The human brain tends to follow a rhythm. Sound has a significant impact on our physical and mental health. This sound technology uses binaural beat by generating two tones of marginally different frequencies in each individual ear to facilitate the improved focus of attention, emotion, calming, and sensory organization. Binaural beat helps in memory boosting, relaxation, and work performance. Again because of hearing a binaural beat sound, brainwave stimuli can be diagnosed to pick up a person’s sensitive information. Using this technology in brain-computer interfacing, it is possible to establish a communication between the brain and the computer. Thus, it enables us to go beyond our potential. The aim of this study is to assess the impact and explore the potential contribution of binaural beat to enhancement of human brain performance.

In most of the cluster-based routing protocols for wireless sensor networks (WSNs), cluster heads (CHs) are selected from the normal sensors which may expire rapidly due to fast energy diminution for such an additional workload. As a consequence, the network lifetime of such cluster-based routing protocol reduces drastically. To resolve these constraints, in this study, we proposed a gateway-based routing protocol-namely Energy-Aware Gateway Based Routing Protocol (EAGBRP) for WSNs. In our proposed protocol, the deployed sensor nodes of a WSN were divided into five logical regions based on their location in the sensing field. The base station (BS) was installed out of the sensing area, and two gateway nodes were inaugurated at two predefined regions of the sensing area. The CH in each region is independent of the other regions and selected based on a weighted election probability. We implemented our proposed routing protocol through simulations. To evaluate the performance of our EAGBRP, we simulated SEP, M-GEAR, and MGBEHA (4GW) protocols. The network lifetime, throughput, and residual energy parameters are utilized for performance analysis. It is revealed from the performance analysis results that WSNs with EAGBRP achieve maximum network lifetime and throughput over other considered protocols with minimum energy consumption.

In recent few years, hand gesture recognition is one of the advanced grooming technologies in the era of human computer interaction and computer vision due to a wide area of application in the real world. But it is a very complicated task to recognize hand gesture easily due to gesture orientation, light condition, complex background, translation and scaling of gesture images. To remove this limitation, several research works have developed which is successfully decrease this complexity. However, the intention of this paper is proposed and compared four different hand gesture recognition system and apply some optimization technique on it which ridiculously increased the existing model accuracy and model running time. After employed the optimization tricks, the adjusted gesture recognition model accuracy was 93.21% and the run time was 224 seconds which was 2.14% and 248 seconds faster than an existing similar hand gesture recognition model. The overall achievement of this paper could be applied for smart home control, camera control, robot control, medical system, natural talk, and many other fields in computer vision and human-computer interaction.

Face recognition is truly one of the demanding fields of biometric image processing system. Within this paper, we have implemented Back Propagation Neural Network for face recognition using MATLAB, where feature extraction and face identification system completely depend on Principal Component Analysis (PCA). Face images are multidimensional and variable data. Hence we cannot directly apply Back Propagation Neural Network to classify face without extracting the core area of face. So, the dimensionality of face image is reduced by the Principal Component Analysis algorithm then we have to explore unique feature for all stored database images called eigenfaces of eigenvectors. These unique features or eigenvectors are given as parallel input to the Back Propagation Neural Network (BPNN) for recognition of given test images. Here test image is taken from the integrated webcam which is applied to the BPNN trained network. The maximum output of the tested network gives the index of recognized face image. BPNN employing PCA is more robust and reliable than PCA based face recognition system.

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