Search

Social distancing is an utmost reliable practice to minimise the spread of coronavirus disease (COVID-19). As the new variant of COVID-19 is emerging, healthcare organisations are concerned with controlling the death and infection rates. Different COVID-19 vaccines have been developed and administered worldwide. However, presently developed vaccine quantity is not sufficient to fulfil the needs of the world's population. The precautionary measures still rely on personal preventive strategies. The sharp rise in infections has forced governments to reimpose restrictions. Governments are forcing people to maintain at least 6 feet (ft) of safe physical distance to stay safe. With summers, low-light conditions can become challenging. Especially in the cities of underdeveloped countries, where poor ventilated and congested homes cause people to gather in open spaces such as parks, streets, and markets. Besides this, in summer, large friends and family gatherings mostly take place at night. It is necessary to take precautionary measures to avoid more drastic results in such situations. To support the law and order bodies in maintaining social distancing using Social Internet of Things (SIoT), the world is considering automated systems. To address the identification of violations of a social distancing Standard Operating procedure (SOP) in low-light environments via smart, automated cyber-physical solutions, we propose an effective social distance monitoring approach named DepTSol. We propose a low-cost and easy-to-maintain motionless monocular time-of-flight (ToF) camera and deep-learning-based object detection algorithms for real-time social distance monitoring. The proposed approach detects people in low-light environments and calculates their distance in terms of pixels. We convert the predicted pixel distance into real-world units and compare it with the specified safety threshold value. The system highlights people violating the safe distance. The proposed technique is evaluated by COCO evaluation metrics and has achieved a good speed–accuracy trade-off with 51.2 frames per second (fps) and a 99.7% mean average precision (mAP) score. Besides the provision of an effective social distance monitoring approach, we perform a comparative analysis between one-stage object detectors and evaluate their performance in low-light environments. This evaluation will pave the way for researchers to study the field further and will enlighten the efficiency of deep-learning algorithms in timely responsive real-world applications. [ABSTRACT FROM AUTHOR]

The non-linearity in medical image processing is a critical issue. Because the privacy of the medical image and loss of data is a major concern in recent years. Federated learning is a most advanced form of machine learning in which, rather than transmitting data to local server, a machine learning (ML) algorithm is installed to various devices to train on the information. The parameters from the separate modules will then be transferred to a master ML/ (deep learning) DL model for global training. The research of Image Quality Assessment (IQA) aims to simulate the process of human perception of image quality and construct an objective image quality model as consistent as possible with subjective assessment. The existing IQA methods can be roughly divided into traditional methods and deep learning methods. Traditional methods are knowledge-driven, using prior knowledge or assumptions about the human visual system (HVS) to heuristically design image quality index. Deep learning methods are data-driven, using a large amount of annotated data to learn the mapping from the image to its visual quality end-to-end. To effectively integrate traditional methods into deep networks and investigate the knowledge (model)-driven deep learning methods are the current mainstream trends in IQA research. In this paper, we take the contrary direction and improve traditional methods guided by the cue from deep learning methods. The main works include: 1. the employment of activation function ensure the nonlinear approximation ability of the neural network, here we first extend the two-stage framework widely used in the field of full-reference image quality assessment and propose a nonlinear two-stage framework. 2. Within this framework, we revisit the Edge Strength SIMlarity (ESSIM) algorithm that we previously published in IEEE Signal Processing Letters, and proposed the Nonlinear Edge Strength SIMlarity (NESSIM) algorithm. Experiments on public databases show that NESSIM can obtain good assessment results in traditional methods. • Improve traditional methods guided by the cue from deep learning methods. • Employment of activation function ensure the nonlinear approximation ability of the neural network. • Propose a nonlinear two-stage framework • Proposed the Nonlinear Edge Strength SIMlarity (NESSIM) algorithm. [ABSTRACT FROM AUTHOR]

• A new method for rice variety identification. • The classifier takes both spectral and time series information of each pixel. • Spectral unmixing model is used for labelling each pixel. • Radiometric indices are used to increase classification accuracy. • Experimental results have exhibited an overall accuracy of 98.6%. In the agriculture sector food productivity, security, and sustainability, imposed challenges on farmers, regulatory bodies, and policymakers due to increasing demand and depleting natural resources and environmental concerns. Rice crop holds a prominent place in Pakistan's agriculture sector, it is not only consumed locally but also exported to many countries including China. Gathering crop information such as variety maps, yield estimation, or etc. can help farmers, regulatory bodies, policymakers, and rice mills in decision-making. In Pakistan, crop information is collected through manual field surveys that require a lot of human labor, are costly, and are time-consuming. One cannot ignore human error and bias in the process. A new framework for pixel classification is proposed that uses both spectral and time-series data of Sentinal-2 satellite for mapping two rice varieties "Basmati" and "IRRI, grown in Pakistan. The data were collected from twelve rice fields (approx. 307 acres) of different geographical locations at 16-time instances to cover the complete rice-growing season (May–October) in 2019. A linear spectral unmixing model is used to determine sub-pixel information of water, soil, and vegetation content, which is used for labeling each pixel for supervised learning. The input to our classifier is a 16 × 15 image formed using 15 spectral features (12 spectral bands and 3 radiometric indices) of 16 carefully selected different time instances for each pixel. The output is a pixel-level classification (semantic segmentation) of each pixel into Basmati, IRRI, and others (soil, water, etc.). Experimental results have exhibited an excellent overall accuracy of 98.6% with the proposed approach. The Basmati rice obtained higher accuracy of 99.7% as compared to IRRI rice with an accuracy of 95.2%. [ABSTRACT FROM AUTHOR]