OPAC - Pencarian Artikel Jurnal & Majalah Library USD
Menampilkan semua artikel (Halaman 181 dari 34169, Total: 341690 data)
PEMBANGUNAN PEDESAAN FOKUS PEMBANGUNAN NASIONAL
klik untuk membaca file PDF
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Analisa |
| Volume / Edisi | : | X-3, MARET (No. 3) |
| Halaman | : | 169-185 |
| Abstrak | : | - |
IEEE CIS VP-Publications Vision Statement
| Pengarang | : | Keller, Jim |
| Nama Majalah/Jurnal | : | IEEE Computational intelligence |
| Volume / Edisi | : | 12 (No. 1) |
| Halaman | : | 4-5 |
| Abstrak | : | - |
Protecting the Mixed Signal Domain: Secure ADCs for Internet of Things Devices
| Pengarang | : | Maitreyi Ashok; Ruicong Chen; Taehoon Jeong; Anantha P. Chandrakasan; Hae-Seung Lee |
| Nama Majalah/Jurnal | : | Proceedings of the IEEE |
| Volume / Edisi | : | 113 (No. 6) |
| Halaman | : | 586-604 |
| Abstrak | : | Analog-to-digital converters (ADCs) are a standard building block of most Internet of Things (IoT) devices, used to convert between analog signals of the physical world and digital values for efficient storage and computation. While decades of research have explored methods to optimize the traditional power, performance, and area parameters of these circuits, a new requirement has emerged in the last five years for ADCs—security. Due to the deployment of these devices at the edge outside of a trusted computing base, there is a potential for various eavesdropping and tampering attacks. This can have a severe impact on the privacy and integrity of sensor data, which cannot be corrected for through the secure design of other blocks that follow the front end. In this article, we explore the recent work in ADC security and analyze what has been accomplished as well as what remains to be done for the successful deployment of secure ADCs in commercial systems. |
Deep Reinforcement Learning for Distribution System Operations: A Tutorial and Survey
| Pengarang | : | Daniel Glover; Gayathri Krishnamoorthy; Hongda Ren; Anamika Dubey; Assefaw Gebremedhin |
| Nama Majalah/Jurnal | : | Proceedings of the IEEE |
| Volume / Edisi | : | 113 (No. 6) |
| Halaman | : | 557-585 |
| Abstrak | : | The rapid evolution of modern electric power distribution systems into complex networks of interconnected active devices, distributed generation (DG), and storage poses increasing difficulties for system operators. The large-scale integration of distributed energy resources (DERs) and the rapid exchange of measurement data via communication networks present major opportunities for advancing grid operations but also introduce greater uncertainty, higher data dimensionality, more complex network and device models, and challenging control and optimization problems. Deep reinforcement learning (DRL) algorithms are promising in addressing these challenges. However, they have not been effectively adapted for power systems applications, requiring extensive customization for implementation and evaluation. This has resulted in reproducibility challenges and a steep learning curve for researchers new to applying DRL algorithms to the power systems domain. To bridge these gaps, this tutorial aims to serve as a valuable resource for researchers interested in exploring learning-based algorithms to operate active power distribution networks. Specifically, this work presents a generalized process for translating sequential decision-making problems in power distribution systems into Markov decision process (MDP) formulations, illustrated through concrete grid service examples. Additionally, we introduce a simple environment design strategy to develop and evaluate example DRL algorithms for distribution system applications, complete with an included code repository to guide users through environment construction. |
Deep Learning Empowered Super Resolution: A Comprehensive Survey and Future Prospects
| Pengarang | : | Le Zhang; Ao Li; Qibin Hou; Ce Zhu; Yonina C. Eldar |
| Nama Majalah/Jurnal | : | Proceedings of the IEEE |
| Volume / Edisi | : | 113 (No. 6) |
| Halaman | : | 516-556 |
| Abstrak | : | Super resolution (SR) has garnered significant attention within the computer vision community, driven by advances in deep learning (DL) techniques and the growing demand for high-quality visual applications. With the expansion of this field, numerous surveys have emerged. Most existing surveys focus on specific domains, lacking a comprehensive overview of this field. Here, we present an in-depth review of diverse SR methods, encompassing single-image SR (SISR), video SR (VSR), stereo SR (SSR), and light field SR (LFSR). We extensively cover over 150 SISR methods, nearly 70 VSR approaches, and approximately 30 techniques for SSR and LFSR. We analyze methodologies, datasets, evaluation protocols, empirical results, and complexity. In addition, we conducted a taxonomy based on each backbone structure according to the diverse purposes. We also explore valuable yet understudied open issues in the field. We believe that this work will serve as a valuable resource and offer guidance to researchers in this domain. To facilitate access to related work, we created a dedicated repository available at https://github.com/AVC2-UESTC/Holistic-Super-Resolution-Review. |
Brain Computer Interface A Brain in the Loop Communication System
| Pengarang | : | Xiaorong Gao; Yijun Wang; Xiaogang Chen |
| Nama Majalah/Jurnal | : | Proceedings of the IEEE |
| Volume / Edisi | : | 113 (No. 5) |
| Halaman | : | 479-511 |
| Abstrak | : | The brain–computer interface (BCI) establishes a direct communication system between the brain and a computer or other external devices. Since the inception of BCI technology half a century ago, it has advanced rapidly and developed into an active area of frontier research in modern applied science and technology. This article provides a comprehensive survey on BCI with respect to a brain-in-the-loop communication system. In the present work, we first introduce the underlying architecture of the BCI system from the theoretical and methodological perspectives of communication systems. The key technologies are then detailed, including the construction of BCI system, brain-to-computer (B2C) communication, computer-to-brain (C2B) communication, and multiuser BCI systems. Additionally, this article discusses the various applications of BCI and the challenges they face. Finally, this article discusses BCI’s future development, with an emphasis on the convergence of human intelligence (HI) and artificial intelligence (AI), and the interaction of BCI with wireless communication and the metaverse. |
Vehicle to Everything Cooperative Perception for Autonomous Driving
| Pengarang | : | Tao Huang; Jianan Liu; Xi Zhou; Dinh C. Nguyen; Mostafa Rahimi Azghadi; Yuxuan Xia; Qing-Long Han; Sumei Sun |
| Nama Majalah/Jurnal | : | Proceedings of the IEEE |
| Volume / Edisi | : | 113 (No. 5) |
| Halaman | : | 443-477 |
| Abstrak | : | Achieving fully autonomous driving with enhanced safety and efficiency relies on vehicle-to-everything (V2X) cooperative perception (CP), which enables vehicles to share perception data, thereby enhancing situational awareness and overcoming the limitations of the sensing ability of individual vehicles. V2X CP plays a crucial role in extending the perception range, increasing detection accuracy, and supporting more robust decision-making and control in complex environments. This article provides a comprehensive survey of recent developments in V2X CP, introducing mathematical models that characterize the perception process under different collaboration strategies. Key techniques for enabling reliable perception sharing, such as agent selection, data alignment, and feature fusion, are examined in detail. In addition, major challenges are discussed, including differences in agents and models, uncertainty in perception outputs, and the impact of communication constraints such as transmission delay and data loss. This article concludes by outlining promising research directions, including privacy-preserving artificial intelligence methods, collaborative intelligence, and integrated sensing frameworks to support future advancements in V2X CP. |
PERLOMBAAN SENJATA DI ASIA TIMUR
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Analisa |
| Volume / Edisi | : | X-2, PEBRUARI (No. 2) |
| Halaman | : | 151-164 |
| Abstrak | : | - |
Distributed Reservoir Computing with Sparse Readouts
| Pengarang | : | Scardapane, Simone,Panella, Massimo ,Comminiello, Danilo,Hussain, Amir,Uncini, Aurelio |
| Nama Majalah/Jurnal | : | IEEE Computational intelligence |
| Volume / Edisi | : | 11 (No. 4) |
| Halaman | : | 59-68 |
| Abstrak | : | In a network of agents, a widespread problem is the need to estimate a common underlying function starting from locally distributed measurements. Real-world scenarios may not allow the presence of centralized fusion centers, requiring the development of distributed, message-passing implementations of -10 the standard machine learning training algorithms. In this paper, we are concerned with the distributed training of a particular class of recurrent neural networks, namely echo state networks (ESNs). In the centralized case, ESNs have received considerable attention, due to the fact that they can be trained with standard linear regression routines. Based on this observation, in our previous work we have introduced a decentralized algorithm, framed in the distributed optimization field, in order to train an ESN. In this paper, we focus on an additional sparsity property of the output layer of ESNs, allowing for very efficient implementations ofthe resulting networks. In order to evaluate the proposed algorithm, we test it on two well-known prediction benchmarks, namely the Mackey-Glass chaotic time series and the 10th order nonlinear auto regressive moving average (NARMA) system. |
KONFLIK BATAS-BATAS TERITORIAL DI KAWASAN PERAIRAN ASIA TIMUR
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Analisa |
| Volume / Edisi | : | X-2, PEBRUARI (No. 2) |
| Halaman | : | 125-150 |
| Abstrak | : | - |