Robust pattern decoding in shape-coded structured light

Decoding is a challenging and complex problem in a coded structured light system. In this paper, a robust pattern decoding method is proposed for the shape-coded structured light in which the pattern is designed as grid shape with embedded geometrical shapes. In our decoding method, advancements are made at three steps. First, a multi-template feature detection algorithm is introduced to detect the feature point which is the intersection of each two orthogonal grid-lines. Second, pattern element identification is modelled as a supervised classification problem and the deep neural network technique is applied for the accurate classification of pattern elements. Before that, a training dataset is established, which contains a mass of pattern elements with various blurring and distortions. Third, an error correction mechanism based on epipolar constraint, coplanarity constraint and topological constraint is presented to reduce the false matches. In the experiments, several complex objects including human hand are chosen to test the accuracy and robustness of the proposed method. The experimental results show that our decoding method not only has high decoding accuracy, but also owns strong robustness to surface color and complex textures.     

量子稀疏图码的反馈式迭代译码

量子稀疏图码的译码可以由基于错误图样的和积译码算法来实现.本文在此基础上构建了一个新的反馈式迭代译码算法.其反馈策略不仅仅重新利用了错误图样,而且还利用了稳定子上相应元素的值和信道的错误模型.由此,本方法一方面可以克服传统的量子和积译码算法中遇到的所谓对称简并错误,另一方面还能反馈更多的有用信息到译码器中,帮助其产生有效的译码结果,大大提高译码器的译码能力.另外,本算法并没有增加量子测量的复杂度,而是对测量中所能获得的信息的更充分利用.     

Super-resolved or field of view enlarged imaging based upon spatial depolarization of light

In this paper we present a new approach allowing the surpassing of the diffraction based limitation for the achievable resolution provided by imaging systems. It is based on an encoding-decoding process of various spatial pixels or regions in the field of view of the imaged object by orthogonal and differently time varying polarization states.The reconstruction of the original spatial information is obtained by applying a decoding process in a way similar to the encoding one. Although all the spatial information is summed and mixed together by the system, the decoding provides super-resolved imaging since in every spatial position the undesired spatial information having time varying polarization dependence, that is uncorrelated to the decoding sequence applied on that specific spatial position, is averaged to zero and, on the other hand, the information which corresponds to that specific spatial region is being reinforced.The proposed approach can be used not only for super-resolved imaging but also for imaging module that maintains the same spatial resolution while providing enlarged field of view.     

Communication with chemical chaos in the presence of noise

We use control of chaos to encode information into the oscillations of the Belousov-Zhabotinsky reaction. An arbitrary binary message is encoded by forcing the chaotic oscillations to follow a specified trajectory. The information manipulating control requires only small perturbations to vary the binary message. In this paper we extend our recent theoretical work [Bollt and Dolnik, Phys. Rev. E 64, 1196 (1990)] by introducing a new and simplified encoding technique which can be utilized in the presence of experimental noise. We numerically and theoretically study several practical aspects of controlling symbol dynamics including: modeling noisy time-series, learning underlying symbol dynamics, and evaluation of derivatives for control by observing system responses to an intelligent and deliberate sequence of input parameter variations. All of the modeling techniques incorporated here are ultimately designed to learn and control symbol dynamics of experimental data known only as an observed time-series; the simulation assumes no global model. We find that noise affects reliability of encoding information and may cause coding errors. But, if the level of noise is confined to relatively small values, which are achievable in experiments, the control mechanism is robust to the noise. Thus we can still produce a desired symbolic code. However, scarce errors in encoding may occur due to rare but large fluctuations. These errors may be corrected during the decoding process by a variation of the filtering technique suggested by Rosa et al. [Phys. Rev. Lett. 78, 1247 (1997)]. (c) 1998 American Institute of Physics.     

针对X-Z型Pauli信道的量子稀疏图码的反馈式和积译码算法

本文针对X-Z型Pauli量子信道构建了一个量子稀疏图码的反馈式和积译码算法.相比较之前的基本和积算法,该反馈式译码策略利用了错误图样的比较, 稳定子中相关元素的值,特别的还根据信道的特征充分考虑了各变量所占错误的比重,并由此来调整信息节点的概率分布.该反馈式策略起到了经典译码中的软判决技术的作用,不但克服了对称简并问题带来的不利影响,更重要的是还给译码器提供了更多的有效信息,从而大大提高了译码器的纠错译码能力.另外,反馈式译码和积译码算法是基于GF(4)的,大大拓展了和积译码器关于量子译码 关键词： 量子稀疏图码 和积算法 量子纠错码 量子信息     

Merging Higher Derivative Gravity and Quantum Mechanics

The most general quantum mechanical wave equation for a massive scalar particle in a metric generated by a spherically symmetric mass distribution is considered within the framework of higher derivative gravity (HDG). The exact effective Hamiltonian is constructed and the significance of the various terms is discussed using the linearized version of the above-mentioned theory. Not only does this analysis shed new light on the long standing problem of quantum gravity concerning the exact nature of the coupling between a massive scalar field and the background geometry, it also greatly improves our understanding of the role of HDG's coupling parameters in semiclassical calculations.     

Hiding information using a checkered pattern

Visual cryptography is expected to have various applications since it has potential for visual decoding. However, there are still few practical applications. One reason for this is the difficulty of superposition to decode. To solve the alignment difficulty, we propose an information hiding method which can be physically decode by superimposing a checkered pattern. Superimposing a checkered pattern can also be considered as a kind of image sampling process, and a decoding experiment using an ordinary compact digital camera is presented.     

Att-U-Net：融合注意力机制的U-Net骨导语声增强*

近年来大量全卷积网络、U-Net等编解码网络结构应用于语音增强,它们具有计算复杂度低、模型参数少等优势。然而,与长短时记忆模型等方法相比,这些编解码结构仍存在不能充分利用先后时间之间和高低频率之间的关联信息等缺点,尤其对于长序列数据的输入,编解码结构存在信息丢失的问题。为保持计算效率的同时考虑更充分的时频关联信息建模,本文提出一种融合注意力机制的U-Net网络的骨导语音增强方法（Att-U-Net）,通过在跳跃连接中引入注意力机制,生成一个权重矩阵,将编码层中的全局信息根据权重融入对应的解码层中,使网络在编解码过程中能够关注输入数据中与增强目标相关程度高的重要信息,同时抑制不相关的信息。在骨导语音数据集上的实验表明,融合注意力机制的U-Net网络能在保持模型轻量化的同时有效提升骨导语音的增强效果,增强后的语音在各项客观评价指标上均优于基线模型。通过对编解码网络中间层的可视化分析发现,在解码过程中注意力机制有效地保留了有声段的信息,滤除了骨导语音由于骨导传声特性带来的中频共振,从而使得增强后的骨导语音具有较好的听觉效果。     

Throughput maximization of an IRS-assisted wireless powered network with interference: A deep unsupervised learning approach

We consider an intelligent reflecting surface (IRS)-assisted wireless powered communication network (WPCN) in which a multi antenna power beacon (PB) sends a dedicated energy signal to a wireless powered source. The source first harvests energy and then utilizing this harvested energy, it sends an information signal to destination where an external interference may also be present. For the considered system model, we formulated an analytical problem in which the objective is to maximize the throughput by jointly optimizing the energy harvesting (EH) time and IRS phase shift matrices. The optimization problem is high dimensional non-convex, thus a good quality solution can be obtained by invoking any state-of-the-art algorithm such as Genetic algorithm (GA). It is well-known that the performance of GA is generally remarkable, however it incurs a high computational complexity. To this end, we propose a deep unsupervised learning (DUL) based approach in which a neural network (NN) is trained very efficiently as time-consuming task of labeling a data set is not required. Numerical examples show that our proposed approach achieves a better performance–complexity trade-off as it is not only several times faster but also provides almost same or even higher throughput as compared to the GA.     

Emotional voices in context: a neurobiological model of multimodal affective information processing

Just as eyes are often considered a gateway to the soul, the human voice offers a window through which we gain access to our fellow human beings? minds – their attitudes, intentions and feelings. Whether in talking or singing, crying or laughing, sighing or screaming, the sheer sound of a voice communicates a wealth of information that, in turn, may serve the observant listener as valuable guidepost in social interaction. But how do human beings extract information from the tone of a voice? In an attempt to answer this question, the present article reviews empirical evidence detailing the cerebral processes that underlie our ability to decode emotional information from vocal signals. The review will focus primarily on two prominent classes of vocal emotion cues: laughter and speech prosody (i.e. the tone of voice while speaking). Following a brief introduction, behavioral as well as neuroimaging data will be summarized that allows to outline cerebral mechanisms associated with the decoding of emotional voice cues, as well as the influence of various context variables (e.g. co-occurring facial and verbal emotional signals, attention focus, person-specific parameters such as gender and personality) on the respective processes. Building on the presented evidence, a cerebral network model will be introduced that proposes a differential contribution of various cortical and subcortical brain structures to the processing of emotional voice signals both in isolation and in context of accompanying (facial and verbal) emotional cues.     

Evolutionary computation and multimodal search: A good combination to tackle molecular diversity in the field of peptide design

Summary The awesome degree of structural diversity accessible in peptide design has created a demand for computational resources that can evaluate a multitude of candidate structures. In our specific case, we translate the peptide design problem to an optimization problem, and use evolutionary computation (EC) in tandem with docking to carry out a combinatorial search. However, the use of EC in huge search spaces with different optima may pose certain drawbacks. For example, EC is prone to focus a search in the first good region found. This is a problem not only because of the undesirable and automatic rejection of potentially good search space regions, but also because the found solution may be extremely difficult to synthesize chemically or may even be a false docking positive. In order to avoid rejecting potentially good solutions and to maximize the molecular diversity of the search, we have implemented evolutionary multimodal search techniques, as well as the molecular diversity metric needed by the multimodal algorithms to measure differences between various regions of the search space.     

Mobility-aware coded caching in D2D communication networks

In this paper, we consider a cache-enable device-to-device (D2D) communication network with user mobility and design a mobility-aware coded caching scheme to exploit multicasting opportunities for reducing network traffic. In addition to the static cache memory that can be used to reap coded caching gains, we assign a dynamic cache memory to mobile users such that users who never meet can still exchange contents via relaying. We consider content exchange as an information flow among dynamic cache memories of mobile users and leverage network coding to reduce network traffic. Specifically, we transfer our storage and broadcast problem into a network coding problem. By solving the formulated network coding problem, we obtain a dynamic content replacement and broadcast strategy. Numerical results verify that our algorithm significantly outperforms the random and greedy algorithms in terms of the amount of broadcasting data, and the standard Ford–Fulkerson algorithm in terms of the successful decoding ratio.     

Thermal 3D modeling system based on 3-view geometry

In this paper, we propose a novel thermal three-dimensional (3D) modeling system that includes 3D shape, visual, and thermal infrared information and solves a registration problem among these three types of information. The proposed system consists of a projector, a visual camera and, a thermal camera (PVT). To generate 3D shape information, we use a structured light technique, which consists of a visual camera and a projector. A thermal camera is added to the structured light system in order to provide thermal information. To solve the correspondence problem between the three sensors, we use three-view geometry. Finally, we obtain registered PVT data, which includes visual, thermal, and 3D shape information. Among various potential applications such as industrial measurements, biological experiments, military usage, and so on, we have adapted the proposed method to biometrics, particularly for face recognition. With the proposed method, we obtain multi-modal 3D face data that includes not only textural information but also data regarding head pose, 3D shape, and thermal information. Experimental results show that the performance of the proposed face recognition system is not limited by head pose variation which is a serious problem in face recognition.     

基于注意力残差编解码网络的动态场景图像去模糊

动态场景下的图像去模糊技术是一个具有挑战性的计算机视觉问题.模糊图像不仅影响主观感受,还会影响后续的智能化分析的性能.提出了一种基于注意力残差编解码网络的动态场景图像去模糊方法.首先,编码阶段采用多个残差模块提取特征,加入空间注意力模块感知模糊的空间位置信息;其次,通过在网络中采用全局-局部残差连接策略融合多层卷积特征...     

LGCCT: A Light Gated and Crossed Complementation Transformer for Multimodal Speech Emotion Recognition

Semantic-rich speech emotion recognition has a high degree of popularity in a range of areas. Speech emotion recognition aims to recognize human emotional states from utterances containing both acoustic and linguistic information. Since both textual and audio patterns play essential roles in speech emotion recognition (SER) tasks, various works have proposed novel modality fusing methods to exploit text and audio signals effectively. However, most of the high performance of existing models is dependent on a great number of learnable parameters, and they can only work well on data with fixed length. Therefore, minimizing computational overhead and improving generalization to unseen data with various lengths while maintaining a certain level of recognition accuracy is an urgent application problem. In this paper, we propose LGCCT, a light gated and crossed complementation transformer for multimodal speech emotion recognition. First, our model is capable of fusing modality information efficiently. Specifically, the acoustic features are extracted by CNN-BiLSTM while the textual features are extracted by BiLSTM. The modality-fused representation is then generated by the cross-attention module. We apply the gate-control mechanism to achieve the balanced integration of the original modality representation and the modality-fused representation. Second, the degree of attention focus can be considered, as the uncertainty and the entropy of the same token should converge to the same value independent of the length. To improve the generalization of the model to various testing-sequence lengths, we adopt the length-scaled dot product to calculate the attention score, which can be interpreted from a theoretical view of entropy. The operation of the length-scaled dot product is cheap but effective. Experiments are conducted on the benchmark dataset CMU-MOSEI. Compared to the baseline models, our model achieves an 81.0% F1 score with only 0.432 M parameters, showing an improvement in the balance between performance and the number of parameters. Moreover, the ablation study signifies the effectiveness of our model and its scalability to various input-sequence lengths, wherein the relative improvement is almost 20% of the baseline without a length-scaled dot product.     

一种ZF与SP结合的BLAST译码算法

多输入多输出虽能显著增加信道容量,但译码复杂度与精度一直是亟待解决的核心问题之一。将现有的贝尔实验室分层空时码检测迫零算法与球形译码算法充分结合,重点考察无线多输入多输出信道基本特征即信道条件数与信噪比,提出了一种自适应的贝尔实验室分层空时码解码算法,在保证误比特率性能的条件下,降低了系统的译码复杂度;为无线通信的长期演进研究奠定了良好基础。     

全光纤低相干光纤位移传感技术

提出并演示了一种由两光纤构成的杨氏干涉解调系统的全光纤低相干光纤位移传感器系统。该技术从根本上避免了光源频率、光强的波动，及光在传输过程中由系统带来的损耗、光的偏振方向的改变引起的信号衰落问题。其结构简单，易于调整，测量分辨率可达０．０５４ｍｍ。     

Massive neutrinos and cosmology

The present experimental results on neutrino flavour oscillations provide evidence for non-zero neutrino masses, but give no hint on their absolute mass scale, which is the target of beta decay and neutrinoless double-beta decay experiments. Crucial complementary information on neutrino masses can be obtained from the analysis of data on cosmological observables, such as the anisotropies of the cosmic microwave background or the distribution of large-scale structure. In this review we describe in detail how free-streaming massive neutrinos affect the evolution of cosmological perturbations. We summarize the current bounds on the sum of neutrino masses that can be derived from various combinations of cosmological data, including the most recent analysis by the WMAP team. We also discuss how future cosmological experiments are expected to be sensitive to neutrino masses well into the sub-eV range.     

All-optical frequency-encoded inversion operation with tristate logic using reflecting semiconductor optical amplifiers

Optics has already proved its strong potential in information and data processing because of its inherent parallelism. Several all-optical data processors were proposed since the last few decades. Again it is also known that tristate operations can be well accommodated with optics in data and information processing, as this type of operation can enhance the information quality and capacity. Very recently, the concept of frequency variant encoding /decoding technique has been established because of its basic advantages. The potential advantage of frequency-dependent encoding/decoding is that, as the frequency is the fundamental character of a signal, it will remain unaltered in reflection, refraction, absorption, etc. during transmission. In this communication, the authors therefore propose a method of implementing frequency-encoded inversion logic operations with tristate logic using reflecting semiconductor optical amplifiers (RSOA).     

Personal artist recommendation via a listening and trust preference network

Trust information provided by a user unfolds his/her reliable friends with similar tastes. It not only has the potential to help provide better recommendations but also emancipates the recommendation process from heavy computation for seeking friends. In this paper, by taking into account the latent value of trust information, our personal artist recommendation algorithm via a listening and trust preference network (LTPN for short) is presented. We argue that the excellent recommendation should be acquired via the listening and trust preference network instead of the original listening and trust relation information. Experimental results demonstrate LTPN can not only provide better recommendation but also help relieve the cold start problem caused by new users.