A common countermeasure to detect threatening drones is the electro-optical infrared (EO/IR) system. However, its performance is drastically reduced in conditions of complex background, saturation and light reflection. 3D laser sensor LiDAR is used to overcome the problems of 2D sensors like EO/IR, but it is not enough to detect small drones at a very long distance because of low laser energy and resolution. To solve this problem, A 3D LADAR sensor is under development. In this work, we study the detection methodology adequate to the LADAR sensor which can detect small drones at up to 2 km. First, a data augmentation method is proposed to generate a virtual target considering the laser beam and scanning characteristics, and to augment it with the actual LADAR sensor data for various kinds of tests before full hardware system developed. Second, a detection algorithm is proposed to detect drones using voxel-based background subtraction and variable radially bounded nearest neighbor (V-RBNN) method. The results show that 0.2 m L2 distance and 60% expected average overlap (EAO) indexes are satisfied for the required specification to detect 0.3 m size of small drones.

The use of thermal sensors is increasing in response to dealing with the problems of the visible light spectrum. These sensors measure the temperature of the object and convert it to some readable output. There is a diverse range of temperature sensors, and different sensors are used for different purposes. The choice of the sensor depends on the cost of the sensor, resolution, and level of accuracy. For instance, an IR camera combined with the RGB sensor can produce better human activity recognition. However, increase the cost of the machine. For some applications, a high resolution is not required and a low-cost sensor can satisfy the need. In this survey, we discuss the employment of thermal sensors in HVAC systems, vehicle, and manufacturing industries as they are heavily used in these industries. We reported the types of available thermal sensors and the sensors commonly used in each industry. This is followed by a comprehensive review of the application-specific methods. In the end, we may say that the selection of the thermal sensor has much importance as well as the choice of the suitable algorithms according to the given conditions to avail the maximum accuracy in our results.

Islamophobia is a rising area of concern in the current era where Muslims face discrimination and receive negative perspectives towards their religion, Islam. Islamophobia is a type of racism that is being practiced by individuals, groups, and organizations worldwide. Moreover, the ease of access to social media platforms and their augmented usage has also contributed to spreading hate speech, false information, and negative opinions about Islam. In this research study, we focused to detect Islamophobic textual content shared on various social media platforms. We explored the state-of-the-art techniques being followed in text data mining and Natural Language Processing (NLP). Topic modelling algorithm Latent Dirichlet Allocation is used to find top topics. Then, word embedding approaches such as Word2Vec and Global Vectors for word representation (GloVe) are used as feature extraction techniques. For text classification, we utilized modern text analysis techniques of transformers-based Deep Learning algorithms named Bidirectional Encoders Representation from Transformers (BERT) and Generative Pre-Trained Transformer (GPT). For results comparison, we conducted an extensive empirical analysis of Machine Learning algorithms and Deep Learning using conventional textual features such as the Term Frequency-Inverse Document Frequency, N-gram, and Bag of words (BoW). The empirical based results evaluated using standard performance evaluation measures show that the proposed approach effectively detects the textual content related to Islamophobia. In the corpus of the study under Machine Learning models Support Vector Machine (SVM) performed best with an F1 score of 91%. The Transformer based core NLP models and the Deep Learning model Convolutional Neural Network (CNN) when combined with GloVe performed best among all the techniques except SVM with BoW. GPT, SVM when combined with BoW and BERT yielded the best F1 score of 92%, 92% and 91.9% respectively, while CNN performed slightly poor with an F1 score of 91%.

The ground based electro-optical tracking system (EOTS) and electro-optics and infrared (EO/IR) are the most popular small UAV (C-sUAV) detection systems. The EO/IR systems are able to detect sUAVs at a long distance about several kilometers under clean environment. However, its performance is degraded in various noises like fixed patterns, dead/bad pixels and complex background conditions such as saturated images or foggy environments. In this study, we propose an efficient methodology using high power laser radar for real time CsUAV systems. The goal of our system is to find a 0.5 meter sUAV at 2 kilo meter distance in real time. For that challenging goal, we use a laser radar with dual pan-tilt scanning systems and also apply the variable-radially bounded nearest neighbor (V-RBNN) methodology as a fast clustering method. The experimental results show that the proposed method is able to detect 0.5 meter sUAV and its calculation time is under 20 millisecond per frames in complex background and long range conditions.