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The main objective of this research is to provide better controlling and stability to B767 aircraft, where a study of elevators for pitch control for the described longitudinal movement is conducted. Intelligent controllers are designed and introduced to the surfaces of the linearized model of B767 aircraft, to identify the performance level based on time response and stability analysis by using different transfer functions. Self-tuning PID Controller and designed hybrid intelligent controller with a combination of PID controller and fuzzy controller are proposed along with few sensors installation to obtain results. A comparative assessment and quantitative analysis of controllers are carried out using MATLAB Simulink. This research has shown that the performance of the aircraft has improved significantly.

It is concerned with a new method for solving multiple attribute decision-making (MADM) problems under sine trigonometric Pythagorean neutrosophic normal interval-valued set (shortly, ST-PyNSNIVS). The ST-PyNSNIVS is a generalization of the Pythagorean neutrosophic interval-valued set (shortly, PyNSIVS), the Pythagorean neutrosophic set (shortly, PyNSS), and the interval-valued neutrosophic set (shortly, IVNSS). In this paper, a new concept of sine trigonometric Pythagorean neutrosophic normal interval-valued weighted averaging (shortly, ST-PyNSNIVWA), sine trigonometric Pythagorean neutrosophic normal interval-valued weighted geometric (shortly, ST-PyNSNIVWG), sine trigonometric generalised Pythagorean neutrosophic normal interval-valued weighted averaging (shortly, ST-GPyNSNIVWA) and sine trigonometric generalized Pythagorean neutrosophic normal interval-valued weighted geometric (shortly, ST-GPyNSNIVWG). Under the operators ST-PyNSNIVWA, ST-PyNSNIVWG, ST-GPyNSNIVWA, and ST-GPyNSNIVWG, we also acquired an algorithm and a owchart that interact with the MADM technique. For the suggested models, which are future expansions of real-life instances, we addressed new sorts of two distances, such as euclidean and hamming distances. The PyNSNIV set technique was used to examine the sine trigonometric aggregation operations. They are more practical and straightforward, and the finest option may be reached in a short period of time. As a result, the defined models conclusions are more exact and closely dependent on Σ. We also compare some of the current models to the suggested models, demonstrating the validity and usefulness of the investigated models. Finally, some enthralling and intriguing findings from the study.

We interact the theory of possibility Pythagorean bipolar fuzzy soft sets, possibility bipolar fuzzy soft sets and define complementation, union, intersection, AND and OR. The possibility Pythagorean bipolar fuzzy soft sets are presented as a generalization of soft sets. Notably, we tend to showed De Morgan’s laws, associate laws and distributive laws that are holds in possibility Pythagorean bipolar fuzzy soft set theory. Also, we advocate an algorithm to solve the decision making problem primarily based on soft set model.

The hypothesis of an ordered b-semiring provides a characterization of semiring and ordered semiring in this paper. We have attempted to investigate the Type-1 bi-ideal, Type-2 bi-ideal, Type-1 bi-base and Type-2 bi-base over ordered b-semiring in detail. Some of their characterizations are obtained through Type-1 bi-base and Type-2 bi-base. Let [Formula: see text] be a Type-1 (Type-2) bi-base of ordered b-semiring and [Formula: see text] such that [Formula: see text] but not [Formula: see text], nor [Formula: see text] ([Formula: see text], nor [Formula: see text]). We introduce the notions of one-quasi order and two-quasi order on an ordered [Formula: see text]-semiring to interact with Type-1 and Type-2 bi-bases generated by element and subset. With the help of some examples, we have shown that Type-1 bi-base and Type-2 bi-base have partial order.

In this experimental investigation based on natural composites such as banana, Bristle coir and jute mixed with different percentage based on weight for the insulation on cylindrical exhaust smog passage to identify the heat transfer properties analysis such as thermal conductivity, thermal resistance and temperature differences. In this investigation 50% of Bristle coir used for three combinations of natural composite only change in percentage of weight varied for banana fiber (25%, 20% and 15%)and jute fiber (25%, 30% and 35%). There are three different thicknesses of natural composites such as 2 mm, 4 mm and 6 mm considered for investigation.

We introduce the notion of neutrosophic subbisemiring(shortly NSBS), level sets of NSBS and neutrosophic normal subbisemiring(NNSBS) of a bisemiring. The concept of neutrosophic subbisemiring is a new generalization of fuzzy subbisemiring over bisemiring. We interact the theory for (��, ��) NSBS and NNSBS over bisemiring. Let A be the neutrosophic subset in S, we show that ���� = (��T A, ��I A, ��F A) is an NSBS of S if and only if all non empty level set ����(t,s) is a subbisemiring of S for t, s ��� [0, 1]. Let A be the NSBS of a bisemiring S and V be the strongest neutrosophic relation of S, we observe that A is an NSBS of S if and only if V is an NSBS of S �� S. Let A1, A2, ..., An be the family of NSBSs of S1, S2, ..., Sn respectively. We show that A1 �� A2 �� ... �� An is an NSBS of S1 �� S2 �� ... �� Sn. The homomorphic image of NSBS is an NSBS. The homomorphic preimage of NSBS is an NSBS. Examples are provided to illustrate our results

The purpose of this study is to improve the cooling of brake disc by modifying the wheel rim by fitting it with an axial ventilator. The orientation angle of these blades in axial ventilator is varied between 0 and 10. The numerical investigation is carried out using commercial CFD code ANSYS Fluent. The pressure difference induced in 5 model is maximum leading to a better air flow through the wheel rim resulting with enhanced cooling of the brake disc. The 5 model reduced the brake disc temperature from 450C to 207.84C which is nearly 54%. The surface heat transfer coefficient and Nusselt number of 5 model is found to be maximum. The resulting improvement in heat dissipation enhances the performance and hence the lifetime of the brake disc. Therefore, the wheel rim with axial ventilator 5 model is found to the suitable configuration so as to achieve enhanced convective cooling of brake disc.

The effects of fuel injection strategies on the characteristics of common rail diesel injection engine using mahua methyl ester (MME20) blend have been investigated. Fuel injection strategies such ...

In this paper, we introduce the concept of interval valued fuzzy weak bi-ideals of $Gamma$-near-rings, which is a generalized concept of fuzzy weak bi-ideals of $Gamma$- near-rings. We also characterize some properties and examples of interval valued fuzzy weak bi-ideals of $Gamma$-near-rings.

The rapid growth of computing has made effective human-computer interaction essential. It is important for the growing number of computer users whose professional schedules will not allow the elaborate training and experience that was once necessary to take advantage of computing. Increased attention to usability is also driven by competitive pressures for greater productivity, the need to reduce frustration, and to reduce overhead costs such as user training. As computing affects more aspects of our lives the need for usable systems becomes even more important. In this paper, types of HCI interaction systems are discussed briefly with their application areas and concluded with the future selection of research in order to carry out further.

In this paper, we have introduce the notion of anti fuzzy soft -semigroups, anti fuzzy soft -ideal, bi-ideal, interior ideal-regular, soft-regular anti fuzzy soft-regular and obtain some interesting properties results and discusse in this paper, {"references":["1.\tM. I. Ali, F. Feng, X. Y. Liu and M. Shabir, On some new operations in soft set theory, Comput. Math. Appl. 57 1547-1553, (2009). 2.\tV. Chinnadurai and K. Arulmozhi, Interval valued fuzzy soft semigroups, Annamalai University science journal 50 35-44 (2016). 3.\tChinram and Jirojkul, on bi-Gamma ideals in Gamma semigroups, songklanakarin sci .technol journal. 29, 231-234(2007). 4.\tP. Dheena and S. Coumaressane, Characterization of regular Gamma semigroups through fuzzy ideals, Iranian journal of fuzzy systems. 4(2), 57-68 (2007). 5.\tP. K. Maji, R. Biswas and R. Roy, Soft set theory, Comput7. Math. Appl. 45, 555-562 (2003). 6.\tD. Molodtsov, Soft set theory first results, Comput. Math. Appl. 37 19-31(1999). 7.\tMuhammad Ifran Ali, Muhammad Shabir and K.P. Shum, on soft ideals over semigroups, Southeast Asian Bulletion of Mathematics. 34, 595-610 (2010). 8.\tMuhammad Gulistan, Muhammad Aslam, Saleem Abdullah, Generalized anti fuzzy interior ideals in LA- semigroups, Applied Mathematics and information science letters an international journal. 2(3), 1-6 (2014). 9.\tT. Nagaish, Anti fuzzy bi- Gamma ideals in Gamma semigroups, International Journal of Algebra 5 (28), 1387-1394 (2011). 10.\tSamit kumar majumder, A study of Gamma semigroups in terms of anti fuzzy ideals, Mathematica Aeterna.1 (8)}, 529-536 (2011). 11.\tS. K Sardar, S. K Majumder and Pavel pal, Characterization of Gamma semigroups in terms of anti fuzzy ideals, Annals of fuzzy mathematics. 5(3), 463-473 (2013). 12.\tM. K. Sen and N. K Saha, On Gamma semigroup, I, Bull. Calcutta Math. Soc. 78, 180-186 (1986). 13.\tM. Shabir and Y.Nawaz, Semigroups characterized by the properties of anti fuzzy ideals, J. Adv. Res. Pure Math. 1 (3), 42-59 (2009). 14.\tSujit kumar Sardar, On Fuzzy Ideals in Gamma Semigroups, International Journal of Algebra. 3 (16), 775 -784 (2009). 15.\tThawhat changphas and Boonyen thongkam, on soft Gamma semigroups, Annals of fuzzy Mathematics and Informatics, 4(2), 217-223 (2012). 16.\tL. A Zadeh, Fuzzy sets Inform and control. 8, 338-353 (1965)."]}

In this paper, we have discussed about the fuzzy soft -regular and -intra regular semigroups and their properties.

Image fusion is defined as the process of combining two or more different images into a new single image retaining important features from each image with extended information content. There are two approaches to image fusion, namely Spatial Fusion and Transform fusion. In Spatial fusion, the pixel values from the source images are directly summed up and taken average to form the pixel of the composite image at that location. Transform fusion uses transform for representing the source images at multi scale. The most common widely used transform for image fusion at multi scale is Wavelet Transform since it minimizes structural distortions. But, wavelet transform suffers from lack of shift invariance & poor directionality and Stationary Wavelet Transform and Wavelet Packet Transform overcome these disadvantages. The Multi-Wavelet Transform of image signals produces a non-redundant image representation, which provides better spatial and spectral localization of image formation than discrete wavelet transform. In this paper, Multi-Wavelet Transform, Stationary Wavelet Transform and Wavelet Packet Transform were combined to form Multi-Stationary Wavelet Packet Transform and its performance in fusion of multi-focused images in terms of Peak Signal to Noise Ratio, Root Mean Square Error, Quality Index and Normalized Weighted Performance Metric is presented.

The fast development of digital image processing leads to the growth of feature extraction of images which leads to the development of Image fusion. Image fusion is defined as the process of combining two or more different images into a new single image retaining important features from each image with extended information content. There are two approaches to image fusion, namely Spatial Fusion and Transform fusion. In Spatial fusion, the pixel values from the source images are directly summed up and taken average to form the pixel of the composite image at that location. Transform fusion uses transform for representing the source images at multi scale. The most common widely used transform for image fusion at multi scale is Wavelet Transform since it minimizes structural distortions. But, wavelet transform suffers from lack of shift invariance & poor directionality and these disadvantages are overcome by Stationary Wavelet Transform and Dual Tree Wavelet Transform. The conventional convolution-based implementation of the discrete wavelet transform has high computational and memory requirements. Lifting Wavelets has been developed to overcome these drawbacks. The Multi-Wavelet Transform of image signals produces a non-redundant image representation, which provides better spatial and spectral localization of image formation than discrete wavelet transform. And there are three levels of image fusion namely Pixel level, Area level and region level. This paper evaluates the performance of all levels of multi focused image fusion of using Discrete Wavelet Transform, Stationary Wavelet Transform, Lifting Wavelet Transform, Multi Wavelet Transform, Dual Tree Discrete Wavelet Transform and Dual Tree Complex Wavelet transform in terms of various performance measures.

The fast development of digital image processing leads to the growth of feature extraction of images which leads to the development of Image fusion. Image fusion is defined as the process of combining two or more different images into a new single image retaining important features from each image with extended information content. There are two approaches to image fusion, namely Spatial Fusion and Transform fusion. In Spatial fusion, the pixel values from the source images are directly summed up and taken average to form the pixel of the composite image at that location. The most common widely used transform for image fusion at multi scale is Discrete Wavelet Transform since it minimizes structural distortions. But, wavelet transform suffers from lack of shift invariance and poor directional selectivity. These two disadvantages are overcome by Stationary and Complex Wavelet Transform. But they are more expansive and this can be compromised by Double Density Wavelet Transform. Image fusion can be performed using three levels namely Pixel, feature and decision level. This paper evaluates the performance of feature level fusion of multi focused images using Discrete, Stationary and Dual Tree Complex wavelet transform in terms of various performance measures.

The fast development of digital image processing leads to the growth of feature extraction of images which leads to the development of Image fusion. Image fusion is defined as the process of combining two or more different images into a new single image retaining important features from each image with extended information content. There are two approaches to image fusion, namely spatial fusion and multi scale transform fusion. In spatial fusion, the pixel values from the source images are directly summed up and taken average to form the pixel of the composite image at that location. Multi scale transform fusion uses transform for representing the source image at multi scale. The most common widely used transform for image fusion at multi scale is Discrete Wavelet Transform (DWT) since it minimizes structural distortions. But, wavelet transform suffers due to poor directionality and does not provide a geometrically oriented decomposition in multiple directions. One way to generalize the discrete wavelet transform so as to generate a structured dictionary of base is given by the Discrete Wavelet Packet Transform (DWPT). This benefit comes from the ability of the wavelet packets to better represent high frequency content and high frequency oscillating signals in particular. However, it is well known that both DWT and DWPT are shift varying. The Dual Tree Discrete Wavelet Transform (DTDWT) introduced by Kingsbury, is approximately shift -invariant and provides directional analysis. And there are three levels for image fusion namely pixel level, area level and region level. In this paper, it is proposed to implement area level fusion of multi focused images using Dual Tree Discrete Wavelet Packet Transform (DTDWPT), extending the DTDWT as the DWPT extends the DWT and the performance is measured in terms of various performance measures like root mean square error, peak signal to noise ratio, quality index and normalized weighted performance metric.

The fast development of digital image processing leads to the growth of feature extraction of images which leads to the development of Image fusion. The process of combining two different images into a new single image by retaining salient features from each image with extended information content is known as Image fusion. Two approaches to image fusion are Spatial Fusion and Transform fusion. Discrete Wavelet Transform plays a vital role in image fusion since it minimizes structural distortions among the various other transforms. Lack of shift invariance, poor directional selectivity and the absence of phase information are the drawbacks of Discrete Wavelet Transform. These drawbacks are overcome by Stationary Wavelet Transform and Dual Tree Complex Wavelet Transform. This paper describes the optimal decomposition level of Discrete, Stationary and Dual Tree Complex wavelet transform required for better pixel based fusion of multi focused images in terms of Root Mean Square Error, Peak Signal to Noise Ratio and Quality Index.

Migraine is characterized by attacks of intense pulsatile and throbbing headache, typically unilateral in nature with or without aura. Migraine affects a substantial fraction (10-20 %) of the world population (more women than men). With regard to the pathophysiology of migraine, several theories have been proposed; the major three are vascular (due to cerebral vasodilatation), neurological (abnormal neurological firing) and neurogenic dural inflammation (release of inflammatory neuropeptides). The drugs used to treat migraine can be divided into two groups: agents that abolish the acute migraine headache and agents aimed at prevention. The acutely acting antimigraine agents (5-HT(1B/1D) receptor agonists) stimulated research interest in the field of migraine. Currently prophylactic treatments for migraine include calcium channel blockers, 5-HT(2) receptor antagonists, beta-adrenoceptor blockers and gamma-amino butyric acid (GABA) agonists. Unfortunately, many of these treatments are non-specific and not always effective. Despite progress, the complex etiology of migraine requires further research, the condition often remains undiagnosed and available therapies are underused. In this review, the evidence that linked the different theories of migraine with its pathophysiology is considered. Furthermore, the present therapeutic targets and future approaches for the acute and prophylactic treatment of migraine are critically evaluated.

In License Plate Recognition algorithms the extraction of the license plate region accurately is the seed and lifeline of the entire LPR system. It consists of a sequence of pre-processing algorithms to remove the bottlenecks like, presence of non uniform outdoor illumination conditions during image acquisition, motion blur due to varying vehicle speed and changing dynamic backgrounds. The acquired images are subject to image corruption due to noise. This paper presents an algorithm namely 2-Stage Median Filter to de-noise the images corrupted by fixed value impulse noise in Indian License Plates. First stage is detection of impulse pixel depends on threshold values and second stage is filtering of corrupted pixels which are replaced by the median of uncorrupted pixels in the filtering window. Experimental results show that the proposed algorithms provide a flexible and reliable way in de-noising the highly corrupted image due to dynamic environmental condition of License plate capturing.

The first and the most important stage for any ALPR system is the correct localization of the license plate within the image captured by the LPR camera. Most of the varieties of techniques reported for localization of license plate are applicable for a very controlled environment. This work focuses on localizing the license plate regions from true color still snapshots captured in a very realistic situation. The technique is based on a novel multi-stage approach for analysis of vertical edge gradients from contrast stretched gray-scale images. Horizontally and vertically oriented rank filters are used to detect clusters of high density of bright edges in the area of the number plate. The proposed approach embeds multi resolution and multi scale edge detection, adaptive searching, color analysis, and affine rectification in a hierarchical frame work for sign detection, with different emphasis at each phase to handle the text in different sizes, orientations, color distributions and backgrounds.

In Intelligent Transportation System, License plate recognition system is one of the most important parts and the license plate localization is the key part of it. It affects the latter phase of the character segmentation and recognition directly. During License Plate Localization phase image enhancement technique is applied to solve the problems such as noise, low contrast, shadow etc in the LP image. Image enhancement techniques are also needed to improve skew correction and segmentation. Here a new approach of edge enhancement for enhancing license plates to improve SNR in detection of License Plate after improving the contrast by using Contrast Limited Adaptive Histogram Equalization is introduced. Presence of nonuniform illumination and shadow in LP image hampers LP localization accuracy which is addressed by the MACOA Image Pre-processing algorithm. This technique improves the contrast by using two thresholds, one Adaptive threshold and other Otsu threshold. Adaptive thresholding technique is used for non uniform noised or half shadowed images, while Otsu threshold is used to attenuate uniformly distributed noise.

Automatic license plate localization in realtime environment is a complex task, due to the variety of sizes and colors used in License Plate as well as due to the complexity of the background in different lighting conditions. This paper presents a recognition technique in which the number plate area is localized using a novel feature-based number plate localization algorithm which consists of three different LP segmentation algorithms, two of them based on different local adaptive binarisation on to grayscale versions of the color image and the third algorithm is directly applied onto the color image. Each of the three segmentation results is post-processed by four component reduction steps to reduce the false positives. The selection of the true license plate is performed by a qualification process applied to all remaining license plate candidates obtained after the component reduction of the three sequences. This paper mainly focus on the two fast algorithms — Edge Finding Method and Window Filtering Method for the better development of the number plate detection system. Experiments have been preformed to prove the robustness and accuracy of the approach. The experiment results show that almost 96.1% of input images are correctly localized on the average.

Skew Correction is a processing-stage between LP Localization and Character Segmentation in License Plate Recognition system used to identify a vehicle by its number plate. License plate is skewed in captured image due to the positioning of the vehicle with respect to the camera while capturing the LP image. The skewed license plate affects badly on the accurate character segmentation and recognition. After localization skew correction technique is applied in order to get correct character segmentation followed by character recognition. In this paper, a Centroid based Hough Transform technique is presented for skew correction of license plate which performs better than the other approaches of skew correction including Thresholding, Connected Component analysis, Hough Transform and Centroid method. The performance of the proposed algorithm has been tested on live captured LP images, yielding better performance in car license plate segmentation and hence the presented algorithm is good for all types of LP recognition applications due to its direct and simple approach with minimal computational time.

One of the major bottlenecks in an Automatic License Plate Recognition system (ALPR) is the presence of skew and shear in the captured localized license plate due to the position of vehicle with respect to camera. Skew not only spoils the LP character image segmentation accuracy, but makes the segmentation impossible in quite a many occasions. Skew Correction is a pre-process before License Plate Character Segmentation. Proper skew correction after localization will enables near perfect character segmentation and recognition in ALPR process. There are several techniques available to correct the skew of the localized LP plate including Projection Profile, Threshold method, Connected Component analysis, Centroid method, Convex Hull method, Morphological method, Radon Transform method. This paper proposes a new skew correction algorithm using polar Hough Transform. Major problems faced by most of the skew handling algorithms like restriction on the detectable angle range, restrictions on type or size of fonts, dependence on License Plate layout, presence of dirt and dust on the License Plate and high computational cost are minimized due to the concept of luminance threshold introduced in this algorithm, which correctly identifies the pixels whether black or white. The performance of the proposed algorithm has been tested on real Indian License Plate images, which shows superior performance in car license plate segmentation in Indian ALPR context.

Automatic License Plate Recognition (ALPR) is used in Intelligent Transportation systems wherein they are deployed for automatic ticketing of vehicles at car parking area, tracking vehicles during traffic signal violations and related applications. Out of the three major modules of ALPR systems, namely 1. localization of license plate from vehicle image, 2. segmentation of the characters images from the localized license plate and 3. Recognition of segmented characters images as license plate number, localization of potential license plate from the vehicle images is the most challenging task due to the huge variations in size, shape, color, texture and spatial orientations of license plate regions in such images. LP Localization fails often due to the presence of complex background and non-uniform illumination of license plate due to varying lighting conditions. In this paper, we propose a smart, simple and efficient algorithm for Indian license Plate Localization using Top Hat Transformation, which suppresses the background of image and remove the non uniform illumination. The algorithm is tested with live ALPR field images, confirming the robustness of the proposed method against adverse imaging condition.

Two extensively studied and analyzed factors related to Wireless Sensor Networks (WSNs) are optimization of power or energy consumption of node or network and efficient routing mechanism for data transfer between sensor nodes. These two aspects play a major role in the effectiveness and efficiency of the WSN being deployed. This paper, is a study of these aspects of WSN and the various mechanisms that have been proposed to provide an efficient network with reasonable functional time. In this paper, (i) we have studied the various factors that affect the energy consumption of the node and modeled it in a layered approach for better understanding (ii) we have created a framework for the Power Optimized Network Protocol

Contrast improvement is useful when the details in an image is not clear due to various reasons and are widely available, but their applications are not well defined. Contrast improvement has been applied to different fields and many algorithms have been proposed for contrast improvement in the past decade. In this paper, it is propose to improve the contrast of the radiographic images using Fast Multilevel Fuzzy Edge detectors (FMFE) in spatial domain and to compare its performance with the Sobel edge detectors in terms of Peak Signal to Noise Ratio, Correlation Coefficients and Structural Stability.

The science of obtaining a compact representation of a signal while maintaining all the necessary information is referred to as Compression. It can be classified into two types, Lossless and Lossy compression. Lossy compression can be broadly classified into two types, namely Scalar Quantization (SQ) and Vector Quantization (VQ) .VQ since about 1980, become a popular technique for source coding of image and speech data [1]. SQ involves processing the input samples individually using some distortion measure while VQ involves processing the input samples in groups into a set of well-defined vectors using some distortion measure. The direct use of VQ suffers from a serious complexity barrier. The classical technique of TSVQ was introduced by Buzo et al. [2]. This paper discusses the Tree Structured Vector Quantization design for image compression with compact codebook approach.

A new adaptive single pixel based lossless intra coding technique employs pixel based DPCM(Differential Pulse Code Modulation) is presented as an enhancement of H.264/MPEG-4 AVC(Advanced Video Coding) standard.. In this paper, we have addressed a technique to trace a single significant pixel in the source block adaptively based on perceptual considerations, applied pixel wise DP CM for spatial prediction for residual transform coding. However, the block style ofH.264/AVC is not troubled for the transform encoding and decoding process. From the experiments, it follows that the new adaptive single pixel based lossless intra coding technique offers a better image quality and good compression ratio as compared with the current standard. If the visual significance of source block is considered for spatial prediction, it offers better results. In this paper, we highlight the exclusion of excess visual data present in both Luma and Chromo components of a video frame for lossless Intra coding of H.264/AVC

The techniques for watermarking of digital images are available to prevent unauthorized distribution of multimedia data image. In this paper, a watermarking scheme is proposed in which the image is decomposed in to wavelet coefficients and a visual recognizable logo and content based watermark information is embedded in the wavelet coefficients. The wavelet coefficients corresponding to the points located in a neighborhood that have maximum entropy are used for embedding the visual logo and the relations between the neighboring coefficients in the selected wavelet sub bands are embedded into middle frequency pairs of the first scale coefficients. This method embeds the maximum amount of watermark while the watermark is imperceptible. This technique provides two levels of protection while maintaining the image quality. The embedding of relation coefficients provide the capability to identify malicious changes made on the image. The experimental results confirm that the proposed techniques provide secure watermarking and robust to a variety of attacks.

An effective median filter for salt & pepper impulse noise removal is presented. This computationally efficient filtering technique is implemented by a two pass algorithm: In the first pass, identification of corrupted pixels that are to be filtered are perfectly detected into a flag image using a variable sized detection window approach; In the second pass, using the detected flag image, the pixels to be modified are identified and corrected by a more suitable median. Experimental results show that the proposed algorithm performs far more superior than many of the median filtering techniques reported in terms of retaining the fidelity of the image highly corrupted by impulse noises even to the tune of ninety percent impulse noise. The proposed algorithm is free from patchy effects, does not extend black or white blocks in the image as has been found in many other adaptive median based techniques and is very effective in cases when images are corrupted with large percentage of impulse noises. This algorithm works very well for images with lower percentage of impulse noises also.

This paper describes a robust, oblivious data embedding scheme which uses lattice vector quantization. The embedding is done in the DCT domain. The signature image is vector quantized and the indices obtained are coded using error-correcting codes (BCH codes). The codewords obtained are multiplied by a scaling factor and perturbed into the DCT coefficients of the host image which is lattice vector quantized. Experimental results of the peak signal to noise ratio (PSNR) of the watermarked image for different host images and the PSNR of the recovered signature image for different values of scaling factors stand a testimony for the validity of this method.

Role of 5-HT3 receptors in cholinergic hypofunctional models of cognitive impairment in the elevated plus maze model and a passive avoidance model is studied. Cognitive impairment was caused by scopolamine (1 mg/kg, ip) in mice and 5-HT3 ligands mCPBG (1 and 5 mg/kg, ip) and ondansetron (0.5 and 5 mg/kg, ip) were administered before the pre-learning phase to study the effects on acquisition, while post-learning administration was used to determine the effects on consolidation. Ondansetron improved acquisition and retention in cholinergic hypofunctional models while mCPBG potentiated selected impaired cognitive indices. The results indicate the role of 5-HT3 receptors in cognition and that an ideal evaluation of 5-HT3 ligands in cognition should distinguish true cognitive effects from locomotor, motivational and emotional effects.