Underdetermined blind source separation based on Fuzzy C-Means and Semi-Nonnegative Matrix Factorization

Conventional blind source separation is based on over-determined with more sensors than sources but the underdetermined is a challenging case and more convenient to actual situation. Non-negative Matrix Factorization (NMF) has been widely applied to Blind Source Separation (BSS) problems. However, the separation results are sensitive to the initialization of parameters of NMF. Avoiding the subjectivity of choosing parameters, we used the Fuzzy C-Means (FCM) clustering technique to estimate the mixing matrix and to reduce the requirement for sparsity. Also, decreasing the constraints is regarded in this paper by using Semi-NMF. In this paper we propose a new two-step algorithm in order to solve the underdetermined blind source separation. We show how to combine the FCM clustering technique with the gradient-based NMF with the multi-layer technique. The simulation results show that our proposed algorithm can separate the source signals with high signal-to-noise ratio and quite low cost time compared with some algorithms

Energy-based Self-attentive Learning of Abstractive Communities for Spoken Language Understanding

Abstractive community detection is an important spoken language understanding task, whose goal is to group utterances in a conversation according to whether they can be jointly summarized by a common abstractive sentence. This paper provides a novel approach to this task. We first introduce a neural contextual utterance encoder featuring three types of self-attention mechanisms. We then train it using the siamese and triplet energy-based meta-architectures. Experiments on the AMI corpus show that our system outperforms multiple energy-based and non-energy based baselines from the state-of-the-art. Code and data are publicly available.Comment: Update baseline

Tele-Autonomous control involving contact

Object localization and its application in tele-autonomous systems are studied. Two object localization algorithms are presented together with the methods of extracting several important types of object features. The first algorithm is based on line-segment to line-segment matching. Line range sensors are used to extract line-segment features from an object. The extracted features are matched to corresponding model features to compute the location of the object. The inputs of the second algorithm are not limited only to the line features. Featured points (point to point matching) and featured unit direction vectors (vector to vector matching) can also be used as the inputs of the algorithm, and there is no upper limit on the number of the features inputed. The algorithm will allow the use of redundant features to find a better solution. The algorithm uses dual number quaternions to represent the position and orientation of an object and uses the least squares optimization method to find an optimal solution for the object's location. The advantage of using this representation is that the method solves for the location estimation by minimizing a single cost function associated with the sum of the orientation and position errors and thus has a better performance on the estimation, both in accuracy and speed, than that of other similar algorithms. The difficulties when the operator is controlling a remote robot to perform manipulation tasks are also discussed. The main problems facing the operator are time delays on the signal transmission and the uncertainties of the remote environment. How object localization techniques can be used together with other techniques such as predictor display and time desynchronization to help to overcome these difficulties are then discussed

Microcalcifications Detection using PFCM and ANN

This work presents a method to detect Microcalcifications in Regions of Interest from digitized mammograms. The method is based mainly on the combination of Image Processing, Pattern Recognition and Artificial Intelligence. The Top-Hat transform is a technique based on mathematical morphology operations that, in this work is used to perform contrast enhancement of microcalcifications in the region of interest. In order to find more or less homogeneous regions in the image, we apply a novel image sub-segmentation technique based on Possibilistic Fuzzy c-Means clustering algorithm. From the original region of interest we extract two window-based features, Mean and Deviation Standard, which will be used in a classifier based on a Artificial Neural Network in order to identify microcalcifications. Our results show that the proposed method is a good alternative in the stage of microcalcifications detection, because this stage is an important part of the early Breast Cancer detectio

A Systematic Survey of Classification Algorithms for Cancer Detection

Cancer is a fatal disease induced by the occurrence of a count of inherited issues and also a count of pathological changes. Malignant cells are dangerous abnormal areas that could develop in any part of the human body, posing a life-threatening threat. To establish what treatment options are available, cancer, also referred as a tumor, should be detected early and precisely. The classification of images for cancer diagnosis is a complex mechanism that is influenced by a diverse of parameters. In recent years, artificial vision frameworks have focused attention on the classification of images as a key problem. Most people currently rely on hand-made features to demonstrate an image in a specific manner. Learning classifiers such as random forest and decision tree were used to determine a final judgment. When there are a vast number of images to consider, the difficulty occurs. Hence, in this paper, weanalyze, review, categorize, and discuss current breakthroughs in cancer detection utilizing machine learning techniques for image recognition and classification. We have reviewed the machine learning approaches like logistic regression (LR), Naïve Bayes (NB), K-nearest neighbors (KNN), decision tree (DT), and Support Vector Machines (SVM)

A Systematic Review of Existing Data Mining Approaches Envisioned for Knowledge Discovery from Multimedia

The extensive use of multimedia technologies extended the applicability of information technology to a large extent which results enormous generation of complex multimedia contents over the internet. Therefore the number of multimedia contents available to the user is also exponentially increasing. In this digital era of the cloud-enabled Internet of Things (IoT), analysis of complex video and image data plays a crucial role.It aims to extract meaningful information as the distributed storages and processing elements within a bandwidth constraint network seek optimal solutions to increase the throughput along with an optimal trade-off between computational complexity and power consumption. However, due to complex characteristics of visual patterns and variations in video frames, it is not a trivial task to discover meaningful information and correlation. Hence, data mining has emerged as a field which has diverse aspects presently in extracting meaningful hidden patterns from the complex image and video data considering different pattern classification approach. The study mostly investigates the existing data-mining tools and their performance metric for the purpose of reviewing this research track.It also highlights the relationship between frequent patterns and discriminativefeatures associated with a video object. Finally, the study addresses the existing research issues to strengthen up the future direction of research towards video analytics and pattern recognition

FRMDN: Flow-based Recurrent Mixture Density Network

Recurrent Mixture Density Networks (RMDNs) are consisted of two main parts: a Recurrent Neural Network (RNN) and a Gaussian Mixture Model (GMM), in which a kind of RNN (almost LSTM) is used to find the parameters of a GMM in every time step. While available RMDNs have been faced with different difficulties. The most important of them is high$-$dimensional problems. Since estimating the covariance matrix for the high$-$dimensional problems is more difficult, due to existing correlation between dimensions and satisfying the positive definition condition. Consequently, the available methods have usually used RMDN with a diagonal covariance matrix for high$-$dimensional problems by supposing independence among dimensions. Hence, in this paper with inspiring a common approach in the literature of GMM, we consider a tied configuration for each precision matrix (inverse of the covariance matrix) in RMDN as (\(\Sigma _k^{ - 1} = U{D_k}U\)) to enrich GMM rather than considering a diagonal form for it. But due to simplicity, we assume \(U\) be an Identity matrix and \(D_k\) is a specific diagonal matrix for \(k^{th}\) component. Until now, we only have a diagonal matrix and it does not differ with available diagonal RMDNs. Besides, Flow$-$based neural networks are a new group of generative models that are able to transform a distribution to a simpler distribution and vice versa, through a sequence of invertible functions. Therefore, we applied a diagonal GMM on transformed observations. At every time step, the next observation, \({y_{t + 1}}\), has been passed through a flow$-$based neural network to obtain a much simpler distribution. Experimental results for a reinforcement learning problem verify the superiority of the proposed method to the base$-$line method in terms of Negative Log$-$Likelihood (NLL) for RMDN and the cumulative reward for a controller with fewer population size