A robust,real-time control scheme for multifunction myoelectric control

This paper represents an ongoing investigation of dexterous and natural control of upper extremity prostheses using the myoelectric signal (MES). The scheme described within uses pattern recognition to process four channels of MES, with the task of discriminating multiple classes of limb movement. The method does not require segmentation of the MES data, allowing a continuous stream of class decisions to be delivered to a prosthetic device. It is shown in this paper that, by exploiting the processing power inherent in current computing systems, substantial gains in classifier accuracy and response time are possible. Other important characteristics for prosthetic control systems are met as well. Due to the fact that the classifier learns the muscle activation patterns for each desired class for each individual, a natural control actuation results. The continuous decision stream allows complex sequences of manipulation involving multiple joints to be performed without interruption. Finally, minimal storage capacity is required, which is an important factor in embedded control systems.     

A new strategy for multifunction myoelectric control

A novel approach to the control of a multifunction prosthesis based on the classification of myoelectric patterns is described. It is shown that the myoelectric signal exhibits a deterministic structure during the initial phase of a muscle contraction. Features are extracted from several time segments of the myoelectric signal to preserve pattern structure. These features are then classified using an artificial neural network. The control signals are derived from natural contraction patterns which can be produced reliably with little subject training. The new control scheme increases the number of functions which can be controlled by a single channel of myoelectric signal but does so in a way which does not increase the effort required by the amputee. Results are presented to support this approach     

A wavelet-based continuous classification scheme for multifunction myoelectric control

This work represents an ongoing investigation of dexterous and natural control of powered upper limbs using the myoelectric signal. When approached as a pattern recognition problem, the success of a myoelectric control scheme depends largely on the classification accuracy. A novel approach is described that demonstrates greater accuracy than in previous work. Fundamental to the success of this method is the use of a wavelet-based feature set, reduced in dimension by principal components analysis. Further, it is shown that four channels of myoelectric data greatly improve the classification accuracy, as compared to one or two channels. It is demonstrated that exceptionally accurate performance is possible using the steady-state myoelectric signal. Exploiting these successes, a robust online classifier is constructed, which produces class decisions on a continuous stream of data. Although in its preliminary stages of development, this scheme promises a more natural and efficient means of myoelectric control than one based on discrete, transient bursts of activity.     

A hand vein recognition system based on DSP and CPLD

The hand vein recognition system based on digital signal processing (DSP) and complex programmable logic device (CPLD) is designed according to the requirements for equipment volume, accuracy and reaction speed. The overall structure and detailed implementation of the system hardware architecture are discussed in this paper. Moreover, the design philosophy and specific realization of system software as well as core algorithms are explored. The recognition system owns many good characteristics, such as high-degree integration, simple structure, flexible programming, convenient application and so on, which make it suitable for circumstances with high requirements for personal identification.     

Incomplete fingerprint recognition based on feature fusion and pattern entropy

Considering the inherent characteristics of incomplete fingerprint： local feature loss and global information distortion, the recognition progress has been mainly restricted by two critical problems： how to precisely extract informative features and still with compact representation of the incomplete fingerprint; and how to effectively measure the similarity between fingerprint images. In this paper, to handle the first problem, both the minutiae and orientation field feature are extracted and then fused to get a more comprehensive feature with scale and rotation invariability. Dealing with the second one, the pattern entropy is introduced to robustly measure the similarity of two incomplete fingerprints. Extensive experiments have been conducted on both those popular fingerprint databases and our extended databases containing more incomplete fingerprints. Meanwhile, thorough performance comparisons have been made with existing approaches. Experimental results show that our approach has more efficient ability especially in incomplete fingerprint recognition, and also performs well in both accuracy and efficiency.     

Antenna selection for MIMO system based on pattern recognition

This paper proposes a novel Multiple-Input Multiple-Output (MIMO) transmission scheme based on Pattern Recognition (PR), which is termed as the PR aided Transmission Antenna Selection MIMO (PR-TAS aided MIMO). As the conventional TAS algorithms need to search all possible legitimate antenna subsets, they may impose some redundant calculations. In order to avoid this problem, we employ some pattern recognition methods to carry out the TAS algorithm in this paper. To be specific, two PR algorithms, namely the K-Nearest Neighbor (KNN) algorithm and the Support Vector Machine (SVM) algorithm, are introduced and redesigned to obtain a TAS with lower complexity but higher efficiency. Moreover, in order to improve the performance of the SVM, we propose a new feature extraction of channel matrix for the TAS. Our simulation results show that the proposed KNN and SVM based PR-TAS algorithms are capable of striking a flexible tradeoff between the complexity and the Bit Error Rate (BER), and the new feature can effectively improve the BER performance compared with the conventional feature extraction method.     

基于模式识别的餐饮自助结算系统

针对自选式餐饮服务中人工结算效率低,排队拥挤的情况,文中提出一种基于模式识别的自助结算系统,通过识别餐盘的颜色、形状特征来区分价格种类进而自动结算。采用了灰度变换、高斯滤波、椭圆拟合的方法对模式特征进行预提取,采用Sobel边缘检测算子、快速Hough变换等算法对目标进行识别。试验表明该方法对非完整边缘目标的识别具有准确性和适应性,同时系统采用DSP实现,对一次目标识别结算不超过3s,可以满足快速结算的要求。     

基于局部纹理特征的实时目标跟踪系统设计

为了有效、稳定、高速地跟踪目标,设计了一套高精度的目标跟踪算法和高性能的目标跟踪硬件系统。为了满足系统高精度跟踪的需要,在深入分析LBP算法优缺点的基础上,提出了一种基于LBP算法与差值匹配算法相结合的改进的纹理特征提取算法;为了满足系统对实时性跟踪的要求,结合算法特点,设计了一套基于高速DSP和FPGA的目标跟踪硬件系统。实验结果表明：改进算法的目标跟踪精度比传统跟踪算法提高很多,而且能够在20 ms内完成目标位置的计算。设计的系统已应用到实际工程中,且能满足目标跟踪系统对实时性、高精度、抗旋转等的要求。     

基于自适应特征提取的数显仪表识别系统

为了适应数显仪表各种不同的室外环境,建立了自适应于不同光照条件下的仪表自动识别系统。针对数显仪表图像的点亮区域多为红色,不亮部分偏灰白的特征,设计基于RGB彩色空间滤波方法,较好地保留了点亮区域的亮度信息;并提出改进的最大类间方差法,实现了数显仪表图像字符的自适应提取。根据数显仪表数字特征信息,采用穿线识别法,实现了数显仪表的自动识别。通过大量实测实验表明,提出的自适应特征提取算法对不同角度、不同光照环境拍摄的仪表图像都有良好的适应性,应用于数显仪表自动识别系统可得到95%的识别准确率,平均运行时间为1.136 s。     

Chronological pattern indexing: An efficient feature extraction method for hand gesture recognition with Leap Motion

Recently, Hand-Gesture-Recognition (HGR) systems has appreciably change the way of interaction between humans and computers thanks to advanced sensor technologies like the Leap-Motion-Controller (LMC). Despite the success achieved by many state-of-the-art methods, they have not worked on the rich temporal information existing in the sequential hand gesture data and characterizing the discriminative representation of different hand gesture classes. In this paper, we suggest a novel Chronological-Pattern-Indexing (CPI) approach which encodes the temporal orders of patterns for hand gesture time series data acquired by the LMC sensor. We extract a set of temporal patterns from different optimized projections. Then, we compare their temporal order and we encode the whole sequence with the index of the first coming pattern. We repeat these steps until we generate an efficient feature vector modeling the chronological dynamics of the hand gesture. The experiments demonstrate the potential of the proposed CPI approach for HGR systems.     

一种基于肤色特征提取的手势检测识别方法

针对手势识别的准确率和识别速度等问题,提出一种基于肤色特征提取的手势检测识别方法。采用OpenCV自有的Haar分类器自动提取面部区域并建立肤色模型,再以肤色模型检测肤色点集合,去除与面部区域重合点集获得手部轮廓,通过综合计算掌心位置,手掌轮廓凸包和凸性缺陷实现手势识别。经验证,该方法能够稳定地定位手掌区域,识别准确度高并能够在保证识别率的基础上实现对手势的快速识别。     

A pattern recognition system for handoff algorithms

In wireless cellular systems, handoff algorithms decide when and to which base station to handoff. Traditional handoff algorithms generally cannot keep both the average number of unnecessary handoffs and the handoff decision delay low. They do not exploit the relative constancy of path loss and shadow fading effects at any given location around a base station. This information can in fact be used to improve the efficiency of handoff algorithms, as we do in our new handoff algorithms using statistical pattern recognition. Handoff algorithms with both a negligible number of unnecessary handoffs and a negligible decision delay can therefore be realized     

基于环形核的旋转不变性特征提取方法

针对人脸在XOZ平面内旋转,即在图像所在平面内人脸产生的旋转,因特征值变化大导致人脸识别率降低的问题,提出了一种新颖的基于环形核的旋转不变性特征(Circular Kernel Feature,CKF)提取方法.所提算法有两个创新点,第一点是给出了环形核的建立方式,定位人脸上明显的特征部分.第二点是提供了特征的旋转不变计算方式.首先建立环形核,定位人脸上明显特征的坐标区域;然后,用旋转不变的计算方式获取定位区域的特征值.在Georgia Tech人脸数据库上的实验证明:人脸旋转前后CKF的值相较Gabor,LBP等特征值的变化小了98％.     

A real-time industrial pattern classification system

Using the partitioned matrix approach, a parallel hardware architecture for a parametric (Bayes) classifier is designed. The architecture consists of simple, regularly structured processing elements operating in parallel. As a result, the proposed design is suitable for VLSI implementation. A comparative analysis shows that the approach is more efficient and can significantly reduce the cost required for implementing the classifier, while maintaining high speed     

基于模糊模式识别的车型识别系统

车型的识别在智能交通系统中有重要作用,文中主要介绍了一种基于压力传感器和红外传感器来提取车辆特征,基于模糊模式识别方法进行识别的车型识别系统.     

基于Gabor的特征提取算法在人脸识别中的应用

针对人脸识别中的特征提取问题,提出一种新的基于Gabor的特征提取算法,利用Gabor小波变换良好的提取区分能力和LDA所具有的判别性优势来进行特征提取。首先利用Gabor小波变换来提取人脸特征。然后对得到的高维特征采用PCA进行初次降维,再利用LDA实现再次降维,得到最终的特征向量。在ORL和YALE人脸库上的实验验证了该算法的有效性。     

Integrated circuits for a real-time large-vocabulary continuousspeech recognition system

The architecture and implementation of a word processing subsystem for a real-time speech recognition system using hidden Markov models are described. The bottleneck of this system, which is the acquisition of data, is demonstrated, and an architecture that speeds up this bottleneck using on-chip dual-ported cache memories is presented. The architecture is described in a textual form, and the layout data were completely automatically generated. The chips have been fabricated through MOSIS using a 2-μm CMOS n-well technology. The functionality of the processors was successfully tested using the scan-path test methodology. The clock rate for the scan-path test was 5 MHz to guarantee proper operation of the circuits for this clock rate. All the processors were first time working silicon     

改进的基于结构光投影的三维物体识别

提出一种在识别后期采用修正的无零级条纹调节联合变换相关代替传统联合变换相关(CJTC)的新的识别方法.此方法在原基础上对功率谱作了一系列修正.理论分析、计算机模拟实验和对实物模型的识别实验结果都表明该方法不仅具有本征三维识别的特点,而且相对于CJTC而言其能够抑制相关面上的中央零级项,很大程度上锐化了一级相关峰,明显地提高了信噪比和识别力.该方法进一步拓宽了三维物体识别的应用前景.     

实时语音识别系统在家庭监护机器人的实现

文中阐述的是家庭监护机器人项目中语音识别系统设计的部分,通过DSP、DMA和ARM Cortex-A8的并行处理,利用双缓冲的方法,在嵌入式Linux上实现了基于ATK的实时语音识别系统。文中对该系统的软硬件进行了设计。在硬件方面,给出语音识别系统的硬件组成原理,并提供了关键部分原理图;在软件方面,提出实时语音识别的方法,给出应用程序实现流程。最后通过真人说话来进行语音识别实验,实时语音识别率达到了94.67%以上,实验验证了系统的软件硬件设计的正确性。     

A robust gross-to-fine pattern recognition system

This paper presents a model-based vision recognition engine for planar contours that are scale invariant of known models. Features are obtained by using a constant-curvature criterion and used to carry out efficient coarse-to-fine recognition. A robust shape matching is proposed for comparing contour fragments from scenes with partial occluding. In order to carry out an early pruning of a large portion of the models, hypotheses are only generated for a subset of contours with enough discriminative information. Poor scene contours are used later in validating or invalidating a relatively small set of hypotheses. Since hypotheses are selectively verified, blocking is avoided by extending current matching through pairing of hypotheses, predictive matching, and retrieving the next weighted hypotheses. This avoids the processing of a large number of initial hypotheses. The authors' evaluation shows that a high recognition error results from the use of too small a bucket size because the indexes may fall at random, producing nonrepeatable results. They use a multidimensional hashing scheme with space separation between dense parameter areas to create additional hashing tables. The robustness of the recognition is based on engineering a coarse bucket size to the best tolerance with respect to various sources of noise. Partially occluded scenes having three objects can be recognized with a success rate of 84%. The results are reproducible against changes in scale, rotation, and translation. Due to the selection of robust initial hypotheses and the structure of the selective matching system, the processing time essentially depends on scene complexity with a marginal dependence on database size