Recognition Module Research Articles

Retraction Note: Orthogonal frequency multiplexing division based modulation recognition using deep belief network with tangent search optimization in wireless optic communication

Retraction Note: Orthogonal frequency multiplexing division based modulation recognition using deep belief network with tangent search optimization in wireless optic communication

FE-SKViT: A Feature-Enhanced ViT Model with Skip Attention for Automatic Modulation Recognition

Automatic modulation recognition (AMR) is widely employed in communication systems. However, under conditions of low signal-to-noise ratio (SNR), recent studies reveal limitations in achieving high AMR accuracy. In this work, we introduce a novel network architecture that leverages a transformer-inspired approach tailored for AMR, called Feature-Enhanced Transformer with skip-attention (FE-SKViT). This innovative design adeptly harnesses the advantages of translation variant convolution and the Transformer framework, handling intra-signal variance and small cross-signal variance to achieve enhanced recognition accuracy. Experimental results on RadioML2016.10a, RadioML2016.10b, and RML22 datasets demonstrate that the Feature-Enhanced Transformer with skip-attention (FE-SKViT) excels over other methods, particularly under low SNR conditions ranging from −4 to 6 dB.

AI-Based Object Detection, Distance Measurement and Speaking System for Blind Stick

The sense of sight is fundamental to how humans perceive and navigate their surroundings. For visually impaired individuals, this lack of vision creates significant challenges in detecting obstacles and measuring distances, making daily navigation difficult and potentially dangerous. In response to this problem, we propose an AI-based object detection, distance measurement, and speaking system integrated with a blind stick to enhance mobility and independence for the visually impaired. This system leverages cutting-edge artificial intelligence and image processing techniques to provide real-time object detection and distance measurement. By combining ultrasonic sensors with an AI-powered object recognition module, the system accurately identifies obstacles and calculates their distance, offering directional audio feedback to the user. Designed to be compact and user-friendly, the blind stick system provides seamless assistance in both indoor and outdoor environments. The object detection process utilizes advanced algorithms like YOLO (You Only Look Once) to identify obstacles. Distance measurement is performed using ultrasonic sensors to ensure accurate proximity readings. When an obstacle is detected, the system provides immediate auditory feedback, alerting users to the direction and distance of the object. The Google Maps API enhances usability by offering location awareness and route planning, empowering users to navigate with confidence. This AI-based blind stick system aims to improve the mobility and safety of visually impaired individuals, offering a more autonomous experience in their daily lives.

Tightly Coupled LIDAR/IMU/UWB Fusion via Resilient Factor Graph for Quadruped Robot Positioning

Continuous accurate positioning in global navigation satellite system (GNSS)-denied environments is essential for robot navigation. Significant advances have been made with light detection and ranging (LiDAR)-inertial measurement unit (IMU) techniques, especially in challenging environments with varying lighting and other complexities. However, the LiDAR/IMU method relies on a recursive positioning principle, resulting in the gradual accumulation and dispersion of errors over time. To address these challenges, this study proposes a tightly coupled LiDAR/IMU/UWB fusion approach that integrates an ultra-wideband (UWB) positioning technique. First, a lightweight point cloud segmentation and constraint algorithm is designed to minimize elevation errors and reduce computational demands. Second, a multi-decision non-line-of-sight (NLOS) recognition module using information entropy is employed to mitigate NLOS errors. Finally, a tightly coupled framework via a resilient mechanism is proposed to achieve reliable position estimation for quadruped robots. Experimental results demonstrate that our system provides robust positioning results even in LiDAR-limited and NLOS conditions, maintaining low time costs.

Polish Speech and Text Emotion Recognition in a Multimodal Emotion Analysis System

Emotion recognition by social robots is a serious challenge because sometimes people also do not cope with it. It is important to use information about emotions from all possible sources: facial expression, speech, or reactions occurring in the body. Therefore, a multimodal emotion recognition system was introduced, which includes the indicated sources of information and deep learning algorithms for emotion recognition. An important part of this system includes the speech analysis module, which was decided to be divided into two tracks: speech and text. An additional condition is the target language of communication, Polish, for which the number of datasets and methods is very limited. The work shows that emotion recognition using a single source—text or speech—can lead to low accuracy of the recognized emotion. It was therefore decided to compare English and Polish datasets and the latest deep learning methods in speech emotion recognition using Mel spectrograms. The most accurate LSTM models were evaluated on the English set and the Polish nEMO set, demonstrating high efficiency of emotion recognition in the case of Polish data. The conducted research is a key element in the development of a decision-making algorithm for several emotion recognition modules in a multimodal system.

Locking-Fluorescence Signals Regulated CRISPR/Cas12a Biosensor Based on Metal-Organic Framework for Sensitive Detection of Salmonella typhimurium.

The efficient, sensitive, and rapid detection of Salmonella typhimurium (S. typhimurium) in food and food products is important to ensure food safety and health. This study developed a fluorescence biosensing assay that integrated recombinase-aided amplification (RAA) and CRISPR/Cas12a with a zeolitic imidazolate framework-8@fluorescein sodium (ZIF-8@FLS) nanocomposite for the sensitive detection of S. typhimurium. In this approach, using RAA as a preamplification module, CRISPR/Cas12a-AChE as a target recognition and dual-enzyme cascade amplification module, and the prepared ZIF-8@FLS with high porosity and rapid pH responsiveness as a fluorescence signal explosive amplification module, the RAA-CRISPR/Cas12a-ZIF-8@FLS biosensor was constructed. Under optimal conditions, it exhibited an excellent linear relationship for S. Typhimurium, with a sensitive detection limit as low as 1.3 × 102 CFU/mL and could complete sample detection within 2 h relying on the RAA and ZIF-8@FLS explosive fluorescence rapid response, demonstrating its significant advantages in specificity, sensitivity, and reliability in food-borne pathogens detection.

MAMSC: a semantic enhanced representation model for public opinion key node recognition based on multianchor mapping in semantic communities

PurposeInformation is presented in various modalities such as text and images, and it can quickly and widely spread on social networks and among the general public through key communication nodes involved in public opinion events. Therefore, by tracking and identifying key nodes of public opinion, we can determine the direction of public opinion evolution and timely and effectively control public opinion events or curb the spread of false information.Design/methodology/approachThis paper introduces a novel multimodal semantic enhanced representation based on multianchor mapping semantic community (MAMSC) for identifying key nodes in public opinion. MAMSC consists of four core components: multimodal data feature extraction module, feature vector dimensionality reduction module, semantic enhanced representation module and semantic community (SC) recognition module. On this basis, we combine the method of community discovery in complex networks to analyze the aggregation characteristics of different semantic anchors and construct a three-layer network module for public opinion node recognition in the SC with strong, medium and weak associations.FindingsThe experimental results show that compared with its variants and the baseline models, the MAMSC model has better recognition accuracy. This study also provides more systematic, forward-looking and scientific decision-making support for controlling public opinion and curbing the spread of false information.Originality/valueWe creatively combine the construction of variant autoencoder with multianchor mapping to enhance semantic representation and construct a three-layer network module for public opinion node recognition in the SC with strong, medium and weak associations. On this basis, our constructed MAMSC model achieved the best results compared to the baseline models and ablation evaluation models, with a precision of 91.21%.

An inverted tetrahedron-mediated DNA walker for sulfadimethoxine detection.

Aninverted DNA tetrahedron-mediated modular DNA walker was developed for the determination of sulfadimethoxine. The inverted DNA tetrahedron scaffold raises several advantages of recognition module including appropriate lateral space, multiple recognition domains, and cost-effectiveness. The proposed inverted DNA tetrahedron-based recognition module exhibited better binding affinity and kinetics toward target antibiotic than that of other DNA tetrahedron counterparts. Upon specific binding with target, the released bipedal DNA walking strand hops to the signal amplification module and moves stochastically with assistant of nicking enzyme. By coupling these two modules, a good linear relationship between the fluorescence intensity of supernatant and the concentration of sulfadimethoxine was achieved in the range 0.1-100nM, and the limit of detection was 64.7pM. Furthermore, this modular DNA walker had also successfully applied tospiked honey and milk samples with satisfactory recoveries from 91.5 to 108.8%, demonstrating its practical sensing capability.

INTELLIGENT VIDEO ANALYSIS TECHNOLOGY FOR AUTOMATIC FIRE CONTROL TARGET RECOGNITION BASED ON MACHINE LEARNING

Context. Target recognition is a priority in military affairs. This task is complicated by the fact that it is necessary to recognize moving objects, different terrain and landscape create obstacles for recognition. Combat actions can take place at different times of the day, accordingly, it is necessary to take into account the perspective of lighting and general lighting. It is necessary to detect the object in the video by segmenting the video frames, recognize and classify. Objective of the study is to develop a technology for the analysis of the development of a technology for recognizing targets in real time as a component of the fire control system, due to the use of artificial intelligence, YOLO and machine learning. Method. The article develops a video stream analysis technology for automatic target recognition of the fire control system based on machine learning. The paper proposes the development of a target recognition module as a component of the fire control system within the framework of the proposed information technology using artificial intelligence. The YOLOv8 pattern recognition model family was used to develop the target recognition module. The methods used during the study of the formed dataset. – Bounding Box: Noise – Up to 15% of pixels (limiting frame: adding salt and pepper noise to the image – up to 15% of pixels). – Bounding Box: Blur – Up to 2.5px (bounding box: adding Gaussian blur to the image – up to 2.5 pixels). – Cutout – 3 boxes with 10% size each (cut out a part of the image – 3 boxes of 10% size each). – Brightness Between –25% and +25% (changing the brightness of the image to increase the resistance of the model to changes in lighting and camera settings – from –25% to +25%). – Rotation – Between –15 and +15 (rotation of the image object – clockwise or counterclockwise by degrees from –15 to +15). – Flip – Horizontal (flip the image object horizontally). Results. The data is collected from open sources, in particular, from videos posted in open sources on the YouTube platform. The main task of data preprocessing is the classification of three classes of objects on video or in real time – APC, BMP and TANK. The dataset is formed using the Roboflow platform based on the labeling tools and subsequently the augmentation tools. The dataset consists of 1193 unique images – approximately equally for each class. The training was conducted using Google Colab resources. It took 100 epochs to train the model. Conclusions. Analysis is performed according to mAP50 (average precision as 0.85), mAP50-95 (0.6), precision (0.89) and recall (0.75). Big losses are due to the fact that the background was not taken into account during the research – training the module on the basis of confirmed data (images) of the background without technology

MSGNet: A Multi-Feature Lightweight Learning Network for Automatic Modulation Recognition

MSGNet: A Multi-Feature Lightweight Learning Network for Automatic Modulation Recognition

Extended multi-stream temporal-attention module for skeleton-based human action recognition (HAR)

Graph convolutional networks (GCNs) are an effective skeleton-based human action recognition (HAR) technique. GCNs enable the specification of CNNs to a non-Euclidean frame that is more flexible. The previous GCN-based models still have a lot of issues: (I) The graph structure is the same for all model layers and input data. GCN model's hierarchical structure and human action recognition input diversity make this a problematic approach; (II) Bone length and orientation are understudied due to their significance and variance in HAR. For this purpose, we introduce an Extended Multi-stream Temporal-attention Adaptive GCN (EMS-TAGCN). By training the network topology of the proposed model either consistently or independently according to the input data, this data-based technique makes graphs more flexible and faster to adapt to a new dataset. A spatial, temporal, and channel attention module helps the adaptive graph convolutional layer focus on joints, frames, and features. Hence, a multi-stream framework representing bones, joints, and their motion enhances recognition accuracy. Our proposed model outperforms the NTU RGBD for CS and CV by 0.6% and 1.4%, respectively, while Kinetics-skeleton Top-1 and Top-5 are 1.4% improved, UCF-101 has improved 2.34% accuracy and HMDB-51 dataset has significantly improved 1.8% accuracy. According to the results, our model has performed better than the other models. Our model consistently outperformed other models, and the results were statistically significant that demonstrating the superiority of our model for the task of HAR and its ability to provide the most reliable and accurate results.

LPI Radar Waveform Recognition Based on Hierarchical Classification Approach and Maximum Likelihood Estimation

The importance of information gathering is emphasized to minimize casualties and economic losses in warfare. Through electronic warfare, which utilizes electromagnetic waves, it is possible to discern the enemy’s intentions and respond accordingly, thereby leading the battle advantageously. Consequently, related research is actively underway. The development of various radar signal modulation techniques has revealed limitations in the existing modulation recognition methods, necessitating the development of distinguishing features to overcome these limitations. This paper proposes and analyzes distinguishing features that can differentiate various modulation schemes. Eleven distinguishing features were employed, and twenty-two types of modulated signals, including analog, digital, and composite modulation, were classified using hierarchical classification approach and maximum likelihood estimation (MLE). The proposed method achieves a recognition performance of 99.76% at an SNR of 20 dB and 98.45% at an SNR of 8 dB.

Research on the detection and recognition system of target vehicles based on fusion algorithm

<p>In modern society, the monitoring of vehicles is paramount for upholding traffic safety and order. This paper delves into a Graphical User Interface (GUI)-driven system for vehicle target detection and integration. This system seamlessly integrates three pivotal functionalities: vehicle type recognition, license plate recognition, and driver face recognition. It automates the vehicle inspection assessment process through an intuitive user interface. Specifically, for license plate recognition, the system leverages traditional computer vision techniques, including color and grayscale processing, radon transform, morphological operations, edge detection, character segmentation, and character recognition. The driver face recognition module incorporates methods such as image gray scaling, Gaussian filtering for denoising, face detection, and feature extraction. Meanwhile, vehicle type recognition is achieved by extracting Histogram of Oriented Gradients (HOG) features and utilizing a Support Vector Machine (SVM) classifier. Experimental findings highlight the system’s remarkable recognition accuracy, offering a highly efficient and dependable solution for the automated inspections of vehicles.</p>

Dexterous Manipulation Based on Object Recognition and Accurate Pose Estimation Using RGB-D Data.

This study presents an integrated system for object recognition, six-degrees-of-freedom pose estimation, and dexterous manipulation using a JACO robotic arm with an Intel RealSense D435 camera. This system is designed to automate the manipulation of industrial valves by capturing point clouds (PCs) from multiple perspectives to improve the accuracy of pose estimation. The object recognition module includes scene segmentation, geometric primitives recognition, model recognition, and a color-based clustering and integration approach enhanced by a dynamic cluster merging algorithm. Pose estimation is achieved using the random sample consensus algorithm, which predicts position and orientation. The system was tested within a 60° field of view, which extended in all directions in front of the object. The experimental results show that the system performs reliably within acceptable error thresholds for both position and orientation when the objects are within a ±15° range of the camera's direct view. However, errors increased with more extreme object orientations and distances, particularly when estimating the orientation of ball valves. A zone-based dexterous manipulation strategy was developed to overcome these challenges, where the system adjusts the camera position for optimal conditions. This approach mitigates larger errors in difficult scenarios, enhancing overall system reliability. The key contributions of this research include a novel method for improving object recognition and pose estimation, a technique for increasing the accuracy of pose estimation, and the development of a robot motion model for dexterous manipulation in industrial settings.

Noncovalent Inhibition and Covalent Inactivation of Proline Dehydrogenase by Analogs of N-Propargylglycine.

The flavoenzyme proline dehydrogenase (PRODH) catalyzes the first step of proline catabolism, the oxidation of l-proline to Δ1-pyrroline-5-carboxylate. The enzyme is a target for chemical probe discovery because of its role in the metabolism of certain cancer cells. N-propargylglycine is the first and best characterized mechanism-based covalent inactivator of PRODH. This compound consists of a recognition module (glycine) that directs the inactivator to the active site and an alkyne warhead that reacts with the FAD after oxidative activation, leading to covalent modification of the FAD N5 atom. Here we report structural and kinetic data on analogs of N-propargylglycine with the goals of understanding the initial docking step of the inactivation mechanism and to test the allyl group as a warhead. The crystal structures of PRODH complexed with unreactive analogs in which N is replaced by S show how the recognition module mimics the substrate proline by forming ion pairs with conserved arginine and lysine residues. Further, the C atom adjacent to the alkyne warhead is optimally positioned for hydride transfer to the FAD, providing the structural basis for the first bond-breaking step of the inactivation mechanism. The structures also suggest new strategies for designing improved N-propargylglycine analogs. N-allylglycine, which consists of a glycine recognition module and allyl warhead, is shown to be a covalent inactivator; however, it is less efficient than N-propargylglycine in both enzyme inactivation and cellular assays. Crystal structures of the N-allylglycine-inactivated enzyme are consistent with covalent modification of the N5 by propanal.

BodyFlow: An Open-Source Library for Multimodal Human Activity Recognition

Human activity recognition is a critical task for various applications across healthcare, sports, security, gaming, and other fields. This paper presents BodyFlow, a comprehensive library that seamlessly integrates human pose estimation and multiple-person estimation and tracking, along with activity recognition modules. BodyFlow enables users to effortlessly identify common activities and 2D/3D body joints from input sources such as videos, image sets, or webcams. Additionally, the library can simultaneously process inertial sensor data, offering users the flexibility to choose their preferred input, thus facilitating multimodal human activity recognition. BodyFlow incorporates state-of-the-art algorithms for 2D and 3D pose estimation and three distinct models for human activity recognition.

Textual studies of the era of big data and neural networks

This article analyses the emergence of new technologies for working with big data which can be highly helpful to philologists, studying the diachronic development. First of all, that applies to the study and publication of Old Russian manuscripts with traditional liturgical texts used for church service. These manuscripts existed in a huge number of folios, and in the process of copying were subjected to considerable textual unification. That makes it very difficult to study them by the laborious methods of traditional textual criticism. Now, when the full text of the monuments can be automatically processed, the creation of the Linguistic intellectual environment (LIE) has been lounged. This tool will provide new opportunities for the study of Slavonic liturgical texts from different historical periods. As a result, we will create a corpus of liturgical texts of the 11th–17th centuries, obtained using a program for automatic text recognition of manuscripts, with annotation und search module. The user of the LIE will be able to receive a complete list of variant readings for each fragment of a liturgical book of the widest range of manuscripts. In fact, we are talking of a new type of publication of traditional liturgical texts, when the user can set the parameters for the edition in accordance with his research interests.

HCTC: Hybrid Convolutional Transformer Classifier for Automatic Modulation Recognition

Automatic modulation recognition (AMR) methods used in advanced wireless communications systems can identify unknown signals without requiring reference information. However, the acceptance of these methods depends on the accuracy, number of parameters, and computational complexity. This study proposes a hybrid convolutional transformer classifier (HCTC) for the classification of unknown signals. The proposed method utilizes a three-stage framework to extract features from in-phase/quadrature (I/Q) signals. In the first stage, spatial features are extracted using a convolutional layer. In the second stage, temporal features are extracted using a transformer encoder. In the final stage, the features are mapped using a deep-learning network. The proposed HCTC method is investigated using the benchmark RadioML database and compared with state-of-the-art methods. The experimental results demonstrate that the proposed method achieves a better performance in modulation signal classification. Additionally, the performance of the proposed method is evaluated when applied to different batch sizes and model configurations. Finally, open issues in modulation recognition research are addressed, and future research perspectives are discussed.

Leveraging Incremental Learning for Dynamic Modulation Recognition

Modulation recognition is an important technology used to correctly identify the modulation modes of wireless signals and is widely used in cooperative and confrontational scenarios. Traditional modulation-recognition algorithms require the assistance of expert experiences, which constrains their applications. With the rapid development of artificial intelligence in recent years, deep learning (DL) is widely advocated for intelligent modulation recognition. Typically, DL-based modulation-recognition algorithms implicitly assume a relatively static scenario in which the signal samples of all the modulation modes can be completely collected in advance. In practical situations, the radio environment is quite dynamic and the signal samples with new modulation modes may appear sequentially, in which the current DL-based modulation-recognition algorithms may require unacceptable time and computing resource consumption to re-train the optimal model from scratch. In this study, we leveraged incremental learning (IL) and designed a novel IL-based modulation-recognition algorithm that consists of an initial stage and multiple incremental stages. The main novelty of the proposed algorithm lies in the new loss function design in each incremental stage, which combines the distillation loss of recognizing old modulation modes and the cross-entropy loss of recognizing new modulation modes. With the proposed algorithm, the knowledge of the signal samples with new modulation modes can be efficiently learned in the current stage without forgetting the knowledge learned in the previous stages. The simulation results demonstrate that the proposed algorithm could achieve a recognition accuracy close to the upper bound with a much lower computing time and it outperformed the existing IL-based benchmarks.

Engineering of a Multi-Modular DNA Nanodevice for Spatioselective Imaging and Evaluation of NK Cell-Mediated Cancer Immunotherapy.

Granzyme A (GzmA) secreted by natural killer (NK) cells has garnered considerable interest as a biomarker to evaluate the efficacy of cancer immunotherapy. However, current methodologies to selectively monitor the spatial distribution of GzmA in cancer cells during NK cell-targeted therapy are extremely challenging, primarily due to the existence of diverse cell populations, the low levels of GzmA expression, and the limited availability of GzmA probes. Herein we develop a multi-modular, structurally-ordered DNA nanodevice for evaluating NK cell-mediated cancer immunotherapy (MODERN), that permits spatioselective imaging of GzmA in cancer cells through GzmA-induced apurinic/apyrimidinic endonuclease 1 (APE1) inactivation. The MODERN incorporates multiple functional modules, including an APE1-gated recognition module, a photo-activated amplification module, an aptamer-mediated tumor-target module, and a polycatenane DNA module, enabling improved sensitivity and specificity towards intracellular GzmA. The MODERN was activated (on) in cancer cells due to the overexpression of APE1, whereas it remained silent (off) in the NK-treated cancer cells owing to the GzmA-induced APE1 inactivation. Furthermore, we demonstrated that GzmA-induced APE1 inactivation blocks the cellular repair of target cells, resulting in efficient cell death. This MODERN that relies on the specific inactivation of APE1 by GzmA should be beneficial for evaluating the efficacy of cancer immunotherapy.