Reinforcement mastering is an area of synthetic intelligence that enables machines to adopt actions that optimize their environment or maximize a pre-defined reward. This paper investigates the potential makes use of ...
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This paper mainly considers the wireless communication scenario where equipment in different working conditions switches its channel state in the actual industrial field. To address the problem of uncertain communicat...
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this paper outlines the design of a low-process control system for embedded systems applications, utilizing a RISC-V processor. The design incorporates functional units of a single-cycle Datapath and a control unit de...
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ISBN:
(纸本)9798350370058;9798350370164
this paper outlines the design of a low-process control system for embedded systems applications, utilizing a RISC-V processor. The design incorporates functional units of a single-cycle Datapath and a control unit developed in Verilog HDL. Focus on testing RISC-V instructions to achieve low-process control in embedded systems applications by employing proper instruction mnemonics. The approach enhances the low-process control and efficiency in embedded systems applications and system design through single-cycle control. The designed low-process control proves valuable by computing the processor time (T) needed in instruction execution. Low-process control 1 exhibits an 88.21% improvement over an AVR processor control, and low-process control 2 demonstrates a 49.43% improvement, thereby ensuring efficient process control. The findings contribute to the understanding of RISC-V architecture implementation in an FPGA platform, featuring LED visualization to depict equivalent machine code generated in a single-cycle process. This study advances the theoretical framework of system optimization and control with the use of FPGA platforms.
The complexity of absolute time process control in industries has increased. Despite this, these processes generally involve significant quantities of data that must be analyzed and controlled quickly to maintain the ...
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Intermediate representations (IRs) are fundamental to classical and quantum computing, bridging high-level quantum programming languages and the hardware-specific instructions required for execution. This paper review...
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Intermediate representations (IRs) are fundamental to classical and quantum computing, bridging high-level quantum programming languages and the hardware-specific instructions required for execution. This paper reviews the development of quantum IRs, focusing on their evolution and the need for abstraction layers that facilitate portability and optimization. Monolithic quantum IRs, such as QIR (Lubinski et al. in Front Phys 10:940293, 2022. https://***/10.3389/fphy.2022.940293), QSSA (Peduri et al. in Proceedings of the 31st ACM SIGPLAN internationalconference on compiler construction. CC 2022. Association for computing Machinery, New York, 2022), or Q-MLIR (McCaskey and Nguyen in Proceedings-2021 IEEE internationalconference on Quantum computing and Engineering, QCE, 2021), their effectiveness in handling abstractions, and their hybrid support between quantum-classical operations are evaluated. However, a key limitation is their inability to address qubit locality, an essential feature for distributed quantum computing (DQC). To overcome this, InQuIR (Nishio and Wakizaka in InQuIR: Intermediate Representation for Interconnected Quantum Computers, 2023. https://***/abs/2302.00267) was introduced as an IR specifically designed for distributed systems, providing explicit control over qubit locality and inter-node communication. While effective in managing qubit distribution, InQuIR's dependence on manual manipulation of communication protocols increases complexity for developers. NetQIR (V & aacute;zquez-P & eacute;rez et al. in NetQIR: An Extension of QIR for Distributed Quantum computing, 2024. https://***/abs/2408.03712), an extension of QIR for DQC, emerges as a solution to achieve the abstraction of quantum communications protocols. This review emphasizes the need for further advancements in IRs for distributed quantum systems, which will play a crucial role in the scalability and usability of future quantum networks.
Among all forms of communication, writing is the most popular and universally recognised. Writing proficiency is thus crucial for reaching professional and corporate objectives. Unfortunately, it could be difficult in...
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The financial services sector in India has leveraged the power of Industry 4.0 extensively on account of digital innovation and automation of financial processes. The rising competition in the financial services secto...
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In recent years, the Intent-driven Network (IBN) has been proposed to further enhance the intelligence of communication systems. In IBN, users can express their resource expectations through intents while the IBN perf...
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ISBN:
(纸本)9798350361261;9798350361278
In recent years, the Intent-driven Network (IBN) has been proposed to further enhance the intelligence of communication systems. In IBN, users can express their resource expectations through intents while the IBN performs resource scheduling to fulfill these intents. Many challenges of complex networks can be tackled by IBN such as the mobile edge computing (MEC) system. In a MEC system, limited computing resources are competed by the users which may cause the intent conflict. To solve this, in this paper, we introduce the IBN concept to the MEC system, where an intent conflict detection module is proposed. The proposed module is based on the Open Network automation Platform (ONAP) architecture. Moreover, by formulating the computing resource conflict problem as a Markov decision process (MDP) model, we employ an improved deep Qnetwork (DQN) algorithm to improve the efficiency of resource utilization. Simulation results demonstrate the completion time of intents is remarkably reduced in the proposed intent conflict resolution scheme.
Since most communications in vehicular networks rely on wireless transmissions, information broadcast by vehicles is highly susceptible to interception and eavesdropping by third parties, making it vulnerable to vario...
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This paper investigates an optimized approach to coordinated resource allocation and power control for wireless communication networks. To address limitations in existing methods, such as scalability and fairness, a n...
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ISBN:
(纸本)9798350387919;9798350387902
This paper investigates an optimized approach to coordinated resource allocation and power control for wireless communication networks. To address limitations in existing methods, such as scalability and fairness, a novel algorithm is implemented that leverages federated learning regression-based decision trees to achieve efficient resource allocation and improved network performance. The system model considers a dense communication network with different user equipment types and incorporates constraints like power limitations and quality of service requirements. Simulation results demonstrate that the implemented approach significantly outperforms existing techniques in terms of throughput, energy efficiency, and latency.
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