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Condition monitoring and fault diagnosis of plant machineries are of great concern in industries. Early fault detection in machineries can save millions of dollars in emergency maintenance costs. The vibration signal analysis is incipient fault detection technique. It is based on the principle that normal vibration pattern of the machine changes due to fault or some dynamic process changes in machine. Generally used vibration acquisition system for condition monitoring of the machines are complex and costly. But small industries require a cost effective and simple vibration monitoring system. This paper aims to enlighten the details of such developed vibration acquisition system which is designed and fabricated using an accelerometer ADXL335 and to calculate the unique features of vibration signals through Non-Dimensional Symptom Parameters (NSPs) and Discrimination Index (DI) under variable load conditions using Discrete Wavelet Transform (DWT).
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In the recent years With the immense advancements in science and technology, there is a fast increase in the volume of images, video collections. Because of immense development in the photographic technology, information is available in large amount and it became very difficult to access it properly. Traditionally the text-based systems are used for this purpose. However, in this system the images are described with keywords. As the database size grows larger, retrieval of a particular image, using text-based method becomes tedious and inadequate. In order to overcome these kinds of difficulties, the emerging approach is Content Based Image Retrieval (CBIR). It is an application of computer vision technique;address the problem in searching for digital images inthe large database. In CBIR system, images are indexed by theirvisual content, such as color, texture and shape, this emerging approach includes Local Binary Patterns, and Center-Symmetric Line Edge Patterns. In this paper, for extracting texture features Center Symmetric – Line Edge Binary Patterns operator is used. The efficiency of CS-LEBP based retrievalsystem ismeasured with Precision and Recall parameters. The experimental results show that the CS-LEBP based retrieval system gives better performance than the LBP and LEBP based retrieval systems.
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Generally quality metrics are used to measure the quality of improvement in the images after they are processed and compared with the original and other different alternatives methods. Measurement of image quality is very crucial to many image processing applications. Compression is one of the applications where it is required to monitor the quality of decompressed / decoded image. JPEG compression is the lossy compression which is most prevalent technique for image codecs. But it suffers from blocking artifacts Here in this paper Various objective evaluation algorithms for measuring image quality like MSE, PSNR, SSIM and PSNR-B are simulated and compared w.r.t. JPEG compression application. Different deblocking filters are used to reduce blocking artifacts and deblocked images are compared through various quality metrics. As the degree of blocking depends on the quantization step, the quality metrics are also simulated and compared by varying the quantization step size. We discussed a new concept called ‘Modified PSNR-B’ which is under review process that gives even better results compared to the existing PSNR-B which includes the Blocking Effect Factor (BEF).
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A new and efficient algorithm for high-density salt and pepper noise removal in images and videos is proposing. The existing non-linear filter like Standard Median Filter (SMF), Adaptive Median Filter (AMF), Decision Based Algorithm (DBA) and Robust Estimation Algorithm (REA) shows better results at low and medium noise densities. At high noise densities, their performance is poor. Salt and Pepper noise is consider as Impulse noise. The objective of filtering is to remove the impulses so that the noise free image is fully recover with minimum signal distortion. The new algorithm is having lower computation time when compares to other standard algorithms. Results of the algorithm is compares with various existing algorithms and it is proving that the new method has better visual appearance and quantitative measures at higher noise densities as high as 90%.
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In Real Time,Automatic face identification of characters in movieshas drawn significant research interests and led to manyinterestingapplications. It is a challenging problem due to the hugevariation in the appearance of each character. Although existingmethods demonstrate promising results in clean environment, theperformances are limited in complexmovie scenes due to the noisesgenerated during the face tracking and face clustering process. We present two schemes of global face-name matchingbased framework for robust character identification. The contributionsof this work include the following. 1) A noise insensitivecharacter relationship representation is incorporated. 2)We introducean edit operation based graph matching algorithm. 3) Complexcharacter changes are handled by simultaneously graph partitionand graph matching. 4) Beyond existing character identificationapproaches, we further perform an in-depth sensitivity analysisby introducing two types of simulated noises. The proposedschemes demonstrate state-of-the-art performance on movie characteridentification in various genres of movies.
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Recent advances in network and communication technologies have led to an increasing interest in Networked Control Systems (NCS). Feedback is present in many deployed communication systems and yet the theory of feedback has made little impact on communications in practice. Here we are going to consider noisy channels in feedback system. A discrete time stochastic feedback control system with a noisy communication channel between the sensor and the controller is considered. The sensor has limited memory. At each time, the sensor transmits encoded symbol over the channel and updates its memory. The controller receives a noisy version of the transmitted symbol, and generates a control action based on all its past observations and actions. We show how to exploit a noisy feedback link to implement high-reliability communication. We specify a variable-length coding strategy that achieves the error exponent (in delay) of erasure decoding using any noisy feedback channel which has a positive zero-rate random coding error exponent. Building on this result, we give a second approach that, depending only on the capacity of the feedback link, achieves an error exponent up to half of the Burnashev exponent—the maximum exponent that can be achieved with a noiseless feedback link. The resulting exponent can be far larger than the exponent of erasure decoding, particularly at rates close to capacity. The objective is to choose encoding, memory updating and control strategies to minimize the expected total costs over a finite horizon, or the expected discounted cost over an infinite horizon, or the expected average cost per unit time over an infinite horizon.
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The way we distribute and consume electricity is undergoing a quiet revolution that will lead to lower energy bills. Energy used in buildings accounts for almost half of the total amount of energy consumed in the Indian Community today. The key element to implementing system optimization is the application of sensors and controls. These allow processes to be monitored and systems adjusted to minimize energy consumption. The groundwork of this revolution has been established by the rise of intelligent technologies: the digital hardware and software embedded within energy system, from the grid to the home, including sensors, controls, intelligent appliances, and others. The purpose of automation systems in buildings is to identify the areas in building stock where energy is used in excess. The system including ARM microcontroller to control appliances like fan, tube, AC, etc. In this way according to occupancy comfort the devices will be turn on or off automatically. By implementing this system in buildings energy which is used unnecessary that energy will consume. The objective is to establish proposed system for Building automation and control system components to significantly improve the energy conserving of buildings.
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Image enhancement and Finger print detection using Gabor filter is one of highly computational image verification process. Gabor filter play an important role in many application areas for the enhancement of various types of images and the extraction of Gabor features. Gabor filter can optimally capture global and local textures. The incoming signal or image data in pixel form will be filtered out or convoluted by Gabor filter. The main characteristics of this approach are the usage of memory to store the image pixel and coefficient of Gabor filter before the convolution matrix take place. The result will be convolution of image data and Gabor filter coefficient. Discrete wavelet Transform is also widely used in image processing algorithm. Finger print enhancement using Gabor filter is implemented already in MATLAB. The paper focuses the modeling and simulation of Gabor filter in hardware chip. The language used for hardware implementation is VHDL and synthesized in Xilinx ISE 14.2. Modelsim10.1 b is used to check the waveform simulation of chip. In this paper comparison is done of hardware utilization and timing parameters for the finger print image enhancement results using Gabor filter and DWT.
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The demand for images, video sequences and computer animations has increased drastically over the years. This has resulted in image and video compression becoming an important issue in reducing the cost of data storage and transmission. JPEG is currently the accepted industry standard for still image compression, but alternative methods are also being explored. Fractal Image Compression (FIC) is one of them. This scheme works encoding by partitioning an image into blocks and using Contractive Mapping to map range blocks to domains. The encoding step in fractal image compression has high computational complexity whereas, decoding step involves starting from all zeros image to achieve final image which is same as original image by applying self Transformations.
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Analysis of ECG signal is proposed using FIR filters. Heart beat rate of given ECG is calculated using zero crossing algorithm.FIR filter is designed by using hybrid window. The proposed window (hybrid window) is a combination of hamming and Bartlett window. The designed filter removes power line interference of 60Hz from the noisy ECG signal The proposed window gives better response than individual windows.
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Data compression affects most aspects of computing today. In fact it is greatly employed in information retrieval and dissemination to reduce the amount of physical traffic and save time. Compression is useful because it helps reduce the consumption of resources such as data space or transmission capacity. Technically, it may be defined as representation of information sources like an image or an audios signal or a data file as accurately as possible using the fewest number of bits resulting in reduced space required to store the files on the disk and also decrease in time to transmit large files. A variety of compression techniques both free and commercial are available to implement compression.
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Various efforts have been taken between power area and performance due to trade-off [3]. This paper focuses on the design and implementation of low power Booth multiplier by using VHDL. The speed, area and delay of radix 2 and modified radix 4 Booth multipliers are compared. This implementation explains in the form of RTL Schematic and comparison is also done by using RTL Schematic. Booth Multiplier is used to reduce number of iteration step in order to perform multiplication as compare to Conventional steps. Booth algorithm ‘scans’ the multiplier operand and skips the chains of this algorithm which can reduce the number of additions required to produce the result compared to Conventional Multiplication algorithm, where each bit of the multiplier is multiplied with the multiplicand and the partial products are aligned and added together.
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The feeding technique of a microstrip antenna along with its dimensions determines its resonant frequency. In this article, a Particle Swarm Optimization (PSO) based technique for accurate determination of resonant frequency of rectangular aperturecoupled microstrip antenna is reported. It is then compared against two common evolutionary techniques viz. genetic algorithm and differential evolution algorithm in terms of speed of convergence. Results show that resonant frequency of the designed optimal antenna almost matches with desired value (fr = 7.842 GHz with S11 = -25.6 dB for the desired design frequency 8 GHz). It is also observed that the particle swarm optimization technique converges faster than the evolutionary algorithms.
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So many filters are use to remove the noise from ECG signal, because it does not need the signal statistical characteristics. In this paper we present a novel adaptive filter for removing the artifacts from ECG signals based on Constrained Stability Least Mean Square (CSLMS) algorithm. This paper presents a novel adaptation for filtering Cardiac signals in continuous stationary and non-stationary environments in biotelemetry systems, which are characterized by sudden changes of the signal statistics due to physiological, nonphysiological reasons and noises due to free space propagation.The considered CSLMS algorithm is based on the concept of difference quantities and the constraint of equilibrium in the sequence of a posteriori estimation errors. Different filter structures are presented to eliminate the diverse forms of noise. Finally, we have applied this algorithm on ECG signals and compared it performance with the conventional LMS algorithm. Our results show that the performance of the CSLMS based algorithm is superior to that of the LMS based algorithm in noise reduction.
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Electromyography is a muscle examination method which tracks and interprets electrical activity that provides to muscle contractions. Surface Electromyography (SEMG) is widely used as a diagnostic tool in estimation of muscle strength, calculation of muscle fatigue and ergonomics, sports physiology and rehabilitation. Obtaining and examining carefully EMG signals provide valuable information in determining and examining abnormalities in the muscle and motor system. In this research we detect abnormalities in the muscle.
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While forming reliable communication networks, we must guarantee that, after failure of a node or links, the surviving network still allows communication between all other nodes by choosing alternate path which gives strict requirement on the connectivity of the corresponding graph. A general network design problem which requires the underlying network to be resilient to link failures is known as the edge-connectivity survivable network design problem. In this work we present a new approach to find a Minimum Spanning Tree (NAFMST) used to parallelize efficiently Kruskal’s Minimum Spanning Tree algorithm. This algorithm is known for exhibiting inherently sequential Characteristics. More specifically, the strict order by which the algorithm checks the edges of a given graph is the main reason behind the lack of explicit parallelism. Our proposed scheme attempts to overcome the imposed restrictions and improve the performance of the algorithm.
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The problem of underdetermined blind audio source separation is usually addressed under the framework of sparse signal representation. In this paper, we develop a novel algorithm for this problem based on compressed sensing which an emerging technique for efficient data reconstruction is. The proposed algorithm consists of two stages. The unknown mixing matrix is firstly estimated from the audio mixtures in the transform domain, as in many existing methods, by a K- means clustering algorithm. Different from conventional approaches, in the second stage, the sources are recovered by using a compressed sensing approach. This is motivated by the similarity between the mathematical models adopted in compressed sensing and source separation. Numerical experiments including the comparison with a recent sparse representation approach are provided to show the good performance of the proposed method.
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Cyclic Redundancy Check (CRC) is a widely used error detection method in data communications and storage devices. It is also applied to many other fields such as the testing of integrated circuits and the detection of logical faults. CRC codes are a special subset of linear block codes that are typically implemented as a Linear Feedback Shift Register (LSFR). Many different CRC polynomials are possible; designed and constructed to have desirable errordetection properties. In this paper, the implementation of the CRC algorithm is carried out using Verilog on an Actel Smart Fusion FPGA.
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Maximum Power Point Tracking (MPPT) is a technique that gridtie inverters (GTI), solar battery chargers and similar devices use to get the maximum possible power from one or more photovoltaic devices, typically solar panels. It is the purpose of the MPPT system to sample the output of the cells and apply the proper resistance (load) to obtain maximum power for any given environmental conditions. MPPT devices are typically integrated into an electric power converter system that provides voltage or current conversion, filtering, and regulation for driving various loads, including power grids, batteries, or motors. There are more algorithms are used in MPPT in which we are going to implement Perturb and observe (P&O) Algorithm in our project. It is simple, top-level efficiency and ease of implementation, the perturb and observe (P&O) algorithm is the most commonly applied maximum power point tracking (MPPT) control scheme in photovoltaic (PV) applications. Two techniques are usually used for implementing the P&O algorithm, reference voltage perturbation where a reference value for the PV generator output voltage is used as the control parameter and direct duty ratio perturbation where the duty ratio of the MPPT converter is used directly as the control parameter.
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