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International Conference on Information and Communication Technology Convergence (ICTC)

作者： Jiseung Youn Sunghyun Cho Dept. of Computer Science & Engineering Major in Bio-Artificial Intelligence Hanyang University Seoul Republic of Korea Dept. of Computer Science & Engineering Hanyang University ERICA Ansan Republic of Korea

ISBN: (数字)9798350364637

ISBN: (纸本)9798350364644

As Internet of Things (IoT) technologies are inves-tigated to support key 5G services, significant research efforts are being made to enable machine-type communication (MTC). Cell-free IoT (CF-IoT) systems, characterized by favorable prop-agation and macro diversity, are being studied in conjunction with grant-free random access (GFRA) as a potential enabler to achieve massive connectivity for MTC. However, existing GFRA schemes in CF-IoT transmit preambles at a fixed power level resulting in severe preamble collisions at nearby access points (APs). In this paper, we analyzes the analysis of preamble collisions in massive connectivity scenario and proposes a sim-ple power control scheme to address preamble collision. The proposed scheme mitigates preamble collisions by transmitting preambles only to the nearest AP in high-density access scenes. Simulation results show that the proposed method improves the random access success rate compare to existing GFRA method by 10%.

关键词： 5G mobile communication Simulation Power control Information and communication technology Internet of Things Convergence

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Task-Based Design and Policy Co-Optimization for Tendon-driven Underactuated Kinematic Chains

arXiv

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arXiv 2024年

作者： Islam, Sharfin He, Zhanpeng Ciocarlie, Matei Dept. of Mechanical Engineering Dept. of Computer Science Columbia University New York United States

Underactuated manipulators reduce the number of bulky motors, thereby enabling compact and mechanically robust designs. However, fewer actuators than joints means that the manipulator can only access a specific manifold within the joint space, which is particular to a given hardware configuration and can be low-dimensional and/or discontinuous. Determining an appropriate set of hardware parameters for this class of mechanisms, therefore, is difficult - even for traditional taskbased co-optimization methods. In this paper, our goal is to implement a task-based design and policy co-optimization method for underactuated, tendon-driven manipulators. We first formulate a general model for an underactuated, tendon-driven transmission. We then use this model to co-optimize a three-link, two-actuator kinematic chain using reinforcement learning. We demonstrate that our optimized tendon transmission and control policy can be transferred reliably to physical hardware with real-world reaching experiments. © 2024, CC BY-SA.

关键词： Actuators

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Neural Harvest : Advancements in Crop Yield Forecasting

Neural Harvest : Advancements in Crop Yield Forecasting

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International Conference on Inventive Computation Technologies (ICICT)

作者： Srinivasa Reddy Seelam Charitha Sai Thivari Bhagath Parabattina Narendra Kumar G Sumanth K Dept. of Computer Science and Engineering Lakireddy Bali Reddy College of Engineering Mylavaram India

ISBN: (数字)9798350359299

ISBN: (纸本)9798350359305

Crop yield forecasting involves predicting the amount of crops that a farmer can expect from their field. This prediction is based on several factors, including soil type and environmental conditions. It is a crucial issue for the farming industry, which is the backbone of any nation’s economy. Thanks to advancements in Artificial Intelligence, farmers can benefit from accurate crop yield estimations. This research study presents a Feed Forward Neural Network architecture that enables farmers to get accurate crop yield estimates and finally discusses about the implementation of the procedure and its outcome.

关键词： Computational modeling Neural networks Estimation computer architecture Production Soil Predictive models

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qCrop: An IoT Based Framework to Enhance Crop Productivity in Smart Agriculture

qCrop: An IoT Based Framework to Enhance Crop Productivity i...

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IFIP International Conference on Very Large Scale Integration (VLSI-SoC)

作者： Mahdi Shamsa Laavanya Rachakonda Saraju P. Mohanty Elias Kougianos Dept. of Computer Science and Engineering University of North Texas Denton USA Dept. of Computer Science University of North Carolina Wilmington Wilmington USA Dept. of Electrical Engineering University of North Texas Denton USA

ISBN: (数字)9798331539672

ISBN: (纸本)9798331539689

Food is crucial in our life. Despite food manufacturers' efforts to meet consumers' needs with manufactured food, it cannot attain the identical level of quality and flavor as natural food. Some fruits and vegetables are classified as food that cannot be manufactured. Also, they play an essential role worldwide. This paper focuses on how farmers can protect their farms from an object that might threaten the crops and cause damage to them. It proposes a framework that farmers may utilize to safeguard the crops against any species of birds. Considering the adverse impact of bird attacks on crop production, this system effectively addresses this problem and consistently enhances the quality and quantity of them. It utilizes computer vision technology to establish a secure environment for the crop. This system is called Quality of Crop Device (qCrop), and it works with a You Only Look Once (Yolov8m) model to detect birds with high accuracy to protect farms.

关键词： YOLO Smart agriculture Productivity computer vision Accuracy Computational modeling Crops Very large scale integration Birds

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Advancing Skill-Based Pathways to Good Jobs in Artificial Intelligence at Community Colleges 14

Advancing Skill-Based Pathways to Good Jobs in Artificial In...

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14th IEEE Integrated STEM Education Conference, ISEC 2024

作者： Saaid, Yasmin Rathore, Heena Griffith, Henry Trio Ubms San Antonio College San AntonioTX United States Texas State University Dept. of Computer Science San MarcosTX United States San Antonio College Dept. of Engineering San AntonioTX United States

ISBN: (纸本)9798350352801

This paper provides recommendations for developing skill-based employment pathways in artificial intelligence (AI) at community colleges. These recommendations are motivated by a summary assessment of influencing factors, including current AI workforce composition, employer perceptions of skill-based hiring, and existing implementations of comparable models. Our analysis indicates that future development should be expedited to capture favorable trends in labor market demand and current hiring protocols. Leveraging curriculum developed by industry leaders, and ensuring the integration of skill validation mechanisms, are also suggested as a means of addressing existing barriers to entry for individuals without four-year degrees. © 2024 IEEE.

关键词： artificial intelligence education community colleges skill-based hiring

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Exploring the EmoBone dataset with Bi-Directional LSTM and Attention for Emotion Recognition via Bone Conducted Speech

Exploring the EmoBone Dataset with Bi-Directional LSTM and A...

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International Conference on computer and Information Technology (ICCIT)

作者： Md. Sarwar Hosain Yosuke Sugiura Mousumi Haque M. Shahidur Rahman Tetsuya Shimamura Dept. of Information and Computer Sciences Saitama University Saitama Japan Dept. of Information and Communication Engineering University of Rajshahi Rajshahi Bangladesh Dept. of Computer Science and Engineering Shahjalal University of Science and Technology Sylhet Bangladesh

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

This research investigation explores the use of deep learning for emotion recognition using bone-conducted (BC) speech. It focuses on the EmoBone dataset, which is the first dataset specially created for this purpose. Bone-conducted speech offers several advantages, such as improved speech quality in noisy environments, reduced interference from ambient sounds, and enhanced user privacy, as the sound is directly transmitted to the auditory system through bone conduction. Although deep learning is commonly employed for voice emotion recognition, these methods frequently encounter challenges, including the degradation problem and information loss in the deeper stages of deep neural networks. To resolve these issues, we propose implementing a bi-directional long short-term memory (BiLSTM) network that utilizes BC speech and incorporates attention mechanisms. This network will effectively utilize information and mitigate the adverse impacts caused by degradation. However, the attention mechanism significantly improved the performance of the model, resulting in an accuracy gain of approximately 91.45%. The confusion matrices demonstrate that the attention mechanism enhances recognition accuracy for all emotional categories. This study exhibits the capability of enhancing emotion recognition in BC speech applications by integrating BiLSTM with attention.

关键词： Degradation Deep learning Emotion recognition Attention mechanisms Accuracy Transfer learning Bidirectional long short term memory Speech recognition Speech enhancement Bones

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Towards Real-Time Vietnamese Traffic Sign Recognition on Embedded Systems 15

Towards Real-Time Vietnamese Traffic Sign Recognition on Emb...

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15th International Conference on Information and Communication Technology Convergence, ICTC 2024

作者： Tran, Phuong-Nam Pham, Nhat Truong Phan, Nam Van Hai Phane, Duc Tai Nguyen, Cuong Tuan Dang, Duc Ngoc Minh Kyung Hee University Dept. of Computer Science and Engineering Gyeonggi-do Yongin-si Korea Republic of Sungkyunkwan University Dept. of Integrative Biotechnology Suwon Korea Republic of Fpt University Dept. of Computing Fundamental Ho Chi Minh City Viet Nam Vietnamese-German University Faculty of Engineering Binh Duong Viet Nam

ISBN: (纸本)9798350364637

AI development has brought many significant changes in various aspects of our daily lives in recent years. Integrating AI technology into various applications has revolutionized multiple domains, and one particularly vital area is traffic sign recognition, which significantly enhances driver safety. This paper presents an approach to traffic sign recognition specifically designed for the Jetson Nano 2GB device. By utilizing the YOLOv8 Nano model, the proposed approach achieves a remarkable frame rate of up to 32 frames per second (FPS). To optimize inference speed on Jetson with limited memory, the approach incorporates TensorRT and quantization techniques. In addition, this paper introduces a dataset called the Vietnamese Traffic Sign Detection database 100 (VTSDB100). This dataset is an extension of the VTSDB46 dataset and encompasses a comprehensive collection of 100 different classes of traffic signs. These signs were captured in diverse locations within Ho Chi Minh City, Vietnam, providing a rich and diverse dataset for training and evaluating traffic sign recognition models. Extensive experiments and analyses were conducted using various object detection methods on the VTSDB100 dataset. The findings highlight the potential of deploying the proposed approach on resource-constrained devices and provide valuable insights for further research and development in AI-powered driver safety systems. © 2024 IEEE.

关键词： Traffic signs

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Stop Reinventing the Wheel! Promoting Community Software in Computing Education 22

Stop Reinventing the Wheel! Promoting Community Software in ...

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27th Annual Conference on Innovation and Technology in computer science Education, ITiCSE-WGR 2022

作者： Blanchard, Jeremiah Hott, John R. Berry, Vincent Carroll, Rebecca Edmison, Bob Glassey, Richard Karnalim, Oscar Plancher, Brian Russell, Seán Dept. of Engineering Education University of Florida GainesvilleFL United States Dept. of Computer Science University of Virginia CharlottesvilleVA United States Polytech Engineering School LIRMM-Univ Montpellier CNRS Montpellier France Dept. of Computer Science Full Sail University Winter ParkFL United States Dept. of Computer Science Virginia Tech BlacksburgVA United States School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden School of Information and Physical Sciences University of Newcastle CallaghanNSW Australia Dept. of Computer Science Barnard College Columbia University New York CityNY United States School of Computer Science University College Dublin Dublin Ireland

ISBN: (纸本)9798400700101

Historically, computing instructors and researchers have developed a wide variety of tools to support teaching and educational research, including exam and code testing suites and data collection solutions. However, these tools often find limited adoption beyond their creators. As a result, it is common for many of the same functionalities to be re-implemented by different instructional groups within the Computing Education community. We hypothesise that this is due in part to discoverability, availability, and adaptability challenges. Further, instructors often face institutional barriers to deployment, which can include hesitance of institutions to rely on community developed solutions that often lack a centralised authority and may be community or individually maintained. To this end, our working group explored what solutions are currently available, what instructors needed, and the reasons behind the above-mentioned phenomenon. To do so, we reviewed existing literature and surveyed the community to identify the tools that have been developed by the community;the solutions that are currently available and in use by instructors;what features are needed moving forward for classroom and research use;what support for extensions is needed to support further Computing Education research;and what institutional challenges instructors and researchers are currently facing or have faced in using community software solutions. Finally, the working group identified factors that limited adoption of solutions. This work proposes ways to integrate and improve the availability, discoverability, and dissemination of existing community projects, as well as ways to manage and overcome institutional challenges. © 2022 Owner/Author.

关键词： Open source software

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PixMamba: Leveraging State Space Models in a Dual-Level Architecture for Underwater Image Enhancement

arXiv

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arXiv 2024年

作者： Lin, Wei-Tung Lin, Yong-Xiang Chen, Jyun-Wei Hua, Kai-Lung Dept. of Computer Science and Information Engineering National Taiwan University of Science and Technology Taiwan Microsoft Taiwan

Underwater Image Enhancement (UIE) is critical for marine research and exploration but hindered by complex color distortions and severe blurring. Recent deep learning-based methods have achieved remarkable results, yet these methods struggle with high computational costs and insufficient global modeling, resulting in locally under- or over-adjusted regions. We present PixMamba, a novel architecture, designed to overcome these challenges by leveraging State Space Models (SSMs) for efficient global dependency modeling. Unlike convolutional neural networks (CNNs) with limited receptive fields and transformer networks with high computational costs, PixMamba efficiently captures global contextual information while maintaining computational efficiency. Our dual-level strategy features the patch-level Efficient Mamba Net (EMNet) for reconstructing enhanced image feature and the pixel-level PixMamba Net (PixNet) to ensure fine-grained feature capturing and global consistency of enhanced image that were previously difficult to obtain. PixMamba achieves state-of-the-art performance across various underwater image datasets and delivers visually superior results. Code is available at: https://***/weitunglin/pixmamba. © 2024, CC BY.

关键词： Computational efficiency

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Dynamic Road Management in the Era of CAV

arXiv

引用

arXiv 2024年

作者： Younis, Mohamed Lee, Sookyoung Lalouani, Wassila Tan, Dayuan Gupte, Sanket Dept. of Computer Science and Electrical Engineering University of Maryland Baltimore County BaltimoreMD United States Dept. of Computer Science and Engineering Ewha Womans University Seoul Korea Republic of

Traffic management and on-road safety have been a concern for the transportation authorities and the engineering communities for many years. Most of the implemented technologies for intelligent highways focus on safety measures and increased driver awareness, and expect a centralized management for the vehicular traffic flow. Leveraging recent advances in wireless communication, researchers have proposed solutions based on vehicle-to-vehicle (V2V) and vehicle-to-Infrastructure (V2I) communication in order to detect traffic jams and better disseminate data from on-road and on-vehicle sensors. Moreover, the development of connected autonomous vehicles (CAV) have motivated a paradigm shift in how traffic will be managed. Overall, these major technological advances have motivated the notion of dynamic traffic management (DTM), where smart road reconfiguration capabilities, e.g., dynamic lane reversal, adaptive traffic light timing, etc. will be exploited in real-time to improve traffic flow and adapt to unexpected incidents. This chapter discusses what the challenges in realizing DTM are and covers how CAV has revolutionized traffic management. Moreover, we highlight the issues for handling human-driven vehicles while roads are transitioning to CAV only traffic. Particularly, we articulate a new vision for inter-vehicle communication and assessment of road conditions, and promote a novel system for traffic management. Vehicle to on-road sensors as well as inter-vehicle connectivity will be enabled through the use of hand-held devices such as smartphones. This not only enables real-time data sharing but also expedites the adoption of DTM without awaiting the dominant presence of autonomous vehicle on the road. The proposed traffic management system incorporates computation and communication capabilities in traffic lights and road-side units, and accounts for the human-factor in controlling the traffic flow. The goal is to allow autonomous and driver-centric routing decis

关键词： Traffic congestion

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