In this paper, we first recall the concept of 2-tuples distribution matrix, and further study its properties. Based on these properties, we find four special classes of 2-tuples distribution matrices. Then, we provide a new sufficient and necessary condition for n-variable rotation symmetric Boolean functions to be 2-correlation immune. Finally, we give a new method for constructing such functions when n=4t - 1 is prime, and we show an illustrative example.

This paper proposes graph linear notations and an extension of them with regular expressions. Graph linear notations are a set of strings to represent labeled general graphs. They are extended with regular expressions to represent sets of graphs by specifying chosen parts for selections and repetitions of certain induced subgraphs. Methods for the conversion between graph linear notations and labeled general graphs are shown. The NP-completeness of the membership problem for graph regular expressions is proved.

We introduce the Hexagonal Convolutional Neural Network (HCNN), a modified version of CNN that is robust against rotation. HCNN utilizes a hexagonal kernel and a multi-block structure that enjoys more degrees of rotation information sharing than standard convolution layers. Our structure is easy to use and does not affect the original tissue structure of the network. We achieve the complete rotational invariance on the recognition task of simple pattern images and demonstrate better performance on the recognition task of the rotated MNIST images, synthetic biomarker images and microscopic cell images than past methods, where the robustness to rotation matters.

In this paper, the notion of 2-tuples distribution matrices of the rotation symmetric orbits is proposed, by using the properties of the 2-tuples distribution matrix, a new characterization of 2-resilient rotation symmetric Boolean functions is demonstrated. Based on the new characterization of 2-resilient rotation symmetric Boolean functions, constructions of 2-resilient rotation symmetric Boolean functions (RSBFs) are further studied, and new 2-resilient rotation symmetric Boolean functions with prime variables are constructed.

In this report, solar cell characteristics were evaluated by doping the active layer CH3NH3PbI3 (MAPbI3) with 3.0 vol% and 6.0 vol% of potassium ion (KI) in an inverse-structured perovskite solar cells (PSCs). The Tauc plots of the absorbance characteristics and the ionization potential characteristics show that the top end of the valence band shifted by 0.21eV in the shallow direction from -5.34eV to -5.13eV, and the energy band gap decreased from 1.530eV to 1.525eV. Also, the XRD measurements show that the lattice constant decreased from 8.96Å to 8.93Å when KI was doped. The decrease in the lattice constant indicates that a part of the A site is replaced from methylammonium ion (MAI) to KI. In the J-V characteristics of the solar cell, the mean value of Jsc improved from 7.0mA/cm2 without KI to 8.8mA/cm2 with 3.0 vol% of KI doped and to 10.2mA/cm2 with 6.0 vol% of KI doped. As a result, the mean value of power-conversion efficiency (PCE) without KI was 3.5%, but the mean value of PCE improved to 5.2% with 3.0 vol% of KI doped and to 4.5% with 6.0 vol% of KI doped. Thus, it has shown that it is effective to dope KI to MAIPBI3, which serves as the active layer, even in the inverse-structured PSCs.

Translation of long and complex sentence has always been a challenge for machine translation. In recent years, neural machine translation (NMT) has achieved substantial progress in modeling the semantic connection between words in a sentence, but it is still insufficient in capturing discourse structure information between clauses within complex sentences, which often leads to poor discourse coherence when translating long and complex sentences. On the other hand, the hypotactic structure, a main component of the discourse structure, plays an important role in the coherence of discourse translation, but it is not specifically studied. To tackle this problem, we propose a novel Chinese-English NMT approach that incorporates the hypotactic structure knowledge of complex sentences. Specifically, we first annotate and build a hypotactic structure aligned parallel corpus to provide explicit hypotactic structure knowledge of complex sentences for NMT. Then we propose three hypotactic structure-aware NMT models with three different fusion strategies, including source-side fusion, target-side fusion, and both-side fusion, to integrate the annotated structure knowledge into NMT. Experimental results on WMT17, WMT18 and WMT19 Chinese-English translation tasks demonstrate that the proposed method can significantly improve the translation performance and enhance the discourse coherence of machine translation.

Estimating the spatial parameters of the signals by using the effective data of a single snapshot is essential in the field of reconnaissance and confrontation. Major drawback of existing algorithms is that its constructed covariance matrix has a great degree of rank loss. The performance of existing algorithms gets degraded with low signal-to-noise ratio. In this paper, a three-parallel linear array based algorithm is proposed to achieve two-dimensional direction of arrival estimates in a single snapshot scenario. The key points of the proposed algorithm are: 1) construct three pseudo matrices with full rank and no rank loss by using the single snapshot data from the received signal model; 2) by using the rotation relation between pseudo matrices, the matched 2D-DOA is obtained with an efficient parameter matching method. Main objective of this work is on improving the angle estimation accuracy and reducing the loss of degree of freedom in single snapshot 2D-DOA estimation.

In this paper, by using the properties of the cyclic Hadamard matrices of order 4t, an infinite class of (4t-1)-variable 2-resilient rotation symmetric Boolean functions is constructed, and the nonlinearity of the constructed functions are also studied. To the best of our knowledge, this is the first class of direct constructions of 2-resilient rotation symmetric Boolean functions. The spirit of this method is different from the known methods depending on the solutions of an equation system proposed by Du Jiao, et al. Several situations are examined, as the direct corollaries, three classes of (4t-1)-variable 2-resilient rotation symmetric Boolean functions are proposed based on the corresponding sequences, such as m sequences, Legendre sequences, and twin primes sequences respectively.

Well annotated dataset is crucial to the training of object detectors. However, the production of finely annotated datasets for object detection tasks is extremely labor-intensive, therefore, cloud sourcing is often used to create datasets, which leads to these datasets tending to contain incorrect annotations such as inaccurate localization bounding boxes. In this study, we highlight a problem of object detection with noisy bounding box annotations and show that these noisy annotations are harmful to the performance of deep neural networks. To solve this problem, we further propose a framework to allow the network to modify the noisy datasets by alternating refinement. The experimental results demonstrate that our proposed framework can significantly alleviate the influences of noise on model performance.

Image dehazing is of great significance in computer vision and other fields. The performance of dehazing mainly relies on the precise computation of transmission map. However, the computation of the existing transmission map still does not work well in the sky area and is easily influenced by noise. Hence, the dark channel prior (DCP) and luminance model are used to estimate the coarse transmission in this work, which can deal with the problem of transmission estimation in the sky area. Then a novel weighted variational regularization model is proposed to refine the transmission. Specifically, the proposed model can simultaneously refine the transmittance and restore clear images, yielding a haze-free image. More importantly, the proposed model can preserve the important image details and suppress image noise in the dehazing process. In addition, a new Gaussian Adaptive Weighted function is defined to smooth the contextual areas while preserving the depth discontinuity edges. Experiments on real-world and synthetic images illustrate that our method has a rival advantage with the state-of-art algorithms in different hazy environments.

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 this letter, a two-stage QR decomposition scheme based on Givens rotation with novel modified real-value decomposition (RVD) is presented. With the modified RVD applied to the result from complex Givens rotation at first stage, the number of non-zero terms needed to be eliminated by real Givens rotation at second stage decreases greatly and the computational complexity is thereby reduced significantly compared to the decomposition scheme with the conventional RVD. Besides, the proposed scheme is suitable for the hardware design of QR decomposition. Evaluation shows that the proposed QR decomposition scheme is superior to the related works in terms of computational complexity.

Multimedia data and information management is an important task according to the development of media processing technology. Multimedia is a useful resource that people understand complex situations such as the elderly care domain. Appropriate annotation is beneficial in several tasks of information management, such as storing, retrieval, and summarization of data, from a semantic perspective. However, the metadata annotation for multimedia data remains problematic because metadata is obtained as a result of interpretation depending on domain-specific knowledge, and it needs well-controlled and comprehensive vocabulary for annotation. In this study, we proposed a collaborative methodology for developing ontologies and annotation with domain experts. The method includes (1) classification of knowledge types for collaborative construction of annotation data, (2) division of tasks among a team composed of domain experts, ontology engineers, and annotators, and (3) incremental approach to ontology development. We applied the proposed method to 11 videos on elderly care domain for the confirmation of its feasibility. We focused on annotation of actions occurring in these videos, thereby the annotated data is used as a support in evaluating staff skills. The application results show the content in the ontology during annotation increases monotonically. The number of “action concepts” is saturated and reused among the case studies. This demonstrates that the ontology is reusable and could represent various case studies by using a small number of “action concepts”. This study concludes by presenting lessons learnt from the case studies.

In this letter, a low latency, high throughput and hardware efficient sorted MMSE QR decomposition (MMSE-SQRD) for multiple-input multiple-output (MIMO) systems is presented. In contrast to the method of extending the complex matrix to real model and thereafter applying real-valued QR decomposition (QRD), we develop a highly parallel decomposition scheme based on coordinate rotation digital computer (CORDIC) which performs the QRD in complex domain directly and then converting the complex result to its real counterpart. The proposed scheme can greatly improve the processing parallelism and curtail the nullification and sorting procedures. Besides, we also design the corresponding pipelined hardware architecture of the MMSE-SQRD based on highly parallel Givens rotation structure with CORDIC algorithm for 4×4 MIMO detectors. The proposed MMSE-SQRD is implemented in SMIC 55nm CMOS technology achieving up to 50M QRD/s throughput and a latency of 59 clock cycles with only 218 kilo-gates (KG). Compared to the previous works, the proposed design achieves the highest normalized throughput efficiency and lowest processing latency.

Automatic tracking and classification are essential for studying the behaviors of wild animals. Owing to dynamic far-shooting photos, the occlusion problem, protective coloration, the background noise is irregular interference for designing a computerized algorithm for reducing human labeling resources. Moreover, wild dolphin images are hard-acquired by on-the-spot investigations, which takes a lot of waiting time and hardly sets the fixed camera to automatic monitoring dolphins on the ocean in several days. It is challenging tasks to detect well and classify a dolphin from polluted photos by a single famous deep learning method in a small dataset. Therefore, in this study, we propose a generic Cascade Small Object Detection (CSOD) algorithm for dolphin detection to handle small object problems and develop visualization to backbone based classification (V2BC) for removing noise, highlighting features of dolphin and classifying the name of dolphin. The architecture of CSOD consists of the P-net and the F-net. The P-net uses the crude Yolov3 detector to be a core network to predict all the regions of interest (ROIs) at lower resolution images. Then, the F-net, which is more robust, is applied to capture the ROIs from high-resolution photos to solve single detector problems. Moreover, a visualization to backbone based classification (V2BC) method focuses on extracting significant regions of occluded dolphin and design significant post-processing by referencing the backbone of dolphins to facilitate for classification. Compared to the state of the art methods, including faster-rcnn, yolov3 detection and Alexnet, the Vgg, and the Resnet classification. All experiments show that the proposed algorithm based on CSOD and V2BC has an excellent performance in dolphin detection and classification. Consequently, compared to the related works of classification, the accuracy of the proposed designation is over 14% higher. Moreover, our proposed CSOD detection system has 42% higher performance than that of the original Yolov3 architecture.

Recently, local features computed using convolutional neural networks (CNNs) show good performance to image retrieval. The local convolutional features obtained by the CNNs (LC features) are designed to be translation invariant, however, they are inherently sensitive to rotation perturbations. This leads to miss-judgements in retrieval tasks. In this work, our objective is to enhance the robustness of LC features against image rotation. To do this, we conduct a thorough experimental evaluation of three candidate anti-rotation strategies (in-model data augmentation, in-model feature augmentation, and post-model feature augmentation), over two kinds of rotation attack (dataset attack and query attack). In the training procedure, we implement a data augmentation protocol and network augmentation method. In the test procedure, we develop a local transformed convolutional (LTC) feature extraction method, and evaluate it over different network configurations. We end up a series of good practices with steady quantitative supports, which lead to the best strategy for computing LC features with high rotation invariance in image retrieval.

We discuss herein whether an optical wireless communication (OWC) system can be a candidate for post 5G or 6G cellular communication. Almost once per decade, cellular mobile communication is transformed by a significant evolution, with each generation developing a distinctive concept or technology. Interestingly, similar trends have occurred in OWC systems based on visible light and light fidelity (Li-Fi). Unfortunately, OWC is currently relegated to a limited role in any 5G scenario, but the debate whether this is unavoidable has yet to be settled. Whether OWC is adopted post 5G or 6G is not the vital issue; rather, the aim should be that OWC coexists with 5G and 6G communication technologies. In working toward this goal, research and development in OWC will continue to extend its benefits and standardize its systems so that it can be widely deployed in the market. For example, given that a standard already exists for a visible-light beacon identifier and Li-Fi, a service using this standard should be developed to satisfy user demand. Toward this end, we propose herein a method for visible-light beacon identification that involves using a rolling shutter to receive visible-light communications with a smartphone camera. In addition, we introduce a rotary LED transmitter for image-sensor communication.

The authors have been investigating the deterioration process of Au-plated slip-ring and Ag-Pd brush system with lubricant to realize stable and long lifetime. Through the past tests, it can be made clear that lubricant is very important for long lifetime, and a simple model of the deterioration process was proposed. However, it is still an issue how the lubricant is deteriorated and also what the relation between lubricant deterioration and contact voltage behavior is. In this paper, the contact voltage waveforms were regularly recorded during the test, and analyzed to obtain the time change of peak voltage and standard deviation during one rotation. Based on these results, it is discussed what happens at the interface between ring and brush with the lubricant. And the following results are made clear. The fluctuation of voltage waveforms, especially peaks of pulse-like fluctuation more easily occurs for minus rings than for plus rings. Further, peak values of the pulse-like fluctuation rapidly decreases and disappear at lower rotation speed as mentioned in the previous works. In addition, each peaks of the pulse-like fluctuation is identified at each position of the ring periphery. From these results, it can be assumed that lubricant film exists between brush and ring surface and electric conduction is realized by tunnel effect. In other words, it can be made clear that the fluctuation would be caused by the lubricant layer, not only by the ring surface. Finally, an electric conduction model is proposed and the above results can be explained by this model.

This paper introduces a novel skin-stretcher device for gently urging head rotation. The device pulls and/or pushes the skin on the user's neck by using servo motors. The user is induced to rotate his/her head based on the sensation caused by the local stretching of skin. This mechanism informs the user when and how much the head rotation is requested; however it does not force head rotation, i.e., it allows the user to ignore the stimuli and to maintain voluntary movements. We implemented a prototype device and analyzed the performance of the skin stretcher as a human-in-the-loop system. Experimental results define its fundamental characteristics, such as input-output gain, settling time, and other dynamic behaviors. Features are analyzed, for example, input-output gain is stable within the same installation condition, but various between users.

The task of image annotation is becoming enormously important for efficient image retrieval from the web and other large databases. However, huge semantic information and complex dependency of labels on an image make the task challenging. Hence determining the semantic similarity between multiple labels on an image is useful to understand any incomplete label assignment for image retrieval. This work proposes a novel method to solve the problem of multi-label image annotation by unifying two different types of Laplacian regularization terms in deep convolutional neural network (CNN) for robust annotation performance. The unified Laplacian regularization model is implemented to address the missing labels efficiently by generating the contextual similarity between labels both internally and externally through their semantic similarities, which is the main contribution of this study. Specifically, we generate similarity matrices between labels internally by using Hayashi's quantification method-type III and externally by using the word2vec method. The generated similarity matrices from the two different methods are then combined as a Laplacian regularization term, which is used as the new objective function of the deep CNN. The Regularization term implemented in this study is able to address the multi-label annotation problem, enabling a more effectively trained neural network. Experimental results on public benchmark datasets reveal that the proposed unified regularization model with deep CNN produces significantly better results than the baseline CNN without regularization and other state-of-the-art methods for predicting missing labels.