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Human–Computer Interfaces (HCI) deals with the study of interface between humans and computers. The use of radar and other RF sensors to develop HCI based on Hand Gesture Recognition (HGR) has gained increasing attention over the past decade. Today, devices have built-in radars for recognizing and categorizing hand movements. In this article, we present the first ever review related to HGR using radar sensors. We review the available techniques for multi-domain hand gestures data representation for different signal processing and deep-learning-based HGR algorithms. We classify the radars used for HGR as pulsed and continuous-wave radars, and both the hardware and the algorithmic details of each category is presented in detail. Quantitative and qualitative analysis of ongoing trends related to radar-based HCI, and available radar hardware and algorithms is also presented. At the end, developed devices and applications based on gesture-recognition through radar are discussed. Limitations, future aspects and research directions related to this field are also discussed.

Summary This paper proposes a new framework for early hotspot detection in the photovoltaic (PV) panels using color image descriptors and a machine learning algorithm. In the proposed approach, the acquired thermographic images of PV panels are divided into non‐overlapping regions, and then color image descriptors are computed for the regions. The color descriptors are then used as features to train different machine learning algorithms to classify the PV panels into three classes (ie, normal, hotspot, and defective). After extensive testing and comprehensive analysis, the experimental results show that Red‐Green Scale‐Invariant Feature Transform (rgSIFT) descriptor with k‐Nearest Neighbor (k‐NN) outperforms all other images descriptors and machine learning combinations with an accuracy rate of 98.7%. The experimental results also show the effects of the size of non‐overlapping regions on the classification accuracy. It is observed that the classification accuracy decreases as size is increased or decreased around the optimal non‐overlapping region image size of 71 × 71 pixels. The proposed method has a significant role in carbon‐free cities and can easily be implemented to inspect the PV system.

This paper proposes a novel bilateral control design based on an estimated reaction force without a force sensor for a three-degree of freedom hydraulic servo system with master–slave manipulators. The proposed method is based upon sliding mode control with sliding perturbation observer (SMCSPO) using a bilateral control environment. The sliding perturbation observer (SPO) estimates the reaction force at the end effector and second link without using any sensors. The sliding mode control (SMC) is used as a bilateral controller for the robust position tracking and control of the slave device. A bilateral control strategy in a hydraulic servo system provides robust position and force tracking between master and slave. The difference between the reaction force of the slave produced by the effect of the remote environment and the operating force applied to the master by the operator is expressed in the target impedance model. The impedance model is applied to the master and allows the operator to feel the reaction force from the environment. This research experimentally verifies that the slave device can follow the trajectory of the master device using the proposed bilateral control strategy based on the estimated reaction force. This technique will be convenient for three or more degree of freedom (DOF) hydraulic servo systems used in dismantling nuclear power plants. It is worthy to mention that a camera is used for visual feedback on the safety of the environment and workspace.

Brain tumors reduce life expectancy due to the lack of a cure. Moreover, their diagnosis involves complex and costly procedures such as magnetic resonance imaging (MRI) and lengthy, careful examination to determine their severity. However, the timely diagnosis of brain tumors in their early stages may save a patient’s life. Therefore, this work utilizes MRI with a machine learning approach to diagnose brain tumor severity (glioma, meningioma, no tumor, and pituitary) in a timely manner. MRI Gaussian and nonlinear scale features are extracted due to their robustness over rotation, scaling, and noise issues, which are common in image processing features such as texture, local binary patterns, histograms of oriented gradient, etc. For the features, each MRI is broken down into multiple small 8 × 8-pixel MR images to capture small details. To counter memory issues, the strongest features based on variance are selected and segmented into 400 Gaussian and 400 nonlinear scale features, and these features are hybridized against each MRI. Finally, classical machine learning classifiers are utilized to check the performance of the proposed hybrid feature vector. An available online brain MRI image dataset is utilized to validate the proposed approach. The results show that the support vector machine-trained model has the highest classification accuracy of 95.33%, with a low computational time. The results are also compared with the recent literature, which shows that the proposed model can be helpful for clinicians/doctors for the early diagnosis of brain tumors.