Over the past few years, there has been a noticeable transition from centralized Industrial Control systems (ICS) to distributedsystems. However, the challenges of distributedsystems (e.g., communication delays and ...
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The data-driven industry has benefited greatly from the edge computing platform. The general architecture places data storage and computation close to the source of the data. Recent industrial applications and machine...
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To keep up with today's dense metropolitan areas and their accompanying traffic problems, a growing number of towns are looking for more advanced and swift urban taxi drones. The safety parameters that must be tak...
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ISBN:
(纸本)9798350300246
To keep up with today's dense metropolitan areas and their accompanying traffic problems, a growing number of towns are looking for more advanced and swift urban taxi drones. The safety parameters that must be taken into consideration may be the most important element in the widespread use of such technology. Most recent aviation mishaps have happened during the landing phase, making this a particularly important safety consideration for Vertical and/or Short Take-Off and Landing (V/STOL) drones. In this study, we focused on improving the fault tolerance of the processor architectures used by the predecessors of Autonomous Landing Guidance Assistance systems (ALGAS), which in turn improves their decision-making capabilities. Furthermore, this is achieved by proposing a fault-tolerant processing architecture that depends on the Gamma Distribution Sliding Window Unit (GDSWU). This proposed GDSWU has been designed completely using VHDL, and the targeted FPFA was the Intel Cyclone V 5CGXFC9D6F27C7 chip. The GDSWU could operate at a maximum frequency of 369.96 MHz, as calculated by the synthesis results of the INTEL Quartus Prime program. The suggested GDSWU core only requires 20.36 mW for dynamic core and I/O power consumption.
Rapid advances in high-throughput sequencers have made it possible to obtain large amounts of whole genome data quickly and inexpensively. As the amount of data increases, the increase in computation time has become a...
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Despite rapid developments in quantum computing, current systems remain limited in practical applications due to their constrained qubit counts and quality. Technologies such as superconducting, trapped ions, and neut...
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ISBN:
(纸本)9798331541378
Despite rapid developments in quantum computing, current systems remain limited in practical applications due to their constrained qubit counts and quality. Technologies such as superconducting, trapped ions, and neutral atom quantum computing are progressing towards fault tolerance. However, they face challenges in scalability and control. Recent efforts have concentrated on multi-node quantum systems that connect smaller quantum devices to execute larger circuits. Future demonstrations aim to utilize quantum channels for system coupling, but current methods often resort to classical communication with circuit cutting techniques. This involves dividing large circuits into smaller subcircuits and reconstructing them after execution. Existing cutting methods face challenges such as lengthy search times with increasing numbers of qubits and gates. Moreover, they often struggle to efficiently use resources across various worker configurations in a multi-node system. To address these challenges, we propose FitCut, a novel approach that transforms quantum circuits into weighted graphs. FitCut employs a community-based, bottom-up approach to cut circuits based on resource constraints such as qubit counts on each worker. Additionally, it includes a scheduling algorithm that optimizes resource utilization across workers. Implemented with Qiskit and evaluated extensively, FitCut significantly outperforms existing tools such as Qiskit Circuit Knitting Toolbox, reducing time costs by factors ranging from 3 to 2000 and improving resource utilization rates by up to 388% on the worker side, leading to a system-wide improvement of 286% in accumulated circuit depth.
In this paper, we introduce PIMAP, an IoT-based system for continuous, real-time patient monitoring that operates in a fully autonomous fashion, i.e. without the need for human intervention. To our knowledge, PIMAP is...
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ISBN:
(纸本)9781665439299
In this paper, we introduce PIMAP, an IoT-based system for continuous, real-time patient monitoring that operates in a fully autonomous fashion, i.e. without the need for human intervention. To our knowledge, PIMAP is the first open system that integrates the basic patient monitoring workflow for continuous and autonomous operation and includes sensed data collection, storage, analysis, and real-time visualization. PIMAP's open design allows it to integrate a variety of sensors (custom and off-the-shelf), analytics, and visualization. Other novel features of PIMAP include its deployment flexibility, i.e., its ability to be deployed in different configurations depending on the specific application needs, setting, and resources, as well as PIMAP's self-profiling and self-tuning capabilities. While PIMAP can be applied to various patient monitoring applications and settings, in this paper we focus on the unsolved problem of preventing pressure injuries.
Many real-world applications can be formulated as multi-agent cooperation problems, such as network packet routing and coordination of autonomous vehicles. The emergence of deep reinforcement learning (DRL) provides a...
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ISBN:
(数字)9781665471770
ISBN:
(纸本)9781665471770
Many real-world applications can be formulated as multi-agent cooperation problems, such as network packet routing and coordination of autonomous vehicles. The emergence of deep reinforcement learning (DRL) provides a promising approach for multi-agent cooperation through the interaction of the agents and environments. However, traditional DRL solutions suffer from the high dimensions of multiple agents with continuous action space during policy search. Besides, the dynamicity of agents' policies makes the training non-stationary. To tackle the issues, we propose a hierarchical reinforcement learning approach with high-level decision-making and low-level individual control for efficient policy search. In particular, the cooperation of multiple agents can be learned in high-level discrete action space efficiently. At the same time, the low-level individual control can be reduced to single-agent reinforcement learning. In addition to hierarchical reinforcement learning, we propose an opponent modeling network to model other agents' policies during the learning process. In contrast to end-to-end DRL approaches, our approach reduces the learning complexity by decomposing the overall task into sub-tasks in a hierarchical way. To evaluate the efficiency of our approach, we conduct a real-world case study in the cooperative lane change scenario. Both simulation and real-world experiments show the superiority of our approach in the collision rate and convergence speed.
Context-awareness is becoming more relevant for smarter modern-day applications. With billions of IoT devices able to monitor a plethora of parameters in near real-time, inferring contextual information at scale while...
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This paper presents a comprehensive study on the enhancement of smart home automation and monitoring systems through the application of advanced activity recognition algorithms and sensor deployment optimization. We s...
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Wireless sensor Networks (WSN) contains spatially distributedsensor nodes that collaborate with each other. However, the WSN is susceptible since the wireless medium is unpredictable. Several conventional approaches ...
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