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The development of sustainable materials, considering the design aspects is one of the real challenges among the researchers. In general. the renewable resources used in several applications, should not harm ecosystems and maintain the environmental sustainability. In this context, local agricultural by-products or wastes are focused on as an alternative material due to their lightweight and can be easily disposed of after their usage. Agro-waste and biomass materials are plentifully available in the world. The fibers obtained from the agricultural by-products are often used as fillers or reinforcement. Presently, biodegradable polymer matrixes are becoming popular and are finding in number of applications in manufacturing components used in automobiles, medical, domestic, and others. Polylactide (PLA) is a compostable and biodegradable thermoplastic polymer that is derived from renewable agro-sources such as potatoes, corn, or sugarcane. When suitable filler materials are reinforced with these polymers, an improvement in the strength can be seen. In the present work, the rice straw reinforced polylactide (PLA) composites are fabricated with the standard procedure, in accordance with ASTM standards. The experimental studies for the three types of polylactide composites are carried out to characterize their Impact and hardness strength. It is found a higher impact strength for 20 wt% un-treated fiber content of PLA composites. The hardness value is better for treated fiber content PLA composites.

To separate examples from neuroimaging information, different measurable strategies and AI calculations have been investigated for the analysis of Alzheimer's sickness among more established grown-ups in both clinical and examination applications; in any case, recognizing Alzheimer's and solid cerebrum information has been trying in more seasoned grown-ups (age greater than 70) because of exceptionally comparable examples of mind decay and picture forces. As of late, bleeding edge profound learning innovations have quickly ventured into various fields, including clinical picture investigation. This paper diagrams cutting edge DL-formed pipelines utilized to recognize Alzheimer's Magnetic resonance imaging (MRI) and functional MRI (fMRI) from typical sound control information for a given age gathering. Utilizing these pipelines, which were performed on a GPU-based superior processing stage, the information were carefully and cautiously preprocessed. Then, scale-and move invariant low-to undeniable intensity highlights were acquired from elevated amount of preparing pictures utilizing convolutional neural organization (CNN) engineering. In this examination, fMRI information were utilized without precedent for profound learning applications for the motivations behind clinical picture investigation and Alzheimer's infection forecast.

The beginning of dementia can be generally credited to Alzheimer's illness (AD). The reformist idea of AD grounds the synapse crumbling that significantly prompts actual reliance and mental incapacity which upsets an individual's ordinary life. A Personal Computer helped symptomatic framework is very much predominant in assisting doctors in identifying AD continuously. In this undertaking we proposed a methodology for execution investigation of Alzheimer's stage detection framework and characterization framework dependent on deep features utilizing a pre-prepared Deep Learning Neural Network (DNN). The performance of classifier in the Deep Learning networks is principal and influence the overall performance of the system. The performance of Rectified Linear Activation Function (ReLU), Soft Max and Support Vector Machines (SVM) are evaluated in the proposed system.

Nowadays, medical imaging and telemedicine are increasingly being utilized on a huge scale. The expanding interest in storing and sending medical images brings a lack of adequate memory spaces and transmission bandwidth. To resolve these issues, compression was introduced. The main aim of lossless image compression is to improve accuracy, reduce the bit rate, and improve the compression efficiency for the storage and transmission of medical images while maintaining an acceptable image quality for diagnosis purposes. In this paper, we propose lossless medical image compression using wavelet transform and encoding method. Basically, the proposed image compression system comprises three modules: (i) segmentation, (ii) image compression, and (iii) image decompression. First, the input medical image is segmented into region of interest (ROI) and non-ROI using a modified region growing algorithm. Subsequently, the ROI is compressed by discrete cosine transform and set partitioning in hierarchical tree encoding method, and the non-ROI is compressed by discrete wavelet transform and merging-based Huffman encoding method. Finally, the compressed image combination of the compressed ROI and non-ROI is obtained. Then, in the decompression stage, the original medical image is extracted using the reverse procedure. The experimentation was carried out using different medical images, and the proposed method obtained better results compared to different other methods.

Two women presenting with fragility fractures during lactation had bone mineral density (BMD) reduced more greatly than usually associated with lactation. The first woman was 29 years old with a BMD T-score of - 3.2 SD at the spine and- 2.0 SD at the femoral neck. The second woman was 35 years old with a BMD T-score of - 4.5 SD at the spine and - 2.8 SD at the femoral neck. Both women had increased cortical porosity and reduced trabecular density. Investigation identified an elevated serum tryptase, and marrow biopsy confirmed the diagnosis of mastocytosis. Lactation causes bone loss, but the occurrence of fractures in the setting of severe deficits in BMD and microstructural deterioration signals the need to consider additional causes of bone loss.

Objectives: To design a system with high accuracy, sensitivity and specificity to detect brain tumour. Methods/ Statistical Analysis: Over the years, extensive research works have been done for brain tumour diagnosis using various imaging modalities. Magnetic Resonance Imaging (MRI) method has been proven to be effective in imaging the human brain for detection and classification of the tumours for clinical diagnosis. In this paper, Tetrolet Transform (TT) and Support Vector Machine (SVM) Classifier have been utilized for recognition of tumour from brain MRI images. Findings: The brain MRI image is transformed into a set of features that best describes the information content of the image. So as to use the Tetrolet transform for recognition of tumour, MRI brain tumour image is decomposed by utilizing the Tetrolet transform at a predefined level. Statistical features such as mean, standard deviation, and variance of each tetromino sub bands are computed and extracted. The t-test class separability model is applied to the calculated features keeping in mind the objective to choose the best features from the database images. The receiver operating characteristics (ROC) curves and confusion matrix are make use of, to decide the accuracy level of the proposed method. The experimental results show an accuracy level of 98.8% with fifth level of decomposition and SVM based classification while testing with images from Repository of Molecular Brain Neoplasia Data (REMBRANDT) database. The performance of the proposed system have been evaluated by using the parameters namely- accuracy, sensitivity and specificity. Application/Improvements: This technique is applied to the training images and the selected features are put away in a feature vector. 10-fold cross validation method is utilized to test viability of the proposed technique.

Recently the concept of hard turning has gained much attention in the metal cutting industry. In hard turning, multiple operations can be performed in single step, thereby it replaces the traditional process cycle. But it involves very large quantities of cutting fluid. Procurement, storage and disposal of cutting fluid involve expenses and environmental problem. Pure dry turning is a solution to this problem as it does not require any cutting fluid at all. But pure dry turning requires ultra hard cutting tools and extremely rigid machine tools, and also it is difficult to implement in the existing shop floor as the machine tool may not be rigid enough to support hard turning. In this context, turning with minimal fluid application is a viable alternative wherein, extremely small quantities of cutting fluid are introduced at critical contact zones as high velocity pulsing slugs, so that for all practical purposes it resembles pure dry turning and at the same time free from all the problems related to large scale use of cutting fluid as in conventional wet turning. In this study, fluid application parameters that characterize the minimal fluid application scheme were optimized and its effect on cutting performance and tool vibration was studied. From the results, it was observed that minimal fluid application in the optimized mode brought forth low vibration levels and better cutting performance.

Machining is a complicated process where many variables can directly affect the desired results. Among them, tool vibration is the most critical phenomenon which affects the life of the cutting tool, quality of the components machined and functional behavior of the machined tools. This tool vibration can be controlled by increasing the rigidity of the cutting tool through damper. The present investigation aims at suppressing tool vibration during end milling process using magnetorheological fluid damper which acts as a viscoelastic spring with non-linear vibration characteristics. Magnetorheological fluid damper along with fixtures was designed and fabricated according to the required size. Cutting experiments were conducted to arrive at set of compositional parameters that can reduce tool vibration and hence promote better cutting performance during end milling of AISI 4340 steel of 45 HRC using hard metal insert with sculptured rake face. From the results, it was observed that the use of magnetorheological fluid damper reduces tool vibration and improves cutting performance effectively.

In boring process due to larger overhanging length of tool holder, tool vibration is considered as a significant factor which results in poor surface finish, noise generation, accelerated tool wear and reduced life period of machine tool. This tool vibration is a result of interaction between metal cutting process and the dynamics of machine tool. In order to reduce tool vibration it is necessary to develop suitable mechanisms which will in-turn increase the productivity in manufacturing industries. This investigation aims at analyzing the tool holder with impact damper on tool vibration during boring of hardened AISI4340 steel using hard metal insert with sculptured rake face. Impact damper used in this study consists of a concentrated mass of predetermined size and shape mounted on the tool shank at a specific location by means of springs. Material of impact damper and the location of the damper on the tool shank for achieving effective damping was determined by using computational analysis. When the damper was mounted on the tool shank, its vibration characteristics got altered and provided an inherent damping capability to the tool holder by suppressing tool vibration. Experimental work was carried out to study the influence of impact damper on tool vibration and cutting performance. From both computational and experimental results, it was observed that tool vibration was reduced effectively when impact damper was embedded with tool holder.

W ostatnich latach, w obróbce skrawaniem wzrasta zainteresowanie koncepcja toczenia na twardo, poniewaz moze ono zastapic tradycyjny proces toczenia, utwardzania i szlifowania stosowany przy wykonywaniu twardych, odpornych na zuzycie czesci metalowych. Głównym problemem przy twardym toczeniu sa wibracje narzedzia, które musza byc monitorowane i kontrolowane, gdyz wpływaja na wykonczenie powierzchni elementu obrabianego. W celu kontrolowania wibracji narzedzia przy obróbce skrawaniem autorzy zastosowani tłumik z płynem o własciwosciach reologicznych sterowanych polem magnetycznym. Podjeto takze próbe monitorowania wibracji na podstawie parametrów skosnosci i kurtozy sygnałów akustycznych (AE) emitowanych przez uchwyt narzedzia, mierzonych w warunkach bez tłumika i z tłumikiem magnetoreologicznym. Przeprowadzono szereg eksperymentów z toczeniem stali AISI4340 o twardosci 46 HRC przy uzyciu narzedzia z płytka z twardej stali, o geometrycznie kształtowanym ostrzu, firmy Taegu Tec. Otrzymano zbiór parametrów roboczych, wyznaczajac na ich podstawie lepsze charakterystyki tłumienia i osiagajac minimalizacje wibracji narzedzia. Wyniki eksperymentów wskazuja, ze obecnosc tłumika magnetoreologicznego redukuje wibracje i ze istnieje silna zaleznosc miedzy wibracjami narzedzia i wartoscia skuteczna sygnału emisji akustycznych (AERMS). Praca przyczynia sie do znacznie lepszego zrozumienia funkcji tłumika magnetoreologicznego i czujnika emisji akustycznych przy monitorowaniu stanu narzedzia przy toczeniu utwardzonej stali AISI4340

Infective endocarditis still remains a dreaded illness among treating physicians because of the disease course, its need for meticulous antibiotic management, complications, and overall morbidity. Peripheral mycotic aneurysms are a rarely reported complication of infective endocarditis. Mycotic aneurysms occur in about 5%-10% of cases of infective endocarditis, and most of them involve the intracranial vessels. Here, we report a case of native valve endocarditis in a 74-year-old man caused by Kocuria rosea. He presented with septic shock and acute kidney injury. His illness was complicated by a right popliteal artery mycotic aneurysm. He was treated with intravenous ceftriaxone and vancomycin. The mycotic aneurysm needed aneurysmectomy and anastomosis with a graft.

Summary This paper aimed towards the analysis of various multipath routing techniques with and without load balancing as a comparative study. Most of the routing techniques targeted to find best path from source to destination. The basic routing techniques are based on single path; there is only one path from sending end to receiving end. There after multipath routing techniques has been proposed to send the data in multiple paths and eliminates the drawbacks in single path routing. This study gives better idea about multipath routing techniques along with load balance for avoiding congestion in wired packet networks.

Todays Sensor Networks collect and transport huge data to the cloud. The collected data should be tagged to the location for making it more sensible informative. To fulfill the objective, the sensor nodes should be aware of its location and the process of obtaining is named as localization. Localization is a highly studied one, which gained a lot of attention from various researchers, depends mostly on precision, energy efficient and scalability. An insight of various localization techniques, the methods used are discussed and the performance of various existing localization techniques is listed in terms of measurement error. However, still a lot of technical challenges are to be focused based on demanded applications.

Nowadays the digital images are so common in various fields including computer vision, medicine, military, astronomy, industrial automation and etc. But in most of the cases where the capturing of digital images is so crucial, the process of capturing of images is difficult. The objects may be moving or to catch the object the capturing device may need to move fast or the capturing device may respond a little late which result in a blurred image. As just mentioned blurring may be resulted by various factors independently or cumulatively the inverse operation is not just simple. The solution must invert all artifacts imposed in blurring process. In this paper, a review of various methods used to perform the inverse operation was presented. Some of the methods presented are finally compared using different types of blurred images by calculating peak signal to noise ratio and structural similarity.

This paper proposes a new queuing technique in Adaptive Multi-Path routing based on packetpriority. Simultaneous Multi-Path routing is used to improve the data performance over a congested network.The traditional method of transmitting the data is done by using Simultaneous Multi Path Communication(SMPC). Two types of SMPCs are proposed, in which SMPC-I, which uses multiple paths independently andSMPC-P, which uses path priority control algorithm in conjunction with SMPC. SMPC-I and SMPC-P maintainsthroughput regardless of path length and priority control. In addition to available SMPC methods, proposedmethod provides better solution to improve the performance of communication network. A theoretical approachof AMPC-PP proposed here based on packet priority for important data over normal data on a congestionnetwork with route adaptability.

Machining with minimal fluid application is a new technique for reducing the usage of cutting fluid that ensures green manufacturing on shop floor. During minimal fluid application, a high velocity minimal pulsing jet of proprietary cutting fluid was applied at the tool work interface. In the present work an auxiliary high velocity minimal pulsing jet of water was applied at the top side of the chip in addition to the pulsing jet of cutting fluid at the tool work interface during turning of hardened AISI 4340 steel. The effect of auxiliary pulsing jet of water on the top side on cutting performance is investigated in comparison with conventional turning with minimal fluid application. From the experimental results, it was observed that the presence of an auxiliary jet of water on the top side of the chip promoted chip curl by Rebinder effect. Increase in chip curl resulted in less tool chip contact length thereby reduces the friction, cutting force and cutting temperature.

Tool vibration is a frequent problem in the manufacturing industry where metal cutting operation takes place. It affects the surface finish of the work piece, tool life, and produce irritating noise. In order to restrain tool vibration in metal cutting, it is necessary to develop and analyze suitable methods which increases stability and also improves the cutting performance. Magnetorheological damper has received great attention due to their ability to reversibly change from a free flowing, linear, viscous liquid to a semi-solid when exposed to magnetic field in just few milliseconds and also found to be effective on suppressing tool vibration. The present investigation aims at studying the effect of magnetic field on the damping abilities of the magnetorheological (MR) damper during hard turning operation. MR damper was characterized and the effect of magnetic field on damping ability of MR damper and cutting performances like tool vibration, cutting force, cutting temperature, tool wear and surface roughness were analyzed. From the result, it was observed that direction of magnetic field parameter on magnetorheological damper reduces tool vibration effectively and brought forth better cutting performance.

This paper deals with a novel method of image compression. Â The proposed algorithm targets the psychovisual redundancy in the given image to improve compression. The properties or features of the image are initially modified (while ensuring that the image quality is not deteriorated) by down sampling along the horizontal and vertical dimensions. Â The transform is then applied on this modified image obtaining a better PSNR after compression than on the original image. Â The results obtained are compared with DCT and adaptive DCT. Also, the proposed algorithm is image independent as it gives consistently better compression performance for different images.

Steel Authority of India Limited (SAIL), through its Corporate R&D i.e. RDCIS, has been working incessantly for the development of new steel products with superior quality attributes to meet the requirements of Indian market. The product development activities have always been supported by in-house/collaborative research work which has led to deeper understanding on the process know-how, resulting in launching of cost-effective products in Indian market. Some of the products developed by SAIL in the flat product category include high strength thin gauge HR coil for automobile sectors, SAILMA 550 HI/SAILMA 600 grade plates for penstocks and earthmovers, SAIL HITEN 690 AR plates for automated teller machines chest, high strength plates with guaranteed through thickness ductility for railways and Defence sectors, boiler quality plates (ASTM 387 Gr. 12) for power sector, API X 70 grade HR coils and plates for Linepipe application. In the long products category, EQR TMT rebar, Roof/Rock Bolt (Fe—600) TMT rebars for mines and tunnels, corrosion resistant TMT rebars for construction sector, Al-killed low carbon CC blooms for cold reducers, Al-killed medium carbon CC blooms for forging have been developed recently through R&D initiatives in SAIL. This paper describes some of the products which were recently developed in SAIL.

Now a days, internet becomes major channel for communicating information one place to other such as text, image, audio, video data. Steganography is a technique in which information can to be communicating secretly by hiding in another such as secondary information with changing its significance. This paper presents a new approach of image steganography using polynomial based optimal LSB and pixel adjustment method. This method main focus on adaptiveness of LSB’s replacement and pixel adjustment for improving the capacity of hiding data and robustness of steganography. Objective analysis is done over the proposed method using MSE, PSNR and Normalized correlation method.

VLSI applications include Digital Signal Processing, Digital control systems, Telecommunications, Speech and Audio processing for audiology and speech language pathology. The latest research in VLSI is the design and implementation of DSP systems which are essential for above applications. The fundamental computation in DSP Systems is convolution. Convolution and LTI systems are the heart and soul of DSP. The behavior of LTI systems in continuous time is described by Convolution integral whereas the behavior in discrete-time is described by Linear convolution. In this paper, Linear convolution is performed using carry save multiplier architecture based on vertical and cross wise algorithm of Urdhva – Tiryagbhyam in Vedic mathematics. Coding is done using Verilog HDL(verilog Hardware Description Language). Simulation and Synthesis are performed using Xilinx FPGA

In this paper, a novel scheme for efficient content-based medical image retrieval, formalized according to the Patterns for Next generation Database systems (PANDA) framework for pattern representation and management. This scheme involves block-based low-level feature extraction from images followed by the clustering of the feature space to form higher-level, semantically meaningful patterns. The clustering of the feature space is realized by an expectation-maximization algorithm that uses an iterative approach to automatically determine the number of clusters. Experiments were performed on a large set of reference radiographic images, using different kinds of features to encode the low-level image content. Through this experimentation, it is shown that the proposed scheme can be efficiently and effectively applied for medical image retrieval from large databases, providing unsupervised semantic interpretation of the results, which can be further extended by knowledge representation methodologies.

Classification is a well known of the significant tools used to recognize and examine most sharp information in images. Satellite image processing has become popular in these days because of benefits that those are giving. In any remote sensing particularly, the decision-making way mainly rely on the efficiency of the classification process. Image classification was performed generally and the classification accuracy was assessed. Here we have explained a healthy procedure to calculate all the parameters of five class classification model for satellite image classification with confusion matrix.

In this research letter, a low resolution (LR) satellite image is enhanced to high resolution (HR) image using various wavelet techniques. One of the key factors is image resolution. Image RE is the method of modifying a portrait so that output portrait is more superior to the novel one for meticulous application. There are many techniques for portrait enhancement in image processing. Mainly three important wavelet techniques are used i.e., Method1 is Discrete wavelet transform (DWT), Method2 is Discrete wavelet transform — Stationary wavelet transforms (DWT-SWT), and Method3 is Dual tree complex wavelet transform (DT-CWT). A low resolution portrait is decomposed into four sub-band (SB's) i.e., Low-Low (SB 1), low-high (SB 2), high-low (SB 3), and high-high (SB 4) using DWT, DWT-SWT, and DT-CWT. The high frequency (HF) SB's are interpolated using bicubic interpolation and combined with interpolated low resolution portrait in case of DWT-SWT and DT-CWT. SB 1, SB 2, SB 3, SB 4 are interpolated using bicubic interpolation and combined with difference of input portrait and interpolated Low frequency SB portrait in case of DWT. Inverse DWT, Inverse DWT-SWT, and Inverse DT-CWT wavelet transform are applied respectively. The output is high resolution (HR) portrait. Image Resolution enhancement loses HF components. So DWT technique is used to preserve HF components. DWT generates artifacts. These artifacts are reduced by DT-CWT. All these three techniques are applied on NOAA-19-HRPT satellite images. The quantitative PSNR, RMSE, and CC are calculated for satellite portraits and compared for these three wavelet techniques.

The classification of Satellite image is an imperative system utilized to retrieve information in remote sensing. Primary data of extraordinary significance to several difficulties can be acquired straightforwardly from Land-cover observing. As it is required to discuss about the issue of supervised Land-cover classification of multispectral satellite images in the perspective of cluster ensemble and self learning. Different information partitions inferred by several clustering methods which are gathered into a better solution by cluster ensembles. supervised iterative Expectation-Maximization (EM) method can be initialized by cluster ensemble based strategy which will be examined in the paper. This will deliver better approximation of cluster parameters. Here definition of Land-cover classes is vital. Another method for producing suitable labeling model for each and every clustering of the consensus is introduced for cluster ensembles. The upgraded parameter set acquired from the EM step is trained by maximum likely-hood classifier to classify the rest of the pixels. The effect of data overlapping from several clusters can be reduced by the self learning classifier. Comparison is made on the performance of the clustering between the proposed method and individual clustering of the ensemble for medium resolution and a very high spatial resolution images.

Bacterial foraging optimization algorithm has been widely accepted as a global optimization algorithm. Since Image segmentation is the basic step in many image processing applications, so faithful segmentation algorithm must be developed for successful implementation of the processing applications. Core aim of image segmentation is to extract the information which is of interest for a particular application. Infact this research addresses the novel segmentation of infected part of fruits based on color features and the image into various color components by using combined approach of Advanced Bacterial foraging optimization Algorithm (ABFOA) approach and RGB decomposition. The original image is decomposed into separate planes of R G and B and then Improved Bacterial Foraging algorithm is applied on three planes separately to calculate three different thresholds. The experimental results clarify the effectiveness of proposed approach to improve the segmentation quality in aspects of precision and computational time. The simulation results reveal that the proposed approach is promising. Segmentation is performed on the basis of Thresholding.

Background/Objectives: Nowadays artificial intelligence plays an important role in manufacturing sector. How to utilize that in an effective manner is a key factor of optimization. So identification of optimal modeling tool for effective manufacturing gives an extraordinary output. Methods/Statistical Analysis: This paper develops an Adaptive Neuro Fuzzy Inference System (ANFIS) model with hybrid model to predict the Surface Roughness (SR) of the H13 tool steel work piece of 45 HRC hardness value. The investigation of operation has been done in hard turning using minimal cutting fluid application. Findings: The effect of the different input parameters like cutting force, temperature and vibration has been analyzed. Here the hybrid type of ANFIS model is created with triangular membership function and the observed results indicated that the predicted output surface roughness is almost very close to the actual output obtained in the experimental work.

The ischemia, which occurs because of blockage in cardiac electrical conduction path and cardiac injury, is detected with ST-Segment variations such as elevation and depressions. The frequency analysis and time analysis, which mostly make use of linear filter techniques and wavelet techniques, do not remove noise to large extent and particularly from the passband of ECG. In this paper Independent Component Analysis (ICA) is used to extract ECG and Trinagular Method is used to extract ST-segments. As ICA can transform noisy ECG into several components with more independence and maximum separation, the desired cleaned ECG can accurately be extracted and the K-point can be identified more accurately with Triangular Method. In earlier methods the end point of ST-Segment (K-point) is identified by using some fixed time backward from T-wave peak. The proposed method gives accurate measurement of ST-Segment and its elevation or depression. Finally ST-Segments are classified according to their shapes.

Space-time coding for fading channels is a Communication technique that realizes the diversity benefits of multiple transmit antennas. Previous work in this area has focused on the narrowband flat fading case where spatial diversity only is available. In this paper, we investigate the use of space-time coding in OFDM based broadband systems where both spatial and frequency diversity are available. We consider a strategy which basically consists of coding across OFDM tones and will therefore be called space-frequency coding. For a spatial broadband channel model taking into account physical propagation parameters and antenna spacing, we derive the design criteria for space-frequency codes and we show that space-time codes designed to achieve full spatial diversity in the narrowband case will in general not achieve full space-frequency diversity. Specifically, we show that the Alamouti scheme across tones fails to exploit frequency diversity. For a given set of propagation parameters and given antenna spacing, we establish the maximum achievable diversity order. Finally, we provide simulation results studying the influence of delay spread, propagation parameters, and antenna spacing on the performance of Space-frequency codes.

The coin-based mobile battery charger developed in this paper is providing a unique service to the rural public where grid power is not available for partial/full daytime and a source of revenue for site providers. The coin-based mobile battery charger can be quickly and easily installed outside any business premises. The mobile phone market is a vast industry, and has spread into rural areas as a essential means of communication.While the urban population use more sophisticated mobiles with good power batteries lasting for several days, the rural population buy the pre owned mobile phones that require charging frequently. Many times battery becomes flat in the middle of conversation particularly at inconvenient times when access to a standard charger isn't possible. The coin-based mobile battery chargers are designed to solve this problem. The user has to plug the mobile phone into one of the adapters and insert a coin; the phone will then be given a micro-pulse for charging. It does not bring a mobile from 'dead' to fully charged state. The charging capacity of the mobile is designed with the help of pre defined values. It is, of course, possible to continue charging the mobile by inserting more coins. This compact and lightweight product is designed to cater for the growing number of rural mobile users worldwide. A suitable microcontroller is programmed for all the controlling applications. The source for charging is obtained from direct power grid and solar energy in case of non availability of grid power.

This paper presents a blind robust digital image watermarking approach based on back propagation neural network in DWT domain. The cover image is decomposed up to 4-levels using DWT. The bitmap of size 64x64 is selected as a watermark. The Back Propagation Neural Network (BPNN) is implemented while embedding and extracting the watermark. In BPNN the errors will be back propagated to the input layer, so that the weights and learning rate parameters may be changed to get the output. The BPNN is trained in such a way that it converges fast and reaches high accuracy. The proposed watermarking algorithm is imperceptible and robust to some normal attacks such as JPEG compression, salt and pepper noise, rotation, median filtering and cropping.

A general controller evaluation method is introduced, based on robustness criteria of well-known PID Controller formulate for process of non-linear systems using experimental of coefficients for flank wear of dynamic models obtained are calibrated with the actual conditions. These developed models will be used for the simulation of flank wear and using control variable such as cutting speed; the flank wear will be controlled. For model validation, the flank wear is estimated using a non-linear model. In the present work, an attempt has been made to control the flank wear during turning of on-line cutting process using the Fuzzy Logic Controller and Neural network based on self-tuning of PID controller approaches. Those approaches are treat the material as dynamic system and involve developing state space models from available material behavior model. The evaluation of performance criteria can be compared for those approaches of PI controller with Neural network and Fuzzy PID controller based on self-tuning of PID controller. Simulation studies are carried-out for the non-linear system using MATLAB software.

This paper presents a new algorithm to reduce the noise from Kalpana Satellite Images using Dual Tree Complex Wavelet Transform technique. Satellite Images are not simple photographs; they are pictorial representation of measured data. Interpretation of noisy raw data leads to wrong estimation of geophysical parameters such as precipitation, cloud information etc., hence there is a need to improve the raw data by reducing the noise for better analysis. The satellite images are normally affected by various noises. This paper mainly concentrates on reducing the Gaussian noise, Poisson noise and Salt & Pepper noise. Finally the performance of the DTCWT wavelet measures in terms of Peak Signal to Noise Ratio and Structural Similarity Index for both noisy & denoised Kalpana images.

In recent years Parallel-prefix topologies has been emerged to offer a high-speed solution for many DSP applications. Here in this paper carrier approximation is introduced to incorporate speculation in Han Carlson prefix method. And overall latency is considerably reduced using single Brent-Kung addition as a pre and post processing unit. In order to improve the reliability error detection network is combined with the approximated adder and it is assert the error correction unit whenever speculation fails during carries propagation from LSB segment to MSB unit. The proposed speculative adder based on Han-Carlson parallel-prefix topology attains better latency reduction than variable latency Kogge-Stone topology. Finally, multiplier-accumulation unit (MAC) is designed using serial shift-based accumulation where the proposed speculative adder is used for partial product addition iteratively. The performance merits and latency reduction of proposed adder unit is proved through FPGA hardware synthesis. Obtained results show that proposed MAC unit outperforms both previously proposed speculative architectures and all other high-speed multiplication methods.

In this article, an analysis of the electromagnetic radiation emitted by Cellular Networks and other wireless communication devices located in urban and rural areas, is carried out by taking the different exposure situations into account. The emitted RF radiation power values are estimated through the derived theoretical analysis. As per the FCC, TRAI and DOT standards, it is less hazardous if the observed radiation is below −30 dBm. From these standards, the compliance distance from the cellular base stations antenna can be determined. The EM radiation emitted from the different RF sources is analyzed by computing the radiation power theoretically at given distance from the cellular base station antenna. This analysis is further enhanced by considering the some more parameters like distance of separation between the situated antennas, frequency of operation besides the parameter of distance from the RF source. This improved analysis adds accuracy in determining the compliance distance beyond which the emitted radiation is not significant.

Leonhard Euler is one of the greatest and most astounding icons in the history of science. His work, dating back to the early eighteenth century, is still with us, very much alive and generating intense interest. Like Shakespeare and Mozart, he has remained fresh and captivating because of his personality as well as his ideas and achievements in mathematics. The reasons for this phenomenon lie in his universality, his uniqueness, and the immense output he left behind in papers, correspondence, diaries, and other memorabilia. Opera Omnia [E], his collected works and correspondence, is still in the process of completion, close to eighty volumes and 31,000+ pages and counting. A volume of brief summaries of his letters runs to several hundred pages. It is hard to comprehend the prodigious energy and creativity of this man who fueled such a monumental output. Even more remarkable, and in stark contrast to men like Newton and Gauss, is the sunny and equable temperament that informed all of his work, his correspondence, and his interactions with other people, both common and scientific. It was often said of him that he did mathematics as other people breathed, effortlessly and continuously. It was also said (by Laplace) that all mathematicians were his students. It is appropriate in this, the tercentennial year of his birth, to revisit him and survey his work, its offshoots, and the remarkable vitality of his themes which are still flourishing, and to immerse ourselves once again in the universe of ideas that he has created. This is not a task for a single individual, and appropriately enough, a number of mathematicians are attempting to do this and present a picture of his work and its modern resonances to the general mathematical community. To be honest, such a project is Himalayan in its scope, and it is impossible to do full justice to it. In the following pages I shall try to make a very small contribution to this project, discussing in a sketchy manner Euler’s work on infinite series and its modern outgrowths. My aim is to acquaint the generic mathematician with

Heart diseases such as arrhythmia and myocardial infarction are the leading causes of death. If these cardiac-related deceases are detected at early stage then the people can be saved from death. With our proposed method, it is possible to estimate the occurrence of these diseases easily at early stage. Generally the variations in QT-interval are differentiated by the occurrence of premature activation (PA) beats in electrocardiogram (ECG). Similarly the elevation or depression of ST-segments is the indication of ischemia. We used the ratio of ST-segment to QT-interval as an index to specify the probability of ischemia and cardiac injury. The ΔST/QT can be used as the index of the severity of the disease. The aim of this paper is to investigate the instability in the ventricular repolarization process by detecting ST/QT. In our proposed method, four 1-min-long ECG signals of same subject were taken and applied independent component analysis (ICA) to extract the cleaned ECG from various artifacts and non-Gaussian noise background. The performance of proposed method can also be measured using SNR. The ST/QT slope signifies the peak rate of ST-segment change with heart rate, whereas The ΔST/QT index represents the average change of ST-segment depression with QT-interval. In our method, we used a well-known BSS algorithm, JADE algorithm, to obtain ICs. The JADE is able to separate ECM artifact from ECG activity in component domain.

The digital Image processing has emerged as an effective tool for analyzing the digital images of various fields and applications of engineering. Threshold technique is the most useful and well known among segmentation methods because of its robustness, simplicity, and high precision. This paper is an attempt to make an efficient segmentation Natural calamity images by Healthy Bacteria Foraging Optimization Algorithm.

Remote sensing images are an important source of information regarding the Earth surface. For many applications like geology, urban planning, forest and land cover/land use, the underlying information from such images is needed. Extraction of this information is usually achieved through a classification process which is one of the most powerful tools in digital image processing. Good classifier is required to extract the information in satellite images. Latest methods used for classification of pixels in multispectral satellite images are supervised classifiers such as Support Vector Machines (SVM), k-Nearest Number (K-NN) and Maximum Likelihood (ML) classifier. SVM may be one-class SVM or multi-class SVM. K-NN is simple technique in high-dimensional feature space. In ML classifier, classification is based on the maximum likelihood of the pixel. The performance metrics for these classifiers are calculated and compared. Totally 200 points have been considered for validation purpose.

Routing of data packets from source to destination is the primary function of Network layer. If a single path is chosen for data transfer from source to destination, congestion and packet transfer delay increases. In order to minimize this problem, multi-path routing protocol is preferred. There are many multipath routing techniques available; such as SMPC, SMPC-I, SMPC-P. In multi-path routing, not all the paths may have the same capacity, and then there is every possibility of occurrence of congestion at a router in the path. The proposed method provides a better solution for minimizing the congestion by rerouting the data packets over other paths, which are not utilized by the same in multi-path routing. This method avoids the unnecessary dropping of packets at a congested router and improves the network performance.