Abstract -- QCA is a developing technology that consists of various developing nano-electronic innovations that provide a progressive move to the nano level. Microelectronic manufacturers have been improving the speed and size of electronic devices over the last few decades. In this paper, an approximate adder implemented with quantum- dot cellular automata (QCA) is proposed. The proposed approximate adder is built with a new type of XOR gate that has a more complex structure than previously proposed structures. The most fundamental arithmetic operation is the addition of two binary digits, or bits. The adder is important in many fields, but accuracy is not a concern in most of them. As a result, we proposed a completely unique approximate adder of quantum dot cell automata (QCA). The proposed adder is used to reduce circuit complexity and time delay while maintaining a low error rate. The majority gate in the adder circuit is reduced to minimize the circuit complexity. QCA circuit operation is simulated and verified using QCA Designer bistable vector simulation.

Abstract -- The early detection and diagnosis of COVID-19 and the accurate separation of non-COVID-19 cases at the lowest cost and in the early stages of the disease are among the main challenges in the current COVID-19 pandemic. The COVID-19 pandemic has been causing devastating impacts on the well-being of people around the world as well as the global economy. Motivated by the effort of the open source community on collecting the COVID-19 dataset and the success of Deep Learning on previous studies with chest radiography, this thesis builds a Deep Convolutional Neural Network in order to detect COVID19 using only chest X-Ray images.In the light of the rapidly growing COVID19 pandemic, the need for an expeditious diagnosis of COVID-19 infection became essential. The immediate diagnosis will allow the initiation of the isolation process and adequate treatment as well. While the standard test used for the diagnosis of COVID-19 disease (RT-PCR) is usually time consuming (6 hours up to days in some centers); the need for a highly sensitive test became essential. Many studies have illustrated the utility of chest CT/Ultrasound scan in the diagnoses of COVID-19. This paper evaluates the value of learning techniques classify patient’s normal lung, Covid affected Lung and vaccinated Lung. This demonstrates the potential of the proposed technique in computer aided diagnosis for healthcare applications, especially for COVID-19 classification.

Abstract -- Active Noise Cancellation (ANC) is one of the most effective ways of reducing noise. The active noise reduction headphone is the most successful application of active noise control. Traditional active noise control methods are based on adaptive signal processing with the least mean square algorithm as the foundation. They are linear systems and do not perform satisfactorily in the presence of nonlinear distortions. In this project, ANC is formulated as a supervised learning problem and a deep learning approach, called deep ANC is proposed. Hybrid Active noise cancellation techniques which is the combination of feed forward and feedback techniques, in this project. Large scale multi conditioning is trained to achieve good generalization and robustness against a variety of noises. The goal of ANC systems is to generate an anti-noise with the same amplitude and opposite phase of the primary (unwanted) noise to cancel the primary noise. A Convolutional Recurrent Network (CRN) is trained to estimate the real and imaginary spectrograms of the canceling signal from the reference signal so that the corresponding anti-noise can eliminate or attenuate the primary noise in the ANC system.

Abstract -- Distinguishing identical twins using their face images is a challenge in biometrics. The goal of this study is to construct a biometric system that is able to give the correct matching decision for the recognition of identical twins. We propose a method that uses feature-level fusion, score-level fusion, and decision-level fusion with principal component analysis, histogram of oriented gradients, and local binary patterns feature extractors. In the experiments, face images of identical twins from ND-TWINS-2009-2010 database were used. The results show that the proposed method is better than the state-of-the-art methods for distinguishing identical twins. Variations in illumination, expression, gender, and age of identical twins’ faces were also considered in this study. The experimental results of all variation cases demonstrated that the most effective method to distinguish identical twins is the proposed method compared to the other approaches implemented in this study. The lowest equal error rates of identical twins recognition that are achieved using the proposed method are 2.07% for natural expression, 0.0% for smiling expression, and 2.2% for controlled illumination compared to 4.5, 4.2, and 4.7% equal error rates of the best state-of-the-art algorithm under the same conditions. Additionally, the proposed method is compared with the other methods for non-twins using the same database and standard FERET subsets. The results achieved by the proposed method for non-twins identification are also better than all the other methods under expression, illumination, and aging variations.

Abstract -- This paper considers the exact bit error rate (BER) analysis of a two-user non-orthogonal multiple access (NOMA) system using different modulation. Non- Orthogonal Multiple access (NOMA) is a promising candidate for future mobile networks as it enables improved massive connectivity and low latency. Here, NOMA is tested for Bit- error rate (BER) with the Quadrature phase shift keying (QPSK) modulation and Binary phase shift keying (BPSK). The Bit-error rate of QPSK and BPSK modulated NOMA by multiplexing three users is done in a single frequency carrier. QPSK modulated NOMA and BPSK modulated NOMA are compared to each other to know their better compatibility with each modulation. For simplicity, we have used fixed power allocation. The choice of power allocation has a great significance on NOMA network.