Triboelectric generator is attracting much attention as a power source of electronics application. Electromotive force induced by rubbing is a key for triboelectric generator. From dielectric physics point of view, there are two microscopic origins for electromotive force, i.e., electronic charge displacement and dipolar rotation. A new way for evaluating these two origins is an urgent task. We have been developing an optical second-harmonic generation (SHG) technique as a tool for probing charge displacement and dipolar alignment, selectively, by utilizing wavelength dependent response of SHG to the two origins. In this paper, an experimental way that identifies polarity of electronic charge displacement, i.e., positive charge and negative charge, is proposed. Results showed that the use of local oscillator makes it possible to identify the polarity of charges by means of SHG. As an example, positive and negative charge distribution created by rubbing polyimide surface is illustrated.

In vacuum-deposited film composed of organic polar molecules, polarization charges appear on the film surface owing to spontaneous orientation of the molecule. Because its density (σpol) determines an amount of accumulation charge (σacc) in organic light-emitting diodes and output power in polar molecular-based vibrational energy generators (VEGs), control of molecular orientation is highly required. Recently, several groups have reported that dipole-dipole interaction between polar molecules induces anti-parallel orientation which does not contribute to σpol. In other words, perturbation inducing the attenuation of the dipole interaction is needed to enhance σpol. In this study, to investigate an effect of light irradiation on σpol, we prepared 1,3,5-tris(1-phenyl-1H-benzimidazol-2-yl)benzene (TPBi) film under illumination during its deposition, and evaluated the σacc in TPBi-based bilayer device, which equals to σpol. We found that the σacc was increased by light irradiation, indicating that average orientation of TPBi is enhanced. These results suggest that light irradiation during device fabrication is promising process for organic electronic devices including polar molecule-based VEGs.

Temperature-independent zero-zero-birefringence polymer (TIZZBP), which exhibits very small birefringence over the wide temperature range, is required to realize real-color images for displays, particularly vehicle-mounted displays. Previously, a TIZZBP was synthesized, but they did not put into practical use because of their too complex composition and low mechanical strength. In this paper, we propose a practical TIZZBP that has high heat resistance, high transparency and sufficient mechanical strength, using a simple binary copolymerization system. Our proposed novel polymer exhibits very low photoelastic birefringence and very low orientational birefringence. Both types of birefringence of this TIZZBP satisfy the negligible levels for displays, which are defined as follows: the absolute values of photoelastic coefficient and intrinsic birefringence are less than 1 ×10-12 Pa-1 and 1 ×10-3, respectively. In addition, temperature dependency of orientational birefringence was very low. Orientational birefringence satisfies the negligible level all over the temperature range from around -40°C to 85°C. This temperature range is important because it is the operational temperature range for vehicle-mounted display. Furthermore, our proposed novel TIZZBP showed high heat resistance, high transparency and sufficient mechanical strength. The glass transition temperature was 194°C. The total light transmittance and the haze value is more than 91% and less than 1%, respectively. The tensile strength of non-oriented films was 35 ~ 50 MPa. These results suggest our proposed novel TIZZBP has high practicality in addition to very low birefringence. Therefore, this TIZZBP film will be very useful for various displays including vehicle-mounted displays and flexible displays.

Vision systems are widely adopted in industrial fields for monitoring and automation. As a typical example, industrial vision systems are extensively implemented in vibrator parts feeder to ensure orientations of parts for assembling are aligned and disqualified parts are eliminated. An efficient parts orientation recognition and counting method is thus critical to adopt. In this paper, an integrated method for fast parts counting and orientation recognition using industrial vision systems is proposed. Original 2D spatial image signal of parts is decomposed to 1D signal with its temporal variance, thus efficient recognition and counting is achievable, feeding speed of each parts is further leveraged to elaborate counting in an adaptive way. Experiments on parts of different types are conducted, the experimental results revealed that our proposed method is both more efficient and accurate compared to other relevant methods.

Numerous applications such as autonomous driving, satellite imagery sensing, and biomedical imaging use computer vision as an important tool for perception tasks. For Intelligent Transportation Systems (ITS), it is required to precisely recognize and locate scenes in sensor data. Semantic segmentation is one of computer vision methods intended to perform such tasks. However, the existing semantic segmentation tasks label each pixel with a single object's class. Recognizing object attributes, e.g., pedestrian orientation, will be more informative and help for a better scene understanding. Thus, we propose a method to perform semantic segmentation with pedestrian attribute recognition simultaneously. We introduce an attribute-aware loss function that can be applied to an arbitrary base model. Furthermore, a re-annotation to the existing Cityscapes dataset enriches the ground-truth labels by annotating the attributes of pedestrian orientation. We implement the proposed method and compare the experimental results with others. The attribute-aware semantic segmentation shows the ability to outperform baseline methods both in the traditional object segmentation task and the expanded attribute detection task.

The electric properties of a field-effect transistor not only depend on gate surface sidewall but also on channel orientation when applying channel stain engineering. The change of the gate surface and channel orientation through the rotated FinFETs provides the capability to compare the orientation dependence of performance and reliability. This study characterized the <100> and <110> channels of FinFETs on the same wafer under tensile and compressive stresses by cutting the wafer into rectangular silicon pieces and evaluated their piezoresistance coefficients. The piezoresistance coefficients of the <100> and <110> silicon under tensile and compressive stresses were first evaluated based on the current setup. Tensile stresses enhance the mobilities of both <100> and <110> channels, whereas compressive stresses degrade them. Electrical characterization revealed that the threshold voltage variation and drive current degradation of the {100} surface were significantly higher than those of {110} for positive bias temperature instability and hot carrier injection with equal gate and drain voltage (VG=VD). By contrast, insignificant difference is noted for the subthreshold slope degradation. These findings imply that a higher ratio of bulk defect trapping is generated by gate voltage on the <100> surface than that on the <110> surface.

Head action recognition, as a specific problem in action recognition, has been studied in this paper. Different from most existing researches, our head action recognition problem is specifically defined for the requirement of some practical applications. Based on our definition, we build a corresponding head action dataset which contains many challenging cases. For action recognition, we proposed a real-time head action recognition framework based on HOF and ELM. The framework consists of face detection based ROI determination, HOF feature extraction in ROI, and ELM based action prediction. Experiments show that our method achieves good accuracy and is efficient enough for practical applications.

High frame rate and ultra-low delay matching system plays an increasingly important role in human-machine interactive applications which call for higher frame rate and lower delay for a better experience. The large amount of processing data and the complex computation in a local feature based matching system, make it difficult to achieve a high process speed and ultra-low delay matching with limited resource. Aiming at a matching system with the process speed of more than 1000 fps and with the delay of less than 1 ms/frame, this paper puts forward a local binary feature based matching system with field-programmable gate array (FPGA). Pixel selection based 4-1-4 parallel matching and intensity directed symmetry are proposed for the implementation of this system. To design a basic framework with the high process speed and ultra-low delay using limited resource, pixel selection based 4-1-4 parallel matching is proposed, which makes it possible to use only one-thread resource consumption to achieve a four-thread processing. Assumes that the orientation of the keypoint will bisect the patch best and will point to the region with high intensity, intensity directed symmetry is proposed to calculate the keypoint orientation in a hardware friendly way, which is an important part for a rotation-robust matching system. Software experiment result shows that the proposed keypoint orientation calculation method achieves almost the same performance with the state-of-art intensity centroid orientation calculation method in a matching system. Hardware experiment result shows that the designed image process core supports to process VGA (640×480) videos at a process speed of 1306 fps and with a delay of 0.8083 ms/frame.

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.

At the present time, as downsizing of connectors causes thin gold plated layer and low contact load, serious problem of degradation of contact resistance property is induced. For these contacts, corrosion of the contacts surface under environment and high temperature as soldering and reflow process should be existed. Oxidation of base metal atoms which are diffused from under layer and additives occurs. Contact resistance increases for both surface contamination and low contact load. In order to resolve these problems and wear of surface, application of contact lubricants is useful and effective. However, degradation of the lubricants under such reflow process as high temperature possibly occurs. Therefore, in this study, from view point of change of lubricant quality as viscosity, weight loss, polymerization, oxidation and molecular orientation were clarified. For increase in contact resistance, orientation of lubricant molecular acts as important factor was found. The other factors of the lubricant hardly does not effect on contact resistance.

Depth-image-based-rendering (DIBR) is a popular technique for view synthesis. The rendering process mainly introduces artifacts around edges, which leads to degraded quality. This letter proposes a DIBR-synthesized image quality metric by measuring the Statistics of both Edge Intensity and Orientation (SEIO). The Canny operator is first used to detect edges. Then the gradient maps are calculated, based on which the intensity and orientation of the edge pixels are computed for both the reference and synthesized images. The distance between the two intensity histograms and that between the two orientation histograms are computed. Finally, the two distances are pooled to obtain the overall quality score. Experimental results demonstrate the advantages of the presented method.

We propose a framework to perform auto-radiometric calibration in photometric stereo methods to estimate surface orientations of an object from a sequence of images taken using a radiometrically uncalibrated camera under varying illumination conditions. Our proposed framework allows the simultaneous estimation of surface normals and radiometric responses, and as a result can avoid cumbersome and time-consuming radiometric calibration. The key idea of our framework is to use the consistency between the irradiance values converted from pixel values by using the inverse response function and those computed from the surface normals. Consequently, a linear optimization problem is formulated to estimate the surface normals and the response function simultaneously. Finally, experiments on both synthetic and real images demonstrate that our framework enables photometric stereo methods to accurately estimate surface normals even when the images are captured using cameras with unknown and nonlinear response functions.

This paper proposes an appearance-based novel descriptor for estimating head orientation. Our descriptor is inspired by the Weber-based feature, which has been successfully implemented for robust texture analysis, and the gradient which performs well for shape analysis. To further enhance the orientation differences, we combine them with an analysis of the intensity deviation. The position of a pixel and its intrinsic intensity are also considered. All features are then composed as a feature vector of a pixel. The information carried by each pixel is combined using a covariance matrix to alleviate the influence caused by rotations and illumination. As the result, our descriptor is compact and works at high speed. We also apply a weighting scheme, called Block Importance Feature using Genetic Algorithm (BIF-GA), to improve the performance of our descriptor by selecting and accentuating the important blocks. Experiments on three head pose databases demonstrate that the proposed method outperforms the current state-of-the-art methods. Also, we can extend the proposed method by combining it with a head detection and tracking system to enable it to estimate human head orientation in real applications.

In this study, we investigated Si(100), Si(110) and Si(111) surface flattening process utilizing sacrificial oxidation method, and its effect on Metal-Insulator-Semiconductor (MIS) diode characteristics. By the etching of the 100 nm-thick sacrificial oxide formed by thermal oxidation at 1100°C, the surface roughness of Si(100), Si(110) and Si(111) substrates were reduced. The obtained Root-Mean-Square (RMS) roughness of Si(100) was reduced from 0.22 nm (as-cleaned) to 0.07 nm (after etching), while it was reduced from 0.23 nm to 0.12 nm in the case of Si(110), and from 0.23 nm to 0.11 nm in the case of Si(111), respectively. Furthermore, it was found that time-dependent dielectric breakdown (TDDB) characteristics of MIS diodes for p-Si(100), p-Si(110) and p-Si(111) were improved with the reduction of Si surface RMS roughness.

This paper proposes a novel technology to detect the orientation of an image relying on its contour which is noised to varying degrees. For the image orientation detection, most methods regard to the landscape image and the image taken of a single object. In these cases, the contours of these images are supposed to be immune to the noise. This paper focuses on the the contour noised after image segmentation. A polar orientation descriptor Orientation Context is viewed as a feature to describe the coarse distribution of the contour points. This descriptor is verified to be independent of translation, isotropic scaling, and rotation transformation by theory and experiment. The relative orientation depends on the minimum distance Roulette Distance between the descriptor of a template image and that of a test image. The proposed method is capable of detecting the direction on the interval from 0 to 359 degrees which is wider than the former contour-based means (Distance Phase [1], from 0 to 179 degrees). What's more, the results of experiments show that not only the normal binary image (Noise-0, Accuracy-1: 84.8%) (defined later) achieves more accurate orientation but also the binary image with slight contour noise (Noise-1, Accuracy-1: 73.5%) could obtain more precise orientation compared to Distance Phase (Noise-0, Accuracy-1: 56.3%; Noise-1, Accuracy-1: 27.5%). Although the proposed method (O(op2)) takes more time to detect the orientation than Distance Phase (O(st)), it could be realized including the preprocessing in real time test with a frame rate of 30.

Autonomous driving is not only required to detect pedestrians around vehicles, but also expected to understand the behaviors of pedestrians. Pedestrian body orientation and head orientation are the relevant indicators of the pedestrian intention. This paper proposes an accurate estimation system to recognize the pedestrian body orientation and the pedestrian head orientation from on-board camera and inertial sensors. The proposed system discretizes the body orientation and the head orientation into 16 directions. In order to achieve the accurate orientation estimation, a novel training database is established, which includes strongly labeled data and weakly labeled data. Semi-Supervised Learning method is employed to annotate the weakly labeled data, and to generate the accurate classifier based on the proposed training database. In addition, the temporal constraint and the human physical model constraint are considered in orientation estimation, which are beneficial to the reasonable and stable result of orientation estimation for the pedestrian in image sequences. This estimated result is the orientation in camera space. The comprehension of the pedestrian behavior needs to be conducted in the real world space. Therefore, this paper proposes to model the motion of the host vehicle using inertial sensor, then transforms the estimated orientation from camera space to the real world space by considering the vehicle and pedestrian motion. The represented orientation indicates the behavior of the pedestrian more directly. Finally, a series of experiments demonstrate the effectiveness of the proposed pedestrian orientation system.

In this Letter, a new iris recognition approach based on local Gabor orientation feature is proposed. On one hand, the iris feature extraction method using the traditional Gabor filters can cause time-consuming and high-feature dimension. On the other hand, we can find that the changes of original iris texture in angle and radial directions are more obvious than the other directions by observing the iris images. These changes in the preprocessed iris images are mainly reflected in vertical and horizontal directions. Therefore, the local directional Gabor filters are constructed to extract the horizontal and vertical texture characteristics of iris. First, the iris images are preprocessed by iris and eyelash location, iris segmentation, normalization and zooming. After analyzing the variety of iris texture and 2D-Gabor filters, we construct the local directional Gabor filters to extract the local Gabor features of iris. Then, the Gabor & Fisher features are obtained by Linear Discriminant Analysis (LDA). Finally, the nearest neighbor method is used to recognize the iris. Experimental results show that the proposed method has better iris recognition performance with less feature dimension and calculation time.

This paper presents a filtering method for efficient face image retrieval over large volume of face databases. The proposed method employs a new face image descriptor, called a cell-orientation vector (COV). It has a simple form: a 72-dimensional vector of integers from 0 to 8. Despite of its simplicity, it achieves high accuracy and efficiency. Our experimental results show that the proposed method based on COVs provides better performance than a recent approach based on identity-based quantization in terms of both accuracy and efficiency.

We propose a method to extract scene situation by orientation sensors of multiple mobile phones' environment. By using orientations recorded with videos, we analyzed their view concentrations as a remarkable position of the scene for each frame of videos. In an experiment for a soccer scene, the extracted points can be related to a trajectory of a soccer ball.

This paper proposes a sound source orientation estimation method that is suitable for a distributed microphone arrangement. The proposed method is based on orientation-extended beamforming (OEBF), which has four features: (a) robustness against reverberations, (b) robustness against noises, (c) free arrangements of microphones and (d) feasibility for real-time processing. In terms of (a) and (c), since OEBF is based on a general propagation model using transfer functions (TFs) that include all propagation phenomena such as reflections and diffractions, OEBF causes no model errors for the propagation phenomena, and is applicable to arbitrary microphone arrangements. Regarding (b), OEBF overcomes noise effects by incorporating three additional processes (Amplitude extraction, time-frequency mask and histogram integration) that are also proposed in this paper. As for (d), OEBF is executable in real-time basis as the execution process is the same as usual beamforming processes. A numerical experiment was performed to confirm the theoretical validity of OEBF. The results showed that OEBF was able to estimate sound source positions and orientations very precisely. Practical experiments were carried out using a 96-channel microphone array in real environments. The results indicated that OEBF worked properly even under reverberant and noisy environments and the averaged estimation error was given only 4°.