In the paper, the image series forgery detection algorithm based on the analysis of camera pattern noise is proposed. Distribution characteristics of the camera pattern noise are obtained by extracting the noise compo...
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Telemedicine is a promising direction in the development of medical technologies for the interaction of patients with doctors at a distance. In this paper, we consider the use of telemedicine technologies for the deve...
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ISBN:
(纸本)9783030308599;9783030308582
Telemedicine is a promising direction in the development of medical technologies for the interaction of patients with doctors at a distance. In this paper, we consider the use of telemedicine technologies for the development of smart medical autonomous technology. An example of a smart medical autonomous distributed system for diagnostics is also discussed. To develop this system for medical image analysis we review several processing methods and machine learning algorithms. Some examples of medical system processing results are presented.
The stabilization of the Line of Sight (LOS) of an Electro-Optical System (EOS) locating on a moving platform contributes significantly to the image quality. A portion of perturbation inherited by the base motion can ...
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ISBN:
(数字)9781510630222
ISBN:
(纸本)9781510630222
The stabilization of the Line of Sight (LOS) of an Electro-Optical System (EOS) locating on a moving platform contributes significantly to the image quality. A portion of perturbation inherited by the base motion can be eliminated by numbers of algorithms with powerful processors boards. However, it is difficult to implement these algorithms in embedded systems because of memory capacity and processing speed limitation. This paper introduces a method for identifying gimbal parameters and feedforward compensators. The key parameters including the friction force, cross-coupling effect and misalignment compensator are investigated using feedforward compensator theory and verified by practical experiments. The effectiveness in the stabilization loop is validated through many scenarios of disturbance. The result shows that the good improvement for the stabilization level of inertial stabilization platform has been achieved, and the reduction of LOS RMS errors is up to 40 per cent.
Classification of text based on its substance is an essential part of analysis to organize enormously large text data and to mine the salient information contained in it. It is gaining greater attention with the surge...
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Classification of text based on its substance is an essential part of analysis to organize enormously large text data and to mine the salient information contained in it. It is gaining greater attention with the surge in the volume of on-line data available. Classical algorithms like k-NN (k-nearest neighbor), SvM (Support vector Machine) and their variations have been observed to yield only reasonable results in addressing the problem, leaving enough room for further improvement. A class of algorithms commonly referred to as Sparse Methods has been emerged recently from compressive sensing and found numerous effective applications in many areas of data analysis and imageprocessing. Sparse Methods as a tool for text analysis is an alley that is largely unexplored rigorously. This paper presents exploration of sparse representation-based methods for text classification. Based on the success of sparse representation based methods in different areas of data analysis, we intuitively hypothesized that it should work well on text classification problems as well. This paper empirically reinforces the hypothesis by testing the method on Reuters and WebKB data sets. The empirical results on Reuters and WebKB benchmark data show that it can outperform classical classification algorithms like SvM and k-NN. It has been observed that obtaining the basis of representation and sparse codes are computationally costly operations affecting the performance of the system. We also propose a class-wise dictionary refinement algorithm and dynamic dictionary selection algorithm to make sparse coding faster. The addition of dictionary refinement to the classification system not only reduces the time taken for sparse coding but also gives improved classification accuracy. The outcomes of the study are empirical verification of sparse representation classifier as a text classification tool and a computationally efficient solution for the bottleneck operation of sparse coding. (C) 2019 Elsevi
Modified Gram Schmidt (MGS) is one of the well-known forms of QR decomposition (QRD) algorithms. It has been used in many signal and imageprocessing applications to solve least square problem, linear equations or to ...
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ISBN:
(纸本)9781728112329
Modified Gram Schmidt (MGS) is one of the well-known forms of QR decomposition (QRD) algorithms. It has been used in many signal and imageprocessing applications to solve least square problem, linear equations or to invert matrices. Nevertheless, QRD is considered a computationally expensive operation, and its sequential implementation doesn't meet the requirements of many real time applications. In this paper, we propose an optimized MGS algorithm version based on software pipelining and loop unrolling techniques. The suggested MGS version is parallel and well suited for vLIW architectures. The implementation is done under TI C6678 vLIW DSP and the obtained results show great improvements against the standard MGS and the optimized vendor QRD implementations.
SLAM(Simultaneous Localisation And Mobilisation) is a problem in robotics that revolves around the idea of a robot which can map a location by moving around in a sequential manner. Robot has to move and as well as rec...
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Matrices are employed for diversified applications such as imageprocessing, control systems, video processing, radar signal processing, compressive sensing and many more. Finding inverse of a floating point large sca...
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ISBN:
(数字)9781728196640
ISBN:
(纸本)9781728196657
Matrices are employed for diversified applications such as imageprocessing, control systems, video processing, radar signal processing, compressive sensing and many more. Finding inverse of a floating point large scale matrix is considered to be computationally intensive and their hardware implementation is still a research topic. FPGA implementation of four different floating-point matrix inversion algorithms using a novel combination of high level language programming and model based design is proposed in this paper. The proposed designs can compute inverse of a floating point matrix up to a matrix size of 25×25 and can be easily scaled to large size matrices. The performance evaluation of proposed matrix inversion modules are carried out by their hardware implementation on a Zynq 7000 FPGA based ZED board and the results are reported.
Detecting in prior bearing faults is an essential task of machine health monitoring because bearings are the vital components of rotary machines. The performance of traditional intelligent fault diagnosis methods depe...
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Detecting in prior bearing faults is an essential task of machine health monitoring because bearings are the vital components of rotary machines. The performance of traditional intelligent fault diagnosis methods depend on feature extraction of fault signals, which requires signal processing techniques, expert knowledge, and human labor. Recently, deep learning algorithms have been applied widely in machine health monitoring. With the capacity of automatically learning complex features of input data, deep learning architectures have great potential to overcome drawbacks of traditional intelligent fault diagnosis. This paper proposes a method for diagnosing bearing faults based on a deep structure of convolutional neural network. Using vibration signals directly as input data, the proposed method is an automatic fault diagnosis system which does not require any feature extraction techniques and achieves very high accuracy and robustness under noisy environments. (C) 2018 Elsevier B.v. All rights reserved.
Unhindered and smooth movement of emergency vehicles within a city is a crucial aspect of any intelligent transport system. It is common to observe emergency vehicles such as ambulances and fire engines obstructed by ...
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ISBN:
(数字)9781728141671
ISBN:
(纸本)9781728141688
Unhindered and smooth movement of emergency vehicles within a city is a crucial aspect of any intelligent transport system. It is common to observe emergency vehicles such as ambulances and fire engines obstructed by traffic snarls on Indian roads, especially in the proximity of busy intersections. Existing literature primarily advocates the deployment of RFID technology to terminate the round-robin sequence of the signal system and switch the signal to green in the required direction. However, this technology has proven to be susceptible to electromagnetic interferences and also the economic feasibility is questionable. This paper proposes a model that employs real time imageprocessing and object detection using a convolutional neural network (CNN) architecture called SSD Mobilenet. Unlike a few other architectures, SSD Mobilenet requires very limited computation, hence enabling swift detection. Furthermore, an acoustic signal (sound) processing (pitch detection) algorithm is employed to detect the sirens of emergency vehicles to nullify the potential false positives (e.g. an ambulance in a non-emergency scenario) that creep into object detection using imageprocessing. Both algorithms work in unison, bolstering the accuracy of detection. Upon detection, the signal instantly switches to green, facilitating the expedited movement of emergency vehicles, even in high traffic conditions.
The problem of semi-automatic object extraction for visual scene images was solved. The possibility of implementation of a model-free approach to object extraction using low-level image segmentation methods was demons...
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ISBN:
(数字)9781728169514
ISBN:
(纸本)9781728169521
The problem of semi-automatic object extraction for visual scene images was solved. The possibility of implementation of a model-free approach to object extraction using low-level image segmentation methods was demonstrated. The analysis of drawbacks of the basic methods for image segmentation, that limit their application in the developed automatic tracking algorithm of the video stream processing system for unmanned aerial vehicles (UAvs) was conducted. The algorithm for extracting an object in the image by superpixel merging was proposed. The testing results with real and synthetic images for the proposed algorithm were presented.
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