This study investigates whether learners consider constraints while posing arithmetic word problems. Through log data from an interactive learning environment, we analyzed actions of 39 first grade elementary school students and conducted correlation analysis between the frequency of actions and validity of actions. The results show that the learners consider constraints while posing arithmetic word problems.

This paper presents a strategy, Weighted Voting of Discriminative Regions (WVDR), to improve the face recognition performance, especially in Small Sample Size (SSS) and occlusion situations. In WVDR, we extract the discriminative regions according to facial key points and abandon the rest parts. Considering different regions of face make different contributions to recognition, we assign weights to regions for weighted voting. We construct a decision dictionary according to the recognition results of selected regions in the training phase, and this dictionary is used in a self-defined loss function to obtain weights. The final identity of test sample is the weighted voting of selected regions. In this paper, we combine the WVDR strategy with CRC and SRC separately, and extensive experiments show that our method outperforms the baseline and some representative algorithms.

In this paper, we consider the crosscorrelation of two interleaved sequences of period 4N constructed by Gong and Tang which has been proved to possess optimal autocorrelation. Results show that the interleaved sequences achieve the largest crosscorrelation value 4.

Soft-thresholding is a sparse modeling method typically applied to wavelet denoising in statistical signal processing. It is also important in machine learning since it is an essential nature of the well-known LASSO (Least Absolute Shrinkage and Selection Operator). It is known that soft-thresholding, thus, LASSO suffers from a problem of dilemma between sparsity and generalization. This is caused by excessive shrinkage at a sparse representation. There are several methods for improving this problem in the field of signal processing and machine learning. In this paper, we considered to extend and analyze a method of scaling of soft-thresholding estimators. In a setting of non-parametric orthogonal regression problem including discrete wavelet transform, we introduced component-wise and data-dependent scaling that is indeed identical to non-negative garrote. We here considered a case where a parameter value of soft-thresholding is chosen from absolute values of the least squares estimates, by which the model selection problem reduces to the determination of the number of non-zero coefficient estimates. In this case, we firstly derived a risk and construct SURE (Stein's unbiased risk estimator) that can be used for determining the number of non-zero coefficient estimates. We also analyzed some properties of the risk curve and found that our scaling method with the derived SURE is possible to yield a model with low risk and high sparsity compared to a naive soft-thresholding method with SURE. This theoretical speculation was verified by a simple numerical experiment of wavelet denoising.

Establishing local visual correspondences between images taken under different conditions is an important and challenging task in computer vision. A common solution for this task is detecting keypoints in images and then matching the keypoints with a feature descriptor. This paper proposes a robust and low-dimensional local feature descriptor named Adaptively Integrated Gradient and Intensity Feature (AIGIF). The proposed AIGIF descriptor partitions the support region surrounding each keypoint into sub-regions, and classifies the sub-regions into two categories: edge-dominated ones and smoothness-dominated ones. For edge-dominated sub-regions, gradient magnitude and orientation features are extracted; for smoothness-dominated sub-regions, intensity feature is extracted. The gradient and intensity features are integrated to generate the descriptor. Experiments on image matching were conducted to evaluate performances of the proposed AIGIF. Compared with SIFT, the proposed AIGIF achieves 75% reduction of feature dimension (from 128 bytes to 32 bytes); compared with SURF, the proposed AIGIF achieves 87.5% reduction of feature dimension (from 256 bytes to 32 bytes); compared with the state-of-the-art ORB descriptor which has the same feature dimension with AIGIF, AIGIF achieves higher accuracy and robustness. In summary, the AIGIF combines the advantages of gradient feature and intensity feature, and achieves relatively high accuracy and robustness with low feature dimension.

High-resolution image and video communication in home networks is highly expected to proliferate with the spread of Wi-Fi devices and the introduction of multiple-input multiple-output (MIMO) systems. This paper proposes a joint transmission and coding scheme for broadcasting high-resolution video streams over multiuser MIMO systems with an eigenbeam-space division multiplexing (E-SDM) technique. Scalable video coding makes it possible to produce the code stream comprised of multiple layers having unequal contribution to image quality. The proposed scheme jointly assigns the data of scalable code streams to subcarriers and spatial streams based on their signal-to-noise ratio (SNR) values in order to transmit visually important data with high reliability. Simulation results show that the proposed scheme surpasses the conventional unequal power allocation (UPA) approach in terms of both peak signal-to-noise ratio (PSNR) of received images and correct decoding probability. PSNR performance of the proposed scheme exceeds 35dB with the probability of over 95% when received SNR is higher than 6dB. The improvement in average PSNR by the proposed scheme compared to the conventional UPA comes up to approx. 20dB at received SNR of 6dB. Furthermore, correct decoding probability reaches 95% when received SNR is greater than 4dB.

Network traffic forecasts, as it is well known, can be useful for network resource optimization. In order to minimize the forecast error by maximizing information utilization with low complexity, this paper concerns the difference of traffic trends at large time scales and fits a dual-related model to predict it. First, by analyzing traffic trends based on user behavior, we find both hour-to-hour and day-to-day patterns, which means that models based on either of the single trends are unable to offer precise predictions. Then, a prediction method with the consideration of both daily and hourly traffic patterns, called the dual-related forecasting method, is proposed. Finally, the correlation for traffic data is analyzed based on model parameters. Simulation results demonstrate the proposed model is more effective in reducing forecasting error than other models.

In this letter, we propose a robust and fast tracking framework by combining local and global appearance models to cope with partial occlusion and pose variations. The global appearance model is represented by a correlation filter to efficiently estimate the movement of the target and the local appearance model is represented by local feature points to handle partial occlusion and scale variations. Then global and local appearance models are unified via the Bayesian inference in our tracking framework. We experimentally demonstrate the effectiveness of the proposed method in both terms of accuracy and time complexity, which takes 12ms per frame on average for benchmark datasets.

In this paper, we propose a method of embedding and detecting data in printed images with several formats, such as different resolutions and numbers of blocks, using the camera of a tablet device. To specify the resolution of an image and the number of blocks, invisible markers that are embedded in the amplitude domain of the discrete Fourier transform of the target image are used. The proposed method can increase the variety of images suitable for data embedding.

In the scenario of finite sample size, the performance of the generalized sidelobe canceller (GSC) is still affected by the desired signal even if all signal sources are independent with each other. Firstly, the novel expression of weight vector of the auxiliary array is derived under the circumstances of finite sample size. Utilizing this new weight vector and considering the correlative interferences, the general expression for the interference cancellation ratio (CR) is developed. Then, the impacts of the CR performance are further analyzed for the parameters including the input signal-to-noise ratio (SNR), the auxiliary array size, the correlation coefficient between the desired signal and interference as well as the snapshots of the sample data, respectively. Some guidelines can thus be given for the practical application. Numerical simulations demonstrate the agreement between the simulation results and the analytical results.

We conducted experimental classes in an elementary school to examine how the advantages of using stereoscopic 3D images could be applied in education. More specifically, we selected a unit of the Tumulus period in Japan for sixth-graders as the source of our 3D educational materials. This unit represents part of the coursework for the topic of Japanese history. The educational materials used in our study included stereoscopic 3D images for examining the stone chambers and Haniwa (i.e., terracotta clay figures) of the Tumulus period. The results of our experimental class showed that 3D educational materials helped students focus on specific parts in images such as attached objects of the Haniwa and also understand 3D spaces and concavo-convex shapes. The experimental class revealed that 3D educational materials also helped students come up with novel questions regarding attached objects of the Haniwa, and Haniwa's spatial balance and spatial alignment. The results suggest that the educational use of stereoscopic 3D images is worthwhile in that they lead to question and hypothesis generation and an inquiry-based learning approach to history.

The problem of reproducing high dynamic range (HDR) images on devices with a restricted dynamic range has gained a lot of interest in the computer graphics community. Various approaches to this issue exist, spanning several research areas, including computer graphics, image processing, color vision, and physiology. However, most of the approaches to the issue have several serious well-known color distortion problems. Accordingly, this article presents a tone-mapping method. The proposed method comprises the tone-mapping operator and the chromatic adaptation transform. The tone-mapping method is combined with linear and non-linear mapping using visual gamma based on contrast sensitive function (CSF) and using key of scene value, where the visual gamma is adopted to automatically control the dynamic range, parameter free, as well as to avoid both the luminance shift and the hue shift in the displayed images. Furthermore, the key of scene value is used to represent whether the scene was subjectively light, norm, dark. The resulting image is then processed through a chromatic adaptation transform and emphasis lies in human visual perception (HVP). The experiment results show that the proposed method yields better performance of the color rendering over the conventional method in subjective and quantitative quality and color reproduction.

This paper proposes a component ranking method to identify important components which have great impact on the system reliability. This method, which is opposite to an existing method, believes components which frequently invoke other components have more impact than others and employs component invocation structures and invocation frequencies for making important component ranking. It can strongly support for improving the reliability of software systems, especially large-scale systems. Extensive experiments are provided to validate this method and draw performance comparison.

The Life Intelligence and Office Information System (LOIS) Technical Committee of the Institute of Electronics, Information and Communication Engineers (IEICE) dates its origin to May 1986. This Technical Committee (TC) has covered the research fields of the office related systems for more than 30 years. Over this time, this TC, under its multiple name changes, has served as a forum for research and provided many opportunities for not only office users but also ordinary users of systems and services to present ideas and discussions. Therefore, these advanced technologies have been diffused from big enterprises to small companies and home users responsible for their management and operation. This paper sums up the technology trends and views of the office related systems and services covered in the 30 years of presentations of the LOIS Technical Committees by using the new literature analysis system based on the IEICE Knowledge Discovery system (I-Scover system).

This paper presents an approach to nonlinear impedance measurement exploiting an oscilloscope and Möbius transformation. Proposed system consists of a linear 4-port network and an oscilloscope. One of the port is excited by a high power source. The power is delivered to the second port, which is loaded with a DUT. Another set of two ports are used to observe a voltage set. This voltage set gives the impedance of the DUT through Möbius transformation. We formulated measurability M of the system, and derived the condition that M becomes constant for any DUT. To meet the condition, we propose a linear 4-port network consisting of a quarter-wavelength transmission line and resistors. We confirm the validity and utility of the proposed system by measuring the impedance of incandescent bulbs and an RF diode rectifier.

This paper presents a programmable wideband low pass filter (LPF) with Continuous-Time (CT)/Discrete-Time (DT) hybrid architecture. Unlike the conventional DT LPF, the proposed LPF eliminates sample & hold circuits, enabling to expand available bandwidth. The transfer function and the influence of the circuit imperfection are derived from CT/DT hybrid analysis. A prototype has been fabricated in 40 nm CMOS process. The proposed LPF achieves 2.5 GHz bandwidth by wideband equalization, which offers capacitance ratio (Cratio) and clock frequency (fCK) programmability. The proposed LPF occupies only 0.048 mm2 of active area.

Smart card payment systems provide a convenient billing mechanism for public transportation providers and passengers. In this paper, a smart card-based transit log is used to reveal functionally related regions in a city, which are called zones. To discover significant zones based on the transit log data, two algorithms, minimum spanning trees and agglomerative hierarchical clustering, are extended by considering the additional factors of geographical distance and adjacency. The hierarchical spatial geocoding system, called Geohash, is adopted to merge nearby bus stops to a region before zone discovery. We identify different urban zones that contain functionally interrelated regions based on passenger trip data stored in the smart card-based transit log by manipulating the level of abstraction and the adjustment parameters.

As a next-generation CAN (Controller Area Network), CAN FD (CAN with flexible data rate) has attracted much attention recently. However, how to use the improved bus bandwidth efficiently in CAN FD is still an issue. Contrasting with existing methods using greedy approximate algorithms, this paper proposes a genetic algorithm for CAN FD frame packing. It tries to minimize the bandwidth utilization by considering the different periods of signals when packing them in the same frame. Moreover, it also checks the schedulability of packed frames to guarantee the real-time constraints of each frame and proposed a merging algorithm to improve the schedulability for signal set with high bus load. Experimental results validate that the proposed algorithm can achieve significantly less bandwidth utilization and improved schedulability than existing methods for a given set of signals.

In this paper, a novel approach is proposed for stereo vision-based ground plane detection at superpixel-level, which is implemented by employing a Disparity Texture Map in a convolution neural network architecture. In particular, the Disparity Texture Map is calculated with a new Local Disparity Texture Descriptor (LDTD). The experimental results demonstrate our superior performance in KITTI dataset.

Descriptor aggregation techniques such as the Fisher vector and vector of locally aggregated descriptors (VLAD) are used in most image retrieval frameworks. It takes some time to extract local descriptors, and the geometric verification requires storage if a real-valued descriptor such as SIFT is used. Moreover, if we apply binary descriptors to such a framework, the performance of image retrieval is not better than if we use a real-valued descriptor. Our approach tackles these issues by using a dual representation descriptor that has advantages of being both a real-valued and a binary descriptor. The real value of the dual representation descriptor is aggregated into a VLAD in order to achieve high accuracy in the image retrieval, and the binary one is used to find correspondences in the geometric verification stage in order to reduce the amount of storage needed. We implemented a dual representation descriptor extracted in semi-real time by using the CARD descriptor. We evaluated the accuracy of our image retrieval framework including the geometric verification on three datasets (holidays, ukbench and Stanford mobile visual search). The results indicate that our framework is as accurate as the framework that uses SIFT. In addition, the experiments show that the image retrieval speed and storage requirements of our framework are as efficient as those of a framework that uses ORB.