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In practice, images are distorted by more than one distortion. For image quality assessment (IQA), existing machine learning (ML)-based methods generally establish a unified model for all the distortion types, or each model is trained independently for each distortion type, which is therefore distortion aware. In distortion-aware methods, the common features among different distortions are not exploited...
Recent research endeavors have shown the potential of using feed-forward convolutional neural networks to accomplish fast style transfer for images. In this work, we take one step further to explore the possibility of exploiting a feed-forward network to perform style transfer for videos and simultaneously maintain temporal consistency among stylized video frames. Our feed-forward network is trained...
Great efforts have been driven to improve the optimal decision for network selection, which is significant for vertical handover to meet user's rapidly increased requirements. Unfortunately, the instability of the basic decisive parameters is usually ignored, resulting in a high misjudgement ratio. Therefore, this paper proposed a novel parameter estimation algorithm to bridge the observed values...
In order to realize the rapid deployment of indoor localization systems, the crowdsourcing method has been proposed to reduce the collection workload. However, compared to conventional methods, the reduced number of received signal strength (RSS) values lends greater influence to noises and erroneous measurements in RSS values. In this paper, a graph-based semi-supervised learning (G-SSL) method is...
In this paper, we first propose a novel no-reference (NR) image quality assessment (IQA) method for retargeted image based on the rank learning approach. Firstly, image features for each retargeted image are extracted, which should not only represent the image characteristics but also be sensitive to the retargeted distortions. Specifically, the image feature should be able to capture the shape distortions,...
Crowdsourcing allows for rapid deployment of indoor localization systems. However, compared to the conventional methods, crowdsourcing might collect fewer received signal strength (RSS) values, hence result in greater influence to outliers in RSS values. In this paper, we propose an algorithm to detect such outliers and to substitute them with more suitable RSS values. In particular, we investigate...
In practice, multiple types of distortions are associated with an image quality degradation process. The existing machine learning (ML) based image quality assessment (IQA) approaches generally established a unified model for all distortion types, or each model is trained independently for each distortion type by using single-task learning, which lead to the poor generalization ability of the models...
As one important field of sparse representation, the research of dictionary learning attracts most researchers interest in signal processing study. Empirical Mode Decomposition (EMD), as an efficient and adaptive signal decomposition method that depends completely on the signal, is considered as an innovative and appropriative the basis function theory. The Intrinsic Mode Functions (IMFs) obtained...
As the phased array radar works in a multi-objective environment, it's necessary to refer to the priority of targets when allocating resources during scheduling. The determination of priorities of targets needs to be fast and efficient, and consider multiple factors. Artificial neural network has adaptive and self-learning ability, and good fault-tolerance features, especially for processing problems...
Location estimation using received signal strength (RSS) in pervasively available Wi-Fi infrastructures has been considered as a popular indoor positioning solution. However, accuracy deterioration due to uncertainty of RSS and offline manual calibration cost limit the deployment of Wi-Fi positioning systems. This paper proposes a signal perturbation technique to enhance existing support vector regression...
This paper expands the standard pronunciation space (SPS) to include pronunciation errors for automatic pronunciation error detection (APED), uses HMMs to represent the different distributions of pronunciation errors, proposes an adaptive unsupervised clustering of pronunciation errors based on the similarity measures between two HMMs, and then refines more detailed acoustic models for APED within...
In this paper, a review on existing methods of extending image quality metric to video quality metric is given. It is found that three processing steps are usually involved which include the temporal channel decomposition, temporal masking and error pooling. They are utilized to extend our previously proposed image quality metric, which separately evaluates additive impairments and detail losses,...
This paper proposes an ANFIS indoor positioning system based on improved genetic algorithm (GA). In the offline phase, fuzzy rules are abstracted by means of subtractive clustering algorithm with training data, generating the structure of each ANFIS positioning subsystem in X and Y directions. Then each positioning subsystem is trained with improved-GA. In this training algorithm, BP algorithm acts...
A novel indoor location algorithm based on dynamic Radio Maps construction in wireless local area network (WLAN) is proposed. The limitation of previous static Radio Map method is that reconstruction work must be taken to adapt the variation of indoor wireless environment. By taking received signal strength (RSS) values varying over time and space into account, a dynamic Radio Map is constructed to...
Much attention has been paid to WLAN indoor positioning algorithm for its high accuracy and low cost to meet the location based services (LBS). This paper proposes a novel positioning algorithm based on positioning characteristics extraction in WLAN indoor environment. Each RSS signal from an individual access point is taken as input of the RBF neural networks to establish the mapping between RSS...
Neural network optimized by genetic algorithm (GA) based WLAN indoor location method is proposed. GA based artificial neural network (GA-ANN) method can effectively reduce the storage cost, enhance real-time ability, and greatly improves the accuracy of indoor location. By analyzing the inherent shortage in neural network when applying in indoor environment, make use of genetic algorithm to encode...
This paper proposes the WiFi indoor location determination method based on adaptive neuro-fuzzy inference system (ANFIS) with principal component analysis (PCA). It reduces the WiFi signal vectors dimensions and saves the storage cost and simplifies the fuzzy rules generated by subtractive clustering method for ANFIS training. In the off-line phase, the received signal strength (RSS) or signal to...
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