As technology has advanced, data has become more and more important. The more breakthroughs are achieved, the more data is needed to support them. As a result, more storage is required in the system's memory. Compression is therefore required. Before it can be stored, the data must be compressed. To ensure that information is not lost, efficient compression is necessary. This also makes sure that there is no redundancy in the data that is being kept and stored. Compressive sensing has emerged as a new field of compression thanks to developments in sparse optimization. Rather than relying just on compression and sensing formulations, the theory blends the two. The objective of this thesis is to analyze the concept of compressive sensing and to study several reconstruction algorithms. Additionally, a few of the algorithms were put into practice. This thesis also included a model of the ECG, which is vital in determining the health of the heart. For the most part, the ECG is utilized to diagnose heart illness, and a modified synthetic ECG can be used to mimic some of these arrhythmias.

My thesis is to understand the concept of compressive sensing algorithms. Compressive sensing will be a future alternate technique for the Nyquist rate, specific to some applications where sparsity property plays a major role. Software implementation of compressive sensing (CS) takes more time to reconstruct a signal from CS measurements, so we use the orthogonal matching pursuit and basis pursuit algorithms. We have used an image size of 256x256 is used for reconstruction and also implemented a field-programmable gate array (FPGA) of the orthogonal matching pursuit using an image.

Emotions have significant importance in human life in learning, decision-making, daily interaction, and perception of the surrounding environment. Hence, it has become very essential to detect and recognize a person's emotional states and to build a connection between humans and computers. This process is called brain-computer interaction (BCI) and is a vast field of research in neuroscience. Hence, in the past few years, emotion recognition has gained adequate attention in the research community. In this thesis, an emotion recognition system is designed and analyzed using EEG signals. Several existing feature extraction techniques are studied, analyzed, and implemented to extract features from the EEG signals. An SVM classifier is used to classify the features into various emotional states. Four emotional states are detected, namely, happy, sad, anger, and relaxed state. The model is tested, and simulation results are presented with an interpretation. Furthermore, this study has mentioned and discussed the efficacy of the results achieved. The findings from this study could be beneficial in developing emotion-sensitive technologies, such as augmented modes of communication for severely disabled individuals who are unable to communicate their feelings directly.

A modified, single error-correcting, and double error detecting Hamming code, hereafter referred to as modified SEC-DED Hamming code, is proposed in this research. The code requires fewer logic gates to implement than the SEC-DED Hamming code. Also, unlike the popular Hsiao's code, the proposed code can determine the error in the received word from its syndrome location in the parity check matrix. A detailed analysis of the area and power utilization by the encoder and decoder circuits of the modified SEC-DED Hamming code is also discussed. Results demonstrate that this code is an excellent alternative to Hsiao's code as the area and power values are very similar. In addition, the ability to locate the error in the received word from its syndrome is also of particular interest. Primitive polynomials play a crucial role in the hardware realizations for error-correcting codes. This research describes an implementation of a scalable primitive polynomial circuit with coefficients in GF(2). The standard cell area and power values for various degrees of the circuit are analyzed. The physical design of a degree 6 primitive polynomial computation circuit is also provided. In addition to the codes, a background of the already existing SPX GCD computation algorithm is …