Dyslexia is a learning disability that negatively impacts an individual's ability to read, write, spell, and sometimes speak. It results in difficulties in recognizing and decoding words and patterns, despite norm...
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ISBN:
(数字)9798331513269
ISBN:
(纸本)9798331513276
Dyslexia is a learning disability that negatively impacts an individual's ability to read, write, spell, and sometimes speak. It results in difficulties in recognizing and decoding words and patterns, despite normal level of education and intelligence. Studies have shown that early detection of dyslexia is vital for improving learning abilities in young children. Many virtual platforms exist for diagnosing and rehabilitating dyslexia; however, most require tests that measure reading skills. Since developing reading capabilities can delay the detection of dyslexia, a gaming platform based on Hebb-Williams mazes has been developed. This platform does not rely on reading skills and can diagnose dyslexia in young children. This paper presents a machine learning driven approach using two algorithms - Random Forest and Linear SVM - to classify reading abilities based on data from participants performing virtual maze tasks, which are indicative of symptoms of dyslexia. Results from this study indicate that it is possible to predict dyslexia with up to 95% accuracy based on a participant's performance in virtual gaming environments.
The rapid development of Internet of Things (IoT) technology has enabled the widespread deployment of health monitoring systems. Traditionally, the health monitoring system has been limited by centralized processing a...
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ISBN:
(数字)9798350392296
ISBN:
(纸本)9798350392302
The rapid development of Internet of Things (IoT) technology has enabled the widespread deployment of health monitoring systems. Traditionally, the health monitoring system has been limited by centralized processing and storage in the cloud, leading to latency issues and potential data loss. This paper introduces a smart sleep monitoring system based on edge computing, utilizing microservices architecture and caching techniques. The proposed system employs edge computing to enable data processing closer to the source, reducing latency and improving real-time monitoring capabilities. Caching is employed to reduce database load and optimize random access memory (RAM) usage. This research addresses latency and response time challenges on IoT health monitoring platforms in environments with poor network quality while optimizing database load and resource usage on Jetson Nano as the edge computing device. Using Electrocardiogram (ECG) data as input, the proposed system yields impressive performance metrics. The research results indicate that the proposed system can increase throughput by 26.92 KB/s, reduce response time by 18.8 ms, and decrease latency by 20.86 ms compared to the previous work. Message Queuing Telemetry Transport (MQTT) integration reduces CPU usage by approximately 40% and RAM usage by about 81.24%.
In this work, we propose an optical OFDM system using phase modulation followed by optical filtering and direct detection. A fiber Bragg grating is used as an optical filter for phase to amplitude conversion. The perf...
In this work, we propose an optical OFDM system using phase modulation followed by optical filtering and direct detection. A fiber Bragg grating is used as an optical filter for phase to amplitude conversion. The performance of the proposed system is investigated for both 16 QAM- and 64 QAM-OFDM signals considering different numbers of training signals for frequency-domain channel estimation. With adequate choice of the training sequence length, BER results below 10 −4 are reported for the 16 QAM based signal.
This paper presents a proposal of a computer vision tool based on Open CV that allows the pre-visual identification of non-uniform constellation levels, channel coding, and Signal-to-Noise Ratio (SNR) estimation on AT...
This paper presents a proposal of a computer vision tool based on Open CV that allows the pre-visual identification of non-uniform constellation levels, channel coding, and Signal-to-Noise Ratio (SNR) estimation on ATSC. Nowadays, the challenge of digital television systems is to transmit high quality videos employing the new technologies, such as Ultra High Definition (UHD). Thus, the new standards as ATSC 3.0 have incorporated several modifications on their physical layers. Among them, it is possible to highlight the use of non-uniform constellations, advanced channel error coding and layer division multiplexing (LDM). However, the pre-visual understanding of the received constellations has become hard since the complex symbols are not distributed evenly in the complex plane and there are different layers to be seen simultaneously. So, the techniques of computer vision have a great potential to analyze and to extract an initial information from the images related to the received constellation, to identify the modulation level, channel coding rate and SNR to each layer, without the necessity of complete demodulation.
Making inference with spatial extremal dependence models can be computationally burdensome since they involve intractable and/or censored likelihoods. Building on recent advances in likelihood-free inference with neur...
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Making inference with spatial extremal dependence models can be computationally burdensome since they involve intractable and/or censored likelihoods. Building on recent advances in likelihood-free inference with neural Bayes estimators, that is, neural networks that approximate Bayes estimators, we develop highly efficient estimators for censored peaks-over-threshold models that use augmented data to encode censoring information in the neural network input. Our new method provides a paradigm shift that challenges traditional censored likelihood-based inference methods for spatial extremal dependence models. Our simulation studies highlight significant gains in both computational and statistical efficiency, relative to competing likelihood-based approaches, when applying our novel estimators to make inference with popular extremal dependence models, such as max-stable, r-Pareto, and random scale mixture process models. We also illustrate that it is possible to train a single neural Bayes estimator for a general censoring level, precluding the need to retrain the network when the censoring level is changed. We illustrate the efficacy of our estimators by making fast inference on hundreds-of-thousands of high-dimensional spatial extremal dependence models to assess extreme particulate matter 2.5 microns or less in diameter (PM2:5) concentration over the whole of Saudi Arabia.
Exceptional point (EP)-based optical sensors exhibit exceptional sensitivity but poor detectivity. Slightly off EP operation boosts detectivity without much loss in sensitivity. We experimentally demonstrate a high-de...
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The advancement of modern multimedia and data-intensive classes of applications demands the development of hardware that delivers better performance. Due to the evolution of 5G, Edge-Computing, the Internet of Things,...
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We demonstrate Purcell enhancement of a single T center integrated in a silicon photonic crystal cavity, increasing the fluorescence decay rate by a factor of 6.89 and achieving a photon outcoupling rate of 73.3 kHz. ...
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