Quantum Bayesian computation

Quantum Bayesian computation is an emerging field that levers the computational gains available from quantum computers. They promise to provide an exponential speed-up in Bayesian computation. Our article adds to the literature in three ways. First, we describe how quantum von Neumann measurement provides quantum versions of popular machine learning algorithms such as Markov chain Monte Carlo and deep learning that are fundamental to Bayesian learning. Second, we describe quantum data encoding methods needed to implement quantum machine learning including the counterparts to traditional feature extraction and kernel embeddings methods. Third, we show how quantum algorithms naturally calculate Bayesian quantities of interest such as posterior distributions and marginal likelihoods. Our goal then is to show how quantum algorithms solve statistical machine learning problems. On the theoretical side, we provide quantum versions of high dimensional regression, Gaussian processes and stochastic gradient descent. On the empirical side, we apply a quantum FFT algorithm to Chicago house price data. Finally, we conclude with directions for future research.     

A stationary population model with an interior interface-type boundary

We propose a stationary system that might be regarded as a migration model of some population abandoning their original place of abode and becoming part of another population, once they reach the interface boundary. To do so, we show a model where each population follows a logistic equation in their own environment while assuming spatial heterogeneities. Moreover, both populations are coupled through the common boundary, which acts as a permeable membrane on which their flow moves in and out. The main goal we face in this work will be to describe the precise interplay between the stationary solutions with respect to the parameters involved in the problem, in particular the growth rate of the populations and the coupling parameter involved on the boundary where the interchange of flux is taking place.     

Constrained radar waveform optimization for a cooperative radar-communication system

The coexistence of radar-sensing and communication systems research has received a surge of interest in recent times to tackle the issue of spectrum inadequacy. Designing an optimized radar waveform for a coexistence scenario has been a challenging task for accomplishing the convergence of radar-sensing and communication functionalities, without degrading the performance at either end. This paper proposes a novel global optimization-based Spatial Branch and Bound (SBnB) approach to optimize the phase coefficients of a Non-Linear Frequency Modulated (NLFM) waveform in a CRCS framework. In addition, the Modified-Power Ratio Constraint-Cramér–Rao Lower Bound (M-PRC-CRLB), a local optimization-based approach is proposed to optimize the phase coefficients of an NLFM waveform. The spectral energy distribution and auto-correlation characteristics of an NLFM waveform are comprehensively investigated for various values of polynomial order (N) and at different threshold Signal-to-Noise-Ratio (SNR) values. To compare the proposed waveform design approaches (M-PRC-CRLB, SBnB) with the existing waveform design approaches namely, Minimum Estimation Error Variance (MEEV) and PRC- CRLB, a Peak-to-Side-Lobe-Ratio (PSLR), and Integrated-Side-Lobe-Ratio (ISLR) are evaluated at various polynomial orders and threshold SNR values. Furthermore, the performance of a CRCS is assessed using the radar estimation rate and communication data rate. The simulation results reveal that the proposed optimized radar waveform design approaches provide improved performance compared to the existing radar waveform design approaches in terms of radar estimation rate. Further, the proposed global optimization-based SBnB approach achieves a comparable performance of the communication data rate. In addition, the proposed approaches accomplish enhanced spectral utilization, controlled side-lobe energy levels, reduced range-domain ambiguities, and a higher information rate in a CRCS.     

地质灾害图形图象分析系统在天津地面沉降分析中的应用

地质灾害的分布和发生发展过程具有空间特性, 其影响因素具有自身的特点和复杂性。作者开发的地质灾害图象分析系统(GHMIAS)是以典型地质灾害为主要对象、以空间信息管理和分析为主要功能的应用性地理信息系统, 该系统有独特的空间分析模型扩充, 融矢量、栅格和Windows图形用户对象为一体, 支持多种数据格式相互转换, 具有丰富的制图功能和高质量的制图输出, 可以快速生成灾害专题图件。     

Spatial-based iterative learning control for motion control applications

For many motion control applications spatial constraints are often more important than temporal constraints. In recent work, we have developed a spatial control strategy called the ε-controller for mobile robot applications. The control strategy is based solely on static path geometry with position (in space) feedback. Motivated by this idea, in this paper, we consider the notion of spatial-based iterative learning control (ILC). Specifically, we consider repetitive operation problems where corrections are made to the control signal from trial to trial. Unlike traditional ILC, however, which updates control signals based on the time elapsed along a trajectory, we instead make updates based on path errors and progress along the path. The idea is demonstrated via simulation for a system with bang–bang velocity control. Experimental results using a high-precision, two-axis gimbal mechanism are presented to show the effectiveness of the strategy.     

Photolithographic Encoding of Metal Complexes

A platform technology for the creation of spatially resolved surfaces encoded with a monolayer consisting of different metal complexes was developed. The concept entails the light‐triggered activation of a self‐ assembled monolayer (SAM) of UV‐labile anchors, that is, phenacylsulfides, and the subsequent cycloaddition of selected diene‐functionalized metal complexes at defined areas on the surface. The synthesis and characterization of the metal complexes for the UV‐light assisted anchoring on the surface and a detailed study of a short‐chain oligomer model system in solution confirm the high efficiency of the photoreaction. The hybrid materials obtained by this concept can potentially be utilized for the design of highly valuable catalytic or (opto‐)electronic devices.     

具有遗传性疾病和性状的遗传位点分析

随着信息技术的进步和发展,现代生物学越来越多地将这些技术用于大规模生物数据的收集、分析、挖掘等过程.大量计算机技术,特别是统计方法被用来进行复杂疾病的分析.大量研究表明,人体的许多表型性状差异以及对药物和疾病的易感性等都可能与某些位点相关联,或和包含有多个位点的基因相关联.因此,定位与性状或疾病相关联的位点在染色体或基因中的位置,能帮助研究人员了解性状和一些疾病的遗传机理,也能使人们对致病位点加以干预,防止一些遗传病的发生.利用随机森林方法、Bootstrap重抽样、logistic回归等大数据分析方法,意在解决优化生物学位点关联性分析中单一致病位点识别、多位点相互作用和多性状位点关联性分析等子问题.     

Signal enhancement of a novel multi-address coding lidar backscatters based on a combined technique of demodulation and wavelet de-noising

Multi-address coding (MAC) lidar is a novel lidar system recently developed by our laboratory. By applying a new combined technique of multi-address encoding, multiplexing and decoding, range resolution is effectively improved. In data processing, a signal enhancement method involving laser signal demodulation and wavelet de-noising in the downlink is proposed to improve the signal to noise ratio (SNR) of raw signal and the capability of remote application. In this paper, the working mechanism of MAC lidar is introduced and the implementation of encoding and decoding is also illustrated. We focus on the signal enhancement method and provide the mathematical model and analysis of an algorithm on the basis of the combined method of demodulation and wavelet de-noising. The experimental results and analysis demonstrate that the signal enhancement approach improves the SNR of raw data. Overall, compared with conventional lidar system, MAC lidar achieves a higher resolution and better de-noising performance in long-range detection.     

基于ZigBee技术的信息交互系统设计与实现

基于生物电的人-机交互（HCI）技术作为特殊场景下常规人-机交互方法的一种补充,具有非常广阔的应用前景。为了解决基于生物电的HCI系统中多用户通信问题,文章设计并实现了一种基于ZigBee技术的信息交互系统。该系统主要由ZigBee无线通信模块、信息生成/处理模块、串口通信模块三部分组成,用以实现多用户间使用眼电信号（EOG）进行无线信息交互。在实验室环境下,眼电信号有效检出率98.2%,传输距离在无遮挡、无路由的情况下可达到70m。实验结果表明该系统具有识别率高、稳定性好、配置简单、使用方便等优点,具有较强的实际应用价值。     

Towards the reduction of matrix effects in inductively coupled plasma mass spectrometry without compromising detection limits: The use of argon-nitrogen mixed-gas plasma

The multivariate optimization of a mixed-gas plasma was conducted in an attempt to find conditions minimizing matrix effects without sacrificing the detection limits that are observed with an all argon plasma optimized for maximum sensitivity in inductively coupled plasma mass spectrometry. Compared to the latter, where 49.1 ± 7.1% (n = 17) analyte signal suppression resulted in the presence of 0.1 M Na, 3.8 ± 3.2% suppression (and 2.8 ± 2.1% enhancement in some cases) was observed in the optimized mixed-gas plasma with 0.13% v/v N₂ in the plasma gas and 0.11% in the central channel as a sheath gas around the nebulizer gas flow. Furthermore, improved detection limits were observed for Al, Co, Pd, and V with the optimized mixed-gas plasma compared to an argon plasma at maximum sensitivity. The robustness of this mixed-gas plasma was further demonstrated through the accurate determination of U and Mo in NASS-5 seawater certified reference material using a simple external calibration, without matrix-matching or internal standardization. Indeed, the result obtained for Mo (9.1 ± 1.9 μg/L) was within the 95% confidence interval of the certified value of 9.6 ± 1.0 μg/L, while that obtained for U (3.0 ± 0.2 μg/L) was close to the information value of 2.6 μg/L. Spatial profiling results suggest better energy transfer between the toroidal zone and the central channel in the mixed-gas plasma.