Brain cancer is classified as a malignant tumours that can spread to other parts of the human body, especially the spine and brain instantaneously. Brain tumour has been considered as the biggest problem for human hea...
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We present an investigation of several machine learning (ML) models developed to predict the copper (Cu) and lead (Pb) ion concentrations in drinking water. The system where this prediction is employed is based on a m...
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ISBN:
(数字)9798350330649
ISBN:
(纸本)9798350330656
We present an investigation of several machine learning (ML) models developed to predict the copper (Cu) and lead (Pb) ion concentrations in drinking water. The system where this prediction is employed is based on a microwave block loop gap resonator (BLGR), which surrounds a glass tube with drinking water passing through it. The resonator is coupled to a vector network analyzer (VNA), which collects reflection coefficient (S11) measurements over a 100 MHz - 6 GHz frequency range. It is these S11 measurements, in raw format or compressed using various signal processing techniques, that are used as input into the ML models. Our investigation looks at new convolutional neural networks (CNN) and deep neural networks (DNN) models because such models can easily be deployed on IoT microcontroller devices using tinyML technologies. Extensive simulations using real data demonstrate that DNN models that use as input features essential spectral information created from S11 traces provide performance comparable to that of CNN models but at much shorter training times and significantly smaller model sizes.
Existing works in federated learning (FL) often assume either full client or uniformly distributed client participation. However, in reality, some clients may never participate in FL training (aka incomplete client pa...
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Existing works in federated learning (FL) often assume either full client or uniformly distributed client participation. However, in reality, some clients may never participate in FL training (aka incomplete client participation) due to various system heterogeneity factors. A popular solution is the server-assisted federated learning (SA-FL) framework, where the server uses an auxiliary dataset. Despite empirical evidence of SA-FL's effectiveness in addressing incomplete client participation, theoretical understanding of SA-FL is lacking. Furthermore, the effects of incomplete client participation in conventional FL are poorly understood. This motivates us to rigorously investigate SA-FL. Toward this end, we first show that conventional FL is not PAC-learnable under incomplete client participation in the worst case. Then, we show that the PAC-learnability of FL with incomplete client participation can indeed be revived by SA-FL, which theoretically justifies the use of SA-FL for the first time. Lastly, to provide practical guidance for SA-FL training under incomplete client participation, we propose the SAFARI (server-assisted federated averaging) algorithm that enjoys the same linear convergence speedup guarantees as classic FL with ideal client participation assumptions, offering the first SA-FL algorithm with convergence guarantee. Extensive experiments on different datasets show SAFARI significantly improves the performance under incomplete client participation. Copyright 2024 by the author(s)
The world steel industry is highly dependent on the use of electric arc furnaces (EAFs). The application of the electric arc phenomenon causes many power quality (PQ) problems, such as harmonics or voltage flickering....
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An era of automation is currently being experienced, where everything is becoming more automated day by day. Automation technology has been applied everywhere, from smaller to larger scales. Moreover, real-time commun...
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The design of a wireless sensor network receiver requires an efficient Low Noise Amplifier (LNA), with the transistor being a critical component that influences its performance. Selecting an optimal transistor for LNA...
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Unmanned Aerial Vehicles (UAVs) have become essential in search and rescue operations, especially in disaster management scenarios. Their effective navigation and the integration of a plethora of sensors assist in eff...
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This paper presents the design and development of a low-cost and user-friendly Bengali braille embosser for the visually impaired population in Bangladesh. To produce braille text, the embosser employs a shifting mech...
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Optical Wireless Power Transmission (OWPT) system, using beam shaping, is a promising technology that involves a transmitter side using a laser and a receiver side using solar cells to transfer power wirelessly over l...
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Aspect-based sentiment analysis (ABSA) is vital for text comprehension which benefits applications across various domains. This field involves the two main sub-tasks including aspect extraction and sentiment classific...
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