A phase-space formulation and Gaussian approximation of the filtering equations for nonlinear quantum stochastic systems

This paper is concerned with a filtering problem for a class of nonlinear quantum stochastic systems with multichannel nondemolition measurements. The system-observation dynamics are governed by a Markovian Hudson-Parthasarathy quantum stochastic differential equation driven by quantum Wiener processes of bosonic fields in vacuum state. The Hamiltonian and system-field coupling operators, as functions of the system variables, are assumed to be represented in a Weyl quantization form. Using the Wigner-Moyal phase-space framework, we obtain a stochastic integro-differential equation for the posterior quasi-characteristic function (QCF) of the system conditioned on the measurements. This equation is a spatial Fourier domain representation of the Belavkin-Kushner-Stratonovich stochastic master equation driven by the innovation process associated with the measurements. We discuss a specific form of the posterior QCF dynamics in the case of linear system-field coupling and outline a Gaussian approximation of the posterior quantum state.     

Fractionally Delayed Kalman Filter

The conventional Kalman filter is based on the assumption of non-delayed measurements. Several modifications appear to address this problem, but they are constrained by two crucial assumptions: 1) the delay is an integer multiple of the sampling interval, and 2) a stochastic model representing the relationship between delayed measurements and a sequence of possible non-delayed measurements is known. Practical problems often fail to satisfy these assumptions, leading to poor estimation accuracy and frequent track-failure. This paper introduces a new variant of the Kalman filter, which is free from the stochastic model requirement and addresses the problem of fractional delay.The proposed algorithm fixes the maximum delay(problem specific), which can be tuned by the practitioners for varying delay possibilities. A sequence of hypothetically defined intermediate instants characterizes fractional delays while maximum likelihood based delay identification could preclude the stochastic model requirement. Fractional delay realization could help in improving estimation accuracy. Moreover, precluding the need of a stochastic model could enhance the practical applicability. A comparative analysis with ordinary Kalman filter shows the high estimation accuracy of the proposed method in the presence of delay.     

An analytical delay model

Delay consideration has been a major issue in design and test of high performance digital circuits.The assumption of input signal change occurring only when all internal nodes are stable restricts the increas of clock frequency.It is no longer true for wave pipelining circuits.However,previous logical delay models are based on the assumption.In addition,the stable time of a robust delay test generally depends on the longest sensitizable path delay.Thus,a new delay model is desirable.This paper explores th necessity first.Then,Boolean process to analytically describe the logical and timing behavior of a digital circuit is reviewed.The concept of sensitization is redefined precisely in this paper.Based on the new concept of sensitization,an analytical delay model is introduced.As a result,many untestable delay faults under the logical delay model can be tested if the output waveforms can be sampled at more time points.The longest sensitizable path length is computed for circuit design and delay test.     

The Design and Implementation of a Very Fast Experimental Pipelining Computer

The high speed potential of I.C.components can be exploited by shortening the pipeline clock period.Although there are some factors which dominate the shortening,the design of an experimental computeremploys the principle of maximum time difference at the system level to determine the clock period and theintegrated consideration of architecture,logic design and engineering layout to achieve a system clock periodof 9.8 ns using conventional ECL chips of 2ns gate delay.The multiplier in this model,which is constructedwith 0.7 ns gate delay chips,can work at a cloek period of 5.5 ns.     

Low Cost Scan Test by Test Correlation Utilization

Scan-based testing methodologies remedy the testability problem of sequential circuits; yet they suffer from prolonged test time and excessive test power due to numerous shift operations. The correlation among test data along with the high density of the unspecified bits in test data enables the utilization of the existing test data in the scan chain for the generation of the subsequent test stimulus, thus reducing both test time and test data volume. We propose a pair of scan approaches in this paper; in the first approach, a test stimulus partially consists of the preceding stimulus, while in the second approach, a test stimulus partially consists of the preceding test response bits. Both proposed scan-based test schemes access only a subset of scan cells for loading the subsequent test stimulus while freezing the remaining scan cells with the preceding test data, thus decreasing scan chain transitions during shift operations. The proposed scan architecture is coupled with test data manipulation techniques which include test stimuli ordering and partitioning algorithms, boosting test time reductions. The experimental results confirm that test time reductions exceeding 97%, and test power reductions exceeding 99% can be achieved by the proposed scan-based testing methodologies on larger ISCAS89 benchmark circuits.     

Energy-aware routing for delay-sensitive underwater wireless sensor networks

The energy reduction is a challenging problem in the applications of underwater wireless sensor networks （UWSNs）. The embedded battery is difficult to be replaced and it has an upper bound on its lifetime. Multihop relay is a popular method to reduce energy consumption in data transmission. The energy minimum path from source to destination in the sensor networks can be obtained through the shortest path algorithm. However, because of the node mobility, the global path planning approach is not suitable for the routing in UWSNs. It calls for an energy-efficient routing protocol for the high dynamic UWSNs. In this paper, we propose the modified energy weight routing （MEWR） protocol to deal with the energy-efficient routing of delay- sensitive UWSNs. MEWR is a low flooding routing protocol. It can tolerate the node mobility in UWSNs and achieve a low end-to-end packet delay. MEWR can provide lower energy consumption than the existing low delay routing protocols through the dynamic sending power adjustment. The simulation results demonstrate the effectiveness of MEWR.     

On extended state based Kalman flter for nonlinear time‑varying uncertain systems with measurement bias

This paper addresses the state estimation for a class of nonlinear time-varying stochastic systems with both uncertain dynamics and unknown measurement bias. A novel extended state based Kalman flter (ESKF) algorithm is developed to estimate the original state, the uncertain dynamics and the measurement bias. It is shown that the estimation error of the proposed algorithm is bounded in the mean square sense. Also, the estimation of the measurement bias asymptotically converges to its true value, such that the infuence of measurement bias is eliminated. Furthermore, the asymptotic optimality of the estimation result is proved while the uncertain dynamics approaches to a constant vector. Finally, a simulation study for harmonic oscillator system model is provided to illustrate the efectiveness of proposed method.     

A mathematical morphology-based multi-level filter of LiDAR data for generating DTMs

Light detecting and ranging(LiDAR)technology has become an efective way to generate highresolution digital terrain models(DTMs).To generate DTMs,point measurements from non-ground features,such as buildings,vegetation and vehicles,have to be identifed and removed while preserving the terrain points.This paper proposes an efcient mathematical morphology-based multi-level flter to generate DTMs from airborne LiDAR data.Preliminary non-ground points are frst identifed with the characteristics of the multiecho airborne LiDAR data.The localized mathematical morphology opening operations are then immediately applied to the remaining points.By gradually increasing the window size of the flter and using a dynamic critical gradient threshold,the non-ground points are removed,while the ground points are preserved.Eight samples were chosen from eight sites provided with the ISPRS Commission III,Working Group 3,to evaluate the accuracy of our algorithm.Both the qualitative and quantitative experiment analyses show that our morphologybased multi-level flter method achieves promising results,not only in flat urban areas but also in rural areas,especially in preserving complex terrain details,while non-ground spatial objects are removed.     

Analyzing Sequential Patterns in Retail Databases

Finding correlated sequential patterns in large sequence databases is one of the essential tasks in data mining since a huge number of sequential patterns are usually mined, but it is hard to find sequential patterns with the correlation. According to the requirement of real applications, the needed data analysis should be different. In previous mining approaches, after mining the sequential patterns, sequential patterns with the weak affinity are found even with a high minimum support. In this paper, a new framework is suggested for mining weighted support affinity patterns in which an objective measure, sequential ws-confidence is developed to detect correlated sequential patterns with weighted support affinity patterns. To efficiently prune the weak affinity patterns, it is proved that ws-confidence measure satisfies the anti-monotone and cross weighted support properties which can be applied to eliminate sequential patterns with dissimilar weighted support levels. Based on the framework, a weighted support affinity pattern mining algorithm （WSMiner） is suggested. The performance study shows that WSMiner is efficient and scalable for mining weighted support affinity patterns.     

Scan BIST with biased scan test signals

The conventional test-per-scan built-in self-test （BIST） scheme needs a number of shift cycles followed by one capture cycle. Fault effects received by the scan flipflops are shifted out while shifting in the next test vector like scan testing. Unlike deterministic testing, it is unnecessary to apply a complete test vector to the scan chains. A new scan-based BIST scheme is proposed by properly controlling the test signals of the scan chains. Different biased random values are assigned to the test signals of scan flip-flops in separate scan chains. Capture cycles can be inserted at any clock cycle if necessary. A new testability estimation procedure according to the proposed testing scheme is presented. A greedy procedure is proposed to select a weight for each scan chain. Experimental results show that the proposed method can improve test effectiveness of scan-based BIST greatly, and most circuits can obtain complete fault coverage or very close to complete fault coverage.     

Adaptive Harmonic IIR Notch Filter with Varying Notch Bandwidth and Convergence Factor

This paper proposes an improved adaptive harmonic IIR notch filter. The proposed algorithm utilizes varying notch bandwidth and convergence factor to achieve robust frequency estimation and tracking. A formula to determine the stability bound by using the LMS （least mean squares） algorithm is derived. In addition, the developed algorithm is also devised to prevent the adaptive algorithm from converging to its local minima of the MSE function due to signal fundamental frequency switches in the tracking process.     

Removal of High-Density Salt and Pepper Noises Using Adaptive Mixed Median and Mean Filter

An efficient algorithm for the removal of high-density salt and pepper noise is proposed, which integrates the median filter and mean filter. The proposed algorithm replaces the noisy pixels by mean value when there are more than two non-noisy pixels in the selected window, by median value when there are one or two. If there are no non-noisy pixels in the selected window, the window will be enlarged adaptively until non-noisy pixels appear and then mean filter is used. The proposed algorithm is tested against two standard images and the experimental results show that it produces better results for removal salt and pepper noises in terms of PSNR （peak signal-to-noise ratio）, SSIM （structural similarity） and ｜EF （image enhancement factor）.     

A 65-nm low-power high-linearity Σ△ ADC for audio applications

This paper presents a low-power high linearity Σ△ analog-to-digital converter(ADC)for audio applications.By adopting low noise large output swing operational amplifiers in a 2-1 cascaded modulator,not only can the noise floor be reduced,but the input signal range can also be enlarged.A low-power,area-efficient digital decimation filter was also designed to decrease the area and the power cost.The ADC was fabricated in the SMIC 65 nm single-poly-eight-metal(1P8M)mixed-signal complementary metal-oxide-semiconductor(CMOS)process with a die area of 0.36 mm2.Measurement results showed that a 90 dB peak signal to noise plus distortion ratio(SNDR)and a 93 dB dynamic range(DR)were achieved over the 22.05 kHz audio band.The power dissipation was 2.2 mW from 1.2 V power supply,which is suitable for audio codec applications.     

Flying Platform Tracking for Microwave Air-Bridging in Sky-Net Telecom Signal Relaying Operation

The Sky-Net project proposes a mobile BTS （base transceiver station） for mobile communication service from flying platform. An MAB （microwave air-bridging） is established in order to relay the telecom signal via airborne e-Cell BTS on flying platform. The QoS （quality of service） and data-link stability are highly concerned in Sky-Net system, and therefore, a two-axis antenna tracking mechanism is applied for keeping the directional antenna well aligned to flying platform. The proposed G2G （GPS-to-GPS） tracking algorithm is a simple but efficient method to calculate instant azimuth/elevation angle of flying platform related to ground tracking unit. Goal for this paper tries to reduce the vector error and maintain system bandwidth of MAB for telecom signal relaying in Sky-Net application.     

Multi-target tracking with PHD filter using Doppler-only measurements

In this paper, we address the problem of multi-target detection and tracking over a network of separately located Doppler-shift measuring sensors. For this challenging problem, we propose to use the probability hypothesis density (PHD) filter and present two implementations of the PHD filter, namely the sequential Monte Carlo PHD (SMC-PHD) and the Gaussian mixture PHD (GM-PHD) filters. Performances of both filters are carefully studied and compared for the considered challenging tracking problem. Simulation results show that both PHD filter implementations successfully track multiple targets using only Doppler shift measurements. Moreover, as a proof-of-concept, an experimental setup consisting of a network of microphones and a loudspeaker was prepared. Experimental study results reveal that it is possible to track multiple ground targets using acoustic Doppler shift measurements in a passive multi-static scenario. We observed that the GM-PHD is more effective, efficient and easy to implement than the SMC-PHD filter.     

基于高斯混合PHD滤波的多目标跟踪

多目标的跟踪的主要目的是通过一个存在关联不确定性、检测不确定性以及噪声和虚警的观测序列集,联合估计目标数目和目标状态.传统的多目标跟踪算法中的数据关联算法计算量大不易实现,而基于随机集的PHD滤波算法可避免数据数据关联问题,直接估计目标状态.本文针对目前PHD递推算法难以获得闭和解的问题,阐明了在目标运动模型和新生强度都是线性高斯模型的情况下,每一时刻的后验强度都是高斯混合的.进而推导出表示后验强度的高斯成分的均值,方差和权值的递推方程.由仿真结果可以看出在非线性高斯情况下,本算法对多目标有良好的跟踪性能.     

多目标跟踪的混合高斯PHD滤波

为解决目标数未知或随时间变化时的多目标跟踪问题,将多目标状态和观测信息表示为随机集的形式,建立了多目标跟踪的混合高斯概率假设密度(PHD)滤波方法。当目标初始的先验概率密度满足高斯分布的形式时,通过将状态噪声、观测噪声、目标的繁衍、新目标的产生、目标的存活概率和检测概率表示成混合高斯的形式,之后每个时刻的后验概率密度均能表示成混合高斯的形式。线性混合高斯PHD滤波方法将Kalman滤波引入到PHD滤波中,利用混合高斯成分预测和更新随机集的PHD,并估计出目标的状态。实验结果表明,在杂波环境下混合高斯PHD滤波方法可以有效地跟踪目标状态。     

A distributed particle-PHD filter using arithmetic-average fusion of Gaussian mixture parameters

We propose a particle-based distributed PHD filter for tracking the states of an unknown, time-varying number of targets. To reduce communication, the local PHD filters at neighboring sensors communicate Gaussian mixture (GM) parameters. In contrast to most existing distributed PHD filters, our filter employs an “arithmetic average” fusion. For particles–GM conversion, we use a method that avoids particle clustering and enables a significance-based pruning of the GM components. For GM–particles conversion, we develop an importance sampling based method that enables a parallelization of filtering and dissemination/fusion operations. The resulting distributed PHD filtering framework is able to integrate both particle-based and GM-based local PHD filters. Simulations demonstrate the excellent performance and small communication and computation requirements of our filter.     

基于目标尺度的自适应高斯滤波

将具有形态学意义的目标尺度与传统的线性高斯滤波相结合提出了一种自适应高斯滤波,它的主要思想是：利用求出的目标尺度来控制各像素点高斯滤波的方差和模板大小。针对原有的目标尺度求解算法不能适应可见光图像去噪的问题,一方面,引入中值滤波以去除对目标尺度求解影响较大的强噪声点,另一方面,又调整了求解目标尺度算法中的参数。仿真实验证明,该算法可以在去除噪声的同时保护图像的细节,而且不论从主观上还是客观上都优于传统的几类图像平滑算法,且不需要迭代求解,计算简单。