USING VLSI TO EVALUATE BIOELECTRIC SIGNALS AND BIOMETRIC SYSTEMS FOR HUMAN RECOGNITION

This paper discusses a VLSI implementation of a code compression strategy that can be used in biosignal processors with limited storage space. Improved Code density is becoming more essential in processor design because it decreases the need for limited resource memory while also improving processor efficiency in terms of other important design parameters including power consumption and performance. Code compression further increases portability, allowing for the introduction of System on Chip (SOC). For compressing neuronal signals such as Electroencephalogram (EEG), Electrocardiogram (ECG), and Electromyogram, a dictionary-based data compression technique is implemented here that offers a significant improvement in compression efficiency without adding any additional decompression penalty (EMG). When this strategy is applied in VLSI technology, it saves 28% of memory. Since memory uses a large portion of the processor's resources, this can also minimise power consumption. This paper examines bioelectrical signals for use as a biometric for human identification. The electrical activity of the brain reported in Electrocardiogram (ECG) and Electroencephalogram (EEG) signals has special features among individuals, according to research. Other bioelectrical signals, such as the Galvanic skin response (GSR), Electromyogram (EMG), Electrooculography (EOG), and Mechanomyogram (MMG), may be used as biometrics because they process information differently among individuals. Universality, measurability, uniqueness, and robustness are all desirable characteristics for using these signals as biometrics. They also have the innate attribute of vitality, which denotes the existence signals. Bioelectrical signals are extremely private and exclusive to an individual. The inconsistency of the signal, the lack of standardisation of signal characteristics, and the low energy are all problems with using bioelectrical signals as biometrics. Individuality in bioelectrical messages, on the other hand, is a problem that has yet to be solved. Biometrics is a field that searches across a large archive and collects only the relevant information in order to speed up biometric identification and recognition processes. Compression of images is an essential part of the procedure. To meet the real-time requirements of online application processing, various Very Large Scale Integration (VLSI) architectures have arisen. This paper investigates a number of strategies that aid in the rapid operation of the transform stage of image compression. The survey considers a variety of parameters that could be involved in high-speed optimizations, such as computing time, silicon field, memory space, and so on.