Protective measures against small unmanned aerial vehicles (UAVs) are vital from a national security perspective. As a result, the importance of surveillance systems that automatically identify and classify low radar cross section (RCS) aerial targets increases. In this work, an indigenously developed continuous wave (CW) (X-band: 10 GHz) radar is used to build a diversified “DIAT- $\mu $ SAT” dataset comprising 4849 micro-Doppler signature images of five different small aerial targets. We also proposed a transfer learning-based deep convolutional neural network (DCNN) approach for classifying low RCS aerial targets. We demonstrated the classification accuracy of 95% and 97%, with VGG16 and VGG19 as feature extractors, respectively, with minimal false-negative and -positive results. The open-field experimental classes covered in this work are: 1) a two-blade rotor; 2) a three-short-blade rotor; 3) a three-long-blade rotor; 4) a quadcopter; 5) a bionic bird; and 6) a two-blade-rotor and bionic bird. We also observed a good classification accuracy (>97%) when more than one target is operated simultaneously.

Recognizing suspicious human activities is one of the critical requirements for national security considerations. Nowadays, designing the deep convolution neural network (DCNN) models suitable for micro-Doppler (m-D) signature-based human activity classification is rapidly growing. However, high computation cost and a huge number of parameters limit their direct/effective usability in field applications. This article introduces an m-D signatures’ dataset “DIAT- $\mu $ RadHAR” covering army crawling, boxing, jumping while holding a gun, army jogging, army marching, and stone-pelting/grenade-throwing, generated using an $X$ -band continuous wave (CW) radar. This article also introduces a lightweight DCNN model, “DIAT-RadHARNet,” designed for those human suspicious activity classification. To reduce the computation cost and to improve the generalization ability, DIAT-RadHARNet is designed with four design principles: depthwise separable convolutions, channel weighting (CHW) based on the importance, different size filters in the depthwise part, and operating different size kernels on the same input tensor. The network has 213 793 parameters with a total of 55 layers. Our extensive experimental analysis demonstrates that the DIAT-RadHARNet model efficiently classifies the activities with 99.22% accuracy, giving minimal false positive and false negative outcomes. The time complexity of the proposed DCNN model observed during the testing phase is 0.35 s. The same accuracy and time complexity are obtained even at adverse weather conditions, low-lighting environments, and long-range operations.

The coronavirus COVID-19 pandemic is today’s major public health crisis, we have faced since the Second World War. The pandemic is spreading around the globe like a wave, and according to the World Health Organization’s recent report, the number of confirmed cases and deaths are rising rapidly. COVID-19 pandemic has created severe social, economic, and political crises, which in turn will leave long-lasting scars. One of the countermeasures against controlling coronavirus outbreak is specific, accurate, reliable, and rapid detection technique to identify infected patients. The availability and affordability of RT-PCR kits remains a major bottleneck in many countries, while handling COVID-19 outbreak effectively. Recent findings indicate that chest radiography anomalies can characterize patients with COVID-19 infection. In this study, Corona-Nidaan, a lightweight deep convolutional neural network (DCNN), is proposed to detect COVID-19, Pneumonia, and Normal cases from chest X-ray image analysis; without any human intervention. We introduce a simple minority class oversampling method for dealing with imbalanced dataset problem. The impact of transfer learning with pre-trained CNNs on chest X-ray based COVID-19 infection detection is also investigated. Experimental analysis shows that Corona-Nidaan model outperforms prior works and other pre-trained CNN based models. The model achieved 95% accuracy for three-class classification with 94% precision and recall for COVID-19 cases. While studying the performance of various pre-trained models, it is also found that VGG19 outperforms other pre-trained CNN models by achieving 93% accuracy with 87% recall and 93% precision for COVID-19 infection detection. The model is evaluated by screening the COVID-19 infected Indian Patient chest X-ray dataset with good accuracy.

In the view of national security, radar micro-Doppler (m-D) signatures-based recognition of suspicious human activities becomes significant. In connection to this, early detection and warning of terrorist activities at the country borders, protected/secured/guarded places and civilian violent protests is mandatory. Designing an automated human suspicious activities: army crawling, army jogging, jumping with holding a gun, army marching, boxing, and stone-pelting/grenades-throwing, recognition system using a suitable deep convolutional neural network (DCNN) model is rapidly growing due to its inherent in-depth features extraction capability. As a value addition to this research, an X-band continuous wave (CW) 10 GHz radar has been developed at our radar systems laboratory and used to acquire the m-D signatures, to prepare a dataset (DIAT- $\mu $ RadHAR) corresponding to above mentioned suspicious activities. In order to prepare a realistic dataset, human targets of different heights, weights, and gender are directed to perform the suspicious activities in front of the radar at different ranges between 10 m - 0.5 km and at different target aspect angles (0°, ±15°, ±30° and ±45°). A lightweight DCNN architecture ( $\mu $ RadNet) is also designed and trained with the prepared DIAT- $\mu $ RadHAR dataset comprising 3780 samples. The performance and recognition accuracy of $\mu $ RadNet is statistically computed, and the results are compared to the state-of-the-art (SOTA) CNN models. The $\mu $ RadNet DCNN model outperforms the SOTA CNN models, giving 99.22% of overall classification accuracy, 0.09M parameters, and 0.40G floating point operations (FLOPs) with minimal false negative/positive rates. The time-complexity of the designed lightweight $\mu $ RadNet DCNN model is 0.12 s, which evidences the suitability of our DCNN model for the on-device implementation.

Control flow graphs (CFG) and OpCodes extracted from disassembled executable files are widely used for malware detection. Most of the research in static analysis is focused on binary class malware detection which only classifies an executable as benign or malware. To overcome this issue, CFG based multiclass malware detection system that automatically classifies the malware into their respective families is proposed. The use Bi-normal separation (BNS) as a feature scoring metric. Experimental results show that proposed method using BNS outperforms compared to hitherto use technique of document Frequency for multiclass metamorphic malware detection and achieves detection accuracy of 99.5 per cent.