Статьи журнала - International Journal of Image, Graphics and Signal Processing
Все статьи: 1056
Loudspeaker operation status monitoring system based on power line communication technology
Статья научная
With the rapid development of science and technology, intelligent systems have been applied to various fields. A monitoring system for the operating status of loudspeakers based on power line communication was designed and implemented. In this paper, firstly analyzes the deficiencies of previous research, and then according to the actual situation, it is concluded that the power line communication technology is more suitable for loudspeaker operating status monitoring than other communication technologies. The overall design, hardware design and software design of the entire system was introduced. And in the last, the reliability of the system were proved by many experiments. This system can be used in other applications in addition to the monitoring of the operating status of the loudspeakers.
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Luminance-based Embedding Approach for Color Image Watermarking
Статья научная
In this paper a new non-blind luminance-based color image watermarking technique is proposed. The original 512×512 color host image is divided into 8×8 blocks, and each block is converted to YCbCr color space. A 32×32 monochrome image is used as a watermark and embedded in the selected blocks of the original image. The selected blocks must have log-average luminance that is closer to the log-average luminance of the image. DCT transform is applied to the Y component of each selected block. Each four values of the watermark image are embedded into each selected block of the host image. The watermark values are embedded in the first four AC coefficients leaving the DC value unchanged. The watermark is extracted from the watermarked image using the same selected blocks and DCT coefficients that have been used in the embedding process. This approach is tested against variety of attacks and filters: such as, highpass, lowpass, Gaussian, median, salt and peppers, and JPEG compression. The proposed approach shows a great ability to preserve the watermark against these attacks.
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Lung Tumor Segmentation and Staging from CT Images Using Fast and Robust Fuzzy C-Means Clustering
Статья научная
Lung tumor is the result of abnormal and uncontrolled cell division and growth in lung region. Earlier detection and staging of lung tumor is of great importance to increase the survival rate of the suffered patients. In this paper, a fast and robust Fuzzy c-means clustering method is used for segmenting the tumor region from lung CT images. Morphological reconstruction process is performed prior to Fuzzy c-means clustering to achieve robustness against noises. The computational efficiency is improved through median filtering of membership partition. Tumor masks are then reconstructed using surface based and shape based filtering. Different features are extracted from the segmented tumor region including maximum diameter and the tumor stage is determined according to the tumor staging system of American Joint Commission on Cancer. 3D shape of the segmented tumor is reconstructed from series of 2D CT slices for volume measurement. The accuracy of the proposed system is found as 92.72% for 55 randomly selected images from the RIDER Lung CT dataset of Cancer imaging archive. Lower complexity in terms of iterations and connected components as well as better noise robustness are found in comparison with conventional Fuzzy c-means and k-means clustering techniques.
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Статья научная
Internet of Things (IoT) Networks are multitier deployments which assist on-field data to be sensed, processed, communicated, and used for taking control decisions. These deployments utilize hardware-based components for data sensing & actuation, while cloud components are used for data-processing & recommending control decisions. This process involves multiple low-security, low-computational capacity & high-performance entities like IoT Devices, short range communication interfaces, edge devices, routers, & cloud virtual machines. Out of these entities, the IoT Device, router, & short-range communication interfaces are highly vulnerable to a wide-variety of attacks including Distributed Denial of Service (DDoS), worm hole, sybil, Man in the Middle (MiTM), Masquerading, spoofing attacks, etc. To counter these attacks, a wide variety of encryption, key-exchange, and data modification models are proposed by researchers. Each of these models have their own levels of complexities, which reduces QoS of underlying IoT deployments. To overcome this limitation, blockchain-based security models were proposed by researchers, and these models allow for high-speed operations for small-scale networks. But as network size is increased, delay needed for blockchain mining increases exponentially, which limits its applicability. To overcome this issue, a machine learning based blockchain model for QoS-aware secure IoT deployments is proposed in this text. The proposed MLSMBQS model initially deploys a Proof-of-Work (PoW) based blockchain model, and then uses bioinspired computing to split the chain into multiple sub-chains. These sub-chains are termed as shards, and assists in reduction of mining delay via periodic chain splitting process. The significance of this research is use of Elephant Herd Optimization (EHO) which assists in managing number of blockchain-shards via splitting or merging them for different deployment conditions. This decision of splitting or merging depends on blockchain’s security & quality of service (QoS) performance. Due to integration of EHO for creation & management of sidechains, the findings of this research showcase that the proposed model is capable of improving throughput by 8.5%, reduce communication delay by 15.3%, reduce energy consumption by 4.9%, and enhance security performance by 14.8% when compared with existing blockchain & non-blockchain based security models. This is possible because EHO initiates dummy communication requests, which are arbitrarily segregated into malicious & non-malicious, and usedfor continuous QoS & security performance improvement of the proposed model. Due to this continuous performance improvement, the proposed MLSMBQS model is capable of deployment for a wide variety of high-efficiency IoT network scenarios.
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Mammogram Pre-processing Using filtering methods for Breast Cancer Diagnosis
Статья научная
Cancer is the second most found disease, and Breast cancer is the most common in women. Breast cancer is curable and can reduce mortality, but it needs to be identified early and treated accordingly. Radiologists use different modalities for the identification of Breast cancer. The superiority of Mammograms over other modalities is like minor radiation exposure and can identify different types of cancers. Therefore, mammograms are the most frequently used imaging modality for Breast Cancer Diagnosis. However, noise can be added while capturing the image, affecting the accuracy and analysis of the result. Therefore, using different filtering techniques to pre-process mammograms can enhance images and improve outcomes. For the study, the MIAS dataset has been used. This paper gives a comparative study on filters for Denoising and enhancement of mammograms. The study focuses on filters like Box Filter, Averaging filter, Gaussian Filter, Identical Filter, Convolutional 2D Filter, Median Filter, and Bilateral Filter. Performance measures used to compare these filters are Mean Squared Error (MSE), Structural Similarity Index Measure (SSIM), and Peak Signal-to-noise Ratio (PSNR). All Performance measures are evaluated for all images of MIAS dataset and compared accordingly. Results show that Gaussian Filter, Median Filter, and Bilateral Filter give better results than other filters.
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Management of vehicular traffic system using artificial bee colony algorithm
Статья научная
In this paper, an Adaptive Dynamic Scheduling Algorithm (ADSA) based on Artificial Bee Colony (ABC) was developed for vehicular traffic control. The developed model optimally scheduled green light timing in accordance with traffic condition in order to minimize the Average Waiting Time (AWT) at the cross intersection. A MATLAB based Graphic User Interface (GUI) traffic control simulator was developed. In order to demonstrate the effectiveness of the developed ADSA this paper was validated with the existing work in the literature. The result obtained for the AWT of the developed ADSA had a performance of 76.67%. While for vehicular queues cleared at the intersection the developed ADSA had a performance of 53.33%. The results clearly expressed that the developed ADSA method has been successful in minimizing the Average Waiting Time and vehicular queues at the intersection.
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Статья научная
There are presently about 40 special economic zones (SEZs) across the seven federating emirates of the United Arab Emirates. These SEZs include businesses technology hubs, science cities, recreational parks, and media parks. This study aims to document how the SEZs change or affect the transformation of urban expansion and land use practices in the UAE. The study aims to show the new industrial growth and expansion related with land use that emerge around the SEZs compared to before their establishment. The research is based on sequential analysis of temporal geospatial digital maps generated from archival Landsat TM in 2000 and Landsat 8 OLI of 2015. The study area is located around Alqouz and Albarsha localities situated in the western side of the city of Dubai with a vibrant SEZs. The analysis results show remarkable expansion of 23.6% of industrial and warehouse infrastructure coupled with major residential expansion of 18.6%. The findings of this research would help local authorities and corporation in planning for the future of these global businesses and local sustainability in the context of environmental planning and sustainability.
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Статья научная
Alzheimer's disease is a progressive neurologic disorder that causes the brain to shrink (atrophy) and brain cells to die. A recent study found that 40 million people worldwide suffer from Alzheimer's disease (AD). A few symptoms of this AD disease are problems with language understanding, mood swings, behavioral issues, and short-term memory loss. A key research area for AD is the classification of stages. In this paper, we applied both binary and multi-class classification. In this paper, proposed is a Mask-Region based Convolution Neural Network (R-CNN) for classifying the stages including MCI, LMCI, EMCI, AD, and CN of Alzheimer's Disease. First performing pre-processing by using the skull-stripping algorithm for removing the noise. Second, the patch wise U-Net has been employed to segment the images for improving the classification process. After that, the system's efficiency is examined using MATLAB-based experiments, utilizing images from the Alzheimer's disease Neuroimaging Initiative (ADNI) dataset to evaluate the efficiency in terms of accuracy, precision, recall, specificity, and sensitivity. Our proposed approach to classifying the stages achieves about 98.54%,94.2%, 98.25%, 99.2%, and 99.02%in terms of accuracy with EMCI, CN, MCI, AD, and LMCI respectively. Proposing mask R-CNN with segmentation to classify from CN to AD subjects successfully improved classifier accuracy significantly on the ADNI datasets.
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Статья научная
Lung cancer is distinguished by presenting one of the highest incidences and one of the highest rates of mortality among all other types of cancers. Detecting and curing the disease in the early stages provides the patients with a high chance of survival. This work aims at detecting lung nodules automatically through computerized tomography (CT) image. Accordingly, this article aim at presenting a method to improve the efficiency of the lung cancer diagnosis system, through proposing a region growing segmentation method to segment CT scan lung images. Afterwards, cancer recognition are presenting by Fuzzy Inference System (FIS) for differentiating between malignant, benign and advanced lung nodules. In the following, this paper is testing the diagnostic performances of FIS system by using artificial neural networks (ANNs). Our experiments show that the average sensitivity of the proposed method is 95%.
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Статья научная
Lung cancer is distinguished by presenting one of the highest incidences and one of the highest rates of mortality among all other types of cancers. Detecting and curing the disease in the early stages provides the patients with a high chance of survival. In order to help specialists in the search and recognition of the lung nodules in tomography images, a good number of research centers have been developed in computer-aided detection (CAD) systems for automating the procedures. This work aims at detecting lung nodules automatically through computerized tomography images. Accordingly, this article aim at presenting a method to improve the efficiency of the lung cancer diagnosis system, through proposing a region growing segmentation method to segment CT scan lung images and, then, cancer recognition by FIS (Fuzzy Inference System). The proposed method consists of three steps. The first step was pre-processing for enhancing contrast, removing noise, and pictures less corrupted by Linear-Filtering. In second step, the region growing segmentation method was used to segment the CT images. In third step, we have developed an expert system for decision making which differentiates between normal, benign, malignant or advanced abnormality findings. The FIS can be of great help in diagnosing any abnormality in the medical images. This step was done by extracting the features such as area and color (gray values) and given to the FIS as input. This system utilizes fuzzy membership functions which can be stated in the form of if-then rules for finding the type of the abnormality. Finally, the analysis step will be discussed and the accuracy of the method will be determined. Our experiments show that the average sensitivity of the proposed method is more than 95%.
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Matrix-based Kernel Method for Large-scale Data Set
Статья научная
In the computation process of many kernel methods, one of the important step is the formation of the kernel matrix. But the size of kernel matrix scales with the number of data set, it is infeasible to store and compute the kernel matrix when faced with the large-scale data set. To overcome computational and storage problem for large-scale data set, a new framework, matrix-based kernel method, is proposed. By initially dividing the large scale data set into small subsets, we could treat the autocorrelation matrix of each subset as the special computational unit. A novel polynomial-matrix kernel function is then adopted to compute the similarity between the data matrices in place of vectors. The proposed method can greatly reduce the size of kernel matrix, which makes its computation possible. The effectiveness is demonstrated by the experimental results on the artificial and real data set.
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Mechanism and Algorithm for Indirect Schema Mapping Composition
Статья научная
There are a large number of indirect schema mappings between peers in the network. To improve the efficiency of data exchange and queries, indirect mappings are needed to be composed. Direct mappings can be derived directly by the constraints defined between schemas, but not for indirect mappings’ composition. Defined the combination operations of schema elements in indirect mappings, and gave the expression of indirect mappings. Analyzed the composition of indirect mappings, and proposed a strategy, named schema element back, to solve the problem of indirect mapping composition, and gave the indirect mapping composition generation algorithm based on such strategy. Experiments showed that indirect mapping composition can improve the efficiency of data exchange, and compared with other non-full mapping composition generation algorithms, and indirect mapping composition generated by our algorithm based on schema element back strategy can completely eliminate the infection of media schema with no reduction of the composition efficiency.
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Статья научная
Medical imaging has become a vital part of the early detection, diagnosis, and treatment of many diseases. That’s why image denoising is considered as a crucial pre-processing step in medical imaging to restore the original image from its noisy circumstance without losing image features, such as edges, corners, and other sharp structures. Ultrasound (US), Computed Tomography (CT), and Magnetic Resonance (MR) are the most widely used medical imaging techniques that are often corrupted by hazardous noises, namely, speckle, salt and pepper, Poisson, and Gaussian. To remove noises from medical images, researchers have proposed several denoising methods. Each method has its assumptions, merits, and demerits. In this paper, a detailed comparative analysis of different denoising filtering techniques, for example, median, Wiener, mean, hybrid median, Gaussian, bilateral, non-local means, and anisotropic diffusion are performed based on four widely-used image quality assessment (IQA) metrics, such as Root Mean Squared Error (RMSE), Peak Signal to Noise Ratio (PSNR), Mean Absolute Error (MAE), and Structural Similarity Index (SSIM). The results obtained in this present work reveal that Gaussian, median, anisotropic diffusion, and non-local means filtering methods perform extraordinarily to denoise speckle, salt and pepper, Poisson, and Gaussian noises, respectively, from all US, CT, and MR images.
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Medical Image Denoising Using Bilateral Filter
Статья научная
Medical image processing is used for the diagnosis of diseases by the physicians or radiologists. Noise is introduced to the medical images due to various factors in medical imaging. Noise corrupts the medical images and the quality of the images degrades. This degradation includes suppression of edges, structural details, blurring boundaries etc. To diagnose diseases edge and details preservation are very important. Medical image denoising can help the physicians to diagnose the diseases. Medical images include MRI, CT scan, x-ray images, ultrasound images etc. In this paper we implemented bilateral filtering for medical image denoising. Its formulation & implementation are easy but the performance of bilateral filter depends upon its parameter. Therefore for obtaining the optimum result parameter must be estimated. We have applied bilateral filtering on medical images which are corrupted by additive white Gaussian noise with different values of variances. It is a nonlinear and local technique that preserves the features while smoothing the images. It removes the additive white Gaussian noise effectively but its performance is poor in removing salt and pepper noise.
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Статья научная
This paper presents a natural language text description from video content activities. Here it analyzes the content of any video to identify the number of objects in that video content, what actions and activities are going on has to track and match the action model then based on that generate the grammatical correct text description in English is discussed. It uses two approaches, training, and testing. In the training, we need to maintain a database i.e. subject-verb and object are assigned to extract features of images, and the second approach called testing will automatically generate text descriptions from video content. The implemented system will translate complex video contents into text descriptions and by the duration of a one-minute video with three different object considerations. For this evaluation, a standard DB of YouTube is considered where 250 samples from 50 different domains. The overall system gives an accuracy of 93%.
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Статья научная
Human age recognition from face image relies highly on a reasonable aging description. Considering the disparate and complex face-aging variation of each person, aging description needs to be defined carefully with detailed local information. However, aging description relies highly on the appropriate definition of different aging-affiliated textures. Wrinkles are considered as the most discernible textures in this regard owing to their significant visual appearance in human aging. Most of the existing image-descriptors, however, fail short to preserve diverse variations of wrinkles, such as a) characterizing stronger and smoother wrinkles, appropriately, b) distinguishing wrinkles from non-wrinkle patterns, and c) characterizing the proper texture-structures of the pixels belonging to the same wrinkle. In this paper, we address these issues by presenting a new local descriptor, Local Edge-Prototypic Pattern (LEPP) with the notion that LEPP preserves different variations of wrinkle-patterns appropriately in representing the aging description. In the coding, LEPP sets prototypic restrictions for each neighboring pixel using their relation with center pixel when they belong to an inlying-edge, and utilize such restrictions, afterwards, to prioritize specific neighbors showing significant edge-signature. This strategy appropriately encodes the inlying edge structure of aging-affiliated textures and simultaneously, avoids featureless texture. We visualize the stability of LEPP in terms of its robustness under noise. Our experiments show that LEPP preserves discernible aging variations yielding better accuracies than the state-of-the-art methods in popular age-group datasets.
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Minutiae Distances and Orientation Fields Based Thumbprint Identification of Identical Twins
Статья научная
The twins are classified into two categories namely fraternal and identical twins. Fraternal twins differ in face structures and DNA sequences but, identical twins have the same face structure and share same DNA sequence. Therefore, it is difficult to identify identical twins on the basis of their faces and DNA sequences. In this research paper, we have introduced a new approach for identifying identical twins on the basis of minutiae coordinates, orientation angles, and minutiae distances of their thumbprint images. We tested the proposed method on thumbprint images of an identical twin pair generated by using Incept H3 T&A Terminal and fifty pairs of identical twins of FVC04, and FVC06 datasets. We have found that the proposed approach is superior, and robust in comparison to existing techniques in terms of accuracy, efficiency, and storage space.
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Mobile-Based Skin Disease Diagnosis System Using Convolutional Neural Networks (CNN)
Статья научная
This paper presents a design and development of an Artificial Intelligence (AI) based mobile application to detect the type of skin disease. Skin diseases are a serious hazard to everyone throughout the world. However, it is difficult to make accurate skin diseases diagnosis. In this work, Deep learning algorithms Convolution Neural Networks (CNN) is proposed to classify skin diseases on the HAM10000 dataset. An extensive review of research articles on object identification methods and a comparison of their relative qualities were given to find a method that would work well for detecting skin diseases. The CNN-based technique was recognized as the best method for identifying skin diseases. A mobile application, on the other hand, is built for quick and accurate action. By looking at an image of the afflicted area at the beginning of a skin illness, it assists patients and dermatologists in determining the kind of disease present. Its resilience in detecting the impacted region considerably faster with nearly 2x fewer computations than the standard MobileNet model results in low computing efforts. This study revealed that MobileNet with transfer learning yielding an accuracy of about 85% is the most suitable model for automatic skin disease identification. According to these findings, the suggested approach can assist general practitioners in quickly and accurately diagnosing skin diseases using the smart phone.
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Статья научная
In this paper, we propose a synthesis method for synthesizing the reconfigurable multiple patterns with the minimum number of antenna elements based on the state space model. The proposed method is to obtain the common element locations for the multiple patterns using fewer antenna elements within desired performance bounds. The proposed approach introduces the state-space method to represent the multiple patterns and then uses the multiple pattern data to construct a combined Hankel matrix which is used to estimate the model parameters from which the number of elements and the common element locations can be extracted. Numerical results show the effectiveness of the proposed methods.
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Modeling and Simulating Mutual Testing in Complex Systems by Using Petri Nets
Статья научная
The paper tackles the problem of performing mutual testing in complex systems. It is assumed that units of complex systems can execute tests on each other. Tests among system units are part of system diagnosis that can be carried out both before and during system operation. The paper considers the case when tests are executed during system operation. Modelling and simulating mutual tests will allow evaluation of the efficiency of using joint testing in the system. In the paper, the models that use Petri Nets were considered. These models were used for simulating the execution of tests among system units. Two methods for performing such simulations were evaluated and compared. Recommendations for choosing a more appropriate way were made. Simulation results have revealed minor model deficiencies and possible implementation of mutual testing in complex systems. Improvement of the model was suggested and assessed. A recommendation for increasing the efficiency of system diagnosis based on joint testing was made.
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