Efficient Recognition Research Articles

A novel sensitive fluorescent probe with double channels for highly effective recognition of biothiols

Biothiols play a crucial role in maintaining redox balance in organisms, and anomalous levels of biothiols in human organs can lead to various sicknesses and biological disorders. This work developed a novel sensitive fluorescent probe TZ-NBD with double channels for highly efficient recognition of biothiols. TZ-NBD adopts 4-Chloro-7-nitrobenzofurazan (NBD-Cl) as the recognition moiety with simultaneous fluorescence output. By incorporating NBD-Cl with the other fluorophore, benzothiazole dihydrocyclopentachromene derivative (TZ-OH), the dual-channel sensitive fluorescence probe TZ-NBD was built. The existence of Cys/ Hcy could significantly trigger both the green and red fluorescent emissions, which were derived from fluorophores amine-substituted NBD and TZ-OH, respectively. While exposing to GSH, only the red-channel fluorescence signal could be detected, indicating the release of TZ-OH. The phenomena was mainly attributed to the fact that sulfur-substituted NBD has nearly no fluorescence, while amine-substituted NBD shows obvious green fluorescence. In our study, TZ-NBD exhibited dual-channel sensitivity, fast response, and excellent selectivity to biothiols in vitro. Moreover, TZ-NBD was favorably utilized for recognition of biothiols in vivo. We believe that the sensitive fluorescence probe with double channels can afford an alternate approach for monitoring biothiols in organisms and would be useful for studying diseases associated with biothiols.

An efficient and lightweight multiperson activity recognition framework for robot-assisted healthcare applications

Aging is inevitably associated with a decline in physical abilities and can pose challenges to the social lives of elderly individuals. In long-term care facilities, group exercise is instrumental for keeping elderly residents physically and socially healthy. Accommodating these needs in elderly care can be challenging due to staff shortages and other lacking resources. A robotic exercise coach could be helpful in such contexts. Intelligent human–robot interaction requires accurate and efficient human activity recognition. Several solutions focusing on human activity recognition in healthcare robotics have been proposed. However, multiperson activity recognition remains a challenging task in case of using vision-based or wearable sensors data, and past research has mainly focused on single-person rather than multiperson or group activity recognition. Moreover, the existing state-of-the-art methods for activity recognition mainly use heavyweight Convolutional Neural Network (CNN) models to achieve good accuracy. However, these models have certain drawbacks, such as requiring significant computational resources, higher memory and storage needs, and slower inference times. Another challenge is the limited number of publicly available datasets containing few activities for physical activity recognition. In this work, we propose a lightweight, deep learning-based, multiperson activity recognition system for group exercise training of elderly persons. Considering the limited publicly available datasets, we curated a new dataset named the Routine Exercise Dataset (RED), comprising 19 routine exercise activities recommended for elderly persons. The RED dataset has 14,440 samples collected from 19 participants and is one of the most extensive datasets of its kind. We evaluated our proposed activity recognition method based on proposed feature extraction modules and a one-dimensional multilayer long short-term memory network on 16 datasets, including 10 publicly available benchmark activity recognition datasets, an RED dataset, a publicly available dataset combined with RED dataset, and four noise-corrupted RED datasets. The results indicate the efficiency of the proposed method for real-time activity recognition compared to the state-of-the-art methods. The proposed method achieved F1-scores of 98.64%, 97.95%, and 99% on large-scale datasets named UESTC RGB-D, NTU RGB+D, and RED, respectively. We also developed a Robot Operating System (ROS)-based application to deploy our proposed system in a social robot and test it in real-life scenarios.

Boronate affinity-based surface molecularly imprinted polymer microspheres using polyethyleneimine/dopamine coating for efficient selective recognition and separation of Ginsenoside Rb1

A novel approach combining boronate affinity with surface molecular imprinting was developed for the specific recognition and separation of ginsenoside Rb1. Firstly, the boronic acid-functionalized polymer microspheres were prepared as inner cores via suspension polymerization using methyl methacrylate, ethyleneglycol dimethacrylate, and 4-vinylphenyl boronic pinacol ester (4-VBPE) as polymerization monomers. Then, the Ginsenoside Rb1 (GRb1) template was covalently pre-anchored onto the surface of microspheres through boronate affinity binding after the hydrolysis of 4-VBPE. Finally, dopamine and polyethyleneimine were used as co-coating monomers to form a thickness-controllable imprinting layer onto the polymer microspheres via in-water self-copolymerization. The key factors affecting specific recognition, including the boronic acid content, adsorption media, and polymerization time were carefully optimized. The resulting molecularly imprinted microspheres (MIMs) exhibited strong surface hydrophilicity. In the aqueous media, the optimal adsorption capacity for the template reached 81.45 μmol g−1, and the imprinting factor was 5.32. The specific evaluation showed that the MIMs had a much higher selectivity for GRb1 than its analogues, and the selectivity coefficients were all higher than 2.4. In addition, the MIMs can be applied as adsorbents for solid-phase extraction to selectively separate ginsenosides from American ginseng extract. The eluting recovery ratios of GRb1, GRg2, and GRd reach 92.39%, 88.90%, and 92.49%, respectively.

Towards adequate policy enhancement: An AI-driven decision tree model for efficient recognition and classification of EPA status via multi-emission parameters

Accurate and timely evaluation and assessment of emission data and its impact on environmental status has been a key challenge due to the conventional manual approach utilized for independently computing most emission parameters. To resolve this long-standing issue, we proposed an Artificial Intelligence (AI)-driven Decision Tree model to adequately classify Environmental Protection Agency (EPA) status based on multiple Emission Parameters. The model's performance was systematically evaluated using multiple emission parameters obtained from a two-stroke motorcycle dataset collected in Nigeria across various metrics such as K-S Statistics, Confusion Matrix, Correlation Heat Map, Decision Tree, Validation Curve, and Threshold Plot. The K-S Statistics plot's experimental results showed a considerable correlation between HC, CO, and the target variable, with values ranging from 0.75 to 0.80. At the same time, CO2 and O2 do not correlate with the target variable with values between 0.00 and 0.09. The Confusion Matrix revealed that the proposed model has an overall accuracy of 99.9% with 481 true positive predictions and 75 true negative predictions, indicating the effectiveness of the proposed AI-driven model. In conclusion, our proposed AI-driven model can effectively classify EPA status based on multiple emission parameters with high accuracy, which may spur positive advancement in policy enhancement for proper environmental management.

Efficient Underground Tunnel Place Recognition Algorithm Based on Farthest Point Subsampling and Dual-Attention Transformer.

An autonomous place recognition system is essential for scenarios where GPS is useless, such as underground tunnels. However, it is difficult to use existing algorithms to fully utilize the small number of effective features in underground tunnel data, and recognition accuracy is difficult to guarantee. In order to solve this challenge, an efficient point cloud position recognition algorithm, named Dual-Attention Transformer Network (DAT-Net), is proposed in this paper. The algorithm firstly adopts the farthest point downsampling module to eliminate the invalid redundant points in the point cloud data and retain the basic shape of the point cloud, which reduces the size of the point cloud and, at the same time, reduces the influence of the invalid point cloud on the data analysis. After that, this paper proposes the dual-attention Transformer module to facilitate local information exchange by utilizing the multi-head self-attention mechanism. It extracts local descriptors and integrates highly discriminative global descriptors based on global context with the help of a feature fusion layer to obtain a more accurate and robust global feature representation. Experimental results show that the method proposed in this paper achieves an average F1 score of 0.841 on the SubT-Tunnel dataset and outperforms many existing state-of-the-art algorithms in recognition accuracy and robustness tests.

Phosphorescent Thermal Chiroptical Switch Built through Highly Efficient Chiral Recognition and Amplification

Phosphorescent Thermal Chiroptical Switch Built through Highly Efficient Chiral Recognition and Amplification

Efficient feature recognition and matching technology for IoT‐enabled sports training

AbstractWith the development of the Internet of Things (IoT), the IoT technology is gradually applied to the sport training of athletes. According to the training feature of athletes collected by the IoT equipment, this paper proposes to use the method of feature image sequence analysis and feature extraction to automatically identify the training of athletes. The mathematical model of feature image recognition based on gray difference is established, and the pyramid iterative recognition algorithm is used to reduce the recognition error effectively. In addition, a mathematical model of image sequence feature extraction based on moment invariants is established, and the feature table for athlete matching is discussed in detail. Based on the concept of dynamic establishment of search area and the principle of two‐step template feature recognition and matching, through the analysis of the pictures of high jumpers, the change of the athlete angle of left knee in the process of high jump is obtained, which achieves the purpose of automatic identification of key actions. At the same time, the random error existing in manual recognition is completely eliminated.

Efficient Human Gait Activity Recognition Based on Sensor Fusion and Intelligent Stacking Framework

Efficient Human Gait Activity Recognition Based on Sensor Fusion and Intelligent Stacking Framework

Multi-level attention network: Mixed time–frequency channel attention and multi-scale self-attentive standard deviation pooling for speaker recognition

In this paper, we propose a more efficient lightweight speaker recognition network, the multi-level attention network (MANet). MANet aims to generate more robust and discriminative speaker features by emphasizing features at different levels in the speaker recognition network through multi-level attention. The multi-level attention contains mixed time–frequency channel (MTFC) attention and multi-scale self-attentive standard deviation pooling (MSSDP). MTFC attention combines channel, time, and frequency information to capture global features and model long-term contexts. MSSDP can capture changes in frame-level features and aggregate frame-level features with different scales, generating a long-term, robust, and discriminative utterance-level feature. Therefore, MANet emphasizes the features of different levels. We performed extensive experiments on two popular datasets, Voxceleb and CN-Celeb. The proposed method is compared with the current state-of-the-art speaker recognition methods. It achieved EER/minDCF of 1.82%/0.1965, 1.94%/0.2059, 3.69%/0.3626, and 11.98%/0.4814 on the test sets Voxceleb1-O, Voxceleb1-E, Voxceleb1-H, and CN-Celeb, respectively. It is a more effective lightweight speaker recognition network, superior to most large speaker recognition networks and all lightweight speaker recognition networks tested, with an improved performance of 64% compared to the baseline system ThinResNet-34. Compared to the lightest EfficientTDNN-Small, it has only 0.6 million more parameters but 63% better performance. The performance of MANet is only 4% different compared to the state-of-the-art large model LE-Conformer. In the ablation experiments, our proposed attention method and aggregation model achieved the best experimental performance in Voxceleb1-O with EER/minDCF of 2.46%/0.2708, 2.39%/0.2417, respectively, which indicates that our proposed methods are a significant improvement over previous methods.

Towards efficient video-based action recognition: context-aware memory attention network

Abstract Given the prevalence of surveillance cameras in our daily lives, human action recognition from videos holds significant practical applications. A persistent challenge in this field is to develop more efficient models capable of real-time recognition with high accuracy for widespread implementation. In this research paper, we introduce a novel human action recognition model named Context-Aware Memory Attention Network (CAMA-Net), which eliminates the need for optical flow extraction and 3D convolution which are computationally intensive. By removing these components, CAMA-Net achieves superior efficiency compared to many existing approaches in terms of computation efficiency. A pivotal component of CAMA-Net is the Context-Aware Memory Attention Module, an attention module that computes the relevance score between key-value pairs obtained from the 2D ResNet backbone. This process establishes correspondences between video frames. To validate our method, we conduct experiments on four well-known action recognition datasets: ActivityNet, Diving48, HMDB51 and UCF101. The experimental results convincingly demonstrate the effectiveness of our proposed model, surpassing the performance of existing 2D-CNN based baseline models. Article Highlights Recent human action recognition models are not yet ready for practical applications due to high computation needs. We propose a 2D CNN-based human action recognition method to reduce the computation load. The proposed method achieves competitive performance compared to most SOTA 2D CNN-based methods on public datasets.

Improving deep learning performance by augmenting training data

Satellite image recognition is a crucial application of computer vision that has the po-tential to be applied in various fields such as disaster management, agriculture, and urban planning. The objective of this study is to determine the optimal amount of input data required and select the most effective methods of augmentation necessary for training a convolutional neural network (CNN) for satellite image recognition. To achieve this, we perform a series of experiments to investigate the effect of input data quantity on several performance metrics, including model accuracy, convergence, and generalization. Additionally, we explore the impact of various data augmentation techniques, such as rotation, scaling, and flipping, on model performance. The study suggests several strategies for identifying the saturation point and mitigating the effects of overtraining, in-cluding early stopping and dropout regularization. The findings from this study can significantly contribute to the development of more ef-ficient satellite recognition models. Furthermore, they can help improve the performance of existing models, in addition to providing guidance for future research. The study emphasizes the importance of carefully selecting input data and augmentation methods to achieve optimal performance in CNNs, which is fundamental in advancing the field of computer vision. In addition to the above, the study investigates the potential of transfer learning by pre-training the model on a related dataset and fine-tuning it on the satellite imagery dataset. This approach can reduce the amount of required data and training time and increase model performance. Overall, this study provides valuable insights into the optimal amount of input data and augmentation techniques for training CNNs for satellite image recognition, and its findings can guide future research in this area.

Construction of an Online Cloud Platform for Zhuang Speech Recognition and Translation with Edge-Computing-Based Deep Learning Algorithm

The Zhuang ethnic minority in China possesses its own ethnic language and no ethnic script. Cultural exchange and transmission encounter hurdles as the Zhuang rely exclusively on oral communication. An online cloud-based platform was required to enhance linguistic communication. First, a database of 200 h of annotated Zhuang speech was created by collecting standard Zhuang speeches and improving database quality by removing transcription inconsistencies and text normalization. Second, SAformerNet, a more efficient and accurate transformer-based automatic speech recognition (ASR) network, is achieved by inserting additional downsampling modules. Subsequently, a Neural Machine Translation (NMT) model for translating Zhuang into other languages is constructed by fine-tuning the BART model and corpus filtering strategy. Finally, for the network’s responsiveness to real-world needs, edge-computing techniques are applied to relieve network bandwidth pressure. An edge-computing private cloud system based on FPGA acceleration is proposed to improve model operation efficiency. Experiments show that the most critical metric of the system, model accuracy, is above 93%, and inference time is reduced by 29%. The computational delay for multi-head self-attention (MHSA) and feed-forward network (FFN) modules has been reduced by 7.1 and 1.9 times, respectively, and terminal response time is accelerated by 20% on average. Generally, the scheme provides a prototype tool for small-scale Zhuang remote natural language tasks in mountainous areas.

Unleash the Power of Deep Neural Networks and Transfer Learning for Enhanced Face Recognition

Face recognition has become a crucial technology with diverse applications in various domains such as biometric security, healthcare, personal identification, and law enforcement. It stands out due to its high recognition rate, the richness of features, its universality, and its uniqueness. Having said that, challenges such as varying illumination, poses, facial expressions, face transformations, and backgrounds have hindered recognition accuracy, especially when using traditional techniques that rely on explicit feature extraction before the actual classification. The emergence of deep learning algorithms, such as Convolutional Neural Networks (CNNs), has exhibited prominent potential in image recognition capabilities, but building effective face recognition systems from scratch demands substantial labeled data and computational resources. To overcome these challenges, this paper proposes a novel approach that utilizes transfer learning and a pre-trained AlexNet CNN to improve face recognition capabilities. The model is trained with three different deep-learning optimizers, and the most suitable one is selected for the proposed system. For experiments, we created a custom database with images of five distinct individuals, generating over 1260 face images with each person having at least 250 samples. The dataset was further enriched using data augmentation technique. Using MATLAB with the Deep Learning Toolbox, the results demonstrate the model's recognition rate, peaking up to 100 per cent when the SGD optimizer is employed, 97.88 per cent when RMSProp is the optimizer, and 99.2 per cent for the Adam optimizer while using the same set of hyperparameters. These findings showcase the effectiveness of the approach and open doors for more efficient and accurate face recognition systems in real-world scenarios with large datasets.

A Unified Efficient Deep Learning Architecture for Rapid Safety Objects Classification Using Normalized Quantization-Aware Learning

The efficient recognition and classification of personal protective equipment are essential for ensuring the safety of personnel in complex industrial settings. Using the existing methods, manually performing macro-level classification and identification of personnel in intricate spheres is tedious, time-consuming, and inefficient. The availability of several artificial intelligence models in recent times presents a new paradigm shift in object classification and tracking in complex settings. In this study, several compact and efficient deep learning model architectures are explored, and a new efficient model is constructed by fusing the learning capabilities of the individual, efficient models for better object feature learning and optimal inferencing. The proposed model ensures rapid identification of personnel in complex working environments for appropriate safety measures. The new model construct follows the contributory learning theory whereby each fussed model brings its learned features that are then combined to obtain a more accurate and rapid model using normalized quantization-aware learning. The major contribution of the work is the introduction of a normalized quantization-aware learning strategy to fuse the features learned by each of the contributing models. During the investigation, a separable convolutional driven model was constructed as a base model, and then the various efficient architectures were combined for the rapid identification and classification of the various hardhat classes used in complex industrial settings. A remarkable rapid classification and accuracy were recorded with the new resultant model.

Differing structures and dynamics of two photolesions portray verification differences by the human XPD helicase.

Ultraviolet light generates cyclobutane pyrimidine dimer (CPD) and pyrimidine 6-4 pyrimidone (6-4PP) photoproducts that cause skin malignancies if not repaired by nucleotide excision repair (NER). While the faster repair of the more distorting 6-4PPs is attributed mainly to more efficient recognition by XPC, the XPD lesion verification helicase may play a role, as it directly scans the damaged DNA strand. With extensive molecular dynamics simulations of XPD-bound single-strand DNA containing each lesion outside the entry pore of XPD, we elucidate strikingly different verification processes for these two lesions that have very different topologies. The open book-like CPD thymines are sterically blocked from pore entry and preferably entrapped by sensors that are outside the pore; however, the near-perpendicular 6-4PP thymines can enter, accompanied by a displacement of the Arch domain toward the lesion, which is thereby tightly accommodated within the pore. This trapped 6-4PP may inhibit XPD helicase activity to foster lesion verification by locking the Arch to other domains. Furthermore, the movement of the Arch domain, only in the case of 6-4PP, may trigger signaling to the XPG nuclease for subsequent lesion incision by fostering direct contact between the Arch domain and XPG, and thereby facilitating repair of 6-4PP.

Leveraging a Hybrid Deep Learning Architecture for Efficient Emotion Recognition in Audio Processing

This paper presents a novel hybrid deep learning architecture for emotion recognition from speech signals, which has garnered significant interest in recent years due to its potential applications in various fields such as healthcare, psychology, and entertainment. The proposed architecture combines modified ResNet-34 and RoBERTa models to extract meaningful features from speech signals and classify them into different emotion categories. The model is evaluated on five standard emotion recognition datasets, including RAVDESS, EmoDB, SAVEE, CREMA-D, and TESS, and achieves state-of-the-art performance on all datasets. The experimental results show that the proposed hybrid architecture outperforms existing emotion recognition models, achieving high accuracy and F1 scores for emotion classification. The proposed architecture is promising for real-time emotion recognition applications and can be applied in various domains such as speech-based emotion recognition systems, human-computer interaction, and virtual assistants.

TFF-CNN: Distributed optical fiber sensing intrusion detection framework based on two-dimensional multi-features

Distributed fiber optic vibration sensing system has been widely used in safety monitoring with distinct advantages, and the feature extraction and classification methods of fiber optic signals directly determine the real-time performance and reliability of the monitoring system. The existing research feature extraction methods are unidimensional and time consuming, which cannot balance the goals of high accuracy and low time consumption for safety monitoring systems. In this paper, we propose an efficient recognition framework for fusing signal time–frequency features, called TFF-CNN, based on Gramian Angular Difference Fields(GADF) and FFT co-generation matrix(FFTT) to manually extract two-dimensional time–frequency image features of fiber optic signals, taking the significant advantages of CNN in image processing and combining a two-channel model and a fusion module that simulates human decision-making behavior. The accuracy of TFF-CNN is 99.30 % and the detection response time is only 0.6 s. Compared with other methods in this field, TFF-CNN has the advantages of low false alarm rate and short time consumption, which is more suitable for deployment in security monitoring field with distributed fiber optic sensing system.Index Terms: Distributed optical fiber vibration sensing system(DVS), Two-dimensional multi-feature, CNN, intrusion detection, real-time monitoring.

Handwriting for efficient text entry in industrial VR applications: influence of board orientation and sensory feedback on performance.

Text entry in Virtual Reality (VR) is becoming an increasingly important task as the availability of hardware increases and the range of VR applications widens. This is especially true for VR industrial applications where users need to input data frequently. Large-scale industrial adoption of VR is still hampered by the productivity gap between entering data via a physical keyboard and VR data entry methods. Data entry needs to be efficient, easy-to-use and to learn and not frustrating. In this paper, we present a new data entry method based on handwriting recognition (HWR). Users can input text by simply writing on a virtual surface. We conduct a user study to determine the best writing conditions when it comes to surface orientation and sensory feedback. This feedback consists of visual, haptic, and auditory cues. We find that using a slanted board with sensory feedback is best to maximize writing speeds and minimize physical demand. We also evaluate the performance of our method in terms of text entry speed, error rate, usability and workload. The results show that handwriting in VR has high entry speed, usability with little training compared to other controller-based virtual text entry techniques. The system could be further improved by reducing high error rates through the use of more efficient handwriting recognition tools. In fact, the total error rate is 9.28% in the best condition. After 40 phrases of training, participants reach an average of 14.5 WPM, while a group with high VR familiarity reach 16.16 WPM after the same training. The highest observed textual data entry speed is 21.11 WPM.

Nanomaterial-based magnetic surface molecularly imprinted polymers for specific extraction and efficient recognition of dibutyl phthalate.

A rapid and selective sorbent for the enrichment of dibutyl phthalate (DBP) from water and Chinese Baijiu samples was established using magnetic surface molecularly imprinted polymers (MSMIPs) combined with gas chromatography-mass spectrometer (GC-MS). The MSMIPs were synthesized using a magnetic nanosphere material with silica layer, increasing the polymer surface area as a carrier. Compared with the traditional methods, the addition of magnetic microspheres simplified the process of food substrate purification and significantly shortened the pre-concentration time. The MSMIPs adsorption conforms to the Freundlich isotherm model as multilayer adsorption on an inhomogeneous surface and the pseudo-second-order model. The developed MSMIPs combined with GC-MS method showed good linearity in DBP concentration range of 0.02-1.0mg L-1 with low LOD (0.0054mg L-1) and LOQ (0.018mg L-1), and obtained good recoveries in real samples (95.2-97.2%) with RSD<5.0% (n=9), which were consistent with those from Chinese national standard method.

A Metaverse text recognition model based on character-level contrastive learning

Accurate and efficient text recognition can enhance the accuracy and time efficiency of human–computer interaction and information exchange in noise Metaverse scenarios. For example, it can improve the safety of digital twin-based intelligent transportation and the intelligence of Non-Player Characters. Robust features play a vital role in the performance of scene text recognition models in noise Metaverse situations. To extract robust features, and improve the accuracy and time efficiency of scene text recognition, we propose a Character level text recognition model in Metaverse applications, called MetaChara. It contains two main components: a lightweight text feature extraction module (LightFeature), and a robust character recognition module (RobChara). LightFeature leverages the advantage of global feature aggregation in the primitive representation learning network to handle irregular text images.RobChara incorporates the capability of contrastive learning from the momentum contrast method, improving the robustness of feature extraction in MetaChara. It structures a feature queue for organized storage. By optimizing the similarity of intra-character features and maximizing inter-character differences, it makes the model better adapted to scene text recognition tasks in Metaverse. Experiment results demonstrate that MetaChara is light with 29.14 million parameters and time efficient with an average recognition speed of 1.73 s. It also achieves excellent performance in terms of FLoating-point Operations (FLOPs), registering only 59.60 billion times for each operation. MetaChara achieves an average accuracy of 0.969 for character recognition. We present a case study where MetaChara quickly and accurately recognizes scene texts within the context of autonomous driving in the Metaverse. This demonstrates how MetaChara enhances safety and improves time efficiency for intelligent transportation systems.