Numerous advancements in various fields, including pattern recognition and image classification, have been made thanks to modern computer vision and machine learning methods. The capsule network is one of the advanced machine learning algorithms that encodes features based on their hierarchical relationships. Basically, a capsule network is a type of neural network that performs inverse graphics to represent the object in different parts and view the existing relationship between these parts, unlike CNNs, which lose most of the evidence related to spatial location and requires lots of training data. So, we present a comparative review of various capsule network architectures used in various applications. The paper’s main contribution is that it summarizes and explains the significant current published capsule network architectures with their advantages, limitations, modifications, and applications.

In this Paper, we create an augmented reality cricket broadcasting application that uses player recognition and automatic detection during play to display player personal data. The system uses AdaBoost to detect player and player face and use PAL based face recognition model to recognize the faces of players on the field. The system is trained on a large dataset of cricket game footage and achieves high accuracy in detecting and recognizing players’ faces even with several conditions such as occlusion, non-uniform illumination and pose variation. The system has the potential to enhance the viewing experience of cricket games by providing real-time player identification and statistics. The system can also be used in other sports to provide similar benefits. The paper discusses the system’s methodology, results, and implications for the future of sports broadcasting. Overall, the system provides a promising solution for automatic player face detection and recognition in sports broadcasting.

Network on Chip (NoC) is a communication framework for the Multiprocessor System on Chip (MPSoC). It is a router-based communication system. In NoC architecture, nodes of MPSoC are communicating through the network. Different routing algorithms have been developed by researchers, e.g., XY, intermittent XY, DyAD, and DyXY. The main problems in these algorithms are congestion and faults. Congestion and faults cause delay, which degrades the performance of NoC. A congestion-aware algorithm is used for the distribution of traffic over NoC and for the avoidance of congestion. In this paper, a congestion-aware routing algorithm is proposed. The algorithm works by sending congestion information in the data packet. The algorithm is implemented on a mesh NoC using FPGA. The proposed algorithm decreases latency, increases throughput, and uses less bandwidth in sharing congestion information between routers in comparison to the existing congestion-aware routing algorithms.

Due to the three-dimensional formation and flexibility, a human face may appear different in numerous events. Researchers are developing robust and efficient algorithms for face detection, face recognition, and face expression analysis, causing several difficulties due to face poses, illumination, face expression, head orientation, occlusion, hairstyle, etc. To determine the effectiveness of the algorithms, it needs to be tested using a specific benchmark of face images/databases. Face pose is an important factor that severely reduces the recognition ability. In this paper, two contributions are made: (i) a dataset of face images with multiple poses is introduced. The dataset includes 850 images of 50 individuals under 17 different poses (0^°, 5^°, 10^°, 15^°, 20^°, 25^°, 30^°, 35^°, 55^°, -5^°, -10^°, -15^°, -20^°, -25^°, -30^°, -35^°, -55°). These images were captured closed to real-world conditions in the time span of five months in COMSATS University, Abbottabad Campus. Face images included in this dataset can reveal the efficiency and robustness of future face detection and face recognition algorithms. (ii) A comparative analysis of three face recognition algorithms such as PAL, PCA, and LDA is presented based on the proposed face database.