latent Entropy-quantized and Efficient Channel Attention-aided U-Net), a U-Net-based model designed to improve segmentation accuracy through a dual-attention mechanism. Built on an Inceptionv4 backbone, LEECAU-Net's encoder integrates Latent Entropy-quantized Channel Attention (LECA) and Efficient Channel Attention (ECA) modules. The ECA module enhances the network's ability to focus on essential channel dependencies, while LECA leverages entropy quantification to prioritize channels rich in information, leading to better differentiation between similar structures. We have tested LEECAU-Net on three challenging publicly available datasets: Triple Negative Breast Cancer (TNBC), Multi-organ Nuclei Segmentation (MoNuSeg), and Cervical Nucleus Segmentation (CNS). Across these datasets, our model achieves higher performance metrics than numerous state-of-the-art approaches reported in the literature. The improved results underscore the effectiveness of LEECAU-Net's architecture in handling the complexity of microscopic medical images, making it a valuable tool for advancing automated cellular analysis in biomedical research. The source code and more results are made available at https://***/Cmatermedicalimageanalysis/LEECAU-Net.

关键词： Image segmentation Accuracy Microscopy computational modeling Biological system modeling Source coding Computer architecture Entropy Robustness Medical diagnostic imaging

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Self-Organized Learning Model for behavior emergence and its application to mobile robot

Self-Organized Learning Model for behavior emergence and its...

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3rd international symposium on Communications, Control, and Signal Processing (ISCCSP 2008), vol.3

作者： Manabu Gouko Naoki Tomi Tomoaki Nagano Koji Ito Department of Computational Intelligence and Systems Science Interdisciplinary Graduate School of Science and Engineering Tokyo Institute of Technology Yokohama Japan

In this paper, we propose a self-organized learning model that can generate behaviors for successfully performing various tasks, unlike conventional systems that could perform only a limited task in a limited environment. The model memorizes various relationships between changes in sensory information and a motor command through learning. After the learning, the model can perform various tasks by generating the various behaviors automatically. We investigated the performance of the model by applying it to a mobile robot simulation. In the simulation, we applied two different tasks to the model, which performed the same learning. The results indicate that suitable behaviors for all the tasks emerged spontaneously.

关键词： Mobile robots Intelligent robots Control systems Robot sensing systems robotics and automation Intelligent sensors Robot control Learning Intelligent actuators Indium tin oxide

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Human Stability Analysis on Time-Delayed Teleoperation Tasks

Human Stability Analysis on Time-Delayed Teleoperation Tasks

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2007 ieee international symposium on Industrial Electronics (ISIE 2007), vol.6

作者： Jorge A. Mendez-Iglesias Francisco J. Ruiz-Sanchez Automation and Control Praxair Mexico Tultitlan Mexico Robotics and Advanced Manufacturing Division CINVESTAV Saltillo Coahuila Mexico

In this paper, we present our first experimental results on the real-time stability analysis of the human operator performing on time-delayed teleoperation system. Due to the fact that human operator behavior is non linear, time varying and the complexity of the techniques to analyze in real-time this kind of systems, we used a discrete linearization of the model and determined for each sample if the system is stable or unstable. In this way, the stability analysis is based on the real-time identified model of the human operator and the Routh-Hurwitz stability criteria is applied to the model. The reason to use Routh-Hurwits instead of Jury criteria is due to the required amount of computational steps. Having determined if the system is stable or unstable in the sampling time, we are able to evaluate the human operator performance. In this way, the proportion of stable and unstable points obtained in the execution of a certain task, provides a performance indicator. The main result of this analysis indicates that human performance, measured as the proportion of stable/unstable points, is increased with repetitive executions of the task and independent of the magnitude of the delay in the visual feedback information.

关键词： Humans Stability analysis Real time systems Time varying systems Stability criteria Sampling methods Information analysis Performance analysis Anthropometry Delay

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