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This paper demonstrates the design and development of an autonomous aircraft type drone for dynamic applications including surveillance, disaster management, and military applications, especially, target practice of missiles. The drone is capable of flying at a maximum speed of 90 kilometer/hour, where the maximum altitude is approximately 1 kilometer. Because of the autopilot and global positioning system, installed with the drone, the flight of the multi-functional drone is autonomously controllable from the ground control station. The long endurance drone is designed for uninterrupted flying of ~ 30 minutes at an altitude of 300 meters. The high definition camera of the drone transmits live video stream of any projected area to the ground control station using the video transmitter. The drone automatically returns to the ground control station when its battery voltage reduces down to 21.6 volts or the communication with the ground control station is lost. Most importantly, the drone is able to carry ~ 4.6 kilograms of payload along with it.

Backscatter wireless communication (BWC) is a promising research area for the low power Internet of Things (IoT) devices. A substantial number of research works have been conducted in this area to increase the range of BWC. Along with physical layer achievements in BWC, an efficient medium access control (MAC) protocol is mandatory to attain the optimal benefit. In this paper, a novel MAC protocol is proposed to support BWC in a Wi-Fi network, where the AP and the Wi-Fi clients are capable of in-band full-duplex (IBFD) transmission, but the wireless tags (WTags) are half-duplex capable. In this MAC protocol, the AP suppresses the uplink transmission of the corresponding Wi-Fi client while transmitting the downlink data to support the BWC. Some new control frames have been introduced to facilitate this task. A mathematical analysis of this proposed MAC is presented here together with an evaluation of its performance. The mean overhead time is observed as 121 μs with the downlink utilization factor of 80%. The average throughput of the network decreases as the number of WTag increases. This average throughput is observed as 63.5 Mbps and 59 Mbps (with 30 WTags and 30 Wi-Fi clients) for the data delivery frequency of two times/WTag and eight times/WTag respectively.

Automatic traffic sign detection and recognition is a field of computer vision which is very important aspect for advanced driver support system. This paper proposes a framework that will detect and classify different types of traffic signs from images. The technique consists of two main modules: road sign detection, and classification and recognition. In the first step, colour space conversion, colour based segmentation are applied to find out if a traffic sign is present. If present, the sign will be highlighted, normalized in size and then classified. Neural network is used for classification purposes. For evaluation purpose, four type traffic signs such as Stop Sign, No Entry Sign, Give Way Sign, and Speed Limit Sign are used. Altogether 300 sets images, 75 sets for each type are used for training purposes. 200 images are used testing. The experimental results show the detection rate is above 90% and the accuracy of recognition is more than 88%.

Recent advancements in antenna design and signal processing have made it feasible to transmit and receive at the same time by using same carrier frequency, which is called in-band full-duplex (IBFD) wireless communications. Along with physical layer advancements, efficient medium access control (MAC) design is required to achieve optimum benefit from this latest technology. In this paper, a full-duplex MAC (FD-MAC) is proposed for a WLAN to support asymmetric length of traffic for uplink and downlink, which is named as Asym- MAC. The main feature of this MAC is that multiple users can transmit data to the access point (AP) during the transmission of a single downlink frame from AP. Moreover, there is no contention period and hence, there is no collision among the user terminals and AP for channel access. In Asym-MAC, AP always initiates the transmission. The performance of Asym-MAC is compared with the traditional half-duplex MAC (HD-MAC) and another proposed FD-MAC. Asym-MAC outperforms traditional HD-MAC by about 100% throughput gain. Moreover, this MAC shows higher throughput gain as compared to another existing FD-MAC and the throughput gain is about 67%. The results suggest that a significant throughput gain can be achieved by using this MAC, which can be used for future FD WLANs.

This paper presents a novel framework to categorize aerial images into two classes: images with manmade structures and natural scene images. A novel noise-tolerant three-stage feature extraction framework is presented here which includes extracting edges from the input gray image, applying Gabor filter to compute Gabor energy feature and wavelet decomposition technique to extract the feature vector of computationally affordable size. A probabilistic neural network (PNN) is employed to classify the aerial images. From the database of 112 images (58 are natural scenes and 54 are images with manmade structures), total of 30 images, 15 from each class are used for training phase. For testing the algorithm, 82 images (39 manmade class and 43 of natural class) are used. The proposed method gives 94.87% correct classification for images with manmade structure and 97.67% for natural scene images.