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.     

Time allocation improvement method for UAV-based wireless energy transfer cooperative mobile edge

Wireless power transfer (WPT) and mobile edge computing (MEC) are two advanced technologies that could improve the computing power and range of mobile devices. However, by integrating the unmanned aerial vehicle (UAV) into wireless powered MEC systems, wireless energy transfer will be susceptible to the “double near–far” effect. Therefore, in order to further overcome the influence of the “double near–far” effect, this paper considers the optimization of time slot allocation for UAV-assisted wireless powered cooperative MEC system, which includes an access point (UAV) and two mobile devices. The purpose of the study is to minimize the total transmission energy of the UAV while satisfying the delay and size of the computational tasks, so this paper proposed a 2:1:1 time-slot optimization allocation method. The method exploits the synergy of users so that the mobile device which is closer to the UAV acts as an offloading relay, by combining power and time slot optimization to minimize the total energy consumption of the UAV. Compared with the equal time slot scheme before the improvement, this method can not only utilize the wireless transmission energy to charge the mobile device for more time in the first period, but also can save the time of data transmission of the closer device in the third period, and it can enhance the rate of data transmission of the mobile devices at the same time. The results show that the task capacity of the system computed will be increased compared to the original scheme; the total transmission rate of the whole system is also improved by the same order of magnitude. The simulation results verify the effectiveness and reliability of the algorithm of the paper, and the comprehensive performance of the system can be maximized by the flexible offloading algorithm.     

Channel identification with Improved Variational Mode Decomposition

The identification of the type of wireless propagation channel (e.g., Line of Sight (LOS) or Non Line of Sight (NLOS)) is an important function in the wireless communication design and deployment especially in rich propagation environments. The wireless channel characteristics can be quite specific not only between Line of Sight (LOS) and Non Line of Sight (NLOS) wireless propagation conditions but also in different NLOS environments.In recent times, machine learning approaches have been increasingly used to differentiate and classify channel characteristics and this paper is part of this trend. In particular, this paper proposes the combination of machine learning with a recently proposed signal processing tool called Variational Mode Decomposition (VMD), which is a decomposition algorithm that decomposes a time series into several modes which have specific sparsity properties. VMD itself is a refinement of the Empirical Mode Decomposition (EMD) and demonstrated a superior performance to EMD for classification problems. One issue for the practical deployment of VMD in channel identification problems is the presence of hyper-parameters, which must be tuned for the applied context. The main contribution of this paper is to propose a novel approach for channel identification based on an improvement of VMD called Improved Variational Mode Decomposition (IVMD), where the optimal values of the hyper-parameters of VMD are automatically identified on the basis of the Shannon entropy of the signal output from the channel. Then, various features are extracted from the modes generated by IVMD and a sequential feature selection algorithm is applied to select the optimal features. This paper applies the proposed approach with IVMD to a data set generated by the authors with a wireless channel emulator, where 6 different propagation scenarios (including no fading conditions) are created for WiFi 802.11g signals, where only the preamble is used for channel identification. Even if channel identification based on the normalized preamble is a challenging classification problem, the proposed IVMD is able to outperform significantly the application of basic VMD, EMD and the time and frequency domain representations (as commonly done in literature) of the WiFi signals.     

Characteristics of radio transmission over polymer optical fiber for indoor wireless coverage

The characteristics of radio over polymer optical fiber (RoPOF) are evaluated for indoor or in-building wireless coverage. The frequency responses and the third order intermodulation distortion products of the RoPOF link are investigated. The eye diagrams and EVM values of signals carrying quadrature-phase-shift-keyed (QPSK) data and global system for mobile (GSM) signals are measured to evaluate the transmission performance. The interference measurements with FDM are also carried out. The results show that although the nonlinear distortion and power loss deteriorate the system to some extent, making use of passband transmission, RoPOF can provide fairly good transmission performance for wireless applications.     

The opportunistic transmission of wireless worms between mobile devices

The ubiquity of portable wireless-enabled computing and communications devices has stimulated the emergence of malicious codes (wireless worms) that are capable of spreading between spatially proximal devices. The potential exists for worms to be opportunistically transmitted between devices as they move around, so human mobility patterns will have an impact on epidemic spread. The scenario we address in this paper is proximity attacks from fleetingly in-contact wireless devices with short-range communication range, such as Bluetooth-enabled smart phones.An individual-based model of mobile devices is introduced and the effect of population characteristics and device behaviour on the outbreak dynamics is investigated. The model uses straight-line motion to achieve population, though it is recognised that this is a highly simplified representation of human mobility patterns. We show that the contact rate can be derived from the underlying mobility model and, through extensive simulation, that mass-action epidemic models remain applicable to worm spreading in the low density regime studied here. The model gives useful analytical expressions against which more refined simulations of worm spread can be developed and tested.     

基于RFID标签的分布式温度测量系统设计

射频识别（RFID）技术是一种低成本且高效的非接触式自动识别技术,其具有的识别速度快,识别距离远等优点,使其具有非常广泛的应用前景。设计一种基于RFID的分布式多点温度测量系统,系统由RFID电子标签、读写器、主接收机和PC组成,实现各节点温度的实时采集功能。为了提高各节点的温度测量精度,提出了一种测温补偿算法,采用最小二乘拟合的方案,克服由于器件的不准确而引入的测量误差。结果表明,该系统可以有效完成多点温度测量采集的功能,同时具有非常高的精确性。     

Distributed power allocation for spectrum sharing in mutually interfering wireless systems

Allocation of transmit power is critical for spectrum sharing and coexistence of mutually interfering wireless systems. In this paper we present a novel approach for allocation of transmit power, which is based on a non-greedy procedure that aims at maximizing transmission rate while also controlling interference levels. The proposed approach is fully distributed and requires no central control or coordination. Numerical results obtained from simulations are presented to illustrate the performance of the proposed approach in both sparse and dense environments. In sparse wireless environments, where there are fewer mutually interfering wireless links than available frequency bands, the proposed approach yields power allocations which outperform those obtained by applying alternative power allocation strategies, while in dense environments, where there are more interfering links than available frequency bands, the proposed approach yields power allocations with performance similar to those of existing power strategies. Thus, the distributed power allocation procedure based on the proposed approach is a drop-in replacement algorithm that yields better system throughput than existing algorithms for spectrum sharing.     

Solving permutations in frequency-domain for blind separation of an arbitrary number of speech sources

Blind separation of speech sources in reverberant environments is usually performed in the time-frequency domain, which gives rise to the permutation problem: the different ordering of estimated sources for different frequency components. A two-stage method to solve permutations with an arbitrary number of sources is proposed. The suggested procedure is based on the spectral consistency of the sources. At the first stage frequency bins are compared with each other, while at the second stage the neighboring frequencies are emphasized. Experiments for perfect separation situations and for live recordings show that the proposed method improves the results of existing approaches.     

手机虹膜识别镜头设计

随着近年来图像传感器的快速商用化以及生物识别算法的发展,虹膜识别功能得以应用于移动终端设备。获取虹膜图像是虹膜识别的关键一步,运用ZEMAX光学设计软件设计了一款适用于手机的虹膜识别镜头。该镜头采用豪威科技公司OmniVision_OV2281传感器,采用三片式非球面光学塑料设计,F数为2.3,全视场角为34°,在1/2奈奎斯特频率220 lp/mm处MTF值均大于0.39,且系统总长仅3 mm。根据ZEMAX像质评价方法以及公差分析结果可知,该镜头各项光学指标优良,具有像质好、体积小,质量轻、价格低、容易加工等特点。     

Attention-Shared Multi-Agent Actor–Critic-Based Deep Reinforcement Learning Approach for Mobile Charging Dynamic Scheduling in Wireless Rechargeable Sensor Networks

The breakthrough of wireless energy transmission (WET) technology has greatly promoted the wireless rechargeable sensor networks (WRSNs). A promising method to overcome the energy constraint problem in WRSNs is mobile charging by employing a mobile charger to charge sensors via WET. Recently, more and more studies have been conducted for mobile charging scheduling under dynamic charging environments, ignoring the consideration of the joint charging sequence scheduling and charging ratio control (JSSRC) optimal design. This paper will propose a novel attention-shared multi-agent actor–critic-based deep reinforcement learning approach for JSSRC (AMADRL-JSSRC). In AMADRL-JSSRC, we employ two heterogeneous agents named charging sequence scheduler and charging ratio controller with an independent actor network and critic network. Meanwhile, we design the reward function for them, respectively, by considering the tour length and the number of dead sensors. The AMADRL-JSSRC trains decentralized policies in multi-agent environments, using a centralized computing critic network to share an attention mechanism, and it selects relevant policy information for each agent at every charging decision. Simulation results demonstrate that the proposed AMADRL-JSSRC can efficiently prolong the lifetime of the network and reduce the number of death sensors compared with the baseline algorithms.     

Efficient and stable wireless power transfer based on the non-Hermitian physics

As one of the most attractive non-radiative power transfer mechanisms without cables,efficient magnetic resonance wireless power transfer(WPT)in the near field has been extensively developed in recent years,and promoted a variety of practical applications,such as mobile phones,medical implant devices and electric vehicles.However,the physical mechanism behind some key limitations of the resonance WPT,such as frequency splitting and size-dependent efficiency,is not very clear under the widely used circuit model.Here,we review the recently developed efficient and stable resonance WPT based on non-Hermitian physics,which starts from a completely different avenue(utilizing loss and gain)to introduce novel functionalities to the resonance WPT.From the perspective of non-Hermitian photonics,the coherent and incoherent effects compete and coexist in the WPT system,and the weak stable of energy transfer mainly comes from the broken phase associated with the phase transition of parity-time symmetry.Based on this basic physical framework,some optimization schemes are proposed,including using nonlinear effect,using bound states in the continuum,or resorting to the system with high-order parity-time symmetry.Moreover,the combination of non-Hermitian physics and topological photonics in multi-coil system also provides a versatile platform for long-range robust WPT with topological protection.Therefore,the non-Hermitian physics can not only exactly predict the main results of current WPT systems,but also provide new ways to solve the difficulties of previous designs.     

An Evaluation of Entropy Measures for Microphone Identification

Research findings have shown that microphones can be uniquely identified by audio recordings since physical features of the microphone components leave repeatable and distinguishable traces on the audio stream. This property can be exploited in security applications to perform the identification of a mobile phone through the built-in microphone. The problem is to determine an accurate but also efficient representation of the physical characteristics, which is not known a priori. Usually there is a trade-off between the identification accuracy and the time requested to perform the classification. Various approaches have been used in literature to deal with it, ranging from the application of handcrafted statistical features to the recent application of deep learning techniques. This paper evaluates the application of different entropy measures (Shannon Entropy, Permutation Entropy, Dispersion Entropy, Approximate Entropy, Sample Entropy, and Fuzzy Entropy) and their suitability for microphone classification. The analysis is validated against an experimental dataset of built-in microphones of 34 mobile phones, stimulated by three different audio signals. The findings show that selected entropy measures can provide a very high identification accuracy in comparison to other statistical features and that they can be robust against the presence of noise. This paper performs an extensive analysis based on filter features selection methods to identify the most discriminating entropy measures and the related hyper-parameters (e.g., embedding dimension). Results on the trade-off between accuracy and classification time are also presented.     

多频合路器单元关键技术设计

资源集约化多网融合、多系统共用信号分布组网是大势所趋;无线移动通信的多频合路器单元,就是基于无线通信多网多频兼容技术而设计,目的是解决无线信号的室内分布覆盖问题.文中重点就多频合路器单元设计过程中滤波器建模仿真、无源互调、温漂补偿三个关键技术展开论述.     

Identification of Radio Environments in Software Antenna for High-Speed Millimeter Wave Wireless Communication Systems

Different from smart antenna, software antenna can cope with various radio environments by selecting appropriate algorithms. In this paper, we place emphasis on the identification of radio environments in software antenna for high-speed millimeter wave wireless communication systems. According to the major impairments of radio propagation and the limitation of the adaptive algorithms, we give a criterion to categorize the environments. A spatial smoothing method to identify the environments is also proposed.     

非球面手机屏幕放大镜的设计

为方便老年人、视力障碍者等特殊人群看清手机上的文字及图像,采用ZEMAX光学设计软件,从高折射率材料,镜片面型和类型的选择上,以及从使用双胶合镜或非球面等方面设计了几款手机屏幕放大镜.通过分析手机屏幕放大镜特点,发现用非球面镜片不仅可以改善屏幕周边的像质并可减小镜片厚度.最后,还设计了几种放大镜与手机的契合方式,以克服现有手机放大镜片的不足.     

基于贝叶斯网络的认知引擎设计与重配置

以往的通信行为指导系统未来通信, 以满足用户需求并适应环境变化, 是认知无线电系统的核心所在, 为此提出了一种基于贝叶斯网络的认知引擎, 用于解决在复杂多变的电磁环境与用户需求条件下, 认知无线电系统参数自适应调整的问题. 通过对系统过去通信行为样本数据, 进行结构学习和参数学习建立认知引擎, 将系统当前环境状态和用户需求信息经预处理作为推理的证据, 应用引擎决策出系统此时最佳的工作参数, 完成系统参数重构. 本文利用OPNET工具建立一个移动无线网络完成仿真实验, 仿真结果表明该认知引擎能有效地使移动无线网络适应环境变化, 改善端到端通信性能, 进一步验证了建模方法的可行性.     

Effects of mobile phone exposure on time frequency fine structure of transiently evoked otoacoustic emissions

Mobile phones have become very commonly used worldwide within a short period of time. To date there is only limited knowledge about interaction between electromagnetic fields (EMFs) emitted by mobile phones and the auditory function. Moreover, there is widespread concern that there may be potential for harm. The aim of this study was to assess potential subtle changes in cochlear function by measuring the temporal and spectral fine structure of transiently evoked otoacoustic emissions (TEOAE) in normal hearing subjects after exposure to EMFs emitted by Global System for Mobile Communication (GSM) mobile phones. TEOAEs were recorded in 27 healthy young adults before and after 10 min of real or sham exposure in a double-blind design. TEOAE data were analyzed both globally (broadband analysis) and using the Wavelet Transform (analysis of the time-frequency fine structure). The broadband analysis revealed no significant effect on TEOAEs related to exposure, confirming results of previous studies; in addition, no significant change was detected in the analysis of the elementary wavelet components, suggesting that the temporal and spectral fine structure of TEOAEs is not affected by 10 min exposure to low-intensity EMFs emitted by GSM mobile phones.     

Effects of Small Signal Injection on Laser Diode Pumped Hybrid Bistable System with Large Delay

Effects of small signal injection on an electro-optical bistable system with a very large delay are theoretically and experimentally investigated from the viewpoint of linear stability analysis. The resonance phenomena and frequency locking are observed when sinusoidal and rectangular small signals are additively injected into the system. Experiments confirm that theoretical results obtained by linear stability analysis are good predictions of the effects of both resonance and frequency-locking phenomena and show the dependence of these phenomena on the system parameters. As an application of the frequency locking phenomenon to the dynamical memory, the phase identification of the memory oscillation using the frame pulse externally injected is successfully executed.     

Wideband FSS for electromagnetic architecture in buildings

Interweaving the elements of a frequency selective surface (FSS) is a technique that allows wideband frequency filtering as well as size reduction. FSS structures using this method are well suited to wireless indoor applications where the operating bands are spread over a wide spectrum. Interwoven square loop slots FSS have been designed, fabricated and measured. The FSS structure to be presented offers transparency at the emergency services band at around 400 MHz and suppresses most of the mobile and wireless bands up to 3 GHz. The size of the unit cell developed is around 3% of the corresponding wavelength, significantly smaller than a half wavelength resonant dipole.     

实时主动声定位系统设计*

针对在军事演习或爆炸实验中对冲击波超压传感器位置的精确定位,设计了一种实时主动声定位系统。该系统由声信号发射系统、声信号接收处理系统和无线监控终端系统三部分组成。声信号发射系统与无线监控终端以单片机STC12C5A60S2作为核心控制器,接收处理系统以ARM9系列的S3C2440作为核心控制器。发射系统发射四种已知频率的声信号,接收处理系统接收并处理收到的四种声信号,然后把处理结果反馈到无线监控终端。通过实验表明,该系统在一定条件下,能够稳定地发射、接收并处理声信号,达到实时定位目标,并在200 m范围内将定位误差控制在厘米级内。