Through-wall detection and classification are highly desirable for surveillance, security, and military applications in areas that cannot be sensed using conventional measures. In the domain of these applications, a key challenge is an ability not only to sense the presence of individuals behind the wall but also to classify their actions and postures. Researchers have applied ultrawideband (UWB) radars to penetrate wall materials and make intelligent decisions about the contents of rooms and buildings. As a form of UWB radar, stepped frequency continuous wave (SFCW) radars have been preferred due to their advantages. On the other hand, the success of classification with deep learning methods in different problems is remarkable. Since the radar signals contain valuable information about the objects behind the wall, the use of deep learning techniques for classification purposes will give a different direction to the research. This paper focuses on the classification of the human posture behind the wall using through-wall radar signals and a convolutional neural network (CNN). The SFCW radar is used to collect radar signals reflected from the human target behind the wall. These signals are employed to classify the presence of the human and the human posture whether he/she is standing or sitting by using CNN. The proposed approach achieves remarkable and successful results without the need for detailed preprocessing operations and long-term data used in the traditional approaches.

Tunisian Dialect Sentiment Analysis: A Natural Language Processing-based Approach

Artificial bee colony (ABC) is one of the swarmintelligence optimization algorithms, inspired by foraging anddance behaviors of real honey bee colonies. This study is aninstance of a hybrid algorithm using ABC together withk-nearest neighbor algorithm on diagnosis of coronary arterydisease employing exercises stress test data. The study dataset iscomposed of 134 healthy and 346 unhealthy totally 480 patients.On the proposed algorithm two centroid vectors are obtainedconcerning one for healthy patients and the other for unhealthypatients utilizing ABC for the training part of the dataset. Then,the test part of the dataset is classified using k-nearest neighboralgorithm. The results obtained by the proposed technique showthat this hybrid algorithm could be used as an alternativeclassifier on diagnosis of coronary artery disease employingexercise stress test data

Several super-resolution (SR) techniques are introduced in the literature, including traditional and machine learning-based algorithms. Especially, deep learning-based SR approaches emerge with demands for better quality images providing deeper subpixel enhancement. Dealing with the image enhancement task in the satellite images domain, a new SR method for single image SR, namely Enhanced Deep Pyramidal Residual Networks, is introduced in this study. The proposed method overcomes the potential instability problem of Enhanced Deep Residual Networks for Single Image Super-Resolution (EDSR) approach by gradually increasing the feature maps depending upon Pyramidal Residual Networks architecture. The EDSR itself is a good algorithm in the SR domain. However, it has a strict structure for increasing the block size. To overcome this problem with the aim of increasing the algorithm’s performance, the pyramidal residual networks gradually increasing hypothesis is utilized in the proposed approach, which is the main contribution and novelty of this study. Besides, by using the pyramidal residual networks gradually increasing hypothesis in the proposed approach, the parameter size of the models is also reduced, which affects the computational time. Two different models are proposed by considering addition and multiplication manners, and the proposed models are evaluated using well-known remote sensing datasets NWPU-RESISC45 and UC Merced. The results obtained by the proposed model are compared with the results of traditional image enhancement algorithms together with the EDSR itself, EDSR with deeper structure, Super-Resolution Generative Adversarial Networks approach, and Residual Local Feature Networks approach in terms of peak signal to noise ratio (PSNR) and structural similarity index measure (SSIM) metrics and showed that the proposed models present better quality images. Moreover, considering the computational time and complexity, it is shown that some proposed models achieve approximately 27% less output parameter having similar PSNR and SSIM values and computational time for EDSR itself and 65% less output parameter having better PSNR and SSIM values and 16% lower computational time for EDSR with deeper structure.

Multi-task planning for diverse UAVs and missions can be approached as a Constraint Satisfaction Problem (CSP) where the Temporal CSP (TCSP) method adds time-based sequential task modeling. The Enhanced Temporal Constraint Satisfaction Problem (ETCSP) method innovatively merges dynamic domain features with a MIQP (Mixed Integer Quadratic Programming) based scoring system to optimally assign UAVs to tasks, moving beyond traditional greedy algorithms. This approach includes an enhanced forward checking method that evaluates task suitability and UAV compatibility in real-time using dynamic programming, thus refining search precision. The ETCSP model was tested in two phases, initially assigning various tasks and then employing CSP methods to monitor task changes over time. Results show that the generic TCSP method requires 61 UAVs to complete 70 tasks, while the Enhanced TCSP achieves the same with only 48 UAVs—which is roughly a 21% reduction in UAV usage. Similarly, the Enhanced method completes the task package in about 3800 min and with 1142 L of fuel, compared to 4855 min and 1615 L for the TCSP method, translating to approximately a 22% reduction in time and a 29% reduction in fuel consumption.