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The proposed work investigates the performance enhancement of keystroke biometric recognition using soft biometric with filter and Score Boost Weighting (SBW) scheme. Usually, Keystroke recognition performance is lower due to user’s emotional behaviour or distraction, typing patterns vary from user normal position which causes recognition error of genuine user for degrading the recognition accuracy. To address this problem, this work presents Dual Matcher with fusion to reduce the false rejection of genuine user to improve the accuracy of keystroke recognition. In this paper, soft biometric is used as secondary information to improve the recognition accuracy for primary keystroke biometric system. Specifically, soft biometrics provides additional support for keystroke biometric recognition at the combination approach. The performance of keystroke system can be further improved using SVM as machine learning under the score level fusion in the combination approach. Lastly, the fusion technique is used to combine the primary and secondary biometric. The new approach with score fusion enhances the overall performance of keystroke biometric system with 99% accuracy. Maximum of 2% improvement is achieved compared to existing works.

In medical image processing, segmentation and classification plays a significant part in prediction of affected region from given input image. The novel segmentation and classification model to segment tumor region and to identify the abnormality category from MR brain image is projected. In preprocessing, image filtering by distribution-based adaptive median filtering technique to prove smoothness to the image and to eliminate the noise component is provided. Further the skull region is removed by using adaptive threshold-based edge detection with canny method. In the segmentation, a novel multiangle cellular automata model to predict the region of interest, i.e., tumor spot is discussed. The classification performance is improved by novel texture extraction and optimal feature selection method named as dynamic angle projection pattern and priority particle cuckoo search optimization, respectively. These optimized features are given to support vector machine (SVM) and pointing kernel classifier (PKC) to classify the abnormality level of segmented image. This work can be compared with existing systems for the parameters of sensitivity, specificity, accuracy, FPR, TPR, and ROC. Our proposed classifier PKC’s performance is good when compared with other technique. The sensitivity, specificity, and accuracy of 85.9155, 94.3396, 89.5161, and 95.7746%, 100, and 97.5806% are obtained in SVM and PKC, respectively. SVMs are linear up to 0.8 for higher values of FPR and reaches the stable point prior to the maximum value. But, the stable operation is achieved in proposed PKC at 0.9. Result shows that the improvement in performance of proposed PKC.

MANETs (Mobile Adhoc Network) are a set of portable mobile nodes which are infrastructure less and also self-configurable. Limited energy batteries are the major constrain in MANETs which may not be easily recharged or replaced resulting in short transmission range of each device. Power failure disturb the overall network lifetime. Therefore energy consumption is a major notable issue. Battery power and mobility imposes a major challenge to meet the demands of the upcoming scenarios. Our proposed Power optimized Cross Layer design based Protocol conserves the energy along with optimal network lifetime and boosts the overall performance of the network.

The performance of the wavelets within the field of image process is standard. Multiwavelets is the next step in riffle theory and it takes the performance of wavelets to the next level. In this work the performance of the Integer Multiwavelet transform (IMWT) for lossy compression has been studied. The Proposed IMWT shows sensible performance in lossy reconstruction of the images than that of Existing lossy reconstruction. This work utilizes the performance of the Proposed IMWT for lossy compression of images with encoding techniques like Magnitude set coding and Run Length Encoding. The transform coefficients are unit coded by means of Magnitude set coding and run length coding techniques which in turn results with low bits. The transform coefficient matrix is coded on not taking under consideration of the sign values using the Magnitude Set--Variable Length Integer illustration. The sign data of the coefficients is coded as bit plane with zero thresholds. This Bit plane may be used as it is or coded to scale back the bits per pixels. The Simulation was exhausted using Matlab.

Adaptive equalization is an accepted method to mitigate the Inter-Symbol Interference (ISI) in wireless communication. Frequently, adaptive algorithm must needs transmission of well-known training sequence to track the time varying characteristics of the channel and hence make the most of superfluous bandwidth. It is also not viable to have training sequences in all types of transmissions (e.g. non-cooperative environment). Blind algorithm is a concept used to track the time varying characteristics of the channel in the deficiency of training sequence. Nevertheless, it leads to slow convergence. In this paper, the performance of Sato algorithm and Godard based blind algorithm is compared for PAM signal.

Segmentation of biomedical images plays an important role in many applications especially in medical imaging, forming an important step in enabling qualification in the field of medical research as well as clinical practices. Magnetic resonance imaging is normally used to distinguish and enumerate multiple sclerosis lesions in the brain. Recently multiple sclerosis lesion of segmentation is the challenging issue due to special variation, low size and unclear boundaries. Since usual technique for brain MRI tumour detection and classification is manual investigation but it is varied from person to person and also very time consuming. Many new methods have been proposed to segment lesions automatically. This paper proposed segmentation of MRI brain tumour using cellular automata and classification of tumour by pointing kernel classifier (PKC). The utilisation of modified Cuckoo search with the priority values and the PKC in proposed mix model for optimisation of textural features (M-MOTF) provides the significant improvement in classification performance with low dimensionality. The proposed system has been validated with the support of real time data set from Frederick National Laboratory and the experimental results showed improved performance.

Adaptive equalization is a well known method to minimize the Inter-Symbol Interference (ISI) in wireless communication. Often, adaptive algorithm requires transmission of known training sequence to track the time varying characteristics of the channel and hence utilizes additional bandwidth. It is also impractical to have training sequences in all types of transmissions (e.g. non-cooperative environment). Blind algorithm is a concept to track the time varying characteristics of the channel in the absence of training sequence. However, it leads to slow convergence. In this paper, we compare the performance of adaptive LMS algorithm and SATO based blind algorithm for PAM signal.

Mobile ad-hoc networks (MANET) are collections of battery powered wireless mobile nodes dynamically forming a temporary network without the use of any pre-defined network infrastructure or centralized administration. Nodes die out early due to limited battery power which ultimately affects the network's lifetime. Different actions of the nodes like transmitting, receiving, overhearing consume available energy. In order to increase the system's life-time, energy has to be conserved. Our proposed protocol improves on MAC by using power control for the RTS, CTS and DATA frame transmissions; remarkably reducing energy consumption and increase network throughput and lifetime. We follow a slow start principle for the RTS frame transmission power instead of keeping them at constant maximum transmission power and by using RSS the minimum required transmit power is used for DATA-ACK transmissions. Our scheme also allows for more simultaneous collision-free transmissions to take place which improves the overall performance of the network.

Multiwavelets are an advance of the well established wavelet theory. The performance of the wavelets in the field of image processing is well known. Multiwavelets take wavelets a step ahead in performance. In this work the performance of the Integer Multiwavelet Transform (IMWT) for Lossy compression has been studied. The IMWT shows good performance with reconstruction of the images. This work analyses the performance of the IMWT for lossy compression of images with Magnitude set coding. The Transform coefficients are coded using the Magnitude set coding and run length Encoding techniques. The Transform coefficient matrix is coded without taking into account of the sign by using the Magnitude Set — Variable Length Integer representation. The sign information of the coefficients is coded as bit plane with zero thresholds. The Bit plane so formed can be used as it is or coded to reduce the bpp. The Simulation was done in Mat lab.

Today the performance of the Multiwavelets plays a vital role in the field of image processing which is well known from Wavelet theory. Now a day's Multiwavelets take wavelets a step ahead in its performance. In this paper the performance of the Integer Multiwavelet Transform (IMWT) for Lossless has been studied. The IMWT provides good result with the image reconstructed. In this paper the performance of the IMWT for lossless compression of images with Magnitude set coding have been obtained. The Transform coefficients are coded with Magnitude set coding and run length Encoding techniques. The Transform coefficient matrix is coded without considering the sign, which make uses of Magnitude Set-Variable Length Integer Representation. The sign information of the coefficients is coded inform of bit plane with zero thresholds. The Bit plane obtained can be used as it is or it can be coded to reduce the bits per pixel (bpp). The Simulation was done using Mat lab.

The modern digital high speed wireless communication system demands quick convergence rate and low steady state error. The balancing between the demands can be achieved by opting step size. Thus, it is essential to define new algorithms to equalize channels and mitigate noise in communications. It is renowned that time varying step size blind equalization technique can speed up the convergence rate and minimize the misadjustment. This work presents a variable step size (VSS) approach based on godard blind equalization algorithm to resolve the conflict between the convergence rate and precision of the fixed step-size godard algorithm. The results of this projected approach is compared with the existing variable step size sato algorithm for a pulse amplitude modulated (PAM) input symbol.

This paper presents the architecture, design and implementation of a 16 point FFT processor for a high speed wireless local area network. The 110000-transistor chip is implemented in 0.6 /spl mu/m TLM CMOS, operates worst case at 50 MHz at a supply voltage of 2.5 volts, and consumes 80 mW.

This paper presents the architecture and implementation of a single chip 25 Mbps discrete multi-tone (DMT) modem. It forms the baseband processor for a 25 Mbps wireless local area network operating in the newly created 5 GHz U-NII band. The 243000 transistor chip is implemented in 0.6 /spl mu/m TLM CMOS, occupies 5.1 mm by 5.6 mm, and is designed to run at 50 MHz with a 2.5 V supply.

This paper presents the architecture, design and implementation of a 50 MHz FFT processor for a high speed Wireless Local Area Network. The 110,000 transistor chip is implemented in 0.6 /spl mu/m TLM CMOS and uses a custom design flow that allows the rapid design of high speed, high density and low power, process independent, DSP datapaths and related logic directly from a Verilog description.

This chip, fabricated in a 0.25/spl mu/m 5M1P CMOS process with 3.7M transistors, implements a fully-compliant IEEE 802.11a PHY modem. The IEEE 802.11a standard provides for wireless local area networks (WLANs) with a physical layer based on coded orthogonal frequency domain multiplexing (COFDM) modulation, delivering data rates up to 54Mb/s, operating in the 5GHz UNII frequency bands. The article outlines the role of a physical layer (PHY) modem in such a WLAN system. The PHY modem lies between a medium access controller (MAC) and a 5GHz radio transceiver, and is responsible for demodulating and modulating a baseband analog signal with data from the MAC. In 802.11a the baseband signal is a 64 subcarrier COFDM signal with BPSK, QPSK, QAM16 or QAM64 modulated subcarriers.

Describes a new type of magnetoresistor based on magnetic composite material. This device exhibits a magnetoresistance which is comparable to that of conventional magnetoresistors but can be realised with a very low cost technology. The theoretical analysis of the magnetoresistance characteristics of this device is also described.