Efficient Feature Extraction Algorithm Research Articles

Exploration of efficient SERS features extraction algorithm for rapid detection of thiabendazole residues in apples

Long-term exposure to thiabendazole (TBZ) pesticide residues in fruits can have harmful effects on human health. Therefore, this paper proposes a highly sensitive and time-efficient method for the exploration of efficient SERS feature extraction algorithms to establish an optimal quantitative model for the rapid detection of TBZ residues in apples. Specifically, shape-homogeneous Au@Ag nanoparticles (NPs) were developed as label-free substrates, and the optimal detection time of obtaining SERS signals generated by the binding of TBZ to the substrate sample was determined to be 6 min. Recursive Feature Elimination (RFE) and Competitive Adaptive Reweighted Sampling (CARS) were employed for feature extraction algorithms to optimize characteristic peaks and compare them to full spectra variables and extract effective variables on linear (Ridge, PLS) and nonlinear (SVM, RF) models, respectively. Results showed that the RFE-RF (Recursive Feature Elimination-Random Forest) model was more powerful in variable selection and achieved the highest predictive result of TBZ (RP2 = 0.992, RMSEP = 0.012 μg/mL) and the computed LOD (Limitation of Detection) was 0.1 μg/mL based on PCA (Principal Component Analysis). Comparison with ELISA verified that the designed method is feasible and potentially applicable to guarantee the safety of pesticide residues in fruits.

Rotating machinery fault-induced vibration signal modulation effects: A review with mechanisms, extraction methods and applications for diagnosis

Rotating machinery faults can induce characteristic modulation effects in a vibration signal, and their diagnosis can thus be conducted by extracting fault-induced modulation features. To be specific, such a diagnostic strategy typically involves three main aspects, i.e., fault-induced signal modulation mechanisms, modulation feature extraction methods and applications for diagnosis. To date, the research works on these three aspects all achieved great progresses. A systematic review is thus urgently needed to summarize these achievements, and guide their future directions and developments. This paper aims to fill these gaps and address the needs. Three kinds of typical modulation effects induced by transmission elements are firstly reviewed and summarized. For each kind of modulation effects, a representative phenomenological model is given to formulate and illustrate the relationship between fault-induced modulation patterns and characteristic spectral distributions. As the primary tools to extract signal modulation features, different kinds of time-frequency analysis and signal decomposition methods are then systematically reviewed, including their up-to-date developments, classifications and application scenarios. Some representative works of vibration signal modulation feature extraction-based fault diagnosis are finally introduced along with several typical examples. Based on current research achievements, two potential topics for the future developments of vibration-based diagnostic techniques are the quantitative study of fault feature evolution patterns and the development of more robust and efficient feature extraction algorithms.

Electrophysiology-based Closed Loop Optogenetic Brain Stimulation Devices: Recent Developments and Future Prospects.

Closed loop optogenetic brain stimulation enhances the efficacy of the stimulation by adjusting the stimulation parameters based on direct feedback from the target area of the brain. It combines the principles of genetics, physiology, electrical engineering, optics, signal processing and control theory to create an efficient brain stimulation system. To read the underlying neuronal condition from the electrical activity of neurons, a sensor, sensor interface circuit, and signal conditioning are needed. Also, efficient feature extraction, classification, and control algorithms should be in place to interpret and use the sensed data for closing the feedback loop. Finally, a stimulation circuitry is required to effectively control a light source to deliver light based stimulation according to the feedback signal. Thus, the backbone to a functioning closed loop optogenetic stimulation device is a well-built electronic circuitry for sensing and processing of brain signals, running efficient signal processing and control algorithm, and delivering timed light stimulations. This paper presents a review of electronic and software concepts and components used in recent closed-loop optogenetic devices based on neuro-electrophysiological reading and an outlook on the future design possibilities with the aim of providing a compact and easy reference for developing closed loop optogenetic brain stimulation devices.

Web Service Network Embedding Based on Link Prediction and Convolutional Learning

Extensive efforts have been applied to develop efficient feature extraction algorithms, which aim to achieve optimal results in many fundamental tasks such as Web-based software service clustering, recommendation and composition. However, one common issue for existing methods is that mined features are problem dependent, causing poor generalization ability across different applications. Recent studies show that we can represent networked data (e.g., citation networks and social networks) as low-dimensional vectors with rich structure and content information preserved, which can then greatly facilitate many downstream tasks such as classification and clustering. In this article, we focus on the problem of Web service network embedding, which aims to learn low-dimensional vectors to represent services by encoding both Mashup-API composition structure and service functional content. We first propose a novel probabilistic topic model to predict potential links between Mashups and APIs in the service network. Then, we develop a Service Graph Convolutional Network (Service-GCN) to learn vector representations of services, where each service (e.g., Mashup or API) forms its representation through message passing between neighborhood services over the network. We evaluate the network embedding quality on two real-world datasets for downstream classification and clustering tasks. Experimental results show that the average performance of our method improves 20.7 percent (Micro-F1) in service classification and 19.0 percent (Accuracy) in Mashup clustering compared to the state-of-the-art, which verified the effectiveness of the proposed approach for learning vector representations of Web services.

Degradation State Identification for Hydraulic Pumps Based on Multi-scale Ternary Dynamic Analysis, NSGA-II and SVM

Abstract Degradation state identification for hydraulic pumps is crucial to ensure system performance. As an important step, feature extraction has always been challenging. The non-stationary and non-Gaussian characteristics of the vibration signal are likely to weaken the performance of traditional features. In this paper, an efficient feature extraction algorithm named multi-scale ternary dynamic analysis (MTDA) is proposed. MTDA reconstructs the phase space based on the given signal and converts each embedding vector into a ternary pattern independently, which enhances its capacity of describing the details of non-stationary signals. State entropy (SE) and state transition entropy (STE) are calculated to estimate the dynamical changes and complexity of each signal sample. The excellent performance of SE and STE in detecting frequency changes, amplitude changes, and the development process of fault is verified with the use of four simulated signals. The proposed multi-scale analysis enables them to provide a more precise estimation of entropy. Furthermore, support vector machine (SVM) and nondominated sorting genetic algorithm II (NSGA-II) are introduced to conduct feature selection and state identification. NSGA-II and SVM can conduct the joint optimization of these two goals. The details of the method proposed in this paper are tested using simulated signals and experimental data, and some studies related to the fault diagnosis of rotating machinery are compared with our method. All the results show that our proposed method has better performance, which obtains higher recognition accuracy and lower feature set dimension.

Composite and efficient DDoS attack detection framework for B5G networks

Distributed denial-of-service (DDoS) remains an ever-growing problem that has affected and continues to affect a host of web applications, corporate bodies, and governments. With the advent of fifth-generation (5G) network and beyond 5G (B5G) networks, the number and frequency of occurrence of DDoS attacks are predicted to soar as time goes by, hence there is a need for a sophisticated DDoS detection framework to enable the swift transition to 5G and B5G networks without worrying about the security issues and threats. A range of schemes has been deployed to tackle this issue, but along the line, few limitations have been noticed by the research community about these schemes. Owing to these limitations/drawbacks, this paper proposes a composite and efficient DDoS attack detection framework for 5G and B5G. The proposed detection framework consists of a composite multilayer perceptron which was coupled with an efficient feature extraction algorithm and was built not just to detect a DDoS attack, but also, return the type of DDoS attack it encountered. At the end of the simulations and after testing the proposed framework with an industry-recognized dataset, results showed that the framework is capable of detecting DDoS attacks with a high accuracy score of 99.66% and a loss of 0.011. Furthermore, the results of the proposed detection framework were compared with their contemporaries.

New Fusion Techniques to Extract Image Features and Recognition of Palm Vascular Pattern

A strong and efficient Feature extraction algorithm is highly recommended for individual recognition in human authentication systems. This paper presents the work carried on palm vein image to extract features of the person vein for recognition and classification using improved canny edge detector. This paper describes a novel method to extract valuable features of the people’s vein pattern and achieving high recognition rate. The experiments carried using two algorithms 1) PCACE (principal component analysis with canny edge) algorithm and 2) LDACE (linear discriminant analysis with canny edge) algorithm. These two methods are analyzed on palm vein image and found LDACE algorithm is a best extractor compare to PCACE method. An Equal Error Rate (EER) is applied to evaluate two algorithms. Hidden Markova Model (HMM) is utilized for image feature classification and matching using contactless Palm Under Test (PUT) palm vein database. The percentage of recognition is measured by False Acceptance Ratio (FAR) and False Rejection Ratio (FRR). This method shows robust response with respect to human palm vein identification process computation time and improved recognition rate.

Efficient Feature Extraction Algorithms to Develop an Arabic Speech Recognition System

Efficient Feature Extraction Algorithms to Develop an Arabic Speech Recognition System

Efficient Feature Extraction Algorithms to Develop an Arabic Speech Recognition System

This paper studies three feature extraction methods, Mel-Frequency Cepstral Coefficients (MFCC), Power-Normalized Cepstral Coefficients (PNCC), and Modified Group Delay Function (ModGDF) for the development of an Automated Speech Recognition System (ASR) in Arabic. The Support Vector Machine (SVM) algorithm processed the obtained features. These feature extraction algorithms extract speech or voice characteristics and process the group delay functionality calculated straight from the voice signal. These algorithms were deployed to extract audio forms from Arabic speakers. PNCC provided the best recognition results in Arabic speech in comparison with the other methods. Simulation results showed that PNCC and ModGDF were more accurate than MFCC in Arabic speech recognition.

FSEFST:Feature Selection and Extraction using Feature Subset Technique in High Dimensional Data

Dimensionality reduction is one of the pre-processing phases required when large amount of data is available. Feature selection and Feature Extraction are one of the methods used to reduce the dimensionality. Till now these methods were using separately so the resultant feature contains original or transformed data. An efficient algorithm for Feature Selection and Extraction using Feature Subset Technique in High Dimensional Data (FSEFST) has been proposed in order to select and extract the efficient features by using feature subset method where it will have both original and transformed data. The results prove that the suggested method is better as compared with the existing algorithm

Paddy Seed Classification and Identifying Varieties using Random Assessment Classification

The current research work focuses in developing an accurate and efficient classification and feature extraction algorithm for paddy seed image analysis. The paddy images that are preprocessed by applying hybrid mediangaustransform algorithms were segmented using Paddysegmatch algorithm. The resultant image’s features are extracted by applying the proposed enhanced rapid SURF feature extraction including various features of image. Later, the paddy seeds are classified to form different categories by applying the proposed Random Assessment Classification algorithm. Experimental results on Paddy seed realtime image analysis database show that the proposed method performs better classification accuracy compared with SVM and KNN classification algorithms.

Evaluation of Feature Selection and Feature Extraction Techniques on Multi-Temporal Landsat-8 Images for Crop Classification

Recent advancements in the remotely sensed data products and machine learning algorithms are utilized effectively for classification of crops over a considerable large area. This article proposes the use of feature extraction techniques to be employed on the multi-temporal Landsat-8 OLI sensor’s surface reflectances and derived Normalized Difference Indices datasets to classify different crop types. Numerous dimension reduction techniques, viz., feature selection (random forest and PIC measure based), linear (principal component analysis (PCA) and independent component analysis) and nonlinear feature extraction (kernel PCA and Autoencoder), are evaluated to detect most favourable features which should be apt for classification of crops. Subsequently, the detected features are used in a promising nonparametric classifier, support vector machine, for crop classification. It has been found that all the evaluated feature extraction techniques, employed on the multi-temporal datasets, result in better performance compared to feature selection-based approaches. PCA, being a simple and efficient feature extraction algorithm, is well-suited in this classification study and extracted features can classify the crops with an average overall accuracy of 94.32%. Most of the crop types achieve user and producer accuracy of more than 90%. Multi-temporal images prove to be more advantageous compared to the single-date imagery for crop identification.

A Precise and Robust Segmentation-Based Lidar Localization System for Automated Urban Driving

Real-time and high-precision localization information is vital for many modules of unmanned vehicles. At present, a high-cost RTK (Real Time Kinematic) and IMU (Integrated Measurement Unit) integrated navigation system is often used, but its accuracy cannot meet the requirements and even fails in many scenes. In order to reduce the costs and improve the localization accuracy and stability, we propose a precise and robust segmentation-based Lidar (Light Detection and Ranging) localization system aided with MEMS (Micro-Electro-Mechanical System) IMU and designed for high level autonomous driving. Firstly, we extracted features from the online frame using a series of proposed efficient low-level semantic segmentation-based multiple types feature extraction algorithms, including ground, road-curb, edge, and surface. Next, we matched the adjacent frames in Lidar odometry module and matched the current frame with the dynamically loaded pre-build feature point cloud map in Lidar localization module based on the extracted features to precisely estimate the 6DoF (Degree of Freedom) pose, through the proposed priori information considered category matching algorithm and multi-group-step L-M (Levenberg-Marquardt) optimization algorithm. Finally, the lidar localization results were fused with MEMS IMU data through a state-error Kalman filter to produce smoother and more accurate localization information at a high frequency of 200Hz. The proposed localization system can achieve 3~5 cm in position and 0.05~0.1° in orientation RMS (Root Mean Square) accuracy and outperform previous state-of-the-art systems. The robustness and adaptability have been verified with localization testing data more than 1000 Km in various challenging scenes, including congested urban roads, narrow tunnels, textureless highways, and rain-like harsh weather.

Development of an efficient and resilient algorithm For lane feature extraction in image sensor-based lane detection

Development of an efficient and resilient algorithm For lane feature extraction in image sensor-based lane detection

SAR-Oriented Visual Saliency Model and Directed Acyclic Graph Support Vector Metric Based Target Classification

The performance of a synthetic aperture radar automatic (SAR) target recognition system mainly depends on feature extraction and classification. It is crucial to select discriminative features to train a classifier to achieve desired performance. In this paper, we propose an efficient feature extraction and classification algorithm based on a visual saliency model. First, an SAR-oriented graph-based visual saliency model is introduced. Second, relying on the ability of our saliency model in highlighting the most significant regions, Gabor and histogram of oriented gradients features are extracted from the processed SAR images. Third, in order to have more discriminative features, the discrimination correlation analysis algorithm is used for feature fusion and combination. Finally, a two-level directed acyclic graph (DAG) support vector metric learning is developed that seamlessly takes advantage of a two-level DAG by eliminating weak classifiers and the Mahalanobis distance-based radial basis function kernel which emphasizes relevant features and reduces the influence of noninformative features. Experiments on real SAR data from the MSTAR database are conducted and the experimental results demonstrate that the proposed method outperforms the state-of-the-art methods.

Deep learning feature extraction for multispectral palmprint identification

Person’s identity validation is becoming much more essential due to the increasing demand for high-security systems. A biometric system testifies the authenticity of specific physiological or behavioral characteristics-based biometric technology. This technology has been successfully applied to verification and identification systems. We analyze the multispectral palmprint biometric identification system in unimodal and multimodal modes. In an identification system, the feature extraction is a crucial step. For this reason, we propose an efficient deep learning feature extraction algorithm called discrete cosine transform network (DCTNet). The effectiveness of the proposed approach has been evaluated on two publicly available databases: CASIA and PolyU. The obtained results clearly indicate that the DCTNet deep learning-based feature extraction technique can achieve comparable performance to the best of the state-of-the-art techniques.

To Improve Feature Extraction and Opinion Classification Issues in Customer Product Reviews Utilizing an Efficient Feature Extraction and Classification (EFEC) Algorithm

<span lang="EN-US">Currently, customer's product review opinion plays an essential role in deciding the purchasing of the online product. A customer prefers to acquire the opinion of other customers by viewing their opinion during online products' reviews, blogs and social networking sites, etc. The majority of the product reviews including huge words. A few users provide the opinion; it is tough to analysis and understands the meaning of reviews. To improve user fulfillment and shopping experience, it has become a general practice for online sellers to allow their users to review or to communicate opinions of the products that they have sold. The major goal of the paper is to solve feature extraction problem and opinion classification problem from customers utilized product reviews which extract the feature words and opinion words from product reviews. To propose an Efficient Feature Extraction and Classification (EFEC) algorithm is implementing to extracts a feature from opinion words. The reviewer usually marks both positive and negative parts of the reviewed product, despite the fact that their general opinion on the product may be positive or negative. An EFEC algorithm is utilized to predict the number of positive and negative opinion in reviews. Based on Experimental evaluations, proposed algorithm improves accuracy 15.05%, precision 13.7%, recall 15.59% and F-measure 15.07% of the proposed system compared than existing methodologies</span>

Chinese License Plates Recognition Method Based on A Robust and Efficient Feature Extraction and BPNN Algorithm

The prosperity of license plate recognition technology has made great contribution to the development of Intelligent Transport System (ITS). In this paper, a robust and efficient license plate recognition method is proposed which is based on a combined feature extraction model and BPNN (Back Propagation Neural Network) algorithm. Firstly, the candidate region of the license plate detection and segmentation method is developed. Secondly, a new feature extraction model is designed considering three sets of features combination. Thirdly, the license plates classification and recognition method using the combined feature model and BPNN algorithm is presented. Finally, the experimental results indicate that the license plate segmentation and recognition both can be achieved effectively by the proposed algorithm. Compared with three traditional methods, the recognition accuracy of the proposed method has increased to 95.7% and the consuming time has decreased to 51.4ms.

Reconfigurable hardware architecture for faster descriptor extraction in SURF

Speeded up robust features (SURFs) are considered to be the most efficient feature extraction algorithm and it has been implemented in powerful hardware for real-time operation due to its characteristics of data-intensive computation of high complexity. Especially, the computational load of the descriptor extraction procedure is very significant and the overall performance of SURF can be improved by speeding up the descriptor extraction step with increasing parallel hardware accelerators. However, simply increasing the hardware accelerators is burdensome because of causing significant area and power consumption. Therefore, a reconfigurable hardware architecture is proposed that enables achieving the maximum performance of the descriptor extraction step with making the best use of the existing accelerators without any additional ones. Experimental results show that the proposed architecture improves the performance of the descriptor extraction step by 24.77–47.45% with negligible area overheads when compared with the existing hardware implementations of the SURF algorithm.

Efficient RGB–D data processing for feature–based self–localization of mobile robots

Abstract The problem of position and orientation estimation for an active vision sensor that moves with respect to the full six degrees of freedom is considered. The proposed approach is based on point features extracted from RGB-D data. This work focuses on efficient point feature extraction algorithms and on methods for the management of a set of features in a single RGB-D data frame. While the fast, RGB-D-based visual odometry system described in this paper builds upon our previous results as to the general architecture, the important novel elements introduced here are aimed at improving the precision and robustness of the motion estimate computed from the matching point features of two RGB-D frames. Moreover, we demonstrate that the visual odometry system can serve as the front-end for a pose-based simultaneous localization and mapping solution. The proposed solutions are tested on publicly available data sets to ensure that the results are scientifically verifiable. The experimental results demonstrate gains due to the improved feature extraction and management mechanisms, whereas the performance of the whole navigation system compares favorably to results known from the literature.