基于智能手机的快速可见光室内定位系统

基于智能手机的可见光定位不仅定位精度高,还兼具了可见光通信与移动互联网的优势。由于图像处理的高计算复杂度,现有系统定位速度较低无法支持实时导航。论文提出了一种快速和高精度的可见光室内定位系统,通过设计无闪烁线路编码方案和轻量级图像处理算法降低定位时延,同时还具有抑制调制闪烁和支持多级调光的优点。市售Android智能手机完成了原型系统开发和现场测试,实验结果显示,系统平均定位精度可达到7.5 cm,定位时间可低至22.7 ms(单灯)和35.7 ms(双灯),可支持移动速度高达18 km/h的实时室内导航。     

Ultra-Low-Power,High-Accuracy 434 MHz Indoor Positioning System for Smart Homes Leveraging Machine Learning Models

Global navigation satellite systems have been used for reliable location-based services in outdoor environments. However, satellite-based systems are not suitable for indoor positioning due to low signal power inside buildings and low accuracy of 5 m. Future smart homes demand low-cost, high-accuracy and low-power indoor positioning systems that can provide accuracy of less than 5 m and enable battery operation for mobility and long-term use. We propose and implement an intelligent, highly accurate and low-power indoor positioning system for smart homes leveraging Gaussian Process Regression (GPR) model using information-theoretic gain based on reduction in differential entropy. The system is based on Time Difference of Arrival (TDOA) and uses ultra-low-power radio transceivers working at 434 MHz. The system has been deployed and tested using indoor measurements for two-dimensional (2D) positioning. In addition, the proposed system provides dual functionality with the same wireless links used for receiving telemetry data, with configurable data rates of up to 600 Kbauds. The implemented system integrates the time difference pulses obtained from the differential circuitry to determine the radio frequency (RF) transmitter node positions. The implemented system provides a high positioning accuracy of 0.68 m and 1.08 m for outdoor and indoor localization, respectively, when using GPR machine learning models, and provides telemetry data reception of 250 Kbauds. The system enables low-power battery operation with consumption of <200 mW power with ultra-low-power CC1101 radio transceivers and additional circuits with a differential amplifier. The proposed system provides low-cost, low-power and high-accuracy indoor localization and is an essential element of public well-being in future smart homes.     

UWB LOS/NLOS identification in multiple indoor environments using deep learning methods

Indoor location-aware service is booming in daily life and business activities, making the demand for precise indoor positioning systems thrive. The identification between line-of-sight (LOS) and non-line-of-sight (NLOS) is critical for wireless indoor time-of-arrival-based localization methods. Ultra-Wide-Band (UWB) is considered low cost among the many wireless positioning systems. It can resolve multi-path and have high penetration ability. This contribution addresses UWB NLOS/LOS identification problem in multiple environments. We propose a LOS/NLOS identification method using Convolutional Neural Network parallel with Gate Recurrent Unit, named Indoor NLOS/LOS identification Neural Network. The Convolutional Neural Network extracts spatial features of UWB channel impulse response data. While the Gate Recurrent Unit is an effective approach for designing deep recurrent neural networks which can extract temporal features. By integrating squeeze-and-extraction blocks into these architectures we can assign weights on channel-wise features. We simulated UWB channel impulse response signals in residential, office, and industrial scenarios based on the IEEE 802.15.4a channel model report. The presented network was tested in simulation scenarios and an open-source real-time measured dataset. Our method can solve NLOS identification problems for multiple indoor environments. Thus more versatile compare with networks only working in one scenario. Popular machine learning methods and deep learning methods are compared against our method. The test results show that the proposed network outperforms benchmark methods in simulation datasets and real-time measured datasets.     

高准确度多频调制激光测距算法研究

针对传统的激光测距仪测量准确度低、实时性差等问题,结合正交相位检测和坐标旋转数字式计算机角度解算方法,设计了一种多频激光测距系统.系统中采用改进的正交算法对噪音环境下的测距相位差的正切值进行计算,再通过坐标旋转数字式计算机角度解算方法计算出测距相位差,该方法有效地提高了测距准确度并大大降低了系统的运算量.在采样频率500 MHz、计算字长16位、回波信噪比14 dB时,测量范围为150 m,相位测量误差为0.026 4°,距离测量准确度达到0.11 mm.     

Rapid measurements of hydrogen cyanide concentration in combustion gas via terahertz spectroscopy

The development of a real-time measurement system to determine the concentrations of combustion gases mixed with smoke particles in fire environments is an essential technical issue in the fire safety field. In this study, the absorption line for the J = 8–7 rotational transition at 708.9 GHz of hydrogen cyanide (HCN) produced by the combustion of silk is rapidly measured in a 1 m cell during repeat scans (seven times a minute) via terahertz spectroscopy. The obtained time profile of HCN concentrations agrees well with the time profile obtained via Fourier transform infrared (FT-IR) spectroscopy. The maximum concentrations derived via terahertz spectroscopy are consistent with those derived via FT-IR within an accuracy of 10%. The minimum sensitivity of HCN is 100 ppm as detected via terahertz spectroscopy. The repetition rate, the accuracy, and the sensitivity for the scans demonstrate the potential of terahertz spectroscopy to rapidly diagnose combustion gas flow concentrations in fire environments.     

A novel location estimation based on pattern matching algorithm in underwater environments

In this paper, we present a novel approach based on pattern recognition to treat the underwater localization. The goal is to achieve underwater localization by the pattern matching algorithm. It should be noted that the reflected signals in underwater environments do not affect our location estimation. Therefore, the underwater localization in this study is straightforward and efficient by using the pattern matching algorithm. We exploit the maximum likelihood (ML) to perform our study. Initially, the underwater signals are collected by the sound receiver at some sampling locations. These signals are suitably processed by the ML models and are stored in database. The test location in real-time is estimated through the database. Experimental results show that good accuracy of positioning can be obtained by proposed schemes. The proposed localization schemes can be applied to many other applications in underwater environments.     

Displacement measurement system for inverters using computer micro-vision

We propose a practical system for noncontact displacement measurement of inverters using computer micro-vision at the sub-micron scale. The measuring method of the proposed system is based on a fast template matching algorithm with an optical microscopy. A laser interferometer measurement (LIM) system is built up for comparison. Experimental results demonstrate that the proposed system can achieve the same performance as the LIM system but shows a higher operability and stability. The measuring accuracy is 0.283 μm.     

基于SVR和粒子滤波的室内导航方法

针对室内环境复杂性造成的室内定位精度不足问题,提出一种基于支持向量机回归和粒子滤波的室内导航方法;离线阶段通过采集室内接收信号强度并利用统计学习方法,构建室内RSS与物理位置之间的支持向量回归映射模型,定位阶段使用智能移动设备采集加速度、方向角等运动状态信息和Wifi模块感知的环境信息,并利用粒子滤波将运动数据和回归结果进行融合处理,推算移动用户运动轨迹;室内实验结果表明,本方法最大定位误差为1.891 m,平均误差为0.669 m,有效地提高了室内定位导航精度。     

Application of robust color composite fringe in flip-chip solder bump 3-D measurement

This study developed a 3-D measurement system based on flip-chip solder bump, used fringes with different modulation intensities in color channels, in order to produce color composite fringe with robustness, and proposed a multi-channel composite phase unwrapping algorithm, which uses fringe modulation weights of different channels to recombine the phase information for better measurement accuracy and stability. The experimental results showed that the average measurement accuracy is 0.43μm and the standard deviation is 1.38 µm. The results thus proved that the proposed 3-D measurement system is effective in measuring a plane with a height of 50 μm. In the flip-chip solder bump measuring experiment, different fringe modulation configurations were tested to overcome the problem of reflective coefficient between the flip-chip base board and the solder bump. The proposed system has a good measurement results and robust stability in the solder bump measurement, and can be used for the measurement of 3-D information for micron flip-chip solder bump application.     

Preference of online users and personalized recommendations

In a recent work [T. Zhou, Z. Kuscsik, J.-G. Liu, M. Medo, J.R. Wakeling, Y.-C. Zhang, Proc. Natl. Acad. Sci. 107 (2010) 4511], a personalized recommendation algorithm with high performance in both accuracy and diversity is proposed. This method is based on the hybridization of two single algorithms called probability spreading and heat conduction, which respectively are inclined to recommend popular and unpopular products. With a tunable parameter, an optimal balance between these two algorithms in system level is obtained. In this paper, we apply this hybrid method in individual level, namely each user has his/her own personalized hybrid parameter to adjust. Interestingly, we find that users are quite different in personalized hybrid parameters and the recommendation performance can be significantly improved if each user is assigned with his/her optimal personalized hybrid parameter. Furthermore, we find that users’ personalized parameters are negatively correlated with users’ degree but positively correlated with the average degree of the items collected by each user. With these understandings, we propose a strategy to assign users with suitable personalized parameters, which leads to a further improvement of the original hybrid method. Finally, our work highlights the importance of considering the heterogeneity of users in recommendation.     

Real-time dual-wavelength digital holographic microscopy based on polarizing separation

We report on a manifold advanced dual-wavelength digital holographic microscopy (DHM) configuration with a real-time measurement capability. The proposed configuration based on a polarizing separation scheme can be used for microscopic imaging polarimetry as well as dual wavelength digital holographic microscopy. In this paper, we show the feasibility of the proposed scheme by conducting the dual wavelength DHM experiments on a sample with a step height of 1.34 μm nominally. An averaging technique is treated and three-dimensional (3D) topographic measurements are presented. The results obtained by the proposed polarization separation based single shot DHM approach shows it can provide a real time solution for measuring 3D profile information of small objects with excellent accuracy.     

基于折反射全景相机的视觉里程计研究

为了实现折反射全景相机实时定位,提出了基于特征点法的可以用于折反射全景相机的视觉里程计算法。对折反射全景相机的投影模型重新设计了初始化模块,用于全景相机位姿的初始化。在初始化模块中,提出了适用于折反射全景相机的球面对极几何,通过球面对极几何计算初始化过程中两帧图像间位姿变化关系。为了验证算法鲁棒性和精确度,在相同场景下,将本文算法与使用针孔相机的特征点法的视觉里程计算法进行对比。结果表明,使用折反射全景相机的情况下,室外场景和室内场景下视觉里程计精度分别比针孔相机算法提升了38%和50%。     

A Novel Factor Graph and Cubature Kalman Filter Integrated Algorithm for Single-Transponder-Aided Cooperative Localization

Cooperative localization (CL) of underwater multi-AUVs is vital for numerous underwater operations. Single-transponder-aided cooperative localization (STCL) is regarded as a promising scheme for multi-AUVs CL, benefiting from the fact that an accurate reference is adopted. To improve the positioning accuracy and robustness of STCL, a novel Factor Graph and Cubature Kalman Filter (FGCKF)-integrated algorithm is proposed in this paper. In the proposed FGCKF, historical information can be efficiently used in measurement updating to overcome uncertain observation environments, which greatly helps to improve the performance of filtering progress. Furthermore, Adaptive CKF, sum product, and Maximum Correntropy Criterion (MCC) methods are designed to deal with outliers of acoustic transmission delay, sound velocity, and motion velocity, respectively. Simulations and experiments are conducted, and it is verified that the proposed FGCKF algorithm can improve positioning accuracy and robustness greatly than traditional filtering methods.     

基于朴素贝叶斯算法的社交网络数据挖掘技术研究

近年来,随着互联网技术飞速发展与普及,大量社交网络平台迅速崛起。社交网络平台拉近了日常人际关系,提供了便捷的信息通讯交流通道。同时,针对社交网络平台数据挖掘的技术研究成为不可缺少的网络数据研究领域一部分。现有社交网络数据挖掘技术所采用的传统数据挖掘算法与数据分离模式,存在大数据多元特征条件下,数据挖掘准确度降低、挖掘分类逻辑混乱等现象。针对问题产生根源,提出基于朴素贝叶斯算法的社交网络数据挖掘技术研究。采用基于朴素贝叶斯算法设计的PCIE-FN社交网络数据挖掘平台进行全面化的深入性解决。通过实验证明,提出的基于朴素贝叶斯算法的社交网络数据挖掘技术研究,各项数据满足社交网络数据挖掘日常应用要求。     

基于改进粒子群优化算法的火电厂机组负荷分配

以坑口电厂SIS系统机组负荷优化分配功能模块为应用背景,针对基本粒子群优化算法易陷入局部收敛、收敛速度慢的缺点,提出一种基于惯性权重非线性减小策略的改进粒子群优化算法。并且通过MATLAB与Visual C++混合编程,开发了机组负荷在线优化分配功能模块,提高了算法的计算效率和工程应用价值。     

Implementation of a Biometric Interface in Voice Controlled Wheelchairs

In order to assist physically handicapped persons in their movements, we developed an embedded isolated word speech recognition system (ASR) applied to voice control of smart wheelchairs. However, in spite of the existence in the industrial market of several kinds of electric wheelchairs, the problem remains the need to manually control this device by hand via joystick; which limits their use especially by people with severe disabilities. Thus, a significant number of disabled people cannot use a standard electric wheelchair or drive it with difficulty. The proposed solution is to use the voice to control and drive the wheelchair instead of classical joysticks. The intelligent chair is equipped with an obstacle detection system consisting of ultrasonic sensors, a moving navigation algorithm and a speech acquisition and recognition module for voice control embedded in a DSP card. The ASR architecture consists of two main modules. The first one is the speech parameterization module (features extraction) and the second module is the classifier which identifies the speech and generates the control word to motors power unit. The training and recognition phases are based on Hidden Markov Models (HMM), K-means, Baum-Welch and Viterbi algorithms. The database consists of 39 isolated speaker words (13 words pronounced 3 times under different environments and conditions). The simulations are tested under Matlab environment and the real-time implementation is performed by C language with code composer studio embedded in a TMS 320 C6416 DSP kit. The results and experiments obtained gave promising recognition ratio and accuracy around 99% in clean environment. However, the system accuracy decreases considerably in noisy environments, especially for SNR values below 5 dB (in street: 78%, in factory: 52%).     

New algorithm for footstep localization using seismic sensors in an indoor environment

In this study, we consider the use of seismic sensors for footstep localization in indoor environments. A popular strategy of localization is to use the measured differences in arrival times of source signals at multiple pairs of receivers. In the literature, most algorithms that are based on time differences of arrival (TDOA) assume that the propagation velocity is a constant as a function of the source position, which is valid for air propagation or even for narrow band signals. However a bounded medium such as a concrete slab (encountered in indoor environment) is usually dispersive and damped. In this study, we demonstrate that under such conditions, the concrete slab can be assimilated to a thin plate; considering a Kelvin–Voigt damping model, we introduce the notion of perceived propagation velocity, which decreases when the source-sensor distance increases. This peculiar behavior precludes any possibility to rely on existing localization methods in indoor environment. Therefore, a new localization algorithm that is adapted to a damped and dispersive medium is proposed, using only on the sign of the measured TDOA (SO-TDOA). A simulation and some experimental results are included, to define the performance of this SO-TDOA algorithm.     

Improving performance of FxRLS algorithm for active noise control of impulsive noise

Active noise control (ANC) systems employing adaptive filters suffer from stability issues in the presence of impulsive noise. New impulsive noise control algorithms based on filtered-x recursive least square (FxRLS) algorithm are presented. The FxRLS algorithm gives better convergence than the filtered-x least mean square (FxLMS) algorithm and its variants but lacks robustness in the presence of high impulsive noise. In order to improve the robustness of FxRLS algorithm for ANC of impulsive noise, two modifications are suggested. First proposed modification clips the reference and error signals while, the second modification incorporates energy of the error signal in the gain of FxRLS (MGFxRLS) algorithm. The results demonstrate improved stability and robustness of proposed modifications in the FxRLS algorithm. However, another limitation associated with the FxRLS algorithm is its computationally complex nature. In order to reduce the computational load, a hybrid algorithm based on proposed MGFxRLS and normalized step size FxLMS (NSS-FXLMS) is also developed in this paper. The proposed hybrid algorithm combines the stability of NSS-FxLMS algorithm with the fast convergence speed of the proposed MGFxRLS algorithm. The results of the proposed hybrid algorithm prove that its convergence speed is faster than that of NSS-FxLMS algorithm with computational complexity lesser than that of FxRLS algorithm.     

基于高光谱特征吸收峰的煤岩识别方法

煤炭是我国重要的自然资源,在工业和国民经济发展上起到重要的作用。在井下开采过程中,传统的人工识别煤岩界面采煤机切割煤岩效率较为低下,识别准确度较差,存在较多不确定因素。“无人化”逐渐成为未来井下开采的技术发展趋势。实现无人开采首先需要准确高效的确定煤岩界面,煤岩识别的算法将成为无人化设备的“大脑”。高光谱是近年来发展迅速的一种新兴的技术,在物质识别、分类中具有广泛的应用之处。以高光谱作为煤岩识别的技术手段,采集煤岩高光谱数据,通过对高光谱特征波段的提取来设计算法实现煤岩的识别。煤岩识别是基于煤岩组分的不同,建立在对煤岩的物质构成属性特征上的。煤与岩石元素组分中Al元素的存在形式不同,煤样中的Al元素的存在形式为Al₂O₃,而岩样中Al元素的存在形式为Al(OH)₃。Al-OH的晶格的振动使得其在2 130～2 250 nm波段产生强吸收峰,Al₂O₃在此波段间不具有强吸收峰,因此以2 130～2 250 nm作为光谱波段设计算法。以淮南地区矿区为研究区,在多个矿区进行采样,获取焦煤、气煤、瘦煤等煤样23组;获取底板泥岩、砂岩、页岩等岩样25组。对样品进行研磨处理后使用美国ASD公司生产的FieldSpec4光谱仪,采集煤与岩石样本350～2 500 nm间的反射光谱,通过预处理之后使用连续统去除法、一阶微分法、二阶微分法和SCA-SID模型法对煤岩特征波段2 130～2 250 nm进行特征提取,将提取到的特征向量用随机森林和SVM算法进行训练并将模型应用在测试集上进行分类。最终,测试集上的表现良好,整体识别率较高,一阶微分和连续统去除法的识别为83.3%,Kappa系数分别为0.66和0.68。二阶微分法和SCA-SID模型法的识别率都在90%以上,Kappa系数均为0.83。从模型的时间复杂度和空间复杂上来说,二阶微分法较SCA-SID模型法更具有高效性和可靠性。这些识别的方法为实际工程中井下自动化煤岩识别技术提供了应用参考。