Classical convergence analyses for optimization algorithms rely on the widely-adopted uniform smoothness assumption. However, recent experimental studies have demonstrated that many machine learning problems exhibit n...
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Hybrid software development models are increasingly being used in various industries to provide the benefits of agile methodologies in software development while maintaining traditional model frameworks that the rest ...
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While the MQTT protocol is widely adopted in IoT applications, its usage for Industrial IoT is prevented by the lack of support for time-critical transmissions. For this reason, recent work has proposed the Prioritize...
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Technological advancements in modern vehicles facilitate active research on the Ethernet-based Time-Sensitive Networking (TSN) protocol to overcome the limitations of traffic processing in the traditional In-Vehicle N...
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Satellite image processing is a multidomain task which involves design of image capturing, denoising, segmentation, feature extraction, feature reduction, classification, and post-processing tasks. A wide variety of s...
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Satellite image processing is a multidomain task which involves design of image capturing, denoising, segmentation, feature extraction, feature reduction, classification, and post-processing tasks. A wide variety of satellite image processing models are proposed by researchers, and each of them has different data and process requirements. For instance, the image capturing module might obtain images in layered form, while feature extraction module might require data in 2D or 3D forms. Moreover, performance of these models also varies due to changes in internal process parameters and dataset parameters, which limits their accuracy and scalability when applied to real-time scenarios. To reduce the probability of these limitations, a novel high-efficiency temporal engine for real-time satellite image classification using augmented incremental transfer learning is proposed and discussed in this text. The model initially captures real-time satellite data using Google’s Earth Engine and processes it using a transfer learning-based convolutional neural network (CNN) via backscatter coefficient analysis. These coefficients indicate average intensity value of Precision Image (PRI) when evaluated over a distributed target. Due to extraction of backscattering coefficients, the model is capable of representing crop images in VV (vertical transmit, vertical receive), and HV (horizontal transmit vertical receive) modes. Thereby assisting the CNN model to extract a wide variety of features from input satellite image, which classifies these datasets (original, VV, and VH) into different crop categories. The classified images are further processed via an incremental learning layer, which assists in visual identification of affected regions. Due to use of incremental learning and CNN for classification, the proposed TRSAITL model is capable of achieving an average accuracy of 97.8% for crop type and severity of damage detection, with an average PSNR (Peak Signal-to-Noise Ratio) of 29.
This is because printed circuit boards, also known as PCBs, are essential components of optical sensors and devices, which means that they require an outstanding level of precision and performance. However, deep learn...
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Cyclone forecasting using satellite pictures involves anticipating the cyclone’s intensity in advance of its arrival. The results of this study can inform people’s preparations for the cyclone. In order to save live...
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This paper explores the application of reinforcement learning (RL) to torpedo guidance with a focus on obstacle avoidance and target acquisition in dynamic environments. By employing a dual-actor network approach and ...
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This paper introduces a novel methodology for designing secure hardware accelerator tailored for convolutional neural network (CNN) applications, leveraging security-aware high-level synthesis (HLS). The methodology o...
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This paper proposes a thermal reduction method of DC-link capacitors and SiC MOSFETs in two-level inverters based on discontinuous PWM. The converter reliability has been extensively studied at both the device and the...
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