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Human age estimation is an important research topic and can find its applications in such as commodity recommendation and security monitoring. The establishment of existing estimators basically follows a same pipeline, i.e., an estimator is built from a given training dataset like FG-NET and then evaluated on a holdout testing set to determine its effectiveness. In doing so, a usually-followed assumption...
In modern cognitive ratio systems, the spectrum is becoming increasingly crowded and expensive; thus spectrum sensing becomes more important than ever before. Traditional spectrum sensing assumes Gaussian noise (or of other given distributions) in general. However when secondary users (SUs) have no prior information about the measurement distributions, the spectrum sensing schemes assuming given distribution...
We consider the problem of estimating via Monte Carlo simulation the misclassification probabilities of two sequential multiple testing procedures. The first one stops when all local test statistics exceed simultaneously either a positive or a negative threshold. The second assumes knowledge of the true number of signals, say m, and stops when the gap between the top m test statistics and the remaining...
In this paper, we introduce a formulation for a folding sum transformation and then investigate into its impact on binary classification. The proposed folding sum transformation can reduce dimension of data without a training process. The least squares estimation and a full multivariate polynomial expansion are utilized to apply the folding sum transformation for binary classification. Twelve binary...
This study illustrates an estimation tool for software test that provides the estimated time and the cost of any sort of software test project. There are different well-recognized estimation tools for software development process [9], however, there remains a lack of standard tools for estimation of Software Test phase. Therefore, the authors developed a web base tool (www.4beats.net/tpet/) in order...
Real-time human detection is a challenging task due to appearance variance, occlusion and rapidly changing content; therefore it requires efficient hardware and optimized software. This paper presents a real-time human detection scheme on a Raspberry Pi. An efficient algorithm for human detection is proposed by processing regions of interest (ROI) based upon foreground estimation. Different number...
Sufficient dimension reduction (SDR) is a popular framework for supervised dimension reduction, aiming at reducing the dimensionality of input data while information on output data is maximally maintained. On the other hand, in many recent supervised classification learning tasks, it is conceivable that the balance of samples in each class varies between the training and testing phases. Such a phenomenon,...
A new indoor calibration method is proposed to estimate and compensate mutual coupling and channel gain/phase inconsistency parameters for a uniform linear array (ULA). This calibration method employs only one assistant source in near-field and is unnecessary to conduct complex feed circuits behind the antennas. Firstly, accurate channel gain/phase deviation is estimated via an inside testing structure...
Blind steganalysis is a method used to detect whether there is a hidden message in a media without having to know the steganography algorithm behind it. Digital image is converted into features using feature extraction algorithm subtractive pixel adjacency matrix. A model is built based on the resulting features using machine learning method support vector machine. The support vector machine method...
Eddy Current Testing (ECT) is a fast and effective method for detecting and sizing most of the default in conducting materials. The size estimation of an unknown defect from the measurement of the impedance variations is an important technique in industrial area. This paper considers to solve this problem by the novel combination of the Least Square Support Vector Machines (LS-SVM) and Finite Element...
This paper proposes a new manifold learning method called "Soinnmanifold". Traditional manifold learning method needs a lot of computation and appropriate priori parameters. This has somewhat restricted the domains in which manifold learning can potentially be applied. However, with the high-dimensional inputs, our method can generate a lowdimensional manifold in the high-dimensional space...
This paper presents a thorough microscopic simulation investigation of a recently proposed methodology for highway traffic estimation in the presence of mixed traffic, i.e., traffic comprising both connected and conventional vehicles, which employs only average speed measurements stemming from connected vehicles and a limited number (sufficient to guarantee observability) of flow measurements from...
In present paper, authors develop a model for estimation of earth slope stability based on the artificial neural networks. For this purpose, authors engage multi-layer feed-forward network with Levenberg-Marquardt learning algorithm and 14 hidden nodes, using existing experimental data, and the results of traditional limit equilibrium analyzes of 57 different cases according to the predefined experimental...
The Geologic resource estimation requires the accurate prediction of the regionalized variables such as ore grade at an un-sampled location with the knowledge of sparse borehole information. It plays prominent role in the decision-making process for investment and development of various mining projects and hence judicious selection of the assessment method is essential for making profitable investment...
An agent-based elevator simulation was implemented to test the validity of an advertisement scheduling system. The elevator simulation imitates the elevators and their advertisement system, thus testing the different schedules and sending the results back to the scheduler. To validate the results, certain data sets of the simulation application are compared with expected values. First results have...
This study aims to construct a scientific model in estimating effort and cost of software development projects. Use Case Points (UCP) is very important method to estimate the total effort in software development projects. While the technique of Activity-based Costing (ABC) serves as the calculation of costs in each of the activities, especially the allocation of project resources. ABC technique consists...
The estimated accuracy of an algorithm is the most important element of the typical biometrics research publication. Comparisons between algorithms are commonly made based on estimated accuracies reported in different publications. However, even when the same dataset is used in two publications, there is a very low frequency of the publications using the same protocol for estimating algorithm accuracy...
Speaker diarization is the task of estimating “who spoke when” in a meeting. To realize accurate diarization for real meetings, we have to deal with noise, speaker overlap, reverberation, etc. In this work, we propose to model directional statistics of spatial clusters via a dictionary of probabilistic models. The dictionary is trained using spatial features of possible source locations. Observed...
Pulse wave Imaging (PWI) is a noninvasive technique for tracking the propagation of the pulse waves along the arterial wall. Estimation of regional Pulse Wave Velocity (PWV) using 2D long-axis views of arterial vessels assumes propagation parallel to the imaging plane. 3D ultrasound imaging would aid in objectively estimating the PWV vector. The aims of this study were to introduce a novel method...
This paper presents an efficient battery parameter extraction technique with energy recycling feature. Based on transferring the testing energy to and from a supercapacitor (storage device) through a bidirectional DC-DC converter, the charging and discharging current profile of a battery can be obtained for analyzing the battery characteristics and parameters extraction. With the testing energy stored...
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