Resource Efficient Hardware Implementation for Real-Time Traffic Sign Recognition

Traffic sign recognition (TSR, or Road Sign Recognition, RSR) is one of the Advanced Driver Assistance System (ADAS) devices in modern cars. To concern the most important issues, which are real-time and resource efficiency, we propose a high efficiency hardware implementation for TSR. We divide the TSR procedure into two stages, detection and recognition. In the detection stage, under the assumption that most German traffic signs have red or blue colors with circle, triangle or rectangle shapes, we use Normalized RGB color transform and Single-Pass Connected Component Labeling (CCL) to find the potential traffic signs efficiently. For Single-Pass CCL, our contribution is to eliminate the “merge-stack” operations by recording connected relations of region in the scan phase and updating the labels in the iterating phase. In the recognition stage, the Histogram of Oriented Gradient (HOG) is used to generate the descriptor of the signs, and we classify the signs with Support Vector Machine (SVM). In the HOG module, we analyze the required minimum bits under different recognition rate. The proposed method achieves 96.61% detection rate and 90.85% recognition rate while testing with the GTSDB dataset. Our hardware implementation reduces the storage of CCL and simplifies the HOG computation. Main CCL storage size is reduced by 20% comparing to the most advanced design under typical condition. By using TSMC 90 nm technology, the proposed design operates at 105 MHz clock rate and processes in 135 fps with the image size of 1360 × 800. The chip size is about 1 mm2 and the power consumption is close to 8 mW. Therefore, this work is resource efficient and achieves real-time requirement.

Neural Network-Powered License Plate Recognition System Design

The development of scientific inquiry and research has yielded numerous benefits in the realm of intelligent traffic control systems, particularly in the realm of automatic license plate recognition for vehicles. The design of license plate recognition algorithms has undergone digitalization through the utilization of neural networks. In contemporary times, there is a growing demand for vehicle surveillance due to the need for efficient vehicle processing and traffic management. The design, development, and implementation of a license plate recognition system hold significant social, economic, and academic importance. The study aims to present contemporary methodologies and empirical findings pertaining to automated license plate recognition. The primary focus of the automatic license plate recognition algorithm was on image extraction, character segmentation, and recognition. The task of character segmentation has been identified as the most challenging function based on my observations. The license plate recognition project that we designed demonstrated the effectiveness of this method across various observed conditions. Particularly in low-light environments, such as during periods of limited illumination or inclement weather characterized by precipitation. The method has been subjected to testing using a sample size of fifty images, resulting in a 100% accuracy rate. The findings of this study demonstrate the project’s ability to effectively determine the optimal outcomes of simulations.

Cosic’s Resonance Recognition Model for Protein Sequences and Photon Emission Differentiates Lethal and Non-Lethal Ebola Strains: Implications for Treatment

The Cosic Resonance Recognition Model (RRM) for amino acid sequences was applied to the classes of proteins displayed by four strains (Sudan, Zaire, Reston, Ivory Coast) of Ebola virus that produced either high or minimal numbers of human fatalities. The results clearly differentiated highly lethal and non-lethal strains. Solutions for the two lethal strains exhibited near ultraviolet (~230 nm) photon values while the two asymptomatic forms displayed near infrared (~1000 nm) values. Cross-correlations of spectral densities of the RRM values of the different classes of proteins associated with the genome of the viruses supported this dichotomy. The strongest coefficient occurred only between Sudan-Zaire strains but not for any of the other pairs of strains for sGP, the small glycoprotein that intercalated with the plasma cell membrane to promote insertion of viral contents into cellular space. A surprising, statistically significant cross-spectral correlation occurred between the “spike” glycoprotein component (GP1) of the virus that associated the anchoring of the virus to the mammalian cell plasma membrane and the Schumann resonance of the earth whose intensities were determined by the incidence of equatorial thunderstorms. Previous applications of the RRM to shifting photon wavelengths emitted by melanoma cells adapting to reduced ambient temperature have validated Cosic’s model and have demonstrated very narrowwave-length (about 10 nm) specificity. One possible ancillary and non-invasive treatment of people within which the fatal Ebola strains are residing would be whole body application of narrow band near-infrared light pulsed as specific physiologically-patterned sequences with sufficient radiant flux density to perfuse the entire body volume.

A Study of Visual Recognition of Facial Emotional Expressions in a Normal Aging Population in the Absence of Cognitive Disorders

Objective: To examine and measure the decision-making processes involved in Visual Recognition of Facial Emotional Expressions (VRFEE) and to study the effects of demographic factors on this process. Method: We evaluated a newly designed software application (M.A.R.I.E.) that permits computerized metric measurement of VRFEE. We administered it to 204 cognitively normal participants ranging in age from 20 to 70 years. Results: We established normative values for the recognition of anger, disgust, joy, fear, surprise and sadness expressed on the faces of three individuals. There was a significant difference in the: 1) measurement (F (8.189) = 3896, p = 0.0001);2) education level (x2(12) = 28.4, p = 0.005);3) face (F(2.195) = 10, p = 0.0001);4)series (F (8.189)=28, p = 0.0001);5) interaction between the identity and recognition of emotions (F (16, 181 =11, p = 0.0001). However, performance did not differ according to: 1) age (F (6.19669) = 1.35, p = 0.2) or 2) level of education (F (1, 1587) = 0.6, p = 0.4). Conclusions: In healthy participants, the VRFEE remains stable throughout the lifespan when cognitive functions remain optimal. Disgust, sadness, fear, and joy seem to be the four most easily recognized facial emotions, while anger and surprise are not easily recognized. Visual recognition of disgust and fear is independent of aging. The characteristics of a face have a significant influence on the ease with which people recognize expressed emotions (idiosyncrasy). Perception and recognition of emotions is categorical, even when the facial images are integrated in a spectrum of morphs reflecting two different emotions on either side.

MCS HOG Features and SVM Based Handwritten Digit Recognition System

Digit Recognition is an essential element of the process of scanning and converting documents into electronic format. In this work, a new Multiple-Cell Size (MCS) approach is being proposed for utilizing Histogram of Oriented Gradient (HOG) features and a Support Vector Machine (SVM) based classifier for efficient classification of Handwritten Digits. The HOG based technique is sensitive to the cell size selection used in the relevant feature extraction computations. Hence a new MCS approach has been used to perform HOG analysis and compute the HOG features. The system has been tested on the Benchmark MNIST Digit Database of handwritten digits and a classification accuracy of 99.36% has been achieved using an Independent Test set strategy. A Cross-Validation analysis of the classification system has also been performed using the 10-Fold Cross-Validation strategy and a 10-Fold classification accuracy of 99.26% has been obtained. The classification performance of the proposed system is superior to existing techniques using complex procedures since it has achieved at par or better results using simple operations in both the Feature Space and in the Classifier Space. The plots of the system’s Confusion Matrix and the Receiver Operating Characteristics (ROC) show evidence of the superior performance of the proposed new MCS HOG and SVM based digit classification system.

Real-Time Face Detection and Recognition in Complex Background

This paper provides efficient and robust algorithms for real-time face detection and recognition in complex backgrounds. The algorithms are implemented using a series of signal processing methods including Ada Boost, cascade classifier, Local Binary Pattern (LBP), Haar-like feature, facial image pre-processing and Principal Component Analysis (PCA). The Ada Boost algorithm is implemented in a cascade classifier to train the face and eye detectors with robust detection accuracy. The LBP descriptor is utilized to extract facial features for fast face detection. The eye detection algorithm reduces the false face detection rate. The detected facial image is then processed to correct the orientation and increase the contrast, therefore, maintains high facial recognition accuracy. Finally, the PCA algorithm is used to recognize faces efficiently. Large databases with faces and non-faces images are used to train and validate face detection and facial recognition algorithms. The algorithms achieve an overall true-positive rate of 98.8% for face detection and 99.2% for correct facial recognition.

STUDY OF RECOGNITION TECHNIQUE OF RADAR TARGET'S ONE-DIMENSIONAL IMAGES BASED ON RADIAL BASIS FUNCTION NETWORK

This paper studies the problem applying Radial Basis Function Network(RBFN) which is trained by the Recursive Least Square Algorithm(RLSA) to the recognition of one dimensional images of radar targets. The equivalence between the RBFN and the estimate of Parzen window probabilistic density is proved. It is pointed out that the I/O functions in RBFN hidden units can be generalized to general Parzen window probabilistic kernel function or potential function, too. This paper discusses the effects of the shape parameter a in the RBFN and the forgotten factor A in RLSA on the results of the recognition of three kinds of kernel function such as Gaussian, triangle, double-exponential, at the same time, also discusses the relationship between A and the training time in the RBFN.

Automatic de-noising and recognition algorithm for drilling fluid pulse signal

Wavelet forced de-noising algorithm is suitable for denoising of unsteady drilling fluid pulse signal, including baseline drift rectification and two-stage de-noising processing of frame synchronization signal and instruction signal. Two-stage de-noising processing can reduce the impact of baseline drift and determine automatic peak detection threshold range for signal recognition by distinguishing different features of frame synchronization pulse and instruction pulse. Rising and falling edge relative protruding threshold is defined for peak detection in signal recognition, which can make full use of the degree of the signal peak change and detect peaks flexibly with rising and falling edge relative protruding threshold combination. A synchronous decoding method was designed to reduce position uncertainty of the frame synchronization pulse and eliminate the accumulative error of time base drift, which determines the first instruction pulse position according to position of the frame synchronization pulse and decodes subsequent instruction pulse by taking current instruction pulse as new bit synchronization pulse. Special tool software was developed to tune algorithm parameters, which has a decoding success rate of about 95% for the universal coded signals. For the special coded signals with check byte, decoding success rate using the automatic threshold adjustment algorithm is as high as 99%.

An Integrated Face Tracking and Facial Expression Recognition System

This article proposes a feature extraction method for an integrated face tracking and facial expression recognition in real time video. The method proposed by Viola and Jones [1] is used to detect the face region in the first frame of the video. A rectangular bounding box is fitted over for the face region and the detected face is tracked in the successive frames using the cascaded Support vector machine (SVM) and cascaded Radial basis function neural network (RBFNN). The haar-like features are extracted from the detected face region and they are used to create a cascaded SVM and RBFNN classifiers. Each stage of the SVM classifier and RBFNN classifier rejects the non-face regions and pass the face regions to the next stage in the cascade thereby efficiently tracking the face. The performance of tracking is evaluated using one hour video data. The performance of the cascaded SVM is compared with the cascaded RBFNN. The experiment results show that the proposed cascaded SVM classifier method gives better performance over the RBFNN and also the methods described in the literature using single SVM classifier [2]. While the face is being tracked, features are extracted from the mouth region for expression recognition. The features are modelled using a multi-class SVM. The SVM finds an optimal hyperplane to distinguish different facial expressions with an accuracy of 96.0%.

Using Speech Recognition in Learning Primary School Mathematics via Explain, Instruct and Facilitate Techniques

The application of Information and Communication Technologies has transformed traditional Teaching and Learning in the past decade to computerized-based era. This evolution has resulted from the emergence of the digital system and has greatly impacted on the global education and socio-cultural development. Multimedia has been absorbed into the education sector for producing a new learning concept and a combination of educational and entertainment approach. This research is concerned with the application of Window Speech Recognition and Microsoft Visual Basic 2008 Integrated/Interactive Development Environment in Multimedia-Assisted Courseware prototype development for Primary School Mathematics contents, namely, single digits and the addition. The Teaching and Learning techniques—Explain, Instruct and Facilitate are proposed and these could be viewed as instructors’ centered strategy, instructors’—learners’ dual communication and learners' active participation. The prototype is called M-EIF and deployed only users' voices;hence the activation of Window Speech Recognition is required prior to a test run.

基于鲁棒LS-SVM的控制图模式识别

提出一种基于鲁棒最小二乘支持向量机(LS-SVM)的控制图模式识别方法,并研究其应用于过程质量诊断的可行性、有效性.理论研究和仿真试验结果表明,该方法对于标准的6种控制图模式都具有很高的模式识别率,训练模式识别器所需样本少,且训练结果泛化能力强,计算方法简单迅速. Abstract： A technique based on the robust least squares support vector machines(LS-SVM) used for control charts pattern recognition is proposed, the applied feasibility and validity of this technique in process quality diagnosis is also investigated. Theoretical research and experimental results show that this approach performs well upon the six typical control charts pattern recognition with high recognition accuracy, simple computation and fast training process, and the preeminent generalization ability on the condition of small sample size.

合肥市科技馆速度测试展台控制系统研制

系统采用AT89C55单片机为控制核心,高速红外线光电开关为输入信号,完成直线速度的测试和语音动画输出.在对语音芯片ISD1420深入实验研究的基础上,介绍了芯片工作模式和电气原理设计方法.分析研究了SED1335液晶显示模块,介绍了接口电路设计,以及汉字和图形的数据提取方法.

DM-L Based Feature Extraction and Classifier Ensemble for Object Recognition

Deep Learning is a powerful technique that is widely applied to Image Recognition and Natural Language Processing tasks amongst many other tasks. In this work, we propose an efficient technique to utilize pre-trained Convolutional Neural Network (CNN) architectures to extract powerful features from images for object recognition purposes. We have built on the existing concept of extending the learning from pre-trained CNNs to new databases through activations by proposing to consider multiple deep layers. We have exploited the progressive learning that happens at the various intermediate layers of the CNNs to construct Deep Multi-Layer (DM-L) based Feature Extraction vectors to achieve excellent object recognition performance. Two popular pre-trained CNN architecture models i.e. the VGG_16 and VGG_19 have been used in this work to extract the feature sets from 3 deep fully connected multiple layers namely “fc6”, “fc7” and “fc8” from inside the models for object recognition purposes. Using the Principal Component Analysis (PCA) technique, the Dimensionality of the DM-L feature vectors has been reduced to form powerful feature vectors that have been fed to an external Classifier Ensemble for classification instead of the Softmax based classification layers of the two original pre-trained CNN models. The proposed DM-L technique has been applied to the Benchmark Caltech-101 object recognition database. Conventional wisdom may suggest that feature extractions based on the deepest layer i.e. “fc8” compared to “fc6” will result in the best recognition performance but our results have proved it otherwise for the two considered models. Our experiments have revealed that for the two models under consideration, the “fc6” based feature vectors have achieved the best recognition performance. State-of-the-Art recognition performances of 91.17% and 91.35% have been achieved by utilizing the “fc6” based feature vectors for the VGG_16 and VGG_19 models respectively. The recognition performance has been achieved by considering 30 sample images per class whereas the proposed system is capable of achieving improved performance by considering all sample images per class. Our research shows that for feature extraction based on CNNs, multiple layers should be considered and then the best layer can be selected that maximizes the recognition performance.

基于传感器参数和目标轮廓中心的自动配准算法研究

通过对光电成像型反舰导弹的成像过程分析,提出了一种自动配准算法。其基本思路是将图像变换模型分解,逐步简化。可见光和红外图像配准时的变换为仿射变换。首先,利用传感器参数的调整,消除图像间的比例变化,将仿射变换简化为刚体变换;然后,依赖图像信息,用形态学边缘检测的方法求取目标的轮廓中心,以此为控制点消除图像间的平移变化,实现图像的完全配准;最后,通过观察海天线是否重合以及利用均方根误差原则,对算法的配准效果做详细评估。仿真实验表明,该算法准确、快速,配准精度满足目标识别的要求,可以较好地解决异类传感器弱小目标图像配准的难题。

Promotion of structural plasticity in area V2 of visual cortex prevents against object recognition memory deficits in aging and Alzheimer's disease rodents

Memory deficit,which is often associated with aging and many psychiatric,neurological,and neurodegenerative diseases,has been a challenging issue for treatment.Up till now,all potential drug candidates have failed to produce satisfa ctory effects.Therefore,in the search for a solution,we found that a treatment with the gene corresponding to the RGS14414protein in visual area V2,a brain area connected with brain circuits of the ventral stream and the medial temporal lobe,which is crucial for object recognition memory(ORM),can induce enhancement of ORM.In this study,we demonstrated that the same treatment with RGS14414in visual area V2,which is relatively unaffected in neurodegenerative diseases such as Alzheimer s disease,produced longlasting enhancement of ORM in young animals and prevent ORM deficits in rodent models of aging and Alzheimer’s disease.Furthermore,we found that the prevention of memory deficits was mediated through the upregulation of neuronal arbo rization and spine density,as well as an increase in brain-derived neurotrophic factor(BDNF).A knockdown of BDNF gene in RGS14414-treated aging rats and Alzheimer s disease model mice caused complete loss in the upregulation of neuronal structural plasticity and in the prevention of ORM deficits.These findings suggest that BDNF-mediated neuronal structural plasticity in area V2 is crucial in the prevention of memory deficits in RGS14414-treated rodent models of aging and Alzheimer’s disease.Therefore,our findings of RGS14414gene-mediated activation of neuronal circuits in visual area V2 have therapeutic relevance in the treatment of memory deficits.

相控整流器相序相位自动识别算法

三相全控桥式整流器是应用最广泛的电力电子电路。为了实现正确的移相控制,传统方法是安装同步变压器以获取同步信号,还需借助带载实验和波形分析以确定同步信号与主电路的相位关系。针对这一问题,设计了同步信号产生电路形成2路相位差30°的同步信号,并设计了相序相位推算算法,可以推算出有效同步信号、主回路相序以及该同步信号过零点位于主回路中哪路线电压的自然换相点。给出了对传统相控整流器进行改造的关键算法。本方法相对传统数字相控整流器不增加成本,并且由于去掉了同步变压器而降低设备造价,尤其是适用于任意负载阻抗,并在若干产品中得到应用。

Analytic Methods for Quality Control of Scientific Publications Part VI: Presentation in Research Gate, Journal Indexing, and Recognition

In the world of science, recognition of scientific performance is strongly correlated with publication visibility and interest generated among other researchers, which is evident by downloads and citations. A published paper’s number of downloads and citations are the best indices of its importance and are useful measures of the researchers’ performance. However, the published paper should be valuated and indexed independently, and the prestige of the journal in which it is published should not influence the value of the paper itself. By participating in and presenting at congresses and international meetings, scientists strongly increase the visibility of their results and recognition of their research;this also promotes their publications. Status in Research Gate (RG), the so-called RG Score, the Percentile, and the h-index give researchers feedback about their performance, or their place and prestige within the scientific community. RG has become an excellent tool for disseminating scientific results and connecting researchers worldwide. RG also allows researchers to present achievements other than publications (e.g., membership in recognized associations such as the American Chemist Society, a biography in Marquis Who’s Who in the World, awards received, and/or ongoing projects). This paper discusses questions regarding how the RG Score, Percentile, and h-index are calculated, whether these methods are correct, and alternative criteria. RG also lists papers with falsified results and the journals that publish them. Thus, it may be appropriate to reduce the indices for such journals, authors, and the institutions with which these authors are affiliated.

Age Invariant Face Recognition Using Convolutional Neural Networks and Set Distances

Biometric security systems based on facial characteristics face a challenging task due to variability in the intrapersonal facial appearance of subjects traced to factors such as pose, illumination, expression and aging. This paper innovates as it proposes a deep learning and set-based approach to face recognition subject to aging. The images for each subject taken at various times are treated as a single set, which is then compared to sets of images belonging to other subjects. Facial features are extracted using a convolutional neural network characteristic of deep learning. Our experimental results show that set-based recognition performs better than the singleton-based approach for both face identification and face verification. We also find that by using set-based recognition, it is easier to recognize older subjects from younger ones rather than younger subjects from older ones.

Automatic Alzheimer’s Disease Recognition from MRI Data Using Deep Learning Method

Alzheimer’s Disease (AD), the most common form of dementia, is an incurable neurological condition that results in a progressive mental deterioration. Although definitive diagnosis of AD is difficult, in practice, AD diagnosis is largely based on clinical history and neuropsychological data including magnetic resource imaging (MRI). Increasing research has been reported on applying machine learning to AD recognition in recent years. This paper presents our latest contribution to the advance. It describes an automatic AD recognition algorithm that is based on deep learning on 3D brain MRI. The algorithm uses a convolutional neural network (CNN) to fulfil AD recognition. It is unique in that the three dimensional topology of brain is considered as a whole in AD recognition, resulting in an accurate recognition. The CNN used in this study consists of three consecutive groups of processing layers, two fully connected layers and a classification layer. In the structure, every one of the three groups is made up of three layers, including a convolutional layer, a pooling layer and a normalization layer. The algorithm was trained and tested using the MRI data from Alzheimer’s Disease Neuroimaging Initiative. The data used include the MRI scanning of about 47 AD patients and 34 normal controls. The experiment had shown that the proposed algorithm delivered a high AD recognition accuracy with a sensitivity of 1 and a specificity of 0.93.

A Comparison of Classifiers in Performing Speaker Accent Recognition Using MFCCs

An algorithm involving Mel-Frequency Cepstral Coefficients (MFCCs) is provided to perform signal feature extraction for the task of speaker accent recognition. Then different classifiers are compared based on the MFCC feature. For each signal, the mean vector of MFCC matrix is used as an input vector for pattern recognition. A sample of 330 signals, containing 165 US voice and 165 non-US voice, is analyzed. By comparison, k-nearest neighbors yield the highest average test accuracy, after using a cross-validation of size 500, and least time being used in the computation.