In order to bring forward expansion in both the accuracy and the processing speed of the detection of Alzheimer's disease, an automated approach for Alzheimer's disease diagnosis with passable EfficientNet-B3 ...
详细信息
Latency is inherent in almost all real-world networked applications. In this paper, we propose a distributed allocation strategy over multi-agent networks with delayed communications. The state of each agent (or node)...
详细信息
As a highly repetitive logic operation unit in programmable logic modules, the operating speed of a carry adder circuit is crucial to the overall performance of the programmable logic circuit. In order to optimize cir...
As a highly repetitive logic operation unit in programmable logic modules, the operating speed of a carry adder circuit is crucial to the overall performance of the programmable logic circuit. In order to optimize circuit propagation delay and area overhead, this paper proposes an improved design scheme for carry adder circuits. Firstly, several basic adder structure types and their performance characteristics are compared to determine the most suitable architecture for carry adder circuits in programmable modules. Then, based on the carry-propagate principle of the carry lookahead adder, a novel single MOS transmission gate structure is proposed to optimize the underlying carry logic circuit. Finally, the carry adder circuit is implemented using a full-custom design flow in TSMC 28nm technology. Experimental results show that compared to existing logic designs, the proposed carry adder circuit in this paper reduces propagation delay and area overhead by 14% and 26.5% respectively.
The rapid advancement of technology has brought transformative changes in the field of the healthcare system in recent years. Since they can enhance the quality and accessibility of healthcare services, particularly f...
The rapid advancement of technology has brought transformative changes in the field of the healthcare system in recent years. Since they can enhance the quality and accessibility of healthcare services, particularly for patients with chronic conditions or in rural regions, microcontroller-based health monitoring systems have been extensively researched and developed in recent years. This paper presents a system capable of multiple measurements and improved ability of health parameters with a cost-efficient solution with reliability and portability. The proposed system is able to measure the parameters at an accuracy of 97.8% in terms of heart rate, blood pressure, body temperature, and oxygen saturation. The system also proposes continuous monitoring of the patient’s health status by collecting data from sensors attached to the patient’s finger and transmitting it wirelessly to a central database via Blynk where the healthcare provider can monitor the results in real-time and provide immediate feedback.
The global market of machine condition monitoring is projected to grow at a rate of 8.3% in next five years. The recent technological advancement in IoTs and AI has driven predictive maintenance to be one of the most ...
详细信息
ISBN:
(数字)9798331507213
ISBN:
(纸本)9798331507220
The global market of machine condition monitoring is projected to grow at a rate of 8.3% in next five years. The recent technological advancement in IoTs and AI has driven predictive maintenance to be one of the most effective approach in this domain. Vibration analysis is the most efficient technique used to carry out predictive maintenance, using advanced data- driven intelligent approaches. The vibration data carries most significant information regarding the health state of machine components especially bearings. The proposed hybrid framework utilizes CNN and transformer utilizing the complex weightsharing capabilities of CNNs, combined with ability of transformer to capture the broader context of spatial relationships in large-scale patterns, making it suitable for datasets of varying sizes. A fault detection accuracy of 98.86% is achieved through experimentation on a run-to-failure real-industrial environment dataset composed of vibration data of large-scale coaxial fans.
We derive various error exponents for communication channels with random states, which are available non-causally at the encoder only. For both the finite-alphabet Gel’fand-Pinsker channel and its Gaussian counterpar...
We derive various error exponents for communication channels with random states, which are available non-causally at the encoder only. For both the finite-alphabet Gel’fand-Pinsker channel and its Gaussian counterpart, the dirty-paper channel, we derive random coding exponents, error exponents of the typical random codes (TRCs), and error exponents of expurgated codes. For the two channel models, we analyze some sub-optimal bin-index decoders, which turn out to be asymptotically optimal, at least for the random coding error exponent. For the dirty-paper channel, we show explicitly via a numerical example, that at rates below capacity, the optimal values of the dirty-paper design parameter α in the random coding sense and in the TRC exponent sense are different from one another, and they are both different from the optimal α that is required for attaining the channel capacity. For the Gel’fand-Pinsker channel, we allow for a variable-rate random binning code construction, and prove that the previously proposed maximum penalized mutual information decoder is asymptotically optimal within a given class of decoders, at least for the random coding error exponent.
Dosing models often use differential equations to model biological dynamics. Neural differential equations in particular can learn to predict the derivative of a process, which permits predictions at irregular points ...
Dosing models often use differential equations to model biological dynamics. Neural differential equations in particular can learn to predict the derivative of a process, which permits predictions at irregular points of time. However, this temporal flexibility often comes with a high sensitivity to noise, whereas medical problems often present high noise and limited data. Moreover, medical dosing models must generalize reliably over individual patients and changing treatment policies. To address these challenges, we introduce the Neural Eigen Stochastic Differential Equation algorithm (NESDE). NESDE provides individualized modeling (using a hypernetwork over patient-level parameters); generalization to new treatment policies (using decoupled control); tunable expressiveness according to the noise level (using piecewise linearity); and fast, continuous, closed-form prediction (using spectral representation). We demonstrate the robustness of NESDE in both synthetic and real medical problems, and use the learned dynamics to publish simulated medical gym environments.
The electricity demand is increasing due to environmental concerns, and economic benefits, leading to a shift towards renewable energy sources. However, the intermittent nature of renewables makes forecasting difficul...
The electricity demand is increasing due to environmental concerns, and economic benefits, leading to a shift towards renewable energy sources. However, the intermittent nature of renewables makes forecasting difficult. Microgrids ran help integrate small-scale renewable energy sources, energy storage systems, and non-renewable sources to address this challenge. Demand varies based on factors like time, location, season, climate, and resource availability. The primary aim of this research is to offer a thorough analysis of the latest advancements in isolated Alternating current (AC) microgrids, focusing on discussing Energy Management System (EMS) strategies. Specifically, the study emphasizes the integration of hybrid Renewable Energy Resources (RERs) with Energy Storage Systems (ESSs). Additionally, the paper presents potential research directions and future trends in this field.
Photovoltaic systems (PV) are exposed to various weather conditions during the day and getting the maximum power of a PV system is one of its major challenges. While the conventional methods take time to track the max...
详细信息
Photovoltaic systems (PV) are exposed to various weather conditions during the day and getting the maximum power of a PV system is one of its major challenges. While the conventional methods take time to track the maximum power accompanying the fluctuation around the maximum power. Consequently, several metaheuristic algorithms have been presented to overcome the fluctuating around the maximum power. On the other hand optimization methods will search into entire the PV curve to extract the maximum power. This leads to unnecessary search points during the uniform shading conditions which decrease the convergence speed towards the maximum power. In overcoming the tracking time issues as well as the capacity to distinguish between uniform and partial shading conditions, a quick and efficient tracking technique connected to the grid system was presented in this paper. The proposed system, has been tested using MATLAB/SIMULINK, which includes a boost converter to track the maximum power with a sampling time of 0.05 seconds, While a voltage source converter (VSC) is used to transfer the power generated by solar panels to the grid. The simulation results demonstrated that the proposed method was successfully implemented, with an average tracking time of 0.412 s for different weather conditions. Moreover, the comparison with the conventional method of perturb and observe (P& O) has been presented to evaluate the proposed method’s efficiency.
With the emergence of cloud computing in recent years, many cloud service providers offer various types of services to customers. There is a significant possibility for optimizing the selection of services to serve us...
With the emergence of cloud computing in recent years, many cloud service providers offer various types of services to customers. There is a significant possibility for optimizing the selection of services to serve users as efficiently as possible. Indeed, cloud system providers (CSPs) offer a variety of services with different payment cost criteria. From the customer's point of view, the most important factor for choosing the appropriate CSP is service quality Service quality which is relevant to such different features as security, cost, reputation, finances, performance, etc. In this article, two multi-feature optimization methods, including the Analytical Hierarchical Process (AHP) and Shannon's entropy method are considered to rank different features of CSPs. The AHP method performs the ranking, by using an initial pairwise decision matrix proposed by some experts. But Shannon's entropy method corresponds to the maximum likelihood problem and is performed without the intervention experts' decisions. Therefore, comparing these methods determines the weaknesses and strengths of the experts' decisions. Next, the results obtained by these methods have been combined with the TOPSIS and COPRAS methods to analyze the data offered by users to rank some selected cloud service providers. The TOPSIS and COPRAS methods are performed based on clinging to positive and negative ideals. Besides that, the COPRAS method considers the superiority and dependence between features to suggest the final indicators' ranks.
暂无评论