Статьи журнала - International Journal of Image, Graphics and Signal Processing
Все статьи: 1092
A novel hybrid method based on krill herd and cuckoo search for optimal power flow problem
Статья научная
Solving the Optimal power flow (OPF) problem is an urgent task for power system operators. It aims at finding the control variables’ optimal scheduling subjected to several operational constraints to achieve certain economic, technical and environmental benefits. The OPF problem is mathematically expressed as a nonlinear optimization problem with contradictory objectives and subordinated to both constraints of equality and inequality. In this work, a new hybrid optimization technique, that integrates the merits of cuckoo search (CS) optimizer, is proposed to ameliorate the krill herd algorithm (KHA)'s poor efficiency. The proposed hybrid CS-KHA has been expanded for solving for single and multi-objective frameworks of the OPF problem through 11 case studies. The studied cases reflect various economic, technical and environmental requirements. These cases involve the following objectives: minimization of non- smooth generating fuel cost with valve-point loading effects, emission reduction, voltage stability enhancement and voltage profile improvement. The CS-KHA presents krill updating (KU) and krill abandoning (KA) operator derived from cuckoo search (CS) amid the procedure when the krill updating in order to extraordinarily improve its adequacy and dependability managing OPF problem. The viability of these improvements is examined on IEEE 30-bus, IEEE 57-bus and IEEE 118-bus test system. The experimental results prove the greatest ability of the proposed hybrid meta-heuristic CS-KHA compared to other famous methods.
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Статья научная
The first step in Structures Health Monitoring (SHM), are determining the location, intensity and type of damage in structures. Crack is a damage that often occurs in structural elements and may cause serious ruptures in the structure. One of the important approaches is the wavelet analysis of vibration modes structures. In this study, it has been performed the crack detection method in steel cantilever beam structure, using an optimized wavelet-based model. The wavelet analysis has been performed based on the higher orders of the structure’s mode shapes. The results show that the proposed method is able to accurately detect all kinds of cracks, in which the cracks location are variable. In this study also, cracks with length of 20%, 10%, 5% and 2% of the beam’s depth have been considered and one of the most prominent results is introducing a method for detecting robust and environmental noisy cracks. The proposed method is capable of accurately detecting crack in the cantilever beams in noisy conditions about 20 dB of SNR.
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A review of image restoration based image defogging algorithms
Статья научная
Haze and fog lead to image degradation by various degradation processes like image contrast, image blurring and pixel distortion. It has effected the efficiency of computer and machine vision algorithms. A number of single image and multiple image restoration based image defogging algorithms have aimed to solve the problem in an efficient and fast manner. The objective of the paper is to summarize present state of the art image defogging algorithms. Firstly, an image classification algorithm has been presented and then we summarized present state of the art image restoration based image defogging algorithms. Finally, we summarized image quality assessment methods followed by their comparisons of various image defogging algorithms. Problems of image dehazing and future scope have been discussed thereafter.
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A robust zonal fractal dimension method for the recognition of handwritten telugu digits
Статья научная
Recognition of handwritten digits is most challenging sub task of character recognition due to various shapes, sizes, large variation in writing styles from person to person and also similarity in shapes of different digits. This paper presents a robust Telugu language handwritten digit recognition system. The Telugu language is most popular and one of classical languages of India. This language is spoken by more than 80 million people. The proposed method initially performs preprocessing on input digit pattern for removing noise, slat correction, size normalization and thinning. This paper divides the preprocessed Telugu handwritten digits into four differential zones of 2x2, 3x3, 4x4 and 6x6 pixels and extracts 65 features using Fractal dimension (FD) from each zone. The proposed zonal fractal dimension (ZFD) method uses, Feed forward backward propagation neural network (FFBPNN) for classifying the digits with learning rate of 0.01 and sigmoid function as an activation function on extracted 65 features. This paper evaluated the efficiency of the proposed method based on 5000 Telugu handwritten digit samples, each consists of ten digits from different groups of people and totally 50,000 samples. The performance of classification of the proposed method also evaluated using statistical parameters like recall, precision, F-measure and accuracy.
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Статья научная
The wastage of electrical power cannot be over-emphasized in FCT College of Education, Zuba-Abuja as many lighting bulbs, street-lights, electrical fans are always ON even when not in use. The college community is characterized by electrical power wastage. However, the motivation for this research was to curtail the electrical power wastage and reduce the high cost of electricity. This research was designed to control ON and OFF time of any electrical appliance connected to its output and could as well be used as a digital clock. The objective of this research was to control the ON and OFF time of air conditioning. The design included a microcontroller (ATMEGA328) that was programmed to achieve the timing operation. The Light Emitting Diode (LED) displayed the ton (Time ON) and the t0ff (Time OFF); four keys set the hour and the minutes; and the relay was activated whenever the time set elapsed, causing the air conditioning to be energized/dis-energized automatically. A time of 3:19 was set to test the ON switching. An air conditioning connected to the developed system was activated at exactly 3:19. Also, a time of 5:57 was set to de-activate the already ON electric bulb. The electric bulb was switched OFF at exactly 5:57. The developed switching system was tested and satisfactorily switched ON and OFF air conditioning as desired and pre-set by the user.
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A video based vehicle detection, counting and classification system
Статья научная
Traffic Analysis has been a problem that city planners have dealt with for years. Smarter ways are being developed to analyze traffic and streamline the process. Analysis of traffic may account for the number of vehicles in an area per some arbitrary time period and the class of vehicles. People have designed such mechanism for decades now but most of them involve use of sensors to detect the vehicles i.e. a couple of proximity sensors to calculate the direction of the moving vehicle and to keep the vehicle count. Even though over the time these systems have matured and are highly effective, they are not very budget friendly. The problem is such systems require maintenance and periodic calibration. Therefore, this study has purposed a vision based vehicle counting and classification system. The system involves capturing of frames from the video to perform background subtraction in order detect and count the vehicles using Gaussian Mixture Model (GMM) background subtraction then it classifies the vehicles by comparing the contour areas to the assumed values. The substantial contribution of the work is the comparison of two classification methods. Classification has been implemented using Contour Comparison (CC) as well as Bag of Features (BoF) and Support Vector Machine (SVM) method.
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ADCT-based Robust Methodology for Image Steganography
Статья научная
Steganography is an important area of research in recent years involving a number of applications. It is the science of embedding information into the cover image viz., text, video, and image (payload) without causing statistically significant modification to the cover image. The modern secure image steganography presents a challenging task of transferring the embedded information to the destination without being detected.In this paper, a DCT based robust methodology has been designed. The cover image is segmented into 8*8 blocks and DCT is applied on the image. The text to be hidden is embedded in the diagonal elements of the blocks by substituting a random variable in place of the bits of the text to be embedded. It is observed that the proposed algorithm is more robust with better CER & Normalized coefficient.
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ANN Approach for Classification of Different Origin Fabric Images
Статья научная
This paper focuses on classification of varieties of plants’, animals’ and minerals’ origin fabric materials from images. The morphological operations, namely, erosion and dilation are used. ANN classifier is used to predict the classification rates and the rates of 89%, 87% and 88% are obtained for plants’, animals’ and minerals’ origin fabric images respectively. The overall classification rate of 88% is obtained.
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Статья научная
The field of fraud identification is reaching a very high proportion in the society, thus leading to an increase in the need for fingerprint-based identification. This paper presents ASIC implementation of fingerprint recognition based on Overlap-add method and Integer Wavelet Transforms. In overlap-add method, the present output overlaps the next output and in the integer to integer wavelet, low component at 2nd level decomposition is taken as approximate integer value. The implementation presents an analysis for speed, area and power dissipation between the two algorithms and other methods.
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Accelerating Cross-correlation Applications via Parallel Computing
Статья научная
Software dealing with large-scale signal processing takes long time even on modern hardware. Cross-correlation applications are mostly algorithms rather than data-intensive (that is, they are more CPU-bound than I/O-bound). Parallel implementation of the cross-correlation execution over the local network, or in some cases over a Wide Area Network (WAN), helps reducing the processing time. The aim of this paper is to discuss the possibility of distributing the cross-correlation computational process over the available PCs in the local network. Moreover, the algorithm portion that is sent to a remote PC, within the LAN, will be redistributed over the available CPU cores on that computer yielding to maximum utilization of all available cores in the local area network. The load balancing problem will be addressed as well.
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Статья научная
Medical imaging appliances play a pivotal role in preventive medicine as the industry combat to low patient expense and acquire early disease estimation using nonintrusive methods. There are proprietary software packages which provide fast development for designing image processing algorithms. Another trend is to use open source softwares. With the advancement of VLSI (Very Large Scale Integration) technology, hardware implementation has also become an alternative. Proprietary hardwares provide flexibility, efficient power and timing constraints whereas open source hardwares provide optimum quality and cost constraints. The Present study is useful for image architects, researchers, biologists to learn various proprietary and open sources softwares as well as hardwares utilized for distinct applications of the healthcare industry.
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Accuracy Improvement in Palmprint Authentication System
Статья научная
Biometric authentication has been emerged as a reliable means to control a person's access to physical and virtual places. Despite the various efforts made on biometrics, accuracy of the authentication/identification is the main concern and it has to be completely investigated. The paper presents critical analysis of the matching score values in such a manner that system accuracy is increased. Min Max Threshold Range (MMTR) technique is proposed that provides two levels of authentication and increase in accuracy is observed. The methodology of increase in accuracy is observed on various feature extraction methods.
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Achieving Robustness in Face Recognition by Effective Feature Acquisition
Статья научная
In the past few decades, face recognition has been a widely researched topic, since it is a robust means of authentication. Extraction of features from the face images during face recognition is a very challenging task. Hence, proper selection of appropriate feature extraction algorithms is vital in this regard. Many robust feature extraction techniques do exist. But their proper selection and combination also plays an utmost role. In this study, 2D face recognition was achieved using the combination of local binary pattern (LBP), principal component analysis (PCA) and Support Vector Machines (SVM). Along with retaining most of the information, PCA is used to reduce multidimensional data to lower dimensions. LBP was mainly used to tackle the problems arising due to expressions. As the facial expression changes, the effect gets prevalent on the rest of the organs of the face. Similarly, the intensity of the corresponding pixels of images also changes. Hence, this study aims to overcome these challenges by applying PCA and LBP algorithms on face images to increase the recognition rate. SVM was used to perform classification on these datasets. This hybrid approach of using LBP and PCA in conjunction increased the recognition rate (RR) and decreased the false match rate. Therefore, this method was found to be more suitable for real-time applications.
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Acoustic Modeling of Bangla Words using Deep Belief Network
Статья научная
Recently, speech recognition (SR) has drawn a great attraction to the research community due to its importance in human-computer interaction bearing scopes in many important tasks. In a SR system, acoustic modelling (AM) is crucial one which contains statistical representation of every distinct sound that makes up the word. A number of prominent SR methods are available for English and Russian languages with Deep Belief Network (DBN) and other techniques with respect to other major languages such as Bangla. This paper investigates acoustic modeling of Bangla words using DBN combined with HMM for Bangla SR. In this study, Mel Frequency Cepstral Coefficients (MFCCs) is used to accurately represent the shape of the vocal tract that manifests itself in the envelope of the short time power spectrum. Then DBN is trained with these feature vectors to calculate each of the phoneme states. Later on enhanced gradient is used to slightly adjust the model parameters to make it more accurate. In addition, performance on training RBMs improved by using adaptive learning, weight decay and momentum factor. Total 840 utterances (20 utterances for each of 42 speakers) of the words are used in this study. The proposed method is shown satisfactory recognition accuracy and outperformed other prominent existing methods.
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Acoustic Signal Based Fault Detection in Motorcycles – A Comparative Study of Classifiers
Статья научная
The sound patterns generated by the vehicles give a clue of the health conditions. The paper presents the fault detection of motorcycles based on the acoustic signals. Simple temporal and spectral features are used as input to four types of classifiers, namely, dynamic time warping (DTW), artificial neural network (ANN), k-nearest neighbor (k-NN) and support vector machine (SVM), for a suitability study in automatic fault detection. Amongst these classifiers the k-NN is found to be simple and suitable for this work. The overall classification accuracy exhibited by k-NN classifier is over 90%. The work finds applications in automatic surveillance, detection of non-compliance with traffic rules, identification of unlawful mixture of fuel, detection of over-aged vehicles on road, vehicle fault diagnosis and the like.
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Acoustic Signal based Traffic Density State Estimation using SVM
Статья научная
Based on the information present in cumulative acoustic signal acquired from a roadside-installed single microphone, this paper considers the problem of vehicular traffic density state estimation. The occurrence and mixture weightings of traffic noise signals (Tyre, Engine, Air Turbulence, Exhaust, and Honks etc) are determined by the prevalent traffic density conditions on the road segment. In this work, we extract the short-term spectral envelope features of the cumulative acoustic signals using MFCC (Mel-Frequency Cepstral Coefficients). Support Vector Machines (SVM) is used as classifier is used to model the traffic density state as Low (40 Km/h and above), Medium (20-40 Km/h), and Heavy (0-20 Km/h). For the developing geographies where the traffic is non-lane driven and chaotic, other techniques (magnetic loop detectors) are inapplicable. SVM classifier with different kernels are used to classify the acoustic signal segments spanning duration of 20–40 s, which results in average classification accuracy of 96.67% for Quadratic kernel function and 98.33% for polynomial kernel function, when entire frames are considered for classification.
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Adaptive Image Enhancement Using Image Properties and Clustering
Статья научная
Image Enhancement is the method of improving the visibility of given image. Image Properties are used for the analysis of Quality of the given image. Various image Properties considered to improve the quality of the image. The Classification or grouping of images can be made by applying unsupervised image Classification algorithm. In our proposed method, various image properties are studied and an Adaptive K-means Clustering method is applied for Fractal image with Entropy Properties. The images are to enhanced on the basis of its grouping automatically. The resulted Classification can be acceptable by the user since the grouping is made on the type of the image i.e., Good Visible, Moderate and Blur images.
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Adaptive Modulation and Coding with Channel State Information in OFDM for WiMAX
Статья научная
WiMAX is a broadband wireless communication system which provides fixed as well as mobility services. The mobile-WiMAX offers a special feature that has adopted an adaptive modulation and coding (AMC) in OFDM to provide higher data rates and error free transmission. AMC technique employs the channel state information (CSI) to efficiently utilize the channel and maximize the throughput with better spectral efficiency. In this paper, LSE, MMSE, LMMSE, Low rank (Lr)-LMMSE channel estimators are integrated with the physical layer. The performance of estimation algorithms is analyzed in terms of BER, SNR, MSE and throughput. Simulation results proved that increment in modulation scheme size causes to improvement in throughput along with BER value. There is a trade-off among modulation size, BER value and throughput.
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Adaptive Quantization Index Modulation Audio Watermarking based on Fuzzy Inference System
Статья научная
Many of the adaptive watermarking schemes reported in the literature consider only local audio signal properties. Many schemes require complex computation along with manual parameter settings. In this paper, we propose a novel, fuzzy, adaptive audio watermarking algorithm based on both global and local audio signal properties. The algorithm performs well for dynamic range of audio signals without requiring manual initial parameter selection. Here, mean value of energy (MVE) and variance of spectral flux (VSF) of a given audio signal constitutes global components, while the energy of each audio frame acts as local component. The Quantization Index Modulation (QIM) step size Δ is made adaptive to both the global and local features. The global component automates the initial selection of Δ using the fuzzy inference system while the local component controls the variation in it based on the energy of individual audio frame. Hence Δ adaptively controls the strength of watermark to meet both the robustness and inaudibility requirements, making the system independent of audio nature. Experimental results reveal that our adaptive scheme outperforms other fixed step sized QIM schemes and adaptive schemes and is highly robust against general attacks.
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Adaptive Remote Sensing Texture Compression on GPU
Статья научная
Considering the properties of remote sensing texture such as strong randomness and weak local correlation, a novel adaptive compression method based on vector quantizer is presented and implemented on GPU. Utilizing the property of Human Visual System (HVS), a new similarity measurement function is designed instead of using Euclid distance. Correlated threshold between blocks can be obtained adaptively according to the property of different images without artificial auxiliary. Furthermore, a self-adaptive threshold adjustment during the compression is designed to improve the reconstruct quality. Experiments show that the method can handle various resolution images adaptively. It can achieve satisfied compression rate and reconstruct quality at the same time. Index is coded to further increase the compression rate. The coding way is designed to guarantee accessing the index randomly too. Furthermore, the compression and decompression process is speed up with the usage of GPU, on account of their parallelism.
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