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Serious games have been steadily increasing their use in many sectors of society, yet it seems that a true killer app remains elusive. This paper will review some existing game based efforts and explore what the research and development community is doing to bring game use and game engines more into the mainstream of society. It is the authors'' view that three major impediments that must be worked...
In this work we study the problem of weakly supervised human body detection under difficult poses (e.g., multiview and/or arbitrary poses) within the framework of multi-instance learning (MIL). We first point out the existence of the so-called “vanishing gradient” problem in MIL with a noisy-or rule as its bagging model. This is mainly due to the independence assumption of the noisy-or rule, which...
In this paper, we propose a supervised learning based model for ocular biometrics. Using Speeded-Up Robust Features (SURF) for detecting local features of the eye region, we create a local feature descriptor vector of each image. We cluster these feature vectors, representing an image as a normalized histogram of membership to various clusters, thereby creating a bag-of-visual-words model. We conduct...
Wearable biosensors have become increasingly popular in healthcare due to their capabilities for low cost and long term biosignal monitoring. This paper presents a novel two-stage technique to offer biometric identification using these biosensors through Deep Belief Networks and Restricted Boltzman Machines. Our identification approach improves robustness in current monitoring procedures within clinical,...
Biomarker discovery involves finding correlations between features and clinical symptoms to aid clinical decision. This task is especially difficult in resting state functional magnetic resonance imaging (rs-fMRI) data due to low SNR, high-dimensionality of images, inter-subject and intra-subject variability and small numbers of subjects compared to the number of derived features. Traditional univariate...
In the rehabilitation training and assessment of upper limbs, the conventional kinematic model treats the arm as a serial manipulator and maps the rotations in the joint space to movements in the Cartesian space. While this model brings simplicity and convenience, and thus has been overwhelming used, its accuracy is limited, especially for the distal parts of the upper limb that execute dexterous...
Wearable sensors have the potential to enable clinical-grade ambulatory health monitoring outside the clinic. Technological advances have enabled development of devices that can measure vital signs with great precision and significant progress has been made towards extracting clinically meaningful information from these devices in research studies. However, translating measurement accuracies achieved...
Recognition of traffic signs is vary important in many applications such as in self-driving car/driverless car, traffic mapping and traffic surveillance. Recently, deep learning models demonstrated prominent representation capacity, and achieved outstanding performance in traffic sign recognition. In this paper, we propose a traffic sign recognition system by applying convolutional neural network...
The paper presents a short-term load forecasting model for metro power supply system based on echo state neural network. Echo state neural network composed of input layer, reserve pool, the output layer. Reserve pool as a dynamic network is connected by a large number of random sparse of neurons. Reserve pool is used to overcome the slow convergence speed and avoid neural network into the local minimum...
In the context of medical team leaders training, we present a multiagent communication model that can introduce errors in a team of agents. This model is built from existing work from the literature in multiagents systems and information science, but also from a corpus of dialogues collected during actual field training for medical teams. Our model supports four types of communication errors (misunderstanding,...
Ridge regression is an important algorithm in machine learning and has been widely used in real-world applications like recommendation systems. Trained with a large training dataset, it outputs a curve that models the relationship between a scalar dependent variable and one or more explanatory variables. In general, the more training dataset it is fed, the more accurate the resulting model will be...
Physical rehabilitation training using an intelligent control system is important for the people with lower limb dysfunction. In this paper, the sit-to-stand (STS) trajectory of health persons is researched based on STS biomechanics model. Several novel mechanical structures for intelligent lower limb STS rehabilitation training system are presented. According to the mechanical analysis of its finite...
Bagging ensemble techniques have been utilized effectively by practitioners in the field of bioinformatics to alleviate the problem of class imbalance and to improve the performance of classification models. However, many previous works have used bagging only with a single arbitrary number of iterations. In this study, we raise the question of what is the impact of altering the number of iterations/ensembles...
Endmember variability associated with impervious layer has been a serious problem in spectral mixture analysis (SMA). A reliable spectral library which ideally models the endmember variability is required for precise SMA. Even though many endmember bundles extraction algorithms have been proposed, there are still some problems in these methods which blur the threshold and endmember numbers. In this...
Statistical models built on historical data are often found to be effective in forecasting Indian summer monsoon. However, linear models are found to be inadequate, and non-linear models like neural networks provide better performance. In this article, we study the use of recurrent neural network for long range forecast of Indian monsoon at lead of one season. Recurrent network model the sequential...
Deep convolutional neural networks (CNN) brought revolution without any doubt to various challenging tasks, mainly in computer vision. However, their model designing still requires attention to reduce number of learnable parameters, with no meaningful reduction in performance. In this paper we investigate to what extend CNN may take advantage of pyramid structure typical of biological neurons. A generalized...
The problem of extracting high level information from digital images and videos is frequently faced in the area of computer vision and machine learning. For the recognition of traffic signs, a lot of outstanding methods have been proposed, and deep models demonstrates that their powerful representation capacity, can archieve dominant performances. In this paper a method for recognizing traffic signs...
Iris liveness detection methods have been developed to overcome the vulnerability of iris biometric systems to spoofing attacks. In the literature, it is typically assumed that a known attack modality will be perpetrated. Then liveness models are designed using labelled samples from both real/live and fake/spoof distributions, the latter derived from the assumed attack modality. In this work it is...
Gait recognition aims to identify people through the analysis of the way they walk. The challenge of model-free based gait recognition is to cope with various intra-class variations such as clothing variations and carrying conditions that adversely affect the recognition performances. This paper proposes a novel method which combines Statistical Dependency (SD) feature selection with Globality-Locality...
Hierarchical Model and X (HMAX) is an outstanding bio-inspired model for object recognition. But there are still some limitations such as its poor invariance to rotation and processing speed. To lessen these limitations and improve the model's performance, we extend the HMAX model and propose a novel representation method by combining HMAX with Histogram of Oriented Gradients (HOG), denoted as HOG-HMAX...
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