Improving website security to prevent malicious online activities is crucial,and CAPTCHA(Completely Automated Public Turing test to tell Computers and Humans Apart)has emerged as a key strategy for distinguishing huma...Improving website security to prevent malicious online activities is crucial,and CAPTCHA(Completely Automated Public Turing test to tell Computers and Humans Apart)has emerged as a key strategy for distinguishing human users from automated bots.Text-based CAPTCHAs,designed to be easily decipherable by humans yet challenging for machines,are a common form of this verification.However,advancements in deep learning have facilitated the creation of models adept at recognizing these text-based CAPTCHAs with surprising efficiency.In our comprehensive investigation into CAPTCHA recognition,we have tailored the renowned UpDown image captioning model specifically for this purpose.Our approach innovatively combines an encoder to extract both global and local features,significantly boosting the model’s capability to identify complex details within CAPTCHA images.For the decoding phase,we have adopted a refined attention mechanism,integrating enhanced visual attention with dual layers of Long Short-Term Memory(LSTM)networks to elevate CAPTCHA recognition accuracy.Our rigorous testing across four varied datasets,including those from Weibo,BoC,Gregwar,and Captcha 0.3,demonstrates the versatility and effectiveness of our method.The results not only highlight the efficiency of our approach but also offer profound insights into its applicability across different CAPTCHA types,contributing to a deeper understanding of CAPTCHA recognition technology.展开更多
Being aimed at the weakness of short range target′s threshold value recognition system,the double passage And Gate recognition system was put forward on the correlativity of target signals and randomness of noise ...Being aimed at the weakness of short range target′s threshold value recognition system,the double passage And Gate recognition system was put forward on the correlativity of target signals and randomness of noise signals Through state analysis and inference of state transition probability,both the reliability and early burst probability of the system were obtained in theory展开更多
Perceptual auditory filter banks such as Bark-scale filter bank are widely used as front-end processing in speech recognition systems.However,the problem of the design of optimized filter banks that provide higher acc...Perceptual auditory filter banks such as Bark-scale filter bank are widely used as front-end processing in speech recognition systems.However,the problem of the design of optimized filter banks that provide higher accuracy in recognition tasks is still open.Owing to spectral analysis in feature extraction,an adaptive bands filter bank (ABFB) is presented.The design adopts flexible bandwidths and center frequencies for the frequency responses of the filters and utilizes genetic algorithm (GA) to optimize the design parameters.The optimization process is realized by combining the front-end filter bank with the back-end recognition network in the performance evaluation loop.The deployment of ABFB together with zero-crossing peak amplitude (ZCPA) feature as a front process for radial basis function (RBF) system shows significant improvement in robustness compared with the Bark-scale filter bank.In ABFB,several sub-bands are still more concentrated toward lower frequency but their exact locations are determined by the performance rather than the perceptual criteria.For the ease of optimization,only symmetrical bands are considered here,which still provide satisfactory results.展开更多
This paper presents a new pattern recognition system for Chinese spirit identification by using the polymer quartz piezoelectric crystal sensor based e-nose. The sensors are designed based on quartz crystal microbala...This paper presents a new pattern recognition system for Chinese spirit identification by using the polymer quartz piezoelectric crystal sensor based e-nose. The sensors are designed based on quartz crystal microbalance（QCM） principle,and they could capture different vibration frequency signal values for Chinese spirit identification. For each sensor in an8-channel sensor array, seven characteristic values of the original vibration frequency signal values, i.e., average value（A）,root-mean-square value（RMS）, shape factor value（S_f）, crest factor value（C_f）, impulse factor value（I_f）, clearance factor value（CL_f）, kurtosis factor value（K_v） are first extracted. Then the dimension of the characteristic values is reduced by the principle components analysis（PCA） method. Finally the back propagation（BP） neutral network algorithm is used to recognize Chinese spirits. The experimental results show that the recognition rate of six kinds of Chinese spirits is 93.33% and our proposed new pattern recognition system can identify Chinese spirits effectively.展开更多
We present a new pattern recognition system based on moving average and linear discriminant analysis （LDA）, which can be used to process the original signal of the new polymer quartz piezoelectric crystal air-sensit...We present a new pattern recognition system based on moving average and linear discriminant analysis （LDA）, which can be used to process the original signal of the new polymer quartz piezoelectric crystal air-sensitive sensor system we designed, called the new e-nose. Using the new e-nose, we obtain the template datum of Chinese spirits via a new pattern recognition system. To verify the effectiveness of the new pattern recognition system, we select three kinds of Chinese spirits to test, our results confirm that the new pattern recognition system can perfectly identify and distinguish between the Chinese spirits.展开更多
The COVID-19 pandemic poses an additional serious public health threat due to little or no pre-existing human immunity,and developing a system to identify COVID-19 in its early stages will save millions of lives.This ...The COVID-19 pandemic poses an additional serious public health threat due to little or no pre-existing human immunity,and developing a system to identify COVID-19 in its early stages will save millions of lives.This study applied support vector machine(SVM),k-nearest neighbor(K-NN)and deep learning convolutional neural network(CNN)algorithms to classify and detect COVID-19 using chest X-ray radiographs.To test the proposed system,chest X-ray radiographs and CT images were collected from different standard databases,which contained 95 normal images,140 COVID-19 images and 10 SARS images.Two scenarios were considered to develop a system for predicting COVID-19.In the first scenario,the Gaussian filter was applied to remove noise from the chest X-ray radiograph images,and then the adaptive region growing technique was used to segment the region of interest from the chest X-ray radiographs.After segmentation,a hybrid feature extraction composed of 2D-DWT and gray level co-occurrence matrix was utilized to extract the features significant for detecting COVID-19.These features were processed using SVM and K-NN.In the second scenario,a CNN transfer model(ResNet 50)was used to detect COVID-19.The system was examined and evaluated through multiclass statistical analysis,and the empirical results of the analysis found significant values of 97.14%,99.34%,99.26%,99.26%and 99.40%for accuracy,specificity,sensitivity,recall and AUC,respectively.Thus,the CNN model showed significant success;it achieved optimal accuracy,effectiveness and robustness for detecting COVID-19.展开更多
The license plate recognition system(LPRS)has been widely adopted in daily life due to its efficiency and high accuracy.Deep neural networks are commonly used in the LPRS to improve the recognition accuracy.However,re...The license plate recognition system(LPRS)has been widely adopted in daily life due to its efficiency and high accuracy.Deep neural networks are commonly used in the LPRS to improve the recognition accuracy.However,researchers have found that deep neural networks have their own security problems that may lead to unexpected results.Specifically,they can be easily attacked by the adversarial examples that are generated by adding small perturbations to the original images,resulting in incorrect license plate recognition.There are some classic methods to generate adversarial examples,but they cannot be adopted on LPRS directly.In this paper,we modify some classic methods to generate adversarial examples that could mislead the LPRS.We conduct extensive evaluations on the HyperLPR system and the results show that the system could be easily attacked by such adversarial examples.In addition,we show that the generated images could also attack the black-box systems;we show some examples that the Baidu LPR system also makes incorrect recognitions.We hope this paper could help improve the LPRS by realizing the existence of such adversarial attacks.展开更多
In order to improve the resource allocation mechanism of artificial immune recognition system(AIRS) and decrease the memory cells,a fuzzy logic resource allocation and memory cell pruning based AIRS(FPAIRS) is propose...In order to improve the resource allocation mechanism of artificial immune recognition system(AIRS) and decrease the memory cells,a fuzzy logic resource allocation and memory cell pruning based AIRS(FPAIRS) is proposed.In FPAIRS,the fuzzy logic is determined by a parameter,thus,the optimal fuzzy logics for different problems can be located through changing the parameter value.At the same time,the memory cells of low fitness scores are pruned to improve the classifier.This classifier was compared with other classifiers on six UCI datasets classification performance.The results show that the accuracies reached by FPAIRS are higher than or comparable to the accuracies of other classifiers,and the memory cells decrease when compared with the memory cells of AIRS.The results show that the algorithm is a high-performance classifier.展开更多
The challenge faced by the visually impaired persons in their day-today lives is to interpret text from documents.In this context,to help these people,the objective of this work is to develop an efficient text recogni...The challenge faced by the visually impaired persons in their day-today lives is to interpret text from documents.In this context,to help these people,the objective of this work is to develop an efficient text recognition system that allows the isolation,the extraction,and the recognition of text in the case of documents having a textured background,a degraded aspect of colors,and of poor quality,and to synthesize it into speech.This system basically consists of three algorithms:a text localization and detection algorithm based on mathematical morphology method(MMM);a text extraction algorithm based on the gamma correction method(GCM);and an optical character recognition(OCR)algorithm for text recognition.A detailed complexity study of the different blocks of this text recognition system has been realized.Following this study,an acceleration of the GCM algorithm(AGCM)is proposed.The AGCM algorithm has reduced the complexity in the text recognition system by 70%and kept the same quality of text recognition as that of the original method.To assist visually impaired persons,a graphical interface of the entire text recognition chain has been developed,allowing the capture of images from a camera,rapid and intuitive visualization of the recognized text from this image,and text-to-speech synthesis.Our text recognition system provides an improvement of 6.8%for the recognition rate and 7.6%for the F-measure relative to GCM and AGCM algorithms.展开更多
This study describes the development of a simple biometric facial recognition system, BFMT, which is designed for use in identifying individuals within a given population. The system is based on digital signatures der...This study describes the development of a simple biometric facial recognition system, BFMT, which is designed for use in identifying individuals within a given population. The system is based on digital signatures derived from facial images of human subjects. The results of the study demonstrate that a particular set of facial features from a simple two-dimensional image can yield a unique digital signature which can be used to identify a subject from a limited population within a controlled environment. The simplicity of the model upon which the system is based can result in commercial facial recognition systems that are more cost-effective to develop than those currently on the market.展开更多
Designing accurate and time-efficient real-time traffic sign recognition systems is a crucial part of developing the intelligent vehicle which is the main agent in the intelligent transportation system.Traffic sign re...Designing accurate and time-efficient real-time traffic sign recognition systems is a crucial part of developing the intelligent vehicle which is the main agent in the intelligent transportation system.Traffic sign recognition systems consist of an initial detection phase where images transportaand colors are segmented and fed to the recognition phase.The most challenging process in such systems in terms of time consumption is the detection phase.The trade off in previous studies,which proposed different methods for detecting traffic signs,is between accuracy and computation time,Therefore,this paper presents a novel accurate and time-efficient color segmentation approach based on logistic regression.We used RGB color space as the domain to extract the features of our hypothesis;this has boosted the speed of our approach since no color conversion is needed.Our trained segmentation classifier was tested on 1000 traffic sign images taken in different lighting conditions.The results show that our approach segmented 974 of these images correctly and in a time less than one-fifth of the time needed by any other robust segmentation method.展开更多
In forest variety registration, visual traits of the plants appearance are widely used to discern different tree species. The new recognition system of leaf image strategy which based on neural network established to ...In forest variety registration, visual traits of the plants appearance are widely used to discern different tree species. The new recognition system of leaf image strategy which based on neural network established to administrate a hierarchical list of leaf images, some sorts of edge detection can be performed to identify the individual tokens of every image and the frame of the leaf can be got to differentiate the tree species. An approach based on back-propagation neuronal network is proposed and the programming language for the implementation is also Riven by using Java. The numerical simulations results have shown that the proposed leaf strategt is effective and feasible.展开更多
Emotion recognition from speech data is an active and emerging area of research that plays an important role in numerous applications,such as robotics,virtual reality,behavior assessments,and emergency call centers.Re...Emotion recognition from speech data is an active and emerging area of research that plays an important role in numerous applications,such as robotics,virtual reality,behavior assessments,and emergency call centers.Recently,researchers have developed many techniques in this field in order to ensure an improvement in the accuracy by utilizing several deep learning approaches,but the recognition rate is still not convincing.Our main aim is to develop a new technique that increases the recognition rate with reasonable cost computations.In this paper,we suggested a new technique,which is a one-dimensional dilated convolutional neural network(1D-DCNN)for speech emotion recognition(SER)that utilizes the hierarchical features learning blocks(HFLBs)with a bi-directional gated recurrent unit(BiGRU).We designed a one-dimensional CNN network to enhance the speech signals,which uses a spectral analysis,and to extract the hidden patterns from the speech signals that are fed into a stacked one-dimensional dilated network that are called HFLBs.Each HFLB contains one dilated convolution layer(DCL),one batch normalization(BN),and one leaky_relu(Relu)layer in order to extract the emotional features using a hieratical correlation strategy.Furthermore,the learned emotional features are feed into a BiGRU in order to adjust the global weights and to recognize the temporal cues.The final state of the deep BiGRU is passed from a softmax classifier in order to produce the probabilities of the emotions.The proposed model was evaluated over three benchmarked datasets that included the IEMOCAP,EMO-DB,and RAVDESS,which achieved 72.75%,91.14%,and 78.01%accuracy,respectively.展开更多
At present, the demand for perimeter security system is in-creasing greatly, especially for such system based on distribut-ed optical fiber sensing. This paper proposes a perimeter se-curity monitoring system based on...At present, the demand for perimeter security system is in-creasing greatly, especially for such system based on distribut-ed optical fiber sensing. This paper proposes a perimeter se-curity monitoring system based on phase-sensitive coherentoptical time domain reflectometry(Ф-COTDR) with the practi-cal pattern recognition function. We use fast Fourier trans-form(FFT) to exact features from intrusion events and a multi-class classification algorithm derived from support vector ma-chine(SVM) to work as a pattern recognition technique. Fivedifferent types of events are classified by using a classifica-tion algorithm based on SVM through a three-dimensional fea-ture vector. Moreover, the identification results of the patternrecognition system show that an identification accurate rate of92.62% on average can be achieved.展开更多
Taking leaf black spot,anthracnose and leaf blight of Ophiopogon japonicus as the research objects,lesions were separated by K-Means clustering segmentation technology.PCA(principal component analysis)was carried out ...Taking leaf black spot,anthracnose and leaf blight of Ophiopogon japonicus as the research objects,lesions were separated by K-Means clustering segmentation technology.PCA(principal component analysis)was carried out on the 46-dimensional eigenvectors composed of color,shape and texture features,and then the multi-level classifier designed by SVM(support vector machine)was used to identify lesions.The recognition rate of the developed leaf disease recognition system of O.japonicus achieved 93.3%.The results indicates that the system is of great significance to the prevention and control of O.japonicus diseases and the modernization of O.japonicus industry.展开更多
Recently,user recognitionmethods to authenticate personal identity has attracted significant attention especially with increased availability of various internet of things(IoT)services through fifth-generation technol...Recently,user recognitionmethods to authenticate personal identity has attracted significant attention especially with increased availability of various internet of things(IoT)services through fifth-generation technology(5G)based mobile devices.The EMG signals generated inside the body with unique individual characteristics are being studied as a part of nextgeneration user recognition methods.However,there is a limitation when applying EMG signals to user recognition systems as the same operation needs to be repeated while maintaining a constant strength of muscle over time.Hence,it is necessary to conduct research on multidimensional feature transformation that includes changes in frequency features over time.In this paper,we propose a user recognition system that applies EMG signals to the short-time fourier transform(STFT),and converts the signals into EMG spectrogram images while adjusting the time-frequency resolution to extract multidimensional features.The proposed system is composed of a data pre-processing and normalization process,spectrogram image conversion process,and final classification process.The experimental results revealed that the proposed EMG spectrogram image-based user recognition system has a 95.4%accuracy performance,which is 13%higher than the EMGsignal-based system.Such a user recognition accuracy improvement was achieved by using multidimensional features,in the time-frequency domain.展开更多
The geometrical and topological information of 3D computer aided design （CAD） models should be represented as a neut- ral format file to exchange the data between different CAD systems. Exchange of 3D CAD model data...The geometrical and topological information of 3D computer aided design （CAD） models should be represented as a neut- ral format file to exchange the data between different CAD systems. Exchange of 3D CAD model data implies that the companies must exchange complete information about their products, all the way from design, manufacturing to inspection and shipping. This informa- tion should be available to each relevant partner over the entire life cycle of the product. This led to the development of an international standard organization （ISO） neutral format file named as standard for the exchange of product model data （STEP）. It has been ob- served from the literature, the feature recognition systems developed were identified as planar, cylindrical, conical and to some extent spherical and toroidal surfaces. The advanced surface features such as B-spline and its subtypes are not identified. Therefore, in this work, a STEP-based feature recognition system is developed to recognize t--spline surface features and its sub-types from the 3D CAD model represented in AP203 neutral file format. The developed feature recognition system is implemented in Java programming language and the product model data represented in STEP AP203 format is interpreted through Java standard data access interface （JSDAI）. The developed system could recognize B-spline surface features such as B-Spline surface with knots, quasi uniform surface, uniform surface, rational surface and Bezier surface. The application of extracted B-spline surface features information is discussed with reference to the toolpath generation for STEP-NC （STEP AP238）.展开更多
The hypersonic target detection and recognition system is studied,on the basis of overall planning and design,a multi-agent system(MAS)structure and intelligent+information processing mechanism based on target detecti...The hypersonic target detection and recognition system is studied,on the basis of overall planning and design,a multi-agent system(MAS)structure and intelligent+information processing mechanism based on target detection and recognition are proposed,and the multi-agent operation process is analyzed and designed in detail.In the specific agents construction,the information fusion technology is introduced to defining the embedded agents and their interrelations in the system structure,and the intelligent processing ability of complex and uncertain problems is emphatically analyzed from the aspects of autonomy and collaboration.The aim is to optimize the information processing strategy of the hypersonic target detection and recognition system and improve the robustness and rapidity of the system.展开更多
Nowadays, the vein based recognition system becomes an emerging and facilitating biometric technology in the recognition system. Vein recognition exploits the different modalities such as finger, palm and hand image f...Nowadays, the vein based recognition system becomes an emerging and facilitating biometric technology in the recognition system. Vein recognition exploits the different modalities such as finger, palm and hand image for the person identification. In this work, the fuzzy least brain storm optimization and Euclidean distance(EED) are proposed for the vein based recognition system. Initially, the input image is fed into the region of interest(ROI) extraction which obtains the appropriate image for the subsequent step. Then, features or vein pattern is extracted by the image enlightening, circular averaging filter and holoentropy based thresholding. After the features are obtained, the entropy based Euclidean distance is proposed to fuse the features by the score level fusion with the weight score value. Finally, the optimal matching score is computed iteratively by the newly developed fuzzy least brain storm optimization(FLBSO) algorithm. The novel algorithm is developed by the least mean square(LMS) algorithm and fuzzy brain storm optimization(FBSO). Thus, the experimental results are evaluated and the performance is compared with the existing systems using false acceptance rate(FAR), false rejection rate(FRR) and accuracy. The performance outcome of the proposed algorithm attains the higher accuracy of 89.9% which ensures the better recognition rate.展开更多
Hand veins can be used effectively in biometric recognition since they are internal organs that,in contrast to fingerprints,are robust under external environment effects such as dirt and paper cuts.Moreover,they form ...Hand veins can be used effectively in biometric recognition since they are internal organs that,in contrast to fingerprints,are robust under external environment effects such as dirt and paper cuts.Moreover,they form a complex rich shape that is unique,even in identical twins,and allows a high degree of freedom.However,most currently employed hand-based biometric systems rely on hand-touch devices to capture images with the desired quality.Since the start of the COVID-19 pandemic,most handbased biometric systems have become undesirable due to their possible impact on the spread of the pandemic.Consequently,new contactless hand-based biometric recognition systems and databases are desired to keep up with the rising hygiene awareness.One contribution of this research is the creation of a database for hand dorsal veins images obtained contact-free with a variation in capturing distance and rotation angle.This database consists of 1548 images collected from 86 participants whose ages ranged from 19 to 84 years.For the other research contribution,a novel geometrical feature extraction method has been developed based on the Curvelet Transform.This method is useful for extracting robust rotation invariance features from vein images.The database attributes and the veins recognition results are analyzed to demonstrate their efficacy.展开更多
基金supported by the National Natural Science Foundation of China(Nos.U22A2034,62177047)High Caliber Foreign Experts Introduction Plan funded by MOST,and Central South University Research Programme of Advanced Interdisciplinary Studies(No.2023QYJC020).
文摘Improving website security to prevent malicious online activities is crucial,and CAPTCHA(Completely Automated Public Turing test to tell Computers and Humans Apart)has emerged as a key strategy for distinguishing human users from automated bots.Text-based CAPTCHAs,designed to be easily decipherable by humans yet challenging for machines,are a common form of this verification.However,advancements in deep learning have facilitated the creation of models adept at recognizing these text-based CAPTCHAs with surprising efficiency.In our comprehensive investigation into CAPTCHA recognition,we have tailored the renowned UpDown image captioning model specifically for this purpose.Our approach innovatively combines an encoder to extract both global and local features,significantly boosting the model’s capability to identify complex details within CAPTCHA images.For the decoding phase,we have adopted a refined attention mechanism,integrating enhanced visual attention with dual layers of Long Short-Term Memory(LSTM)networks to elevate CAPTCHA recognition accuracy.Our rigorous testing across four varied datasets,including those from Weibo,BoC,Gregwar,and Captcha 0.3,demonstrates the versatility and effectiveness of our method.The results not only highlight the efficiency of our approach but also offer profound insights into its applicability across different CAPTCHA types,contributing to a deeper understanding of CAPTCHA recognition technology.
文摘Being aimed at the weakness of short range target′s threshold value recognition system,the double passage And Gate recognition system was put forward on the correlativity of target signals and randomness of noise signals Through state analysis and inference of state transition probability,both the reliability and early burst probability of the system were obtained in theory
基金Project(61072087) supported by the National Natural Science Foundation of ChinaProject(20093048) supported by Shanxi ProvincialGraduate Innovation Fund of China
文摘Perceptual auditory filter banks such as Bark-scale filter bank are widely used as front-end processing in speech recognition systems.However,the problem of the design of optimized filter banks that provide higher accuracy in recognition tasks is still open.Owing to spectral analysis in feature extraction,an adaptive bands filter bank (ABFB) is presented.The design adopts flexible bandwidths and center frequencies for the frequency responses of the filters and utilizes genetic algorithm (GA) to optimize the design parameters.The optimization process is realized by combining the front-end filter bank with the back-end recognition network in the performance evaluation loop.The deployment of ABFB together with zero-crossing peak amplitude (ZCPA) feature as a front process for radial basis function (RBF) system shows significant improvement in robustness compared with the Bark-scale filter bank.In ABFB,several sub-bands are still more concentrated toward lower frequency but their exact locations are determined by the performance rather than the perceptual criteria.For the ease of optimization,only symmetrical bands are considered here,which still provide satisfactory results.
基金Project supported by the National High Technology Research and Development Program of China(Grant No.2013AA030901)the Fundamental Research Funds for the Central Universities,China(Grant No.FRF-TP-14-120A2)
文摘This paper presents a new pattern recognition system for Chinese spirit identification by using the polymer quartz piezoelectric crystal sensor based e-nose. The sensors are designed based on quartz crystal microbalance（QCM） principle,and they could capture different vibration frequency signal values for Chinese spirit identification. For each sensor in an8-channel sensor array, seven characteristic values of the original vibration frequency signal values, i.e., average value（A）,root-mean-square value（RMS）, shape factor value（S_f）, crest factor value（C_f）, impulse factor value（I_f）, clearance factor value（CL_f）, kurtosis factor value（K_v） are first extracted. Then the dimension of the characteristic values is reduced by the principle components analysis（PCA） method. Finally the back propagation（BP） neutral network algorithm is used to recognize Chinese spirits. The experimental results show that the recognition rate of six kinds of Chinese spirits is 93.33% and our proposed new pattern recognition system can identify Chinese spirits effectively.
基金Project supported by the National High Technology Research and Development Program of China(Grant No.2013AA030901)
文摘We present a new pattern recognition system based on moving average and linear discriminant analysis （LDA）, which can be used to process the original signal of the new polymer quartz piezoelectric crystal air-sensitive sensor system we designed, called the new e-nose. Using the new e-nose, we obtain the template datum of Chinese spirits via a new pattern recognition system. To verify the effectiveness of the new pattern recognition system, we select three kinds of Chinese spirits to test, our results confirm that the new pattern recognition system can perfectly identify and distinguish between the Chinese spirits.
文摘The COVID-19 pandemic poses an additional serious public health threat due to little or no pre-existing human immunity,and developing a system to identify COVID-19 in its early stages will save millions of lives.This study applied support vector machine(SVM),k-nearest neighbor(K-NN)and deep learning convolutional neural network(CNN)algorithms to classify and detect COVID-19 using chest X-ray radiographs.To test the proposed system,chest X-ray radiographs and CT images were collected from different standard databases,which contained 95 normal images,140 COVID-19 images and 10 SARS images.Two scenarios were considered to develop a system for predicting COVID-19.In the first scenario,the Gaussian filter was applied to remove noise from the chest X-ray radiograph images,and then the adaptive region growing technique was used to segment the region of interest from the chest X-ray radiographs.After segmentation,a hybrid feature extraction composed of 2D-DWT and gray level co-occurrence matrix was utilized to extract the features significant for detecting COVID-19.These features were processed using SVM and K-NN.In the second scenario,a CNN transfer model(ResNet 50)was used to detect COVID-19.The system was examined and evaluated through multiclass statistical analysis,and the empirical results of the analysis found significant values of 97.14%,99.34%,99.26%,99.26%and 99.40%for accuracy,specificity,sensitivity,recall and AUC,respectively.Thus,the CNN model showed significant success;it achieved optimal accuracy,effectiveness and robustness for detecting COVID-19.
基金This work is supported by the National Natural Science Foundation of China under Grant Nos.U1636215,61902082the Guangdong Key R&D Program of China 2019B010136003National Key R&D Program of China 2019YFB1706003.
文摘The license plate recognition system(LPRS)has been widely adopted in daily life due to its efficiency and high accuracy.Deep neural networks are commonly used in the LPRS to improve the recognition accuracy.However,researchers have found that deep neural networks have their own security problems that may lead to unexpected results.Specifically,they can be easily attacked by the adversarial examples that are generated by adding small perturbations to the original images,resulting in incorrect license plate recognition.There are some classic methods to generate adversarial examples,but they cannot be adopted on LPRS directly.In this paper,we modify some classic methods to generate adversarial examples that could mislead the LPRS.We conduct extensive evaluations on the HyperLPR system and the results show that the system could be easily attacked by such adversarial examples.In addition,we show that the generated images could also attack the black-box systems;we show some examples that the Baidu LPR system also makes incorrect recognitions.We hope this paper could help improve the LPRS by realizing the existence of such adversarial attacks.
基金Project(61170199)supported by the National Natural Science Foundation of ChinaProject(11A004)support by the Scientific Research Fund of Education Department of Hunan Province,China
文摘In order to improve the resource allocation mechanism of artificial immune recognition system(AIRS) and decrease the memory cells,a fuzzy logic resource allocation and memory cell pruning based AIRS(FPAIRS) is proposed.In FPAIRS,the fuzzy logic is determined by a parameter,thus,the optimal fuzzy logics for different problems can be located through changing the parameter value.At the same time,the memory cells of low fitness scores are pruned to improve the classifier.This classifier was compared with other classifiers on six UCI datasets classification performance.The results show that the accuracies reached by FPAIRS are higher than or comparable to the accuracies of other classifiers,and the memory cells decrease when compared with the memory cells of AIRS.The results show that the algorithm is a high-performance classifier.
基金This work was funded by the Deanship of Scientific Research at Jouf University under Grant Number(DSR2022-RG-0114).
文摘The challenge faced by the visually impaired persons in their day-today lives is to interpret text from documents.In this context,to help these people,the objective of this work is to develop an efficient text recognition system that allows the isolation,the extraction,and the recognition of text in the case of documents having a textured background,a degraded aspect of colors,and of poor quality,and to synthesize it into speech.This system basically consists of three algorithms:a text localization and detection algorithm based on mathematical morphology method(MMM);a text extraction algorithm based on the gamma correction method(GCM);and an optical character recognition(OCR)algorithm for text recognition.A detailed complexity study of the different blocks of this text recognition system has been realized.Following this study,an acceleration of the GCM algorithm(AGCM)is proposed.The AGCM algorithm has reduced the complexity in the text recognition system by 70%and kept the same quality of text recognition as that of the original method.To assist visually impaired persons,a graphical interface of the entire text recognition chain has been developed,allowing the capture of images from a camera,rapid and intuitive visualization of the recognized text from this image,and text-to-speech synthesis.Our text recognition system provides an improvement of 6.8%for the recognition rate and 7.6%for the F-measure relative to GCM and AGCM algorithms.
文摘This study describes the development of a simple biometric facial recognition system, BFMT, which is designed for use in identifying individuals within a given population. The system is based on digital signatures derived from facial images of human subjects. The results of the study demonstrate that a particular set of facial features from a simple two-dimensional image can yield a unique digital signature which can be used to identify a subject from a limited population within a controlled environment. The simplicity of the model upon which the system is based can result in commercial facial recognition systems that are more cost-effective to develop than those currently on the market.
文摘Designing accurate and time-efficient real-time traffic sign recognition systems is a crucial part of developing the intelligent vehicle which is the main agent in the intelligent transportation system.Traffic sign recognition systems consist of an initial detection phase where images transportaand colors are segmented and fed to the recognition phase.The most challenging process in such systems in terms of time consumption is the detection phase.The trade off in previous studies,which proposed different methods for detecting traffic signs,is between accuracy and computation time,Therefore,this paper presents a novel accurate and time-efficient color segmentation approach based on logistic regression.We used RGB color space as the domain to extract the features of our hypothesis;this has boosted the speed of our approach since no color conversion is needed.Our trained segmentation classifier was tested on 1000 traffic sign images taken in different lighting conditions.The results show that our approach segmented 974 of these images correctly and in a time less than one-fifth of the time needed by any other robust segmentation method.
基金Foundation project： This paper was supported by National Natural Science Foundation of China （No. 30371126）.
文摘In forest variety registration, visual traits of the plants appearance are widely used to discern different tree species. The new recognition system of leaf image strategy which based on neural network established to administrate a hierarchical list of leaf images, some sorts of edge detection can be performed to identify the individual tokens of every image and the frame of the leaf can be got to differentiate the tree species. An approach based on back-propagation neuronal network is proposed and the programming language for the implementation is also Riven by using Java. The numerical simulations results have shown that the proposed leaf strategt is effective and feasible.
基金supported by the National Research Foundation of Korea funded by the Korean Government through the Ministry of Science and ICT under Grant NRF-2020R1F1A1060659 and in part by the 2020 Faculty Research Fund of Sejong University。
文摘Emotion recognition from speech data is an active and emerging area of research that plays an important role in numerous applications,such as robotics,virtual reality,behavior assessments,and emergency call centers.Recently,researchers have developed many techniques in this field in order to ensure an improvement in the accuracy by utilizing several deep learning approaches,but the recognition rate is still not convincing.Our main aim is to develop a new technique that increases the recognition rate with reasonable cost computations.In this paper,we suggested a new technique,which is a one-dimensional dilated convolutional neural network(1D-DCNN)for speech emotion recognition(SER)that utilizes the hierarchical features learning blocks(HFLBs)with a bi-directional gated recurrent unit(BiGRU).We designed a one-dimensional CNN network to enhance the speech signals,which uses a spectral analysis,and to extract the hidden patterns from the speech signals that are fed into a stacked one-dimensional dilated network that are called HFLBs.Each HFLB contains one dilated convolution layer(DCL),one batch normalization(BN),and one leaky_relu(Relu)layer in order to extract the emotional features using a hieratical correlation strategy.Furthermore,the learned emotional features are feed into a BiGRU in order to adjust the global weights and to recognize the temporal cues.The final state of the deep BiGRU is passed from a softmax classifier in order to produce the probabilities of the emotions.The proposed model was evaluated over three benchmarked datasets that included the IEMOCAP,EMO-DB,and RAVDESS,which achieved 72.75%,91.14%,and 78.01%accuracy,respectively.
文摘At present, the demand for perimeter security system is in-creasing greatly, especially for such system based on distribut-ed optical fiber sensing. This paper proposes a perimeter se-curity monitoring system based on phase-sensitive coherentoptical time domain reflectometry(Ф-COTDR) with the practi-cal pattern recognition function. We use fast Fourier trans-form(FFT) to exact features from intrusion events and a multi-class classification algorithm derived from support vector ma-chine(SVM) to work as a pattern recognition technique. Fivedifferent types of events are classified by using a classifica-tion algorithm based on SVM through a three-dimensional fea-ture vector. Moreover, the identification results of the patternrecognition system show that an identification accurate rate of92.62% on average can be achieved.
文摘Taking leaf black spot,anthracnose and leaf blight of Ophiopogon japonicus as the research objects,lesions were separated by K-Means clustering segmentation technology.PCA(principal component analysis)was carried out on the 46-dimensional eigenvectors composed of color,shape and texture features,and then the multi-level classifier designed by SVM(support vector machine)was used to identify lesions.The recognition rate of the developed leaf disease recognition system of O.japonicus achieved 93.3%.The results indicates that the system is of great significance to the prevention and control of O.japonicus diseases and the modernization of O.japonicus industry.
基金supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(No.2017R1A6A1A03015496)the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.NRF-2021R1A2C1014033).
文摘Recently,user recognitionmethods to authenticate personal identity has attracted significant attention especially with increased availability of various internet of things(IoT)services through fifth-generation technology(5G)based mobile devices.The EMG signals generated inside the body with unique individual characteristics are being studied as a part of nextgeneration user recognition methods.However,there is a limitation when applying EMG signals to user recognition systems as the same operation needs to be repeated while maintaining a constant strength of muscle over time.Hence,it is necessary to conduct research on multidimensional feature transformation that includes changes in frequency features over time.In this paper,we propose a user recognition system that applies EMG signals to the short-time fourier transform(STFT),and converts the signals into EMG spectrogram images while adjusting the time-frequency resolution to extract multidimensional features.The proposed system is composed of a data pre-processing and normalization process,spectrogram image conversion process,and final classification process.The experimental results revealed that the proposed EMG spectrogram image-based user recognition system has a 95.4%accuracy performance,which is 13%higher than the EMGsignal-based system.Such a user recognition accuracy improvement was achieved by using multidimensional features,in the time-frequency domain.
文摘The geometrical and topological information of 3D computer aided design （CAD） models should be represented as a neut- ral format file to exchange the data between different CAD systems. Exchange of 3D CAD model data implies that the companies must exchange complete information about their products, all the way from design, manufacturing to inspection and shipping. This informa- tion should be available to each relevant partner over the entire life cycle of the product. This led to the development of an international standard organization （ISO） neutral format file named as standard for the exchange of product model data （STEP）. It has been ob- served from the literature, the feature recognition systems developed were identified as planar, cylindrical, conical and to some extent spherical and toroidal surfaces. The advanced surface features such as B-spline and its subtypes are not identified. Therefore, in this work, a STEP-based feature recognition system is developed to recognize t--spline surface features and its sub-types from the 3D CAD model represented in AP203 neutral file format. The developed feature recognition system is implemented in Java programming language and the product model data represented in STEP AP203 format is interpreted through Java standard data access interface （JSDAI）. The developed system could recognize B-spline surface features such as B-Spline surface with knots, quasi uniform surface, uniform surface, rational surface and Bezier surface. The application of extracted B-spline surface features information is discussed with reference to the toolpath generation for STEP-NC （STEP AP238）.
基金This work was supported by the National Natural Science Foundation of China(61471391).
文摘The hypersonic target detection and recognition system is studied,on the basis of overall planning and design,a multi-agent system(MAS)structure and intelligent+information processing mechanism based on target detection and recognition are proposed,and the multi-agent operation process is analyzed and designed in detail.In the specific agents construction,the information fusion technology is introduced to defining the embedded agents and their interrelations in the system structure,and the intelligent processing ability of complex and uncertain problems is emphatically analyzed from the aspects of autonomy and collaboration.The aim is to optimize the information processing strategy of the hypersonic target detection and recognition system and improve the robustness and rapidity of the system.
文摘Nowadays, the vein based recognition system becomes an emerging and facilitating biometric technology in the recognition system. Vein recognition exploits the different modalities such as finger, palm and hand image for the person identification. In this work, the fuzzy least brain storm optimization and Euclidean distance(EED) are proposed for the vein based recognition system. Initially, the input image is fed into the region of interest(ROI) extraction which obtains the appropriate image for the subsequent step. Then, features or vein pattern is extracted by the image enlightening, circular averaging filter and holoentropy based thresholding. After the features are obtained, the entropy based Euclidean distance is proposed to fuse the features by the score level fusion with the weight score value. Finally, the optimal matching score is computed iteratively by the newly developed fuzzy least brain storm optimization(FLBSO) algorithm. The novel algorithm is developed by the least mean square(LMS) algorithm and fuzzy brain storm optimization(FBSO). Thus, the experimental results are evaluated and the performance is compared with the existing systems using false acceptance rate(FAR), false rejection rate(FRR) and accuracy. The performance outcome of the proposed algorithm attains the higher accuracy of 89.9% which ensures the better recognition rate.
基金This research was funded by Al-Zaytoonah University of Jordan Grant Number(2020-2019/12/11).
文摘Hand veins can be used effectively in biometric recognition since they are internal organs that,in contrast to fingerprints,are robust under external environment effects such as dirt and paper cuts.Moreover,they form a complex rich shape that is unique,even in identical twins,and allows a high degree of freedom.However,most currently employed hand-based biometric systems rely on hand-touch devices to capture images with the desired quality.Since the start of the COVID-19 pandemic,most handbased biometric systems have become undesirable due to their possible impact on the spread of the pandemic.Consequently,new contactless hand-based biometric recognition systems and databases are desired to keep up with the rising hygiene awareness.One contribution of this research is the creation of a database for hand dorsal veins images obtained contact-free with a variation in capturing distance and rotation angle.This database consists of 1548 images collected from 86 participants whose ages ranged from 19 to 84 years.For the other research contribution,a novel geometrical feature extraction method has been developed based on the Curvelet Transform.This method is useful for extracting robust rotation invariance features from vein images.The database attributes and the veins recognition results are analyzed to demonstrate their efficacy.
