Syed Hasan Saeed

Experimental use of electronic nose for analysis of volatile organic compound (VOC)

An electronic nose is an instrument intended to mimic the human sense of smell. Electronic noses (e-nose) employ an array of chemical gas sensors, a sample handling system and a pattern recognition system. Pattern recognition provides a higher degree of selectivity and reversibility to the system leading to an extensive range of applications. These ranges from the food and medical industries to environmental monitoring and process control. Many other types of different gas sensors available. These include conducting polymers (CP), metal oxide semiconductors (MOS), piezoelectric, optical fluorescence, quartz crystal microbalance (QCM) and Amperometric gas sensors. The ideal gas sensor would exhibit reliability, robustness, sensitivity, selectivity and reversibility. High selectivity with high reversibility is difficult to attain. After signal processing and feature extraction the output of the sensors provide a unique “smellprint” for that substances which can be used to classify, measure concentration, or verify quality. The present paper illustrates the function of electronic nose, its application and investigates the effective use of e-nose in detecting gases that have some smell developed by the volatile organic compounds (VOC) like ethanol, acetone and benzene at different concentrations. The response and characteristics prove that the Electronic nose is a reliable instrument which can be used for environment control (air quality, pollutants, and gas emission levels), medical science (urine, skin and breath odour etc.), food industry (coffee, milk, soft drink fish, meat etc.), pharmaceutics, chemical industry, Defence and security industries (detecting humanitarian land mines etc.) and semiconductor industrial processes.

Diagnosis of narcolepsy sleep disorder for different stages of sleep using Short Time Frequency analysis of PSD approach applied on EEG signal

Narcolepsy is a sleep disorder in which the subjects brain is chronically unable to regulate sleep-wake cycles. This study aims at identifying narcoleptics from normal individuals. Our approach involves implementation of Short Time Frequency analysis of PSD approach applied on EEG signals, post filtering ROC-LOC channels. In this research article, a comprehensive analysis of EEG signals for S0, S2 and S3 stages of sleep has been performed. The analysis and calculation is performed in all stages of sleep and a comparative database, with Power Spectral Density comparisons between healthy and affected individuals, been prepared. Hence segregation of narcoleptic occurrence events based on delta and alpha segments, of EEG signals, has been successfully performed herein.

Diagnosis of insomnia sleep disorder using short time frequency analysis of PSD approach applied on EEG signal using channel ROC-LOC

Insomnia is a sleep disorder in which the subject encounters problems in sleeping. The aim of this study is to identify insomnia events from normal or effected person using time frequency analysis of PSD approach applied on EEG signals using channel ROC-LOC. In this research article, attributes and waveform of EEG signals of Human being are examined. The aim of this study is to draw the result in the form of signal spectral analysis of the changes in the domain of different stages of sleep. The analysis and calculation is performed in all stages of sleep of PSD of each EEG segment. Results indicate the possibility of recognizing insomnia events based on delta, theta, alpha and beta segments of EEG signals.

Detection of Sleep Disorder Breathing (SDB) Using Short Time Frequency Analysis of PSD Approach Applied on EEG Signal

Sleep disorders may be one of the reasons for concerned sleep. Distressed sleep involves many inabilities such as to fall asleep, to go back to sleep and common waking up during the night. Sleep disorders can be classified under primary and secondary sleep disorders. By the improved recognition of sleep disorders, the variety of treatments now available. In this analysis of several patients of sleep disordered breathing (SDB) and standard people we have calculated an accurate PSD estimate After the analysis of normalized power of standard person a range is defined by which the comparison of normalized power of patients of sleep disordered breathing (SDB) is done. The result of comparison gives the accurate estimate of PSD for sleep disordered breathing. The analysis of patients can be done on a large scale, the more we analyze patients the more accurate results we will get. Working on all channels, with different PSD estimation methods can be very productive.

Implementation of Switching System for High Speed Data Transmission using VHDL

Switching system is used for data transmission between two communicating Channels. This transmission can be done via trunks or it can be fully wireless, technically termed as Telephone Switching and Mobile switching, respectively. The implementation of the switching system using VHSIC hardware description language is more reliable and efficient than the previous switching system (1). It converts entire bulky switching unit which consists of routers, multiplexers, decoders, counters in to a single integrated circuit . Simulation results are presented for transfer of data from input subscriber to the output subscriber using sequential write / random read mode with the timing diagram(2). To verify the transfer of data from input subscriber to the output subscriber a 32 bit op-code is assumed in which each bit represents a specific function. In our Paper we have worked on Switching System for two different channels using VHDL, each channel have 16 users. They can communicate either intra channel or inter channel. We are also providing Caller ID facility. The incoming data through the inlets are written into the data memory and later read out to the appropriate outlets. The incoming and outgoing data are usually in serial form whereas the data are written into and read out of the memory in parallel form. Thats why it is necessary to perform serial to parallel conversion and parallel to serial conversion at the inlets and outlets respectively. We have selected 32 bit op-code for communication among these channels because its ability to define all the functions. This 32 bit op-code is responsible for caller id and transmission and reception of particular data. I have implemented my work with the help of Xilinx ISE and Modelsim 10.2a.