Short Recognition Time Research Articles

Label-Free Assessment of Neuron-Specific Enolase via Polydopamine over a Carbon-Nanotube-Based Flexible Immunosensor.

A label-free electrochemical immunosensor was developed for the rapid and sensitive detection of neuron-specific enolase (NSE). The electropolymerization of dopamine in conjunction with highly conductive carbon nanotubes offers a simple and quick platform for the direct anchoring of antibodies without the assistance of any coupling agent as well as a blocking agent. The developed immunosensor exhibited a wider detection range from 120 pM (9 ng mL-1) to 3 nM (200 ng mL-1) for NSE with a high sensitivity of 3.9 μA pM-1 cm-2 in 0.1 M phosphate-buffered saline (PBS) at physiological pH (7.4). Moreover, the short recognition time (15 min) for the antigen enabled the detection to be fast and less invasive. Additionally, the evaluation of a rate constant at various concentrations of NSE via feedback mode of scanning electrochemical microscopy (SECM) explained the profound effect of antigen concentration on the rate of flow of electrons. Therefore, the proposed immunosensor can be a promising tool for the early detection of small cell lung cancer in a very short period of time with consistent accuracy.

Principal Component Analysis–Based Feature Recognition Technology for Marine Plankton Images in IoT Systems

Abstract To improve the accuracy of traditional plankton image feature recognition techniques, a principal component analysis (PCA)–based method for plankton image feature recognition in Internet of Things (IoT) systems was designed and proposed. According to the characteristics of a microscopic image of marine living plankton with much noise, a noise suppression method based on wavelet is established to meet the needs of edge information retention and stability in image processing. At the same time, the adaptive threshold gray segmentation, mathematical morphology operation, and canny edge detection operator are combined to complete the marine plankton image segmentation. Based on the global feature and texture information of contours, the feature set is composed. At the same time, PCA is used to reduce the dimensionality of the feature space and complete the feature recognition of plankton images in the IoT system. The test results show that this method has higher accuracy, shorter recognition time, lower cost, and certain applicability.

Research on the Construction of Intelligent Robot Path Recognition System Supported by Deep Learning Network Algorithm

Abstract In this paper, a new intelligent robot path recognition system LADDPG, is constructed by extending the DDPG algorithm with the support of the deep learning network algorithm. The path recognition system is optimized by using the memory system and the decision module guided by the attention mechanism, which solves the problems of the existing DRL intelligent robot path recognition method that cannot store long-time memory and the training time is too long. The system in this paper is validated by conducting path recognition experiments under different road conditions and conducting dynamic obstacle avoidance test experiments. The system can maintain excellent noise immunity and a short path recognition time of 51 ms under the conditions of missing road conditions, circuitous road conditions and large curvature, and in the presence of multiple dynamic obstacles, the system can maintain a collision rate close to 0 while maintaining a very high success rate of 0.98, and the required path recognition time is much lower than that of other methods, with an average reward value of 0.4151. This paper’s system is highly accurate in recognizing intelligent robot paths and has high application value and capability.

Voiceprint Recognition under Cross-Scenario Conditions Using Perceptual Wavelet Packet Entropy-Guided Efficient-Channel-Attention–Res2Net–Time-Delay-Neural-Network Model

(1) Background: Voiceprint recognition technology uses individual vocal characteristics for identity authentication and faces many challenges in cross-scenario applications. The sound environment, device characteristics, and recording conditions in different scenarios cause changes in sound features, which, in turn, affect the accuracy of voiceprint recognition. (2) Methods: Based on the latest trends in deep learning, this paper uses the perceptual wavelet packet entropy (PWPE) method to extract the basic voiceprint features of the speaker before using the efficient channel attention (ECA) block and the Res2Net block to extract deep features. The PWPE block removes the effect of environmental noise on voiceprint features, so the perceptual wavelet packet entropy-guided ECA–Res2Net–Time-Delay-Neural-Network (PWPE-ECA-Res2Net-TDNN) model shows an excellent robustness. The ECA-Res2Net-TDNN block uses temporal statistical pooling with a multi-head attention mechanism to weight frame-level audio features, resulting in a weighted average of the final representation of the speech-level feature vectors. The sub-center ArcFace loss function is used to enhance intra-class compactness and inter-class differences, avoiding classification via output value alone like the softmax loss function. Based on the aforementioned elements, the PWPE-ECA-Res2Net-TDNN model for speaker recognition is designed to extract speaker feature embeddings more efficiently in cross-scenario applications. (3) Conclusions: The experimental results demonstrate that, compared to the ECAPA-TDNN model using MFCC features, the PWPE-based ECAPA-TDNN model performs better in terms of cross-scene recognition accuracy, exhibiting a stronger robustness and better noise resistance. Furthermore, the model maintains a relatively short recognition time even under the highest recognition rate conditions. Finally, a set of ablation experiments targeting each module of the proposed model is conducted. The results indicate that each module contributes to an improvement in the recognition performance.

Crossmodal transfer of perceptual learning: Evidence from the recognition of first and second language characters in young children.

Crossmodal transfer of learning is a neurocognitive process whereby a learner's experience and knowledge acquired through one sensory mode enable him/her to perform a similar task using a different sensory mode. This study examined the transfer of (mostly) visually acquired knowledge of first- and second-language characters to the tactile modality typically not used in that acquisition process. Two experiments were conducted, one to assess letter recognition skills and one to assess digit recognition skills in both Bangla and English, in 30 sighted young children who had mastered those characters through sensory learning in natural settings. Results unequivocally demonstrated that children were able to recognize/classify the first and second language letters or digits presented not only to the (trained) visual modality but to the (untrained) tactile modality as well, and as expected, with greater recognition accuracy and shorter recognition time in the former than the latter modality. Their character recognition performance was found to be significantly influenced not by language but by character type, with digits being more accurately and more speedily recognized than letters. Moreover, language-task modality interaction was found to mediate letter recognition accuracy, digit recognition accuracy, and digit recognition time, whereas character type-task modality interaction was found to significantly mediate character recognition time only. The ecological and theoretical significance of these findings is discussed. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

GloveSense: A Hand Gesture Recognition System Based on Inductive Sensing

Gesture recognition, in particular, hand gesture recognition, has received great attention in the field of human-computer interaction. Although several hand gesture recognition designs have been proposed in the past, they still suffer many limitations in practice. Hence, we present a wearable device, called GloveSense, which is made on a regular glove using conductive threads. The system consists of ten coils, with each pair of coils sewn on both sides of each finger and connected in series. To achieve high sensitivity, a data acquisition system is employed in which a tank circuit made of each sewn coil is measured. Since the utilized commercial data acquisition board has limited channels, a switching mechanism is employed to measure some of the extra coils. The utilized switching mechanism reduces the cross-talk effect between the readings. The GloveSense system is tested for recognition of numbers 1 to 10 in American Sign Language. A dataset of 1400 responses is employed along with a machine learning algorithm for gesture recognition. The proposed system demonstrates a high accuracy of 99% and a short recognition time of less than 1 second.

A method for detecting the quality of cotton seeds based on an improved ResNet50 model.

The accurate and rapid detection of cotton seed quality is crucial for safeguarding cotton cultivation. To increase the accuracy and efficiency of cotton seed detection, a deep learning model, which was called the improved ResNet50 (Impro-ResNet50), was used to detect cotton seed quality. First, the convolutional block attention module (CBAM) was embedded into the ResNet50 model to allow the model to learn both the vital channel information and spatial location information of the image, thereby enhancing the model's feature extraction capability and robustness. The model's fully connected layer was then modified to accommodate the cotton seed quality detection task. An improved LRelu-Softplus activation function was implemented to facilitate the rapid and straightforward quantification of the model training procedure. Transfer learning and the Adam optimization algorithm were used to train the model to reduce the number of parameters and accelerate the model's convergence. Finally, 4419 images of cotton seeds were collected for training models under controlled conditions. Experimental results demonstrated that the Impro-ResNet50 model could achieve an average detection accuracy of 97.23% and process a single image in 0.11s. Compared with Squeeze-and-Excitation Networks (SE) and Coordination Attention (CA), the model's feature extraction capability was superior. At the same time, compared with classical models such as AlexNet, VGG16, GoogLeNet, EfficientNet, and ResNet18, this model had superior detection accuracy and complexity balances. The results indicate that the Impro-ResNet50 model has a high detection accuracy and a short recognition time, which meet the requirements for accurate and rapid detection of cotton seed quality.

Single and composite disturbance event recognition based on the DBN-GRU network in φ-OTDR.

An end-to-end deep learning model based on the deep belief network (DBN) and gated recurrent unit (GRU) is proposed to recognize the single disturbance events and composite disturbance events in the phase-sensitive optical time-domain reflectometer (φ-OTDR). Making use of the DBN to fit the original data, five kinds of single disturbance events can be effectively recognized with the GRU network as the classifier. An average recognition accuracy of 96.72% with a short recognition time of 0.079s can be achieved for single disturbance events. Moreover, the proposed method is also applied for recognizing composite disturbance events. Four kinds of composite disturbance events can be recognized with an average recognition accuracy as high as 90.94%, and the corresponding recognition time is only 0.084s. Up until now, there have been fewer reports about the recognition of composite disturbance events in φ-OTDR systems. High recognition accuracy and short recognition time make the model based on DBN-GRU more capable in a high sensitivity, real-time φ-OTDR system.

A Self-Adaption Recognition Method for English Speech on Cloud Platform Based on Wavelet Neural Network

Although there are various recognition methods for digital English speech (DES) on cloud platform network, the accuracy of them is low and the average time consumption is too long. Due to above shortages, this study put forward a self-adaption recognition method based on wavelet neural network (WNN). At first, the study used zero-crossing rate as feature of voiceless and voiced sound of the DES and used WNN to carry out conversion for the English speech information. Then, the study obtained wavelet-scaling function and parameterized wavelet function via zero-pole model. The parameterized wavelet function was used as the feature vector of the recognition. Wavelet function base of DES feature was generated via stretching and translating transformation. The transformed wavelet function was used to build WNN model. Moreover, error entropy function of the recognition was calculated by introducing momentum factor and partial derivative of the error entropy function to adjust parameter of the built WNN model. Thus, the study achieved the self-adaption recognition of DES. Simulation results show that the proposed method has high recognition accuracy and short recognition time.

Signal identification based on modified filter bank feature and generalized regression neural network for optical fiber perimeter sensing

• Signal recognition method based on the generalized regression neural network (GRNN). • Feature extraction algorithm based on the modified filter bank (FB) feature. • Endpoint detection algorithm based on the short-time energy. • Little parameter adjustment and human influence in the training process. • High identification rate, short recognition time and broad application prospects. A method based on the modified filter bank (FB) feature and a generalized regression neural network (GRNN) is proposed to raise the accuracy and real-time performance of signal recognition in optical fiber perimeter sensing systems. The FB feature is modified by adding root mean square information of the signal power under the Mel filter bank. Four kinds of sensing signals under three types of weather conditions are obtained experimentally by a fenced perimeter sensing system based on an in-line Sagnac interferometer. The endpoint detection algorithm based on short-term energy is performed on the sensing signals to obtain the effective signal segments. Then the modified FB features of signal segments are extracted and randomly divided into training and testing samples. The optimal GRNN classifier model is generated by performing a 10-fold cross-validation on the training samples and used to recognize the testing samples. The average accuracy can reach 98.22 % and average recognition time is only about 0.07 s. This method is expected to meet the requirements for real-time and accuracy in practical application of optical fiber perimeter sensing signal recognition.

Research on reform and breakthrough of news, film, and television media based on artificial intelligence

Abstract With the development of technology, news media and film and television media are spreading faster and faster, and at the same time, the spread of rumors is also accelerated. This article briefly describes the application of artificial intelligence in news media and film and television media using a back-propagation neural network (BPNN) algorithm to reform refutation of rumors in news media and film and television media, and compared it with K-means and support vector machine algorithms in simulation experiments. The results showed that the BPNN-based rumor recognition model had better recognition performance and shorter recognition time; it was more accurate in recognizing Weibo texts that were complete and faster in recognizing bullet screen comments that were short; the BPNN-based rumor recognition model also had the lowest false detection cost and performed stably when being used in actual Weibo platform and bullet screen video website.

Image Recognition for Garbage Classification Based on Transfer Learning and Model Fusion

Garbage is an underutilized resource, and garbage classification is one of the effective ways to make full use of these resources. In order to realize the automation of garbage classification, some deep learning models are used for garbage images recognition. A novel garbage image recognition model Garbage Classification Net (GCNet) based on transfer learning and model fusion is proposed in this paper. After extracting garbage image features, EfficientNetv2, Vision Transformer, and DenseNet, respectively, are combined to construct the neural network model of GCNet. Data augmentation is used to expand the dataset and 41,650 garbage images are contained in the new dataset. Compared with other models through experiments, the results show that the proposed model has good convergence, high recall rate and accuracy, and short recognition time.

Optimization of VGG16 Algorithm Pattern Recognition for Signals of Michelson–Sagnac Interference Vibration Sensing System

Signal recognition accuracy and recognition time are the two most important parameters of pattern recognition in a fiber optic vibration sensing system. To obtain high recognition accuracy and short recognition time, this paper optimized the VGG16 algorithm and carried out VGG16-1D and VGG16-2D recognition on big data signals generated by a Michelson–Sagnac interferometric vibration sensor system. The results indicate that VGG16-1D has a higher accuracy of 98.44% and a shorter recognition time of 0.03 s. The proposed method is a more accurate and faster pattern recognition method for big data signals from optical fiber vibration sensing systems, which is helpful in promoting further applications of optical fiber sensing systems.

Cross-subject fusion based on time-weighting canonical correlation analysis in SSVEP-BCIs

Brain–computer interface technology provides new possibilities for medical rehabilitation and human–computer interaction. The steady-state visual evoked potential based brain–computer interface (SSVEP-BCI) is the preferred solution for controlling equipment because of its ease of operation, low training requirements, and high information transfer rate (ITR). However, the low recognition accuracy of SSVEP in short time limits its further improvement in ITR. To address this issue, this paper proposes time-weighting canonical correlation analysis (TWCCA), a time-domain enhancement CCA-based method to improve the recognition accuracy of SSVEP in short recognition time. The TWCCA method evaluates the SSVEP components of EEG signals at different time periods and performs a time-dimension weighted CCA on the original signal. The method integrates the features of all stimulus targets based on CCA and is insensitive to the specific number of targets, so it does not require a large amount of calibration data. To further shorten the calibration time for a certain user, this paper also proposes a cross-subject fusion method to calibrate the parameters of the target user using the acquired data from other users. The effectiveness of the proposed method is evaluated using EEG signals from the occipital and ear regions. The experiment results show that the TWCCA method significantly improves the average recognition accuracy by 3.86% and the cross-subject fusion method shortens the calibration time by 78.5% without compromising recognition accuracy. The proposed combination of cross-subject fusion method and TWCCA (CF-TWCCA) increases recognition accuracy by 13.16% on average (p<0.001).

Traditional Ceramic Sculpture Feature Recognition Based on the Machine Learning Algorithm

Aiming at the problems of low recognition accuracy, high line noise, and high time cost of feature recognition in traditional ceramic sculpture modeling feature recognition methods, this paper designed a traditional ceramic sculpture modeling feature recognition method based on the machine learning algorithm. By constructing the sparse representation model of a traditional ceramic sculpture image, the posterior probability of a traditional ceramic sculpture image was determined and Gaussian distribution of pixels in the image was carried out to determine the distribution law of pixels in the image and the super-resolution reconstruction of the traditional ceramic sculpture image was realized. The feature of the line state of a traditional ceramic sculpture image was determined and classified by the kernel classification method. Finally, the machine learning algorithm red BP neural network is introduced to construct the traditional ceramic sculpture modeling feature recognition algorithm and the error threshold is constantly iterated to realize the traditional ceramic sculpture modeling feature recognition. The experimental results show that the recognition algorithm designed in this paper has high accuracy in identifying the traditional ceramic sculpture features, can effectively suppress the line noise, and has a short recognition time overhead, which has a certain feasibility.

An efficient epileptic seizure classification system using empirical wavelet transform and multi-fuse reduced deep convolutional neural network with digital implementation

Epilepsy, an incurable brain disorder portrayed by seizures, is the most common neurological disease worldwide. The embryonic detection of epileptic action helps the psychologist for the diagnosis of epileptic seizure and reduces the seizure effect on the patient’s life. Empirical wavelet transform (EWT), and a novel multi-fuse reduced deep convolutional neural network (MF-RDCNN) classifier are integrated to design a computer-aided-diagnosis (CAD) system for meticulous classification of epileptic seizure from electroencephalogram (EEG) signals. The EWT is enforced on recorded EEG signals to extract three distinct band-limited modes (BLMs) and the proposed classifier is used to compute the most informative unsupervised signatures automatically by taking the extracted BLMs as inputs. The experimental results are carried out using three EEG databases provided by Bonn-University, Germany single-channel EEG (dataset-A), Neurology and Sleep Center, New Delhi single-channel EEG (dataset-B), and Boston Children’s Hospital multichannel scalp EEG (dataset-C) to verify the effectiveness of the proposed algorithm. The performance of the proposed method is superior over other prevalent methods and has 100%,99.82% overall mean classification accuracy for two class and three class classification problems respectively using ten-fold cross-validation. Moreover, the proposed method has specificity (SPE) of 99.29%, sensitivity (SEN) of 99.86%, and classification accuracy (ACC) of 99.29% with 0.71% of false positive rate per hour (FPR/h) by taking 40% of data for training, 40% of data for testing, and the remaining 20% of data for validation from the total data incorporated with the database-C. The lesser computational complexity, higher learning speed, short event recognition time, remarkable overall mean classification accuracy, and outstanding overall performance are the major advantages of the proposed EWT-MF-RDCNN method over EWT-RDCNN and RDCNN method for accurate classification of seizure EEG epochs. The digital architecture of the MF-RDCNN classifier is also implemented in a high-speed FPGA processor to develop a CAD system for efficacious diagnosis of epileptic seizure activity online.

Traffic sign recognition based on HOG feature extraction

The substantial increase in the number of motor vehicles in recent years has caused many traffic safety problems and has aroused widespread concern. As the basis of intelligent vehicle environment perception and a necessary condition for realizing the functions of assisted driving system, traffic sign recognition is of great significance for realizing automatic driving of vehicles, improving intelligent transportation systems, and promoting the development of smart cities.This paper mainly identifies traffic signs, using histogram of gradient feature extraction method. The image is collected and preprocessed by a vision sensor. The color threshold segmentation method and morphological processing are used to reduce the interference of the background area and enhance the contour of the sign area. Finally, HOG method is used to collect the gradient of each pixel point in the cell unit or the direction histogram of the edge to identify traffic signs. Through MATALB simulation, it is obtained that the HOG image feature extraction method has high accuracy, small error and short recognition time, which shows the effectiveness of the algorithm.

Effect of voice alarms on temporal characteristics of the evacuation process inside metro train carriages: An experiment study

Controlled experiments involved more than 700 participants inside a metro train model were conducted to investigate effects of voice alarms on the evacuation process. The process was divided into 4 stages from the perspective of collective behavior: the recognition stage (from the emergency happens to the first person standing up), the response stage (from the first person standing up to the last person standing up), the transfer stage (from the last person standing up to the first person leaving the carriage), and the queue stage (from the first person leaving the carriage to the last person leaving the carriage). Results showed that the mean evacuation time of all experiments was 77.61 s, and mean percentages of the four stages were 16.89%, 12.80%, 30.67% and 30.76%, respectively. When focusing on a specific stage, a shorter voice alarm cycle, a closer voice source and a more rapid tone resulted in a shorter recognition time. In the response stage, the response order was mainly affected by physical distance. A closer and more directive voice guided evacuees to find the exit more quickly, and further reduced the transfer time. Opening doors by using emergency devices took about 75% of the transfer time on average. Voice alarm didn’t play a key role in the queue stage, and the queue time was mainly affected by the number and the width of available doors. The results provide references for developing models and designing Public Announcement system of a metro train.

FPGA implementation of epileptic seizure detection using semisupervised reduced deep convolutional neural network

In this article, an optimized variational mode decomposition (OVMD), reduced deep convolutional neural network (RDCNN), and multi-kernel random vector functional link network (MKRVFLN) are combined to recognize the epileptic seizure using electroencephalogram (EEG) signals. An improved particle swarm optimization based on the maximum value of log energy entropy is introduced to compute the optimized values of the number of band-limited intrinsic mode functions (BLIMFs) and the data-fidelity factor. The Kurtosis and correlation coefficient are used to extract the most efficient BLIMF from highly nonlinear and non-stationary multi-class epilepsy EEG signals. The RDCNN structure is designed to extract the most discriminative unsupervised features and feed into the novel supervised MKRVFLN classifier to train efficiently by reducing the cross-entropy loss for recognizing the seizure epochs efficaciously. The Bonn University, Germany, Neurology and Sleep centre, New Delhi single-channel EEG, and Boston Children’s Hospital multichannel scalp EEG (sEEG) datasets are considered to evaluate the overall efficiency of the proposed method. The proposed method has produced 100% classification accuracy (CA) for classification problem (CP) 1 to 4 present in database-A, as well as for all CP incorporated with database-B and also it has 99.88% CA for CP-5 of the Bonn University database using a ten-fold cross-validation strategy. Furthermore, the proposed method has also the sensitivity of 100%, specificity of 99.34%, and CA of 99.37% with a negligible false positive per hour (FPR/h) of 0.663% by adopting 50% training, 30% testing and 20% validation of total data present in Boston database and its overall performance outperforms as compared to other state-of-the-art methods. The less computational complexity, higher learning speed, accurate epileptic seizure recognition, remarkable classification accuracy, short event recognition time, and negligible FPR/h are the main advantages of the proposed OVMD-RDCNN-MKRVFLN method over RDCNN, OVMD-RDCNN, and OVMD-RDCNN-KRVFLN methods. The novel RDCNN-MKRVFLN digital architecture is designed and implemented on a high-speed field-programmable gate array (FPGA) hardware environment to design a computer-aided-diagnosis (CAD) system for online epileptic seizure diagnosis. The simplicity, feasibility, and practicability of the proposed method validate the effectiveness of automatic seizure recognition.

Research on the Efficiency of Intelligent Algorithm for English Speech Recognition and Sentence Translatione

Machine translation has been gradually widely used to improve the efficiency of English translation. This paper briefly introduced the English speech recognition algorithm based on the back-propagation (BP) neural network algorithm and the machine translation algorithm based on the long short-term memory-recurrent neural network (LSTM-RNN) algorithm. Then, the machine translation algorithm was simulated and compared with BP-RNN and RNN-RNN algorithms. The results showed that the BP neural network algorithm had a lower word error rate and shorter recognition time than manual recognition; the LSTM-RNN-based machine translation algorithm had the lowest error rate of the translated words in the speech recognition results, and the translation gained the highest evaluation score from ten professional translators.