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Impedance Cardiography (ICG) is a noninvasive method for indirect measurement of stroke volume, monitoring the cardiac output and observing the other hemodynamic parameters by the blood volume changes in the body. The blood volume changes inside a certain body segment due to a number of physiological processes are extracted in the form of the impedance variations of the body segment. The ICG analysis provides the heart stroke volume in sudden cardiac arrest. In the clinical environment desired ICG signals are influenced by several physiological and non-physiological artifacts.As these artifacts are not stationary in nature, we proposed adaptive filtering techniques to eliminate the artifacts. In this paper we used Least Mean Square (LMS), Least Mean Fourth (LMF), Median LMS (MLMS), Leaky LMS (LLMS), and Dead Zone (DZLMS) adaptive techniques to eliminate artifacts from the desired signals. Several adaptive signal enhancement units (ASEUs) are developed based on these adaptive techniques, and evaluated on the real ICG signal components. The ability of these algorithms is evaluated by performing the experiments to eliminate the various artifacts such as sinusoidal artifacts (SA), respiration artifacts (RA), muscle artifacts (MA) and electrode artifacts (EA). Among these techniques, the DZLMS based ASEU performs better in the filtering process. The signal to noise ratio improvement (SNRI) for this algorithm is calculated as 11.9140 dB, 7.3657 dB, 10.4060 dB and 10.5125 dB respectively for SA, RA, MA and EA. Hence, the DZLMS based ASEUs are well suitable for ICG filtering in the real time health care monitoring systems.

Impedance Cardiography (ICG) evaluation facilitates the volume of heart stroke in the sudden cardiac arrest. It is a noninvasive method for measurement of stroke volume, cardiac output monitoring and observing the hemodynamic parameters by changes in the body blood volume. Bloodvolume changes caused due to various physiological processes is extracted in the form of the variations in the impedance of the body segment. In the real time clinical environment during the extraction the ICG signals are influenced with several artifacts.As these artifacts are not stationary in nature, we can’t predict their characteristics. Hence,we developed several hybrid adaptive filtering mechanisms to improve the ICG signals resolution. Least mean square (LMS) algorithm is the basic enhancement technique in the adaptive filtering. However, in the non-stationery situation the LMS algorithm suffers with low rate of convergence and weight drift problems. In this paper we developed some hybrid variantsof LMS algorithm those are Leaky LMS (LLMS) for ICG signal enhancement. More over to progress the convergence rate, filtering capability and to reduce the computational complexity we also developed various sign versions of LLMS algorithms. The sign variants of LLMS algorithms are sign regressor LLMS (SRLLMS), Sign LLMS (SLLMS), and Sign Sign LLMS (SSLLMS). Severaladaptive signal enhancement units (ASEUs) are developed based on adaptive algorithms and performance is evaluated on the real ICG signal taken from MIT-BIT database. To ensure the efficiency of these algorithms, four experiments were performed to eliminate the various artifacts such as sinusoidal artifacts (SA), respiration artifacts (RA), muscle artifacts (MA) and electrode artifacts (EA). Among these techniques, the ASEU associated with SRLLMS performs better in the artifacts filtering process. The signal to noise ratio improvement (SNRI) for this algorithm is calculated as 9.3388 dBs, 5.7514 dBs, 8.4449 dBs and 8.7358 dBs respectively for SA, RA, MA and EA. Hence, the SRLLMS based ASEUs are more suitable in ICG signal filtering in real time health care sensing systems.

The continuous progress of the wireless communication technology provides an intelligent and efficient transportation system through vehicular ad-hoc networks (VANETS) to mitigate traffic jams and road fatalities, which improves safety of passengers and traffic flow. Many researchers, vehicle manufacturers, and telecommunication industries are working on VANETS to construct the next generation transport system. In VANETS, vehicles, equipped with wireless devices, exchange the traffic-related information with other vehicles and the fixed road side units (RSUs). The information shared between vehicles and RSUs in VANETS must be secure. For secure communications in VANETS, many cryptographic schemes were proposed in different settings, and most of the schemes are using bilinear pairings over elliptic curves. But the computation of a bilinear pairing is very expensive. Also the verification of signatures/messages sent by vehicles increases the computational workload on RSUs. In order to improve computational efficiency and transmission overhead, in this paper, we present an efficient pairing-free certificateless authentication scheme with batch verification for VANETS. We designed the scheme in pairing-free environment which improves the communication and computational efficiency. The proposed scheme supports batch verification, which significantly reduces the computational workload on RSUs in VANETS. The proposed scheme is proven secure in the random oracle model and meets the security requirements, such as authenticity, integrity, traceability, anonymity, and revocation. We compared our scheme with well-known existing schemes, and the efficiency analysis shows that the proposed scheme is more efficient.