In this paper, we contrive a stereo matching algorithm with careful handling of disparity, discontinuity and occlusion. This algorithm works a worldwide matching stereo model which is based on minimization of energy. The global energy comprises two terms, firstly the data term and secondly the smoothness term. The data term is approximated by a color-weighted correlation, then refined in obstruct and low-texture areas in many applications of hierarchical loopy belief propagation algorithm. The results during the experiment are evaluated on the Middlebury data sets, showing that out algorithm is the top performer among all the algorithms listed there

Stereo Matching With Color-Weighted Correlation, Hierarchical Belief Propagation And Occlusion Handling

On-orbit service technology is one of the key technologies of space manipulation activities such as spacecraft life extension, fault spacecraft capture, on-orbit debris removal and so on. It is known that the failure satellites, space debris and enemy spacecrafts in space are almost all non-cooperative targets. Relatively accurate pose estimation is critical to spatial operations, but also a recognized technical difficulty because of the undefined prior information of non-cooperative targets. With the rapid development of laser radar, the application of laser scanning equipment is increasing in the measurement of non-cooperative targets. It is necessary to research a new pose estimation method for non-cooperative targets based on 3D point cloud. The paper aims to discuss these issues.,In this paper, a method based on the inherent characteristics of a spacecraft is proposed for estimating the pose (position and attitude) of the spatial non-cooperative target. First, we need to preprocess the obtained point cloud to reduce noise and improve the quality of data. Second, according to the features of the satellite, a recognition system used for non-cooperative measurement is designed. The components which are common in the configuration of satellite are chosen as the recognized object. Finally, based on the identified object, the ICP algorithm is used to calculate the pose between two frames of point cloud in different times to finish pose estimation.,The new method enhances the matching speed and improves the accuracy of pose estimation compared with traditional methods by reducing the number of matching points. The recognition of components on non-cooperative spacecraft directly contributes to the space docking, on-orbit capture and relative navigation.,Limited to the measurement distance of the laser radar, this paper considers the pose estimation for non-cooperative spacecraft in the close range.,The pose estimation method for non-cooperative spacecraft in this paper is mainly applied to close proximity space operations such as final rendezvous phase of spacecraft or ultra-close approaching phase of target capture. The system can recognize components needed to be capture and provide the relative pose of non-cooperative spacecraft. The method in this paper is more robust compared with the traditional single component recognition method and overall matching method when scanning of laser radar is not complete or the components are blocked.,This paper introduces a new pose estimation method for non-cooperative spacecraft based on point cloud. The experimental results show that the proposed method can effectively identify the features of non-cooperative targets and track their position and attitude. The method is robust to the noise and greatly improves the speed of pose estimation while guarantee the accuracy.

A new pose estimation method for non-cooperative spacecraft based on point cloud

This paper presents an Online Built-In Self-Test (OBIST) architecture which is tailored towards the detection of hardware faults found in solar tracking systems. The goal of the proposed OBIST is two-folded: first, we identify the idle states of the electrical equipment that orients the payload of a dual-axis solar tracker to the maximum sun exposure with the support of an idle state detector which requires minimum hardware overhead; secondly, when the solar tracking device is in idle state, we disconnect its Circuits Under Test (CUTs) consisting of one Optocoupler, one Arduino UNO and two L298N motor drivers from the main data flow stream (initially connected directly to the solar panel) and attach the proposed OBIST equipment via dedicated switches in order to inject test vectors and collect their output signals in a signature-based response manner. The experimental results based on our hardware implementation and software simulation achieve an average of 93.93% coverage for single bit-flip errors (last 8 bits, mutant), 100% coverage for single stuck-at-faults (8, 12 and 16 random bits) as well as 96.96% for all targeted faults, showing that the proposed OBIST architecture is efficient with regard to test coverage and cost points of view.

Online Built-In Self-Test Architecture for Automated Testing of a Solar Tracking Equipment

Intra-pulse modulation recognition of radar signals plays an important role in radar identification. The proposed method recognizes intra-pulse modulation type of intercepted radar signal using the features extracted from pulse description words (PDWs) and complex waveforms generated by multiple channels in digital channelized receiver. The intercepted radar signal is classified to three classes such as linear frequency modulation (LFM), non-linear frequency modulation (NLFM), and frequency shift keying (FSK) by the proposed method. The simulation results show that the proposed method can achieve more than 90% recognition rate over signal-to-noise ratio (SNR) 10dB.

Intra-pulse modulation recognition using pulse description words and complex waveforms

Automatic acoustic scene analysis is a complex task that involves several functionalities: detection (time), localization (space), separation, recognition, etc. This thesis focuses on both acoustic event detection (AED) and acoustic source localization (ASL), when several sources may be simultaneously present in a room. In particular, the experimentation work is carried out with a meeting-room scenario. Unlike previous works that either employed models of all possible sound combinations or additionally used video signals, in this thesis, the time overlapping sound problem is tackled by exploiting the signal diversity that results from the usage of multiple microphone array beamformers.
The core of this thesis work is a rather computationally efficient approach that consists of three processing stages. In the first, a set of (null) steering beamformers is used to carry out diverse partial signal separations, by using multiple arbitrarily located linear microphone arrays, each of them composed of a small number of microphones. In the second stage, each of the beamformer output goes through a classification step, which uses models for all the targeted sound classes (HMM-GMM, in the experiments). Then, in a third stage, the classifier scores, either being intra- or inter-array, are combined using a probabilistic criterion (like MAP) or a machine learning fusion technique (fuzzy integral (FI), in the experiments).
The above-mentioned processing scheme is applied in this thesis to a set of complexity-increasing problems, which are defined by the assumptions made regarding identities (plus time endpoints) and/or positions of sounds. In fact, the thesis report starts with the problem of unambiguously mapping the identities to the positions, continues with AED (positions assumed) and ASL (identities assumed), and ends with the integration of AED and ASL in a single system, which does not need any assumption about identities or positions.
The evaluation experiments are carried out in a meeting-room scenario, where two sources are temporally overlapped; one of them is always speech and the other is an acoustic event from a pre-defined set. Two different databases are used, one that is produced by merging signals actually recorded in the UPC?s department smart-room, and the other consists of overlapping sound signals directly recorded in the same room and in a rather spontaneous way. From the experimental results with a single array, it can be observed that the proposed detection system performs better than either the model based system or a blind source separation based system. Moreover, the product rule based combination and the FI based fusion of the scores resulting from the multiple arrays improve the accuracies further. On the other hand, the posterior position assignment is performed with a very small error rate. 
Regarding ASL and assuming an accurate AED system output, the 1-source localization performance of the proposed system is slightly better than that of the widely-used SRP-PHAT system, working in an event-based mode, and it even performs significantly better than the latter one in the more complex 2-source scenario. Finally, though the joint system suffers from a slight degradation in terms of classification accuracy with respect to the case where the source positions are known, it shows the advantage of carrying out the two tasks, recognition and localization, with a single system, and it allows the inclusion of information about the prior probabilities of the source positions. It is worth noticing also that, although the acoustic scenario used for experimentation is rather limited, the approach and its formalism were developed for a general case, where the number and identities of sources are not constrained.

Acoustic event detection and localization using distributed microphone arrays

This paper presents a design that realizes a dual-channel Data Record Card which achieves 33MB/s data transfer rate and 4GB capacity per channel with CF Card and SDRAM. According to the characteristics of data transfer with CF Card, an advanced data transfer method is provided, which reduces the requirements of memory resources in FPGA without influencing the data transfer rate. And it realizes high speed data transfer between CF Card and low-end FPGA with limited memory resources.

Realization of high speed mass storage Data Record Card with CF Card and SDRAM

This paper presents a high-speed data transmission plan based on VPX bus. The aim is to establish a high-speed data transmission structure which could be used for dealing with massive echo data from the PAR (Phased array radar) in the future. The frequency is up to gigahertz so that it is hard to design the PCB which is used for high-speed serial data transmission between the boards. Stable and reliable interconnection between payload boards is a main problem. This paper uses channel simulation method to design the hardware. A channel simulation model is established between high-speed backplane and payload modules. This design extracts key information from channel tub curve and eye diagram, then design the hardware based on channel capacity and bit error rate. This paper designs a dual-channel highspeed serial RapidIO annular communication between payload modules based on VPX bus. Single channel theory transmission rate is 3.125Gbps. And the result shows that the actual speed of dual-channel is up to 540.70MB/s. It’s nearly the theory speed of the RapidIO communication protocol.

Development of serial RapidIO high-speed data transmission based on VPX bus

This paper proposed a novel recovery algorithm for sparse wideband signals. Conventional recovery methods are mainly required the frequency support as a prior, or are based on some compressed sensing (CS) recovery algorithms, such as simultaneous orthogonal matching pursuit (SOMP), which identifies a single spectrum band one time, and couldn't refine the selected spectrum band that is wrong. The proposed algorithm adopts a backtracking strategy. In this strategy, the proposed algorithm can maintain the correct frequency supports and refine the wrong ones during next iteration. The expectation is that the recursive refinements of the estimate of the frequency support set will lead a higher success rate than SOMP. Simulation results demonstrate the proposed algorithm outperforms SOMP.

A blind spectrum recovery algorithm for sparse wideband signals based on backtracking

With the increment of area of memory in SoC, embedded memory test is becoming increasingly significant. The March algorithm and its derived algorithms, which are the mainstream algorithm in memory test domain, can achieve very good effects in terms of testing time and fault coverage. However, because of the different accesses of different types of memories, the same algorithm cannot apply to testing different types of memories. In SoC, implanting different circuits for testing different types of memories will result in high hardware overhead. To solve this problem, we design a generic embedded memory built-in self-test circuit which, according to different types of memories, chooses the suitable testing algorithms through configuring a set of March algorithm registers. This method will make the testing circuit adapt to different types of memories and the corresponding hardware overhead is relatively low. Verified by Modelsim simulation, this method has a flexible configuration and has a high coverage of common faults.

Design of generic embedded memory built in self test circuit

For the achievement of LFM signal acquisition, pulse compression and storage, the hardware platform is built. A way to implement the pulse compression of the LFM signal based on FPGA is presented. This paper describes in detail the functions and implementation methods of the various functional modules of the FPGA in the pulse compression process. A theoretical simulation on the pulse compression is done, and the actual test results are analyzed after compared with the theoretical simulation.

Qiao Liyan

Papers

Realization of high speed mass storage Data Record Card with CF Card and SDRAM

Development of serial RapidIO high-speed data transmission based on VPX bus

A blind spectrum recovery algorithm for sparse wideband signals based on backtracking

Design of generic embedded memory built in self test circuit

Method of LFM pulse compression implementation based on FPGA