This study presents a high-resolution Diabetic Retinopathy dataset collected from Eye Care Hospital in Aizawl, Mizoram, highlighting its importance for advancing DR detection in underrepresented populations. The datas...
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Haze degrades the accuracy of computer vision algorithms for railway monitoring applications. This study proposed a dehazing algorithm emphasizing image quality, performed on GPUs and CPUs rather than on the IoT or lo...
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ISBN:
(数字)9798331520403
ISBN:
(纸本)9798331520410
Haze degrades the accuracy of computer vision algorithms for railway monitoring applications. This study proposed a dehazing algorithm emphasizing image quality, performed on GPUs and CPUs rather than on the IoT or low power devices, as a CNN-based haze removal model for an edge device. Our optimized CNN model utilized the ADD operation to relieve concatenation between two convolutional layers. The model was tested on a TFlite file, with results showing acceptable SSIM and PSNR image quality assessment values. The average computational time was $\mathbf{2 1 0}$ milliseconds on an Intel Xeon processor while a Raspberry Pi 4 Model B, operating as an offline edge inference device, achieved a processing time of $\mathbf{2 8 0}$ milliseconds per image.
Remote Photoplethysmography (rPPG) is a non-invasive approach for monitoring Heart Rate (HR) that can be used in various applications in healthcare and biometrics. rPPG measurements acquired using facial videos have b...
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This research project proposes the use of interactive videos to enhance teachers' training in educating autistic children. Effective teaching methods for students with autism require teachers to comprehend the con...
This research project proposes the use of interactive videos to enhance teachers' training in educating autistic children. Effective teaching methods for students with autism require teachers to comprehend the condition and employ tailored instructional strategies, including adapting assignments, aiding those with language difficulties, and utilizing visual aids for better organization and focus. Traditional teacher training methods can be both expensive and time-consuming. In contrast, interactive videos provide a proactive and flexible way to access training content, empowering teachers to engage with the material dynamically and take control of their learning experiences. Future work will explore the integration of AI-driven ChatGPT to offer personalized support and create a dynamic training program, with the goal of improving inclusivity and educational quality in autism settings while benefiting teachers, students, and the education system at large.
One of the areas that stand to gain the most from the adoption of Artificial Intelligence (AI) is Cyber Security. Traditional well-known system approaches may be slow and inadequate despite their virtues for a variety...
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Defect classification helps ensure the quality and reliability of semiconductor devices. Even a minor defect can lead to significant performance issues or failures in electronic components. Moreover, a recurring issue...
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In this paper, we compare the performance of two popular NLP models, pre-train fine-tuned BERT and BiLSTM with combined CNN, in terms of the classification and recommendation tasks of research papers. We conduct the p...
In this paper, we compare the performance of two popular NLP models, pre-train fine-tuned BERT and BiLSTM with combined CNN, in terms of the classification and recommendation tasks of research papers. We conduct the performance evaluation of these two models with research journal benchmark dataset. Performance results show that the pre-train fine-tuned BERT model is superior to CNN-BiLSTM combined model in terms of classification performance.
Image inpainting is a valuable technique for enhancing images that have been corrupted. The primary challenge in this research revolves around the extent of corruption in the input image that the deep learning model m...
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Recent advances in artificial intelligence technologies have led to a significant increase in deep learning workloads on mobile devices. Given the limited resources of smartphones, much of the research in mobile deep ...
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ISBN:
(纸本)9798350355260
Recent advances in artificial intelligence technologies have led to a significant increase in deep learning workloads on mobile devices. Given the limited resources of smartphones, much of the research in mobile deep learning has concentrated on offloading these workloads to edge or cloud servers. While computing resources are crucial, storage I/O remains a critical performance bottleneck for mobile devices, yet the file access characteristics of deep learning have not been thoroughly explored. This paper investigates the file access traces of deep learning workloads on mobile devices, comparing them to traditional workloads. The main findings include: 1) Write access constitutes 48-94% of total file accesses, aligning with conventional mobile apps but contrasting with most desktop applications; 2) Write access in mobile deep learning workloads exhibits repetitive long-loop patterns, offering insights for enhancing file cache performance; 3) Despite its prevalence, write access demonstrates low access skewness; 4) Frequency of file accesses proves more informative than recency in predicting re-access likelihood. The insights from this study are expected to guide the efficient management of future smartphone systems by addressing the unique file access dynamics of deep learning.
We present a tunable planar guided-mode resonance (GMR) filter using time-varying permittivity along grating nanobars. Results show that the effective medium concept in the temporal state is exactly the same as the sp...
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