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Stereo-electroencephalographic (SEEG) depth electrodes were used to record neural activity from deep brain structures in this study. By localizing all the electrodes into the individual brain, we found that areas that are inside of central sulcus occurred obvious hand-movement-related modulation when the subjects were performing different hand motion tasks. Then, an asynchronous brain-computer interface...
Face Super Resolution(FSR) is to infer High Resolution(HR) facial images from given Low Resolution(LR) ones with the assistance of LR and HR training pairs. Among existing methods, local patch based methods are superior in visual and objective quality than global based methods. These local patch based methods are based on the consistency assumption that the neighbors in HR/LR space form similar local...
Against the problems existing in the collective running projects, such as not-reasonable-enough monitoring means, not-scientific-enough assessment tools, and so on, an overseeing and evaluating system of running training was designed and implemented based on the radio frequency identification(RFID), which has the advantages of high precision, comprehensive and real-time positioning. The system provides...
Face Super Resolution(FSR) is to infer High Resolution(HR) facial images from given Low Resolution(LR) ones with the assistance of LR and HR training pairs. Among existing methods, local patch based methods are superior in visual and objective quality than global based methods. These local patch based methods are based on the consistency assumption that the neighbors in HR/LR space form similar local...
Aiming virtual battlefield environment team squad level tactics evaluation issue, a tactic editing and automatic evaluation method was proposed. The method can describe tactics and establish rule templates for single soldier and teams. Combined with specific training scenarios and three-dimensional scene, tactical detection could be set and tactics was automatically evaluated. The evaluation results...
In a video based face identification task, a sequence of frames can be utilized to identify the subject in the video. The information extracted from frames can provide samples of the subject in different head poses and facial expressions and under various lighting conditions which enriches the training process. However, some of these frames may not be useful for identification due to noise from various...
By incorporating the priors that human face is a class of highly structured object, position-patch based face hallucination methods represent the test image patch through the same position patches of training faces by employing least square estimation or sparse coding. Due to they cannot provide unbiased approximations or ignore the influence of spatial distances between the test image patch and training...
Most state-of-the-art face hallucination approaches suffer from complicated learning patterns and highly intensive computation, which will lead to low efficiency and considerable computing resources. Therefore, how to restore real face image quickly and efficiently is still an important issue in this field. To solve or partially solve the problem, this paper proposed a novel facial standard deviation...
We comment on a paper describing an image classification approach called Volterra kernel classifier, which was called Volterrafaces when applied to face recognition. The performances were evaluated by the experiments on face recognition databases. We find that their comparisons with the state of the art of three databases were indeed based on unfair settings. The results with the settings of the standard...
In Mixed Excitation Linear Prediction algorithm (MELP), the sub-band Unvoiced/Voiced parameters play an important role in improving the naturalness of synthetic speech. However, the coding efficiency with five bits per frame brings difficulties for very low bit rate speech coding. In this paper, the three consecutive MELP frames are grouped into a super-frame, and the fifteen dim sub-band Unvoiced/Voiced...
This paper describes a novel method for single-image super-resolution (SR) based on a neighbor embedding technique which uses coupled feature spaces under surveillance scenarios. For surveillance face images, traditional neighbor embedding SR approaches could not offer counterintuitive results because consistency between high resolution images and low resolution images is destroyed by serious noise...
Face recognition has become a very important field of AI, with many competing techniques, both holistic and local. Recently, a new framework for embedding holistic face recognition algorithms into a regional voting approach, has been shown to be a very stable and accurate mechanism for face recognition. A new system is proposed, which extends the regional voting concept and adds weights to each region...
This system will simulate the satisfaction rating for the product characteristics through neural networks to search for the product attribute combination that satisfies consumers, and to estimate and forecast whether consumers will be satisfied with the designed products. In doing so, the voices for the consumer demand can be effectively transformed into special features of an actual product and consumer...
It is significant that to get accurate prediction of dynamic traffic flow for intelligent traffic system management and control. A traffic flow prediction model of spatial-temporal 2D (2-dimension) data fusing based on SVM (Support Vector Machines) is put forward in this paper. The section flow results predicted by temporal SVM, spatial SVM and spatial-temporal 2D data fusing are all satisfied the...
Linguistic tagging of images require proper detection of language concepts from pictures, which is a challenging issue. Preparation of representative samples to demonstrate concepts is the first step; learning parameters from those training samples and setting up a classifier is the next step; proper tag set definition, extraction of relative contextual concepts, filtering and inference drawing for...
For a class of pattern recognition problems, such as the face recognition problem, humans do not know the strategies that our brains employ in daily life and therefore there is no algorithm that can emulate our brain ability. Without understanding the psychological processes of brains, an objective of improving accuracy of such systems leads nowhere but to a trial-and-error process of different algorithms...
In this paper, we propose a novel semi-supervised algorithm, which works under a two-view setting. Our algorithm, named kernel canonical component analysis graph (KC-GRAPH), can effectively enhance the performance and the parameter stability of traditional graph-based semi-supervised algorithms by taking the advantage of two views using kernel canonical component analysis (KCCA). Experiments have...
Traditional anti-virus scanner employs static features to detect malicious executables. Unfortunately, this content-based approach can be obfuscated by techniques such as polymorphism and metamorphism. In this paper, we propose a malicious executable detecting method using 35-dimension feature vector. Each dimension stands for a malicious run-time behavior feature represented by corresponding Win32...
Support vector machines have been extensively used in machine learning because of its efficiency and its theoretical background. This paper focuses on nu-transductive support vector machines for classification (nu-TSVC) and construct a new algorithm - Unconstrained nu-Transductive Support Vector Machines (Unu-TSVM). After researching on the special construction of primal problem in nu-TSVM, we transform...
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