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arXiv 2024年

作者： Summe, Thomas M. Joshi, Siddharth Dept. of Computer Science and Engineering University of Notre Dame United States

Recent advances to algorithms for training spiking neural networks (SNNs) often leverage their unique dynamics. While backpropagation through time (BPTT) with surrogate gradients dominate the field, a rich landscape of alternatives can situate algorithms across various points in the performance, bio-plausibility, and complexity landscape. Evaluating and comparing algorithms is currently a cumbersome and error-prone process, requiring them to be repeatedly re-implemented. We introduce Slax, a JAX-based library designed to accelerate SNN algorithm design, compatible with the broader JAX and Flax ecosystem. Slax provides optimized implementations of diverse training algorithms, allowing direct performance comparison. Its toolkit includes methods to visualize and debug algorithms through loss landscapes, gradient similarities, and other metrics of model behavior during training. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine learning

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Borneo Smart Forest Information system for Management of Dipterocarp Plants in Kalimantan Rainforest 8

Borneo Smart Forest Information System for Management of Dip...

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8th IEEE Information Technology International Seminar, ITIS 2022

作者： Wati, Masna Puspitasari, Novianti Hairah, Ummul Widians, Joan Angelina Septiarini, Anindita Tjikoa, Ade Fiqri Universitas Mulawarman Faculty of Engineering Dept. of Informatics Samarinda Indonesia Universitas Gadjah Mada Dept. of Computer Science and Electronics Yogyakarta Indonesia

ISBN: (纸本)9798350398199

Kalimantan's tropical rainforest is home to indigenous plants of the Dipterocarpaceae family (tribe), which is renowned for having the most endemic species. Dipterocarp is a family of pantropical plants, many of which are used in the wood industry. This study aims to provide information on the potential of Dipterocarp in Kalimantan forests. Education and conservation efforts for Dipterocarp trees, a prominent component of the forest ecosystem, are crucial in light of the significant harm done to Kalimantan's rainforests. The authors develop the Borneo Smart Forest information system. This system digitally organizes data on the diversity of Dipterocarp in the Kalimantan rainforest based on Web and QR-Code technology. Borneo Smart Forest (BSF) system for monitoring plant inventory and plant markers can be read by smartphones using QR codes. It gives visitors a comprehensive overview of the plants. A QR code is a printed, two-dimensional barcode with limited space for data storage. This technology allows the plant to show earlier administrator-made additions to the inventory information system. The QR code will direct the user to the BSF website, which has data about the plants. The development of this system is one of the efforts to support the achievement of Dipterocarp conservation. © 2022 IEEE.

关键词： Information systems

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ML-based Digital Twin for anomaly detection: a case-study on Turtle soccer robots 49

ML-based Digital Twin for anomaly detection: a case-study on...

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49th Euromicro Conference on Software engineering and Advanced Applications, SEAA 2023

作者： Liu, Xingyu Van Den Brand, Mark Muctadir, Hossain Muhammad Van De Molengraft, Rene Eindhoven University of Technology Set Cluster Dept. of Mathematics & Computer Science Eindhoven Netherlands Eindhoven University of Technology Department of Mechanical Engineering Eindhoven Netherlands

ISBN: (纸本)9798350342352

In recent years, machine learning (ML) based digital twins (DTs) have seen widespread application in the anomaly detection domain. A search-based literature survey revealed that the majority of the case studies focus on large-scale systems (i.e., nuclear power plant, aerospace, and power grid) producing extensive data. Our work aims to investigate the performance of this technology in smaller-scale systems that generate less data. In this case study, we developed a ML-based DT of the mobility system, the omni wheels, of the Turtle soccer robots. The DT is capable of analyzing historical data collected from the physical robots and differentiating between damaged and undamaged wheels. Our experiments suggest that ML-based DT of small-scale systems is indeed capable of achieving relatively accurate results for anomaly detection use-cases. © 2023 IEEE.

关键词： Sports

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Autonomous Drone Landing on a Moving Naval Base using Vision-Based Robot Control

Autonomous Drone Landing on a Moving Naval Base using Vision...

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2023 International Conference on Electrical, computer, Communications and Mechatronics engineering, ICECCME 2023

作者： Subamanian, Senthil Pon Vignesh Subramanian, Suvetha Meenaa Muthiah, Padmapriya Shajahan, Jalaluddin Mohd Ansari Saveetha University Simats School of Engineering Dept. of Elec. and Comm. Engg. Chennai India City University of New York City College of New York Dept. of Computer Science New York United States Monroe College King Graduate School Dept. of Computer Science New York United States City University of New York City College of New York Dept. of Electrical Engineering New York United States

ISBN: (纸本)9798350322972

This paper proposes a novel technique of an Autonomous Drone landing on top of a moving Naval Vessel Base using Vision-Based Robot Control. To achieve an effective landing a camera is fixed under the drone which captures the images and detects the movement of the landing base. The landing base on top of the vessel is specially designed with a nested tag which is scanned by the fixed camera on the drone from a high altitude. This nested tag is a fiducial marker that is used by the imaging system of the drone as a point of reference in the Naval Vessel Base for precise landing. The controller is designed in the image coordinate frame captured by the imaging system on the drone with the help of the Jacobian Matrix. Whether the vessel remains stationary or moving, it always trembles, especially, when a vessel experiences bad weather at sea, it will be quite challenging for the drone to land on the trembling Naval Vessel Base. These unexpected movements cause the landing tag to disorient from the horizontal view of the captured frames, which makes it difficult to obtain the expected mid-point of the tag. To overcome this complication the translational and rotational motions of the drone are decoupled from the camera using the Virtual Camera Method where the mid-point of the tag is set as the feature point which helps to obtain the expected position during the horizontal plane disorientation of the tag image captured by the camera from the drone. Additionally, Kalman Filter is employed in the imaging system to reduce the observation noise so that precise estimation of the feature point can be achieved. © 2023 IEEE.

关键词： Cameras

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Analyzing Yelp Open-Source Data-Set in Azure Data-bricks

Analyzing Yelp Open-Source Data-Set in Azure Data-bricks

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International Conference on Inventive Computation Technologies (ICICT)

作者： Mugunth D Maheshwaran R Maheswari M S. L. Jany Shabu J. Refonaa Dept. of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai Tamilnadu India

ISBN: (数字)9798350359299

ISBN: (纸本)9798350359305

A data-driven application, Yelp has always served and will always serve as one. It is one of the first companies that allows local businesses to receive reviews from their customers. Communities have always developed it in cooperation with each other. According to Yelp, the data-set contains information regarding businesses, reviews, users, and check-ins, and has continuously been updated since 2015. A project titled "Analyzing Yelp Open-Source Data-set in Azure Data Bricks" will analyze this data-set which has been made available as an open source for sentiment analysis and descriptive analysis. This study analyzes local business performance, business distribution, review ratings, and other factors, as well as check-in rates in American business locations over time. This analysis shows that the Yelp is losing reviews, tips, elite users, and check-ins.

关键词： Sentiment analysis Reviews Decision making Data visualization Machine learning Companies Data science

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Multi-Objective Task Scheduling in Cloud Environments using Dynamic Velocity-Adaptive Particle Swarm Optimization

Multi-Objective Task Scheduling in Cloud Environments using ...

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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： Ravi Kumar Manu Vardhan Dept. of computer science and engineering Natinal Institute of Technology Raipur India

ISBN: (数字)9798350373783

ISBN: (纸本)9798350373790

In the realm of cloud computing, tackling the multi-objective task scheduling problem means finding the best way to assign tasks to virtual machines, taking into account conflicting goals like minimizing execution time, load balance and cost. This intricate task demands smart algorithms to strike a balance between these competing objectives, ensuring the most efficient use of resources and meeting user expectations in the cloud. Different optimization techniques, such as Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO), have been explored to handle this challenge and optimize task scheduling in the cloud environment. In classical PSO, particles can move at varying speeds in each iteration. In this study, we apply Velocity Stagnation Particle Swarm Optimization (VSPSO) variants where velocity stagnation applied during particles movement. This approach provides particles with a third movement option, enabling them to maintain the same velocity as in the previous iteration. VSPSO has a greater capacity to balance the exploration and exploitation. VSPSO adjusts the initial term of the PSO velocity equation to prevent premature convergence. Furthermore, the population of VSPSO is split into two swarms to ensure *** the inherent capabilities of PSO, VSPSO algorithm dynamically updates particle velocities to attain $5-6 \%$ total execution time 6-$\mathbf{1 0 \%}$ reduced cost and optimal balance load. Through meticulous comparisons with other algorithms, including Genetic Algorithm and Ant Colony Optimization, this study scrutinizes the effectiveness of our enhanced PSO approach. The overarching goal is to push the boundaries of multi-objective task scheduling in cloud computing by attaining minimal execution time, reduce cost and optimal balance load on each virtual machines.

关键词： Cloud computing Ant colony optimization Technological innovation Job shop scheduling Costs Processor scheduling Heuristic algorithms

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A comparative study of different scheduling approaches for splittable latency sensitive tasks in Fog-Cloud environment

A comparative study of different scheduling approaches for s...

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Ambient Intelligence in Health Care (ICAIHC), International Conference on

作者： Kone Sai Sandeep Challa Aditya Koundinya Adapa Venkata Prabhas Chinmaya Kumar Swain Sambit Kumar Mishra Dept. of Computer Science Engineering SRM University

IoT has revolutionized the way we live and the work we do by connecting different devices through the Internet. In the present scenario, the number of IoT devices are increasing rapidly due to the increase in technology and the increase in the comforts of life. Nowadays we can see that many of them are using IoT devices regularly, it's estimated that by the end of 2030, there will be 30 billion users who will be using IoT applications. These devices send data to the cloud for processing. Due to the distance of the cloud from the IoT devices, the application requests get delayed service responses. So to handle the latency sensitive applications we require the micro cloud service like fog servers deployed near to the data generation points. The fog layer lies between the IoT devices and Cloud which acts as an intermediate layer. This helps in reducing latency of the tasks and provide better performance. As the number of IoT applications keeps on increasing, the resources available with the fog nodes may not handle the upcoming demands. So to overcome these demands, we are using splittable methods to allocate the tasks to Fog/ Cloud nodes more compactly. If a task can be splitted before the deadline into different modules, then we split the given task and allocate those tasks to different fog nodes/ servers and then collecting back the data from the fog nodes/ servers and merging them into a single unit. With the help of this method, we can increase the performance of the system.

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Human Interaction in Virtual and Mixed Reality Through Hand Tracking

Human Interaction in Virtual and Mixed Reality Through Hand ...

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IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT)

作者： S. Geetha Aditya G Chetan Reddy M Nischith G Dept. of Computer Science and Engineering BNM Institute of Technology Bengaluru India

ISBN: (数字)9798350385922

ISBN: (纸本)9798350385939

This paper explores the potential and possibilities of hand tracking in virtual reality (VR) and mixed reality (MR), focusing on its role in human interaction dynamics. An application was designed in Unity leveraging the XR Interaction toolkit, within which various items across three important domains: daily life, education, and recreation, were crafted to demonstrate the versatility of hand tracking along with hand gesture-based shortcuts for interaction. Integration of elements in MR ensures that users can seamlessly enjoy virtual experiences while remaining connected to their physical surroundings. Precise hand tracking enables effortless interaction with the virtual space, enhancing presence and control with a user-friendly interface. Additionally, the paper explores the effectiveness of integrating hand tracking into education and training scenarios. A computer assembly simulation was created to demonstrate this, featuring component inspection and zoom capabilities along with a large language model (LLM) integrated with hand gestures to provide for interaction capabilities.

关键词： Training Visualization Accuracy Three-dimensional displays Tracking Mixed reality Virtual reality

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BCHJam: a Brain-computer Music Interface for Live Music Performance in Shared Mixed Reality Environments

BCHJam: a Brain-Computer Music Interface for Live Music Perf...

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Internet of Sounds, International Symposium on the

作者： Michele Romani Gregorio Andrea Giudici Domenico Stefani Devis Zanoni Alberto Boem Luca Turchet Dept. of Information Engineering and Computer Science University of Trento Trento Italy

ISBN: (数字)9798350366525

ISBN: (纸本)9798350366532

To date, the integration of brain-computer interfaces and mixed reality headsets in Internet of Musical Things (IoMusT) performance ecosystems has received remarkably little attention from the research community. To bridge this gap, in this paper, we present BCHJam: an IoMusT-based performance ecosystem composed of performers, audience members, brain- computer interfaces, smart musical instruments, and mixed reality headsets. In BCHJam, one or more musicians are fitted with a brain-computer music interface (BCMI) giving them the possibility to actively or passively control the processing of their instrument's audio. Moreover, the BCMI's signal controls mixed reality visual effects displayed in XR headsets worn by audience members. All the components of BCHJam communicate through a Wi-Fi network via Open Sound Control messages. We refined the system through a series of test performance sessions, resulting in the creation of a signal quality filter that improved the musician's experience, along with a tuning of control parameters. The developed ecosystem was validated by realizing a musical performance. We provide a critical reflection on the achieved results and discuss the lessons learned while developing this first of its kind IoMusT performance ecosystem.

关键词： Headphones Instruments Ecosystems Music Mixed reality Visual effects Brain-computer interfaces Internet Wireless fidelity Tuning

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Parallel Neural Computing for Scene Understanding from LiDAR Perception in Autonomous Racing

Parallel Neural Computing for Scene Understanding from LiDAR...

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Intelligent systems and Embedded Design (ISED), International Conference on

作者： Suwesh Prasad Sah Suchismita Chinara Dept. Computer Science and Engineering National Institute of Technology Rourkela India

ISBN: (数字)9798331522612

ISBN: (纸本)9798331522629

Autonomous driving in high-speed racing, as opposed to urban environments, presents significant challenges in scene understanding due to rapid changes in the track environment. Traditional sequential network approaches may struggle to meet the real-time knowledge and decision-making demands of an autonomous agent covering large displacements in a short time. This paper proposes a novel baseline architecture for developing sophisticated models capable of true hardware-enabled parallelism, achieving neural processing speeds that mirror the agent's high velocity. The proposed model (Parallel Perception Network (PPN)) consists of two independent neural networks, segmentation and reconstruction networks, running parallelly on separate accelerated hardware. The model takes raw 3D point cloud data from the LiDAR sensor as input and converts it into a 2D Bird's Eye View Map on both devices. Each network independently extracts its input features along space and time dimensions and produces outputs parallelly. The proposed method's model is trained on a system with two NVIDIA T4 GPUs, using a combination of loss functions, including edge preservation, and demonstrates a 2x speedup in model inference time compared to a sequential configuration. Implementation is available at: github/ParallelPerceptionNetwork. Learned parameters of the trained networks are provided at: huggingface/ParallelPercentionNetwork.

关键词： Point cloud compression Solid modeling Laser radar Three-dimensional displays Computational modeling Neural networks Urban areas computer architecture Parallel processing Feature extraction

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