基于量子协方差成形估计器的MIMO检测方法

协方差成形最小二乘估计器是量子信号处理框架内基于MMSE协方差成形方法的一种新型线性估计器。本文将协方差成形最小二乘估计器应用于MIMO系统检测,提出了基于协方差成形最小二乘估计器的MIMO系统检测算法,通过数值计算进行性能分析,仿真结果表明协方差成形MIMO检测方法的误码率性能优于传统ZF和MMSE检测方法,同时系统性能与目标成形矩阵有关。

基于Unity平台的多操作系统共同开发技术研究及实现

近年来,多操作系统共同开发技术广泛使用,其具有技术要求简化和成本降低的极大优势。Unity就是这样一个支持多达28个平台游戏且一次编译能多处发布的平台。为了应对项目开发时多操作系统开发工作量重复臃肿的问题,结合目前基于MVC模型的软件开发方法和Unity3D平台软件开发技术,实现系统检测算法和系统适配算法,对多操作系统共同开发技术进行研究。其中系统检测算法通过检测得到当前系统的描述字段,系统适配算法依据系统描述字段在编译期对具体模块加以适配,两者相辅相成共同应对由不同操作系统功能实现的区别性所带来的开发问题。结合了一个具体的开发项目案例,解释了上述算法在实践过程中的技术难点与具体实现方式,最终使该案例能够同时在Windows与Android平台上成功发布,验证了该技术的可行性。

GNSS spoofing detection based on uncultivated wolf pack algorithm

In order to solve the problem that the global navigation satellite system（GNSS） receivers can hardly detect the GNSS spoofing when they are deceived by a spoofer,a model-based approach for the identification of the GNSS spoofing is proposed.First,a Hammerstein model is applied to model the spoofer/GNSS transmitter and the wireless channel.Then,a novel method based on the uncultivated wolf pack algorithm（UWPA） is proposed to estimate the model parameters.Taking the estimated model parameters as a feature vector,the identification of the spoofing is realized by comparing the Euclidean distance between the feature vectors.Simulations verify the effectiveness and the robustness of the proposed method.The results show that,compared with the other identification algorithms,such as least square（LS）,the iterative method and the bat-inspired algorithm（BA）,although the UWPA has a little more time-eomplexity than the LS and the BA algorithm,it has better estimation precision of the model parameters and higher identification rate of the GNSS spoofing,even for relative low signal-to-noise ratios.

A Portable Fall Detection and Alerting System Based on k-NN Algorithm and Remote Medicine

This paper presents information on a portable fall detection and alerting system mainly consisting of a custom vest and a mobile smart phone. A wearable motion detection sensor integrated with tri-axial accelerometer, gyroscope and Bluetooth is built into a custom vest worn by elderly. The vest can capture the reluctant acceleration and angular velocity about the activities of daily living(ADLs) of elderly in real time. The data via Bluetooth is then sent to a mobile smart phone running a fall detection program based on k-NN algorithm. When a fall occurs the phone can alert a family member or health care center through a call or emergent text message using a built in Global Positioning System. The experimental results show that the system discriminates falls from ADLs with a sensitivity of 95%, and a specificity of 96.67%. This system can provide remote monitoring and timely help for the elderly.

Low Complexity Multiuser Detection with Recursively Successive Zero-Forcing and SIC Based on Nullspace for Multiuser MIMO-OFDM System

To improve the spectrum efficiency, this paper considers the multiuser detection with the MU-MIMO technology for multiuser MIMO-OFDM system uplink with the same subcarrier shared by multiple users. A low complexity multiuser detection algorithm with recursively successive zero-forcing and successive interference cancellation(RSZF-SIC) based on nullspace is proposed. The RSZF process based on the block diagonalization(BD) technique eliminates the co-channel interference(CCI) by a recursive method based on the nullspace orthogonal theorem. The SIC process detects the user signals respectively with the reasonable user detection sequence based on the results of the RSZF process. The computational complexity of the proposed algorithm is effectively reduced by reducing the total number of singular value decomposition(SVD) operations and the dimension of the SVD matrix in the recursive procedure. The performance of the proposed algorithm is improved in terms of bit error rate and sum capacity of the system, especially in the highSNR regime.

Visual positioning of rectangular lead components based on Harris corners and Zernike moments

With the increasing necessities for reliable printed circuit board(PCB) product, there has been a considerable demand for high speed and high precision vision positioning system. To locate a rectangular lead component with high accuracy and reliability, a new visual positioning method was introduced. Considering the limitations of Ghosal sub-pixel edge detection algorithm, an improved algorithm was proposed, in which Harris corner features were used to coarsely detect the edge points and Zernike moments were adopted to accurately detect the edge points. Besides, two formulas were developed to determine the edge intersections whose sub-pixel coordinates were calculated with bilinear interpolation and conjugate gradient method. The last experimental results show that the proposed method can detect the deflection and offset, and the detection errors are less than 0.04° and 0.02 pixels.

Real-valued multi-area self set optimization in immunity-based network intrusion detection system

The real-valued self set in immunity-based network intrusion detection system （INIDS） has some defects： multi-area and overlapping, which are ignored before. The detectors generated by this kind of self set may have the problem of boundary holes between self and nonself regions, and the generation efficiency is low, so that, the self set needs to be optimized before generation stage. This paper proposes a self set optimization algorithm which uses the modified clustering algorithm and Gaussian distribution theory. The clustering deals with multi-area and the Gaussian distribution deals with the overlapping. The algorithm was tested by Iris data and real network data, and the results show that the optimized self set can solve the problem of boundary holes, increase the efficiency of detector generation effectively, and improve the system＇s detection rate.

Multiple phase detector of M-ary phase shift keying symbols in code division multiple access systems

A novel iterative technique, the phase descent search detection was proposed. This technique constrained the solution （PDS） algorithm, for M-ary phase shift keying （M-PSK） symbols to have a unit magnitude and it was based on coordinate descent iterations where coordinates were the unknown symbol phases. The PDS algorithm, together with a descent local search （also implemented as a version of the PDS algorithm）, was used multiple times with different initializations in a proposed multiple phase detector; the solution with the minimum cost was then chosen as the final solution. The simulation results show that for highly loaded multiuser scenarios, the proposed technique has a detection performance that is close to the single-user bound. The results also show that the multiple phase detector allows detection in highly overloaded scenarios and it exhibits near-far resistance. In particular, the detector has a performance that is significantly better, and complexity that is significantly lower, than that of the detector based on semi-definite relaxation.

Throughput-efficient wireless system and blind detection via improved particle filtering

This paper introduces throughput-efficient wireless system based on an extension to binary phasemodulations,named extended binary phase shift keying(EBPSK),and the corresponding analysis ofpower spectra,especially the extension to channel capacity are given.Importantly,a novel sequential es-timation and detection approach for this EBPSK system is proposed.The basic idea is to design a proba-bilistic approximation method for the computation of the maximum a posterior distribution via particle fil-tering method(PF).Subsequently,a new important function in PF is presented,so that the performanceof the detector has a great improvement.Finally,computer simulation illustrates that EBPSK system hasvery high transmission rate,and also the good performance of the proposed PF detector is demonstrated.

Hybrid Optimization of Support Vector Machine for Intrusion Detection

Support vector machine (SVM) technique has recently become a research focus in intrusion detection field for its better generalization performance when given less priori knowledge than other soft-computing techniques. But the randomicity of parameter selection in its implement often prevents it achieving expected performance. By utilizing genetic algorithm (GA) to optimize the parameters in data preprocessing and the training model of SVM simultaneously, a hybrid optimization algorithm is proposed in the paper to address this problem. The experimental results demonstrate that it’s an effective method and can improve the performance of SVM-based intrusion detection system further.

Research on efficient edge-chasing deadlock detection/resolution for distributed systems

Numerous edge-chasing deadlock detection algonthms were developed lor the cycle detection in distributed systems, but their detections had the n steps speed limitation and n （ n- 1） overhead limitation to detect a cycle of size n under the one-resource request model. Since fast deadlock detection is critical, this paper proposed a new algorithm to speed up the detection process. In our algorithm, when the running of a transaction node is blocked, the being requested resource nodes reply it with the waiting or being waited message simultaneously, so the blocked node knows both its predecessors and successors, which helps it detecting a cycle of size 2 directly and locally. For the cycle of size n （ n 〉 2）, a special probe is produced which has the predecessors information of its originator, so the being detected nodes know their indirect predecessors and direct successors, and can detect the cycle within n - 2 steps. The proposed algorithm is formally proved to be correct by the invariant verification method. Performance evaluation shows that the message overhead of our detection is （ n^2 - n - 2）/2, hence both the detection speed and message cost of the proposed algorithm are better than that of the existing al gorithms.

A distributed deadlock detection algorithm for mobile computing system

The mode of mobile computing originated from distributed computing and it has the un-idempotent operation property, therefore the deadlock detection algorithm designed for mobile computing systems will face challenges with regard to correctness and high efficiency. This paper attempts a fundamental study of deadlock detection for the AND model of mobile computing systems. First, the existing deadlock detection algorithms for distributed systems are classified into the resource node dependent (RD) and the resource node independent (RI) categories, and their corresponding weaknesses are discussed. Afterwards a new RI algorithm based on the AND model of mobile computing system is presented. The novelties of our algorithm are that: 1) the blocked nodes inform their predecessors and successors simultaneously; 2) the detection messages (agents) hold the predecessors information of their originator; 3) no agent is stored midway. Additionally, the quit-inform scheme is introduced to treat the excessive victim quitting problem raised by the overlapped cycles. By these methods the proposed algorithm can detect a cycle of size n within n-2 steps and with (n^2-n-2)/2 agents. The performance of our algorithm is compared with the most competitive RD and RI algorithms for distributed systems on a mobile agent simulation platform. Experiment results point out that our algorithm outperforms the two algorithms under the vast majority of resource configurations and concurrent workloads. The correctness of the proposed algorithm is formally proven by the invariant verification technique.

Research on the Network Intrusion Detection System based on Modified Particle Swarm Optimization Algorithm

In this paper, we conduct research on the network intrusion detection system based on the modified particle swarm optimization algorithm. Computer interconnection ability put forward the higher requirements for the system reliability design, the need to ensure that the system can support various communication protocols to guarantee the reliability and security of the network. At the same time also require network system, the server or products have strong ability of fault tolerance and redundancy, better meet the needs of users, to ensure the safety of the information data and the good operation of the network system. For this target, we propose the novel paradigm for the enhancement of the modern computer network that is innovative.

Real-Time Distributed Fiber Optic Sensor for Security Systems： Performance, Event Classification and Nuisance Mitigation

The success of any perimeter intrusion detection system depends on three important performance parameters： the probability of detection （POD）, the nuisance alarm rate （NAR）, and the false alarm rate （FAR）. The most fundamental parameter, POD, is normally related to a number of factors such as the event of interest, the sensitivity of the sensor, the installation quality of the system, and the reliability of the sensing equipment. The suppression of nuisance alarms without degrading sensitivity in fiber optic intrusion detection systems is key to maintaining acceptable performance. Signal processing algorithms that maintain the POD and eliminate nuisance alarms are crucial for achieving this. In this paper, a robust event classification system using supervised neural networks together with a level crossings （LCs） based feature extraction algorithm is presented for the detection and recognition of intrusion and non-intrusion events in a fence-based fiber-optic intrusion detection system. A level crossings algorithm is also used with a dynamic threshold to suppress torrential rain-induced nuisance alarms in a fence system. Results show that rain-induced nuisance alarms can be suppressed for rainfall rates in excess of 100mm/hr with the simultaneous detection of intrusion events. The use of a level crossing based detection and novel classification algorithm is also presented for a buried pipeline fiber optic intrusion detection system for the suppression of nuisance events and discrimination of intrusion events. The sensor employed for both types of systems is a distributed bidirectional fiber-optic Mach-Zehnder （MZ） interferometer.

Cyclostationary Property Based Spectrum Sensing Algorithms for Primary Detection in Cognitive Radio Systems

To implement the primary signal without interference in cognitive radio systems, cognitive radios can detect the presence of the primary user in low SNR. Currently, energy detector is the most common way of spectrum sensing because of its low computational complexity. However, performunce of the method will be possibly degraded due to the uncertainty noise. This paper illustrates the benefits of one-order and two-order cyclostationary properties of primary user＇s signals in time domain. These feature detection techniques in time domain possess the advantages of simple structure and low computational complexity comparing with spectral feature detection methods. Furthermore, performance of the one-order and two-order feature detection is studied and the analytical results are given. Our analysis and numerical results show that the sensing performance of the one-order feature detection is improved significantly comparing with conventional energy detector since it is robust to noise. Meanwhile, numerical results show that the two-order feature detection technique is better than the one-order feature detection. However, this benefit comes at the cost of hardware burdens and power consumption due to the additional multiplying algorithm.

Gaussian Mixture Statistics Model Based on Automatic Inspection System Used in ITS

The ITS is becoming more and more important in the economic development of China. But most of the ITS used in Chinese major cities need the human to perform the supervision task. As a result, it consumes too much human resources, and also can not achieve the satisfied supervision performance. Thus, in this paper, we will propose an automatic inspection system based on the Gaussian mixture statistics model to alleviate this kind of problem. The proposed method will utilize a Gaussian Mixture model to model the background, and then use the EM algorithm to update the model＇s coefficients frame by frame to make the model adapt to the changing environment. After successful modeling, we can extract out the foreground blocks from background blocks, and finally trigger the automatic alarming system by calculating the number of foreground blocks. From the experiment results, our proposed method can achieve considerable good results.