The optical sensor array inside the high-voltage equipment can effectively realize the on-line measurement of the high-voltage switch operation state, significantly improve the efficiency and accuracy of SF6 Electrica...
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In this study, an adaptive approach is addressed to reduce the noise of retinal Optical Coherence Tomography (OCT) images. Since the layered structure of retinal OCT images creates a dependency between adjacent pixels...
In this study, an adaptive approach is addressed to reduce the noise of retinal Optical Coherence Tomography (OCT) images. Since the layered structure of retinal OCT images creates a dependency between adjacent pixels at particular distances, the presented method is based on the adaptive selection of variable neighborhood windows for each pixel of OCT images. Indeed, by defining this spatial adaptivity, we extend our earlier work in which a pixel-wise fixed window was considered. Here, the variance is calculated in an optimal window for each pixel; so that the ultimate distribution of the variance image follows a gamma model. Besides, the asymmetry observed in the distribution of retinal layers led to suggest Asymmetric Bessel K-form (ABKF). This model is easily transformed into a Gaussian distribution through dividing the image into the root of the variance image. Then, it can be used with Gaussian-based algorithms for OCT denoising application. The results show the impressive performance of the proposed adaptive local BKF model in noise reduction and increasing image contrast as visual and quantitative criteria.
Any OFDM-based wireless communication receiver relies on channel estimation. Across a wireless multipath fading channel, there is a growing need for high-data-rate communication, which typically demands previous chann...
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Network slicing is becoming an effective solution to meet the heterogeneous quality of service (QoS) requirements in internet of vehicles (IoV). Caused by these, the coupling effect of intra- and inter-slice resource ...
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ISBN:
(纸本)9781665450867
Network slicing is becoming an effective solution to meet the heterogeneous quality of service (QoS) requirements in internet of vehicles (IoV). Caused by these, the coupling effect of intra- and inter-slice resource allocation have brought great challenges to *** this end, the intra- and inter-slice resource allocation problems are formulated as hierarchical optimization problem. Further, we propose an one leader and two followers resource allocation (OL-TFRA) algorithm based on Stackelberg game. Firstly, the bandwidth ratio of eMBB slice is determined by the bisection method, then the optimal strategy of inter-slice resource allocation is derived through the leader-follower game. The algorithm identifies the existence and uniqueness of the Stackelberg equilibrium, and takes the equilibrium point as the optimal result of inter- and intra-slice resource allocation. Simulation results reveal the interaction of different slices per-formance. Compared with the exhaustive search algorithm, the proposed algorithm converges faster than the exhaustive search algorithm with 2% utility decreases.
In recent years, learning-based image compression has demonstrated similar or superior performance when compared to conventional approaches in terms of compression efficiency and visual quality. Typically, learning-ba...
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ISBN:
(数字)9781510645233
ISBN:
(纸本)9781510645233;9781510645226
In recent years, learning-based image compression has demonstrated similar or superior performance when compared to conventional approaches in terms of compression efficiency and visual quality. Typically, learning-based image compression takes advantage of autoencoders, which are architectures consisting of two main parts: a multi-layer neural network encoder and its dual decoder. The encoder maps the input image represented in the pixel domain to a compact representation, also known as latent space. Consequently, the decoder reconstructs the original image in the pixel domain from its latent representation, as accurately as possible. Traditionally, image processingalgorithms, and in particular image denoising, are applied to images in the pixel domain before compression, and eventually in some cases as a post-processing stage after decompression. In this context, the combination of denoising operations with the autoencoder might reduce the computational cost while achieving similar performance in accuracy. In this paper, the idea of combining the image denoising task with compression is examined. In particular, the integration of denoising convolutional layers in the decoder of a learning-based compression network is investigated. Results show that, while the rate-distortion performance of the method is slightly reduced, a gain in the computational complexity can be achieved.
Biomedical signals may reveal body component function. Basic biological signals are unpredictable and irregular. This makes temporal data from such signals is difficult to get via observation. signalprocessing techno...
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ISBN:
(数字)9798331518981
ISBN:
(纸本)9798331518998
Biomedical signals may reveal body component function. Basic biological signals are unpredictable and irregular. This makes temporal data from such signals is difficult to get via observation. signalprocessing technologies have been used to extract meaningful information from these signals. so many illnesses may be found and identified. Technology that is non-invasive and beneficial Its high anomaly detection accuracy may help physicians identify and treat patients. control many illnesses. Machine learning was formerly the standard for bio analysis of signals from processes. Currently, deep learning approaches are applied. ECG signals show how the ANS maintains a regular heartbeat. ECG signals are non-linear and unrelated to nature. Some disorders may be more effectively diagnosed using ECG analysis. Diabetes, a serious condition, may be detected by an ECG. Diabetes is incurable. Diabetes may lead to cardiovascular disease, renal disease, and stroke if not effectively managed with medicine and insulin. Early diagnosis improves diabetic management. This thesis aims to illustrate that electrocardiogram (ECG) heart rate variability (HRV) values can detect diabetes. More sophisticated spectral (HOS) properties are required for the interpretation of non-linear HRV data. This research combines regular and diabetic ECG data to uncover HRV-generated HOS characteristics. These collected attributes were given to seven classification algorithms, including naive bayes, SVM, and others. Classifier performance was measured by accuracy, positive prediction value, sensitivity, and specificity. The results are compared to other methods and two parameters of the at its peak, the classifier using SVM achieves a predictive value (PPV) of 98.3% and a specificity rating of 98.4%, with an accuracy of 86.5%.
With the rapid development of computer technology and the Internet era, machine vision is a new science and technology formed by the cross-integration of various disciplines such as image processing theory, advanced i...
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ISBN:
(纸本)9781665491129
With the rapid development of computer technology and the Internet era, machine vision is a new science and technology formed by the cross-integration of various disciplines such as image processing theory, advanced information science and digital signalprocessing methods based on computers. The application of computer in information processing has become an inevitable trend, it can better acquire and understand images, and it can quickly and effectively analyze, extract useful targets and provide decision makers with the required content. This can also improve work efficiency and accuracy to achieve a more precise and automated management level, and play a huge role in life. First of all, this paper uses the ORB algorithm for target recognition for the identification and positioning of static targets, but this algorithm still has the problem of low feature matching accuracy. Therefore, an improved ORB feature matching method based on multiple constraints is proposed; Secondly, it studies the design of the machine vision image target recognition system, including the overall function design of the system, the system process design and the system experimental verification.
In this paper, BP neural network model is used to study and analyse the teaching reform of signal and system course. Referring to the structure of BP neural network, the quality evaluation index system of signal and s...
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作者：
P. BogackiM. DługoszT. TalaśkaR. DługoszAptiv Services Poland
Kraków Poland Institute of Telecommunications
Faculty of Computer Science Electronics and Telecommunications AGH University of Science and Technology Kraków Poland Faculty of Control
Robotics and Electrical Engineering Institute of Automation and Robotics Division of Signal Processing and Electronic Systems Poznan University of Technology Poznan Poland Faculty of Telecommunication
Computer Science and Electrical Engineering Bydgoszcz University of Science and Technology Bydgoszcz Poland
The paper presents a family of novel light blob shape descriptors for use in selected active safety algorithms used in advanced Driver Assistance Systems (ADAS). One of the motivations was to obtain a descriptor that ...
The paper presents a family of novel light blob shape descriptors for use in selected active safety algorithms used in advanced Driver Assistance Systems (ADAS). One of the motivations was to obtain a descriptor that would ensure low computational complexity. This makes it easy to implement both in software and hardware. One assumption is that the location of the center of a given light spot is approximately known. The principle of its operation is then to count white pixels in selected directions, starting from this central point. A key issue here is an efficient way of determining indexes of particular pixels belonging to the image patch, as well as the location of points representing places where the white area turns into black. In the case of a hardware implementation, this can be done using a parallel circuit operating in asynchronous mode, without the need for a control clock.
The entire design structure is being impacted by advancements in electronic technology, which is posing a number of challenges for digital systems. In the fields of communications, image processing, and multimedia, VL...
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ISBN:
(纸本)9781665493970
The entire design structure is being impacted by advancements in electronic technology, which is posing a number of challenges for digital systems. In the fields of communications, image processing, and multimedia, VLSI-based image filter architectures are essential. Numerous applications based on VLSI architecture experience problems with significant size components that result in a filter design error during the floating-point arithmetic stages. The increased complexity of the filter design's component list is the issue encountered in the VLSI architecture. Noise removal often involves using the image filtering technique. There is a greater need for performance-based hardware sorting algorithms for low power embedded real-time image filtering applications. Noise often taints an image while being transmitted from one location to another. The purpose of the denoising technique is to reduce noise while keeping any potential signal features that may be significant. Compare to the prior method, the usage of multipliers can be reduced based on the techniques of distance matrix and hardware resource sharing. The speed of the proposed VLSI architecture for the bilateral filter can be faster than the existing approach.
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