Rishabh Jain

Bio: Rishabh Jain is an academic researcher from VIT University. The author has contributed to research in topics: Cellular network & Network performance. The author has an hindex of 2, co-authored 4 publications receiving 9 citations.

Automatic Attendance System Using Face Recognition Technique

Abstract: Recognition of human face is an important domain in unique identification of humans. It is currently being widely used in many industrial applications, such as video monitoring systems, human–computer interaction, and automatic gate control systems and for securing networks. Every university uses some method of attendance to keep a record of the number of students or people who attended that particular lecture. This paper delineates a method for taking attendance of people in a classroom which integrates the face recognition technology using local binary patterns histograms (LBPH) algorithm, along with face detection by Haar feature-based cascades and distance-based clustering. The proposed system records the attendance of the people in a classroom environment autonomously and provides the user with an output as a spreadsheet describing the attendance.

Dynamic Sectorization and parallel processing for device-to-device (D2D) resource allocation in 5G and B5G cellular network

Abstract: Beyond Fifth Generation (B5G) network aims to provide a very high data rate with minimum latency to an ultra-dense user environment. To achieve this demand, the possible approaches are network-centric and device-centric approach. In a network-centric approach, a new frequency band is introduced, and the existing network infrastructure is modified. The device-centric approach does not require any modification in the existing network infrastructure, and the demand of B5G network is achieved through optimum resource allocation methodology. Device-to-Device (D2D) is an effective device-centric approach that supports the B5G network. In this paper, we propose a dynamic sectorization technique in which eNodeB (eNB) varies the number of sectors dynamically in the network and allocates the Resource Block (RB) to D2D users. Sectoring improves Signal-to-Interference-Noise-Ratio (SINR) and network performance. We derive an expression for the probability of successful transmission and threshold to make a decision on the number of sectors based on available RBs and D2D users in the network. Further, dynamic sectoring helps eNB to perform parallel processing for reducing the denial of the request. Simulation results validate the probability of successful transmission of D2D pairs with available RBs and the effectiveness of parallel processing to reduce the denial of request with improved SINR.

Patient Health Monitoring System and Detection of Atrial Fibrillation, Fall, and Air Pollutants Using IoT Technologies

Abstract: The objective of IoT in healthcare is to empower people to live healthy lives by wearing connected equipment. The healthcare industry has perpetually been in the forefront in the adoption and utilization of information and communication technologies (ICT) for the efficient healthcare administration). Detection of atrial fibrillation is done by checking the variations in the period of the heart rate. If a patient has atrial fibrillation, the period between each heartbeat will vary. A gas sensor is used to check the quality of air and a MEMS sensor to detect the fall of the body. The MEMS sensor is a compact device that collects comprehensive physical information and uses the gateway and cloud to analyze and store information.

Smart Sectorization in Device-to-Device (D2D) Communication

Abstract: The cellular systems require the signals to travel through a base station BS that entirely controls the communication taking place between two devices. However, device to device communication reduces the load on a base station and enables two devices in close proximity to communicate directly. This is known as a device centric approach. Sectorization involves replacing an omnidirectional antenna with directional antennas, achieved by having three or six sectors. In this paper, a smart sectorization scheme is presented that enables the number of sectors to change in accordance with the number of D2D users and hence reduces interference.

Face Recognition Systems: A Survey

Abstract: Over the past few decades, interest in theories and algorithms for face recognition has been growing rapidly. Video surveillance, criminal identification, building access control, and unmanned and autonomous vehicles are just a few examples of concrete applications that are gaining attraction among industries. Various techniques are being developed including local, holistic, and hybrid approaches, which provide a face image description using only a few face image features or the whole facial features. The main contribution of this survey is to review some well-known techniques for each approach and to give the taxonomy of their categories. In the paper, a detailed comparison between these techniques is exposed by listing the advantages and the disadvantages of their schemes in terms of robustness, accuracy, complexity, and discrimination. One interesting feature mentioned in the paper is about the database used for face recognition. An overview of the most commonly used databases, including those of supervised and unsupervised learning, is given. Numerical results of the most interesting techniques are given along with the context of experiments and challenges handled by these techniques. Finally, a solid discussion is given in the paper about future directions in terms of techniques to be used for face recognition.

Facial recognition using Haar cascade and LBP classifiers

Abstract: Facial Recognition is the biometric technique used in face detection. The task for validating or recognizing a face from the multi-media photographs is done using facial recognition technique. With the evolution of advanced society the requirement for face identification has been really important. Detection and identification of faces has been grown worldwide. It owes the demand for security such as authorization, national safety and other vital circumstances. There are number of algorithms for facial detection. This paper aspires to present the comparison of two face recognition techniques Haar Cascade and Local Binary Pattern edified for the classification. As a result the accuracy of Haar Cascade is more than the Local Binary Pattern but the execution time in Haar Cascade is more than Local Binary Pattern.

Review of the Internet of Things for Healthcare Monitoring

Abstract: These days, individuals are increasingly bound by indoor living, investing less energy outside As a result, the importance of monitoring air quality inside to make indoor living progressively appropriate has risen immensely The advancement of the Internet of Things (IoT) will significantly encourage patient analysis and monitoring procedures, as small IP-based wireless sensors can be placed on the patient’s body For example, one’s blood pressure and coronary heart rate can be observed remotely and regularly using physiological parameters Over the past few years, the IoT has experienced one of the most significant developments of the 21st century The IoT describes a network of physical items, or “things,” that are embedded with sensors, software, and other technologies that are able to connect and exchange data with other devices and systems over the internet These devices range from ordinary household objects to sophisticated industrial tools There are more than seven billion connected IoT devices today, and experts are expecting this number to grow to 10 billion by 2020 and 22 billion by 2025 This paper attempts to review and understand the utilization of the IoT in customized healthcare and how excellent healthcare can be acquired at a low cost We will clarify, in brief, how the IoT functions and how wireless and sensing systems are utilized in order to carry out efficient healthcare applications

Suspicious Activity Recognition Using Proposed Deep L4-Branched-Actionnet With Entropy Coded Ant Colony System Optimization

Abstract: Intelligent visual surveillance systems are attracting much attention from research and industry. The invention of smart surveillance cameras with greater processing power has now been the leading stakeholder, making it conceivable to design intelligent visual surveillance systems. It is possible to assure the safety of people in both homes and public places. This work aims to distinguish the suspicious activities for surveillance environments. For this, a 63 layers deep CNN model is suggested and named “L4-Branched-ActionNet”. The suggested CNN structure is centered on the alteration of AlexNet with added four blanched sub-structures. The developed framework is first transformed into a pre-trained framework by conducting its training on an object detection dataset called CIFAR-100 with the SoftMax function. The dataset for suspicious activity recognition is then forwarded to this pretrained model for feature acquisition. The acquired deep features are subjected to feature subset optimization. These extracted features are first coded by applying entropy and then an ant colony system (ACS) is utilized on the entropy-based coded features for optimization. The configured features are then fed into numerous SVM and KNN based classification models. The cubic SVM has the highest efficiency scores, with a performance of 0.9924 in order of accuracy. The proposed model is also validated on the Weizmann action dataset and attained an accuracy of 0.9796. The successful findings indicate the suggested work’s soundness.

Symbiosis Between D2D Communication and Industrial IoT for Industry 5.0 in 5G mm-Wave Cellular Network: An Interference Management Approach

Abstract: Industrial Internet of Things (IIoT) paves way into Industry 5.0, which incorporates human–machine collaboration, thereby making manufacturing industry efficient. As 5G architecture supports massive IoT connectivity and has higher spectrum efficiency, device-to-device (D2D) communication is favorable at 28 GHz. While transmitting data from sensors to end user through IoT network, interference affects the system. Thus, an efficient resource allocation scheme is needed for minimizing interference and increasing data rate. Here, formulated problem is divided into two subproblems, channel assignment and power optimization in order to lower computational complexity. A partial resource multiplexing scheme is proposed that will allocate channels to available D2D users. Later, power optimization problem is formulated which is determined through Lagrangian dual optimization technique. Dynamic sectorization overcomes issue of increase in user traffic. Stability factor, fairness index (FI), and energy efficiency depict the performance superiority of proposed scheme over existing schemes. Simulation results prove efficacy of proposed system.