A new adaptive binarization method is proposed for the vehicle state images obtained from the intelligent operation and maintenance system of rail transit. The method can check the corresponding vehicle status information in the intelligent operation and maintenance system of rail transit more quickly and effectively, track and monitor the vehicle operation status in real time, and improve the emergency response ability of the system. The advantages of the proposed method mainly include two points. For decolorization, we use the method of contrast preserving decolorization[1] obtain the appropriate ratio of R, G, and B for the grayscale of the RGB image which can retain the color information of the vehicle state images background to the maximum, and maintain the contrast between the foreground and the background. In terms of threshold selection, the mean value and standard deviation of gray value corresponding to multi-color background of vehicle state images are obtained by using major cluster estimation[2], and the adaptive threshold is determined by the 2 sigma principle for binarization, which can extract text, identifier and other target information effectively. The experimental results show that, regarding the vehicle state images with rich background color information, this method is better than the traditional binarization methods, such as the global threshold Otsu algorithm[3] and the local threshold Sauvola algorithm[4],[5] based on threshold, Mean-Shift algorithm[6], K-Means algorithm[7] and Fuzzy C Means[8] algorithm based on statistical learning. As an image preprocessing scheme for intelligent rail transit data verification, the method can improve the accuracy of text and identifier recognition effectively by verifying the optical character recognition through a data set containing images of different vehicle statuses.

Scene character recognition has been intensively investigated for a couple of decades because it has a great potential in many applications including automatic translation, signboard recognition, and reading assistance for the visually-impaired. However, scene characters are difficult to recognize at sufficient accuracy owing to various noise and image distortions. In addition, Japanese scene character recognition is more challenging and requires a large amount of character data for training because thousands of character classes exist in the language. Some researchers proposed training data augmentation techniques using Synthetic Scene Character Data (SSCD) to compensate for the shortage of training data. In this paper, we propose a Random Filter which is a new method for SSCD generation, and introduce an ensemble scheme with the Random Image Feature (RI-Feature) method. Since there has not been a large Japanese scene character dataset for the evaluation of the recognition systems, we have developed an open dataset JPSC1400, which consists of a large number of real Japanese scene characters. It is shown that the accuracy has been improved from 70.9% to 83.1% by introducing the RI-Feature method to the ensemble scheme.

Realizing frequency rectangular characteristics using a planar circuit made of a normal conductor material such as a printed circuit board (PCB) is difficult. The reason is that the corners of the frequency response are rounded by the effect of the low unloaded quality factors of the resonators. Rectangular frequency characteristics are generally realized by a low-noise amplifier (LNA) with flat gain characteristics and a high-order bandpass filter (BPF) with resonators having high unloaded quality factors. Here, we use an LNA and a fourth-order flat passband BPF made of a PCB to realize the desired characteristics. We first calculate the signal and noise powers to confirm any effects from insertion loss caused by the BPF. Next, we explain the design and fabrication of an LNA, since no proper LNAs have been developed for this research. Finally, the rectangular frequency characteristics are shown by a circuit combining the fabricated LNA and the fabricated flat passband BPF. We show that rectangular frequency characteristics can be realized using a flat passband BPF technique.

We investigate an interactive evolutionary computation (IEC) using multiple users' gaze information when users partially participate in each design evaluation. Many previous IEC systems have a problem that user evaluation loads are too large. Hence, we proposed to employ user gaze information for evaluating designs generated by IEC systems in order to solve this problem. In this proposed system, users just view the presented designs, not assess, then the system automatically creates users' favorite designs. With the user's gaze information, the proposed system generates coordination that can satisfy many users. In our previous study, we verified the effectiveness of the proposed system from a real system operation viewpoint. However, we did not consider the fluctuation of the users during a solution candidate evaluation. In the actual operation of the proposed system, users may change during the process due to the user interchange. Therefore, in this study, we verify the effectiveness of the proposed system when varying the users participating in each evaluation for each generation. In the experiment, we employ two types of situations as assumed in real environments. The first situation changes the number of users evaluating the designs for each generation. The second situation employs various users from the predefined population to evaluate the designs for each generation. From the experimental results in the first situation, we confirm that, despite the change in the number of users during the solution candidate evaluation, the proposed system can generate coordination to satisfy many users. Also, from the results in the second situation, we verify that the proposed system can also generate coordination which both users who participate in the coordination evaluation can more satisfy.

In order to erase data including confidential information stored in storage devices, an unrelated and random sequence is usually overwritten, which prevents the data from being restored. The problem of minimizing the cost for information erasure when the amount of information leakage of the confidential information should be less than or equal to a constant asymptotically has been introduced by T. Matsuta and T. Uyematsu. Whereas the minimum cost for overwriting has been given for general sources, a single-letter characterization for stationary memoryless sources is not easily derived. In this paper, we give single-letter characterizations for stationary memoryless sources under two types of restrictions: one requires the output distribution of the encoder to be independent and identically distributed (i.i.d.) and the other requires it to be memoryless but not necessarily i.i.d. asymptotically. The characterizations indicate the relation among the amount of information leakage, the minimum cost for information erasure and the rate of the size of uniformly distributed sequences. The obtained results show that the minimum costs are different between these restrictions.

An olfactory display is a device to present smells. Temporal characteristics of three types of olfactory displays such as one based upon high-speed switching of solenoid valves, desktop-type one based on SAW atomizer and wearable-type one based on SAW atomizer were evaluated using three odorants with different volatilities. The sensory test revealed that the olfactory displays based on SAW atomizer had the presentation speeds faster than that of solenoid valves switching. Especially, the wearable one had an excellent temporal characteristic. These results largely depend on the difference in the odor delivery method. The data obtained in this study provides basic knowledge when we make olfactory contents.

We propose a video authentication scheme to verify whether a given video file is recorded by a camera device or touched by a video editing tool. The proposed scheme prepares software characteristics of camera devices and video editing tools in advance, and compares them with the metadata of the given video file. Through practical implementation, we show that the proposed scheme has benefits of fast analysis time, high accuracy and full automation.

In this letter, motivated by the research of Tian et al., two constructions of asymptotically optimal codebooks in regard to the Welch bound with additive and multiplicative characters are provided. The parameters of constructed codebooks are new, which are different from those in the letter of Tian et al.

Contextual information is a crucial factor in natural language processing tasks such as sequence labeling. Previous studies on contextualized embedding and word embedding have explored the context of word-level tokens in order to obtain useful features of languages. However, unlike it is the case in English, the fundamental task in East Asian languages is related to character-level tokens. In this paper, we propose a contextualized character embedding method using n-gram multi-sequences information with long short-term memory (LSTM). It is hypothesized that contextualized embeddings on multi-sequences in the task help each other deal with long-term contextual information such as the notion of spans and boundaries of segmentation. The analysis shows that the contextualized embedding of bigram character sequences encodes well the notion of spans and boundaries for word segmentation rather than that of unigram character sequences. We find out that the combination of contextualized embeddings from both unigram and bigram character sequences at output layer rather than the input layer of LSTMs improves the performance of word segmentation. The comparison showed that our proposed method outperforms the previous models.

Faster R-CNN uses a region proposal network which consists of a single scale convolution filter and fully connected networks to localize detected regions. However, using a single scale filter is not enough to detect full regions of characters. In this letter, we propose a simple but effective way, i.e., utilizing variously sized convolution filters, to accurately detect Chinese characters of multiple scales in documents. We experimentally verified that our method improved IoU by 4% and detection rate by 3% than the previous single scale Faster R-CNN method.

Aiming at the complexity of posture recognition with Kinect, a method of posture recognition using distance characteristics is proposed. Firstly, depth image data was collected by Kinect, and three-dimensional coordinate information of 20 skeleton joints was obtained. Secondly, according to the contribution of joints to posture expression, 60 dimensional Kinect skeleton joint data was transformed into a vector of 24-dimensional distance characteristics which were normalized according to the human body structure. Thirdly, a static posture recognition method of the shortest distance and a dynamic posture recognition method of the minimum accumulative distance with dynamic time warping (DTW) were proposed. The experimental results showed that the recognition rates of static postures, non-cross-subject dynamic postures and cross-subject dynamic postures were 95.9%, 93.6% and 89.8% respectively. Finally, posture selection, Kinect placement, and comparisons with literatures were discussed, which provides a reference for Kinect based posture recognition technology and interaction design.

The natural gradient descent is an optimization method for real-valued neural networks that was proposed from the viewpoint of information geometry. Here, we present an extension of the natural gradient descent to complex-valued neural networks. Our idea is to use the Hermitian extension of the Fisher information matrix. Moreover, we generalize the projected natural gradient (PRONG), which is a fast natural gradient descent algorithm, to complex-valued neural networks. We also consider the advantage of complex-valued neural networks over real-valued neural networks. A useful property of complex numbers in the complex plane is that the rotation is simply expressed by the multiplication. By focusing on this property, we construct the output function of complex-valued neural networks, which is invariant even if the input is changed to its rotated value. Then, our complex-valued neural network can learn rotated data without data augmentation. Finally, through simulation of online character recognition, we demonstrate the effectiveness of the proposed approach.

Clinical researches show that the morbidity of coronary artery disease (CAD) is gradually increasing in many countries every year, and it causes hundreds of thousands of people all over the world dying for each year. As the optical coherence tomography with high resolution and better contrast applied to the lesion tissue investigation of human vessel, many more micro-structures of the vessel could be easily and clearly visible to doctors, which help to improve the CAD treatment effect. Manual qualitative analysis and classification of vessel lesion tissue are time-consuming to doctors because a single-time intravascular optical coherence (IVOCT) data set of a patient usually contains hundreds of in-vivo vessel images. To overcome this problem, we focus on the investigation of the superficial layer of the lesion region and propose a model based on local multi-layer region for vessel lesion components (lipid, fibrous and calcified plaque) features characterization and extraction. At the pre-processing stage, we applied two novel automatic methods to remove the catheter and guide-wire respectively. Based on the detected lumen boundary, the multi-layer model in the proximity lumen boundary region (PLBR) was built. In the multi-layer model, features extracted from the A-line sub-region (ALSR) of each layer was employed to characterize the type of the tissue existing in the ALSR. We used 7 human datasets containing total 490 OCT images to assess our tissue classification method. Validation was obtained by comparing the manual assessment with the automatic results derived by our method. The proposed automatic tissue classification method achieved an average accuracy of 89.53%, 93.81% and 91.78% for fibrous, calcified and lipid plaque respectively.

VLSIs that perform signal processing near infrared sensors cooled to ultra-low temperature are demanded. Delay test of those chips must be executed at ultra-low temperature while functional test could be performed at room temperature as long as hold timing errors do not occur. In this letter, we focus on the hold timing violation and evaluate the feasibility of functional test of ultra-low temperature circuits at room temperature. Experimental evaluation with a case study shows that the functional test at room temperature is possible.

Radar-absorbent materials (RAM) with various characteristics, such as broadband, oblique-incidence, and polarization characteristics, have been developed according to applications in recent years. This paper presents the optimized design method of two flat layers RAM with both broadband and oblique-incidence characteristics for the required RAM performance. The oblique-incidence characteristics mean that the RAM is possible to absorb radio waves continuously up to the maximum incidence angle. The index of the wave-absorption amount is 20dB, corresponding to an absorption rate of 99%. Because determination of the electrical material constant of each layer is the most important task with respect to the received frequency and the incidence angle, we optimized the values by using Non-dominated sorting genetic algorithm-II (NSGA-II). Two types of flat-layer RAM composed of dielectric and magnetic materials were designed and their characteristics were evaluated. Consequently, it was confirmed that oblique-incidence characteristics were better for the RAM composed of dielectric materials. The dielectric RAM achieved an incidence angle of up to 60° with broadband characteristics and a relative bandwidth of 77.01% at the transverse-magnetic (TM) wave incidence. In addition, the magnetic RAM could lower the minimum frequency of the system more than the dielectric RAM. The minimum frequency of the magnetic RAM was 1.38GHz with a relative bandwidth of 174.18% at TM-wave incidence and an incidence angle of 45°. We confirmed that it is possible to design RAM with broadband characteristics and continuous oblique-incidence characteristics by using the proposed method.

The thin film organic photovoltaic cells (OPVs) using organic semiconductors are inferior to oxgen-resistance and water-resistance, and the OPVs have a drawback that the photoelectric conversion efficiency (η) is low. For high efficiency of the OPVs, control of bulk heterojunction (BHJ) structure in the active layer is demanded. Therefore, it is thought that we can control the BHJ structure easily if we can bring a change in the aggregated structure and the crystallinity of the BHJ structure by introducing the third component that is different from the organic semiconductor into the activity layer. In this study, we introduced peptide consisting of phenylalanine of 2 molecules into the active layer prepared by poor solvent addition effect for the organic thin film solar cells and intended to try high efficiency of the organic thin film solar cells and examined the electrochemistry characteristic of the cells.

This paper presents an approach for developing an algorithm for automatic license plate recognition system (ALPR) on complex scenes. A plate-style classification method is also proposed in this paper to address the inherent challenges for ALPR in a system that uses multiple plate-styles (e.g., different fonts, multiple plate lay-out, variations in character sequences) which is the case in the current Philippine license plate system. Methods are proposed for each ALPR module: plate detection, character segmentation, and character recognition. K-nearest neighbor (KNN) is used as a classifier for character recognition together with a proposed confidence scoring to rate the decision made by the classifier. A small dataset of Philippine license plates but with relevant features of complex scenarios for ALPR is prepared. Using the proposed system on the prepared dataset, the performance of the system is evaluated on different categories of complex scenes. The proposed algorithm structure shows promising results and yielded an overall accuracy higher than the existing ALPR systems on the dataset consisting mostly of complex scenes.

Wireless power transfer (WPT) via coupled magnetic resonances has more than ten years history of development. However, it appears frequency splitting phenomenon in the over-coupled region, thus, the output power of the two-coil WPT system achieves the maximum output power at the two splitting angular frequencies and not at the natural resonant angular frequency. By investigating the relationship between the impedances of the transmitter side and receiver side, we found that WPT system is a power superposition system, and the reasons were given to explaining how to appear the frequency splitting and impact on the maximum output power of the system in details. First, the circuit model was established and transfer characteristics of the two-coil WPT system were studied by utilizing circuit theories. Second, the mechanism of the power superposition of the WPT system was carefully researched. Third, the relationship between the impedances of the transmitter side and receiver side was obtained by investigating the impedance characteristics of a two-coil WPT system, and also the impact factors of the maximum output power of the system were obtained by using a power superposition mechanism. Finally, the experimental circuit was designed and experimental results are well consistent with the theoretical analysis.

In recent years, autonomous driving technologies are being developed for vehicles and personal mobility devices including golf carts and autonomous wheelchairs for various use cases, not only outside areas but inside areas like shopping malls, hospitals and airpots. The main purpose of developing these autonomous vehicles is to avoid the traffic accidents caused by human errors, to assist people with walking, and to improve human comfort by relieving them from driving. Most relevant research focuses on the efficiency and safety of autonomous driving, however, in order to use by the widespread of people in the society, it is important to consider passenger comfort inside vehicles as well as safety and efficiency. Therefore, in this work, we emphasize the importance of considering passenger comfort in designing the control loop of autonomous navigation for the concept of comfortable intelligence in the future autonomous mobility. Moreover, passenger characteristics, in terms of ride comfort in an autonomous vehicle, have not been investigated with regard to safety and comfort, depending on each passenger's driving experience, habits, knowledge, personality, and preference. There are still few studies on the optimization of autonomous driving control reflecting passenger characteristics and different stress factors during the ride. In this study, passenger stress characteristics with different stress factors were objectively analyzed using physiological indices (heart rate and galvanic skin response sensors) during autonomous wheelchair usages. Two different experimental results from 12 participants suggest that there are always at least two types of passengers: one who experiences stress and the other who does not, depending on the stress factors considered. Moreover, with regard to the classification result for the stress reduction method, there are two types of passenger groups, for whom the solution method is, respectively, either effective or ineffective.

The deep neural named entity recognition model automatically learns and extracts the features of entities and solves the problem of the traditional model relying heavily on complex feature engineering and obscure professional knowledge. This issue has become a hot topic in recent years. Existing deep neural models only involve simple character learning and extraction methods, which limit their capability. To further explore the performance of deep neural models, we propose two character feature learning models based on convolution neural network and long short-term memory network. These two models consider the local semantic and position features of word characters. Experiments conducted on the CoNLL-2003 dataset show that the proposed models outperform traditional ones and demonstrate excellent performance.