Recognition Step Research Articles

Urban Traffic Congestion State Recognition Supporting Algorithm Research on Vehicle Wireless Positioning in Vehicle–Road Cooperative Environment

Vehicle–road cooperative technology applies wireless communication and a new generation of internet technology to urban traffic management, providing an effective way to solve urban traffic congestion and improve traffic efficiency. This article researches the vehicle wireless positioning fusion algorithm, suitable for the actual vehicle–road collaborative environment, which is an important step of urban traffic congestion state recognition. First, based on the error correction of existing wireless positioning algorithms, a weighting indicator considering distance and positioning compound errors is designed, and a vehicle wireless positioning fusion algorithm based on error weighting to eliminate line-of-sight (LOS) and non-line-of-sight (NLOS) error is proposed. Secondly, the wireless positioning fusion algorithm is verified based on accuracy evaluation indicators such as root mean square error (RMSE), Cramer Rao lower bound (CRLB), geometric differentiation of precision (GDOP), and cumulative distribution probability (CDP), and the sensitivity of the distance propagation model parameters to the positioning error is analyzed. The verification results show that the local vehicle wireless positioning fusion algorithm proposed in this article could be useful to locate vehicles in an actual vehicle–road collaborative environment. The positioning accuracy could reach 46.31 m with 67% probability, while the positioning accuracy could reach 122.53 m with 95% probability. The average positioning accuracy could reach 39.97 m. Compared with the two types of wireless positioning methods based on ranging and non-ranging methods, the positioning accuracy is improved by 7.74% and 17.69%. The algorithm can either use the roadside base stations to carry out the individual vehicle positioning or cooperate with GPS positioning and trilateral positioning to make up for the positioning blind spots caused by the lack of signal, interference, or base station overload in the urban complex road environment and, furthermore, improves the robustness of vehicle positioning. The results could assist in all-day real-time traffic congestion state recognition and other actual scenarios.

A Novel Classification Method with Cubic Spline Interpolation

Classification is the last, and usually the most time-consuming step in recognition. Most recently proposed classification algorithms have adopted machine learning (ML) as the main classification approach, regardless of time consumption. This study proposes a statistical feature classification cubic spline interpolation (FC-CSI) algorithm to classify emotions in speech using a curve fitting technique. FC-CSI is utilized in a speech emotion recognition system (SERS). The idea is to sketch the cubic spline interpolation (CSI) for each audio file in a dataset and the mean cubic spline interpolations (MCSIs) representing each emotion in the dataset. CSI interpolation is generated by connecting the features extracted from each file in the feature extraction phase. The MCSI is generated by connecting the mean features of 70% of the files of each emotion in the dataset. Points on the CSI are considered the new generated features. To classify each audio file according to emotion, the Euclidian distance (ED) is found between each CSI and all MCSIs of all emotions in the dataset. Each audio file is classified according to the nearest MCSI to the CSI representing it. The three datasets used in this work are Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS), Berlin (Emo-DB), and Surrey Audio-Visual Expressed Emotion (SAVEE). The proposed work shows fast classification and high accuracy of results. The classification accuracy, i.e., the proportion of samples assigned to the correct class, using FC-CSI without feature selection (FS), was 69.08%, 92.52%, and 89.1% with RAVDESS, Emo-DB, and SAVEE, respectively. The results of the proposed method were compared to those of a designed neural network called SER-NN. Comparisons were made with and without FS. FC-CSI outperformed SER-NN on Emo-DB and SAVEE, and underperformed on RAVDESS, without using an FS algorithm. It was noticed from experiments that FC-CSI operated faster than the same system utilizing SER-NN.

Retracted] Research on Sports Training Action Image Recognition Based on SDN

Current image recognition methods cannot combine the transmission of image data with the interaction of image features, so the steps of image recognition are too independent, and the traditional methods take longer time and cannot complete the image denoising. Therefore, a recognition method of sports training action image based on software defined network (SDN) architecture is proposed. The SDN architecture is used to integrate the image data transmission and interactive process and to optimize the image processing centralization. The network architecture is composed of application layer, control layer, and infrastructure layer. Based on this, the dimension of image sample set is reduced, and the edge detection operator in any direction is constructed. The image edge filter is realized by calculating the response and threshold of image edge by using lag threshold and nonmaximum suppression (NMS). The Hough transform algorithm is improved to optimize the detection range. Extracting the neighborhood feature of sports training action, the recognition of sports training action image based on SDN architecture is completed. Simulation results show that the proposed method takes less time and the image denoising effect is better. In addition, the F1 test results of the proposed method are higher than those of the literature, and the convergence is better. Therefore, the performance of the proposed method is better.

Facial processing in bipolar disorder is mediated by clinical and biological aspects.

The process of detecting faces can be considered one of the initial steps in face recognition, which is essential for human interaction. We sought to investigate whether a face perception task reliably detects subtle perceptual disturbances between patients with bipolar disorder (BD) and healthy controls. In this multisite study, we examined differences between BD patients and matched healthy controls. Participants were instructed to detect the orientation (either left or right) of a face when it was presented as a face/non-face pair on a computer screen using Bayesian entropy estimation. Data analyses compared performance between the groups. Overall, BD patients exhibited more perceptual disturbances compared with controls. BD patients who took olanzapine had better performance and faster reaction times (RTs) than patients who took lithium or were medication-naive. BD patients who took lithium had better performance and faster RTs than medication-naive patients. The medication-naive BD group exhibited greater disturbances than all other groups. These findings highlight the reliability of the face perception task used herein and may be important for public health initiatives and follow-up studies that seek to understand the diverse effects of other variables that can affect sensory processing in this population.

A Review on Emotional Speech Databases

Due to its numerous practical applications, human emotion recognition from speech is now a challenging and demanding research subject for scientists. The Speech databases, speech features, and classifiers are the important factors for recognizing emotions from speech. The availability of suitable emotional speech databases is the first step for Speech Emotion Recognition (SER). This paper presents a comprehensive literature review of emotional speech databases. The availability of appropriate emotional speech databases in all emotions and languages are summarized. A total of 26 papers for the emotional speech database have been reviewed. It determines which speech databases are often used and also identifies popular speech databases and the languages in which the majority of the databases are available.

Core point pixel-level localization by fingerprint features in spatial domain.

Singular point detection is a primary step in fingerprint recognition, especially for fingerprint alignment and classification. But in present there are still some problems and challenges such as more false-positive singular points or inaccurate reference point localization. This paper proposes an accurate core point localization method based on spatial domain features of fingerprint images from a completely different viewpoint to improve the fingerprint core point displacement problem of singular point detection. The method first defines new fingerprint features, called furcation and confluence, to represent specific ridge/valley distribution in a core point area, and uses them to extract the innermost Curve of ridges. The summit of this Curve is regarded as the localization result. Furthermore, an approach for removing false Furcation and Confluence based on their correlations is developed to enhance the method robustness. Experimental results show that the proposed method achieves satisfactory core localization accuracy in a large number of samples.

Character Detection and Segmentation of Historical Uchen Tibetan Documents in Complex Situations

Tibetan is a low-resource language, and Tibetan culture carried by historical Tibetan documents is an important part of Chinese civilization. The study of historical Tibetan documents is of great significance to the protection of Tibetan culture and the promotion of Chinese culture. Character segmentation is an important step in image analysis and recognition of historical Tibetan documents. However, the following three challenges prevent solving problems of character segmentation in historical Tibetan documents: 1) the text lines have different degrees of tilt and twist; 2) there are many complex situations such as overlapping, crossing, touching and breaking character strokes; and 3) these documents are written by different people with different stroke styles. To resolve these problems, we propose a character segmentation method based on key feature information for historical Tibetan documents. The proposed method consists of three parts: 1) projection and syllable point location information are used to shorten the text lines of historical Tibetan documents and establish a character block database; 2) the local baseline of the character block is detected by using the location information of syllable points or combined with horizontal projection and straight line detection, and the character block is divided into two areas above and below the baseline, and different segmentation methods are adopted; and 3) in view of the large difference in stroke styles, three stroke attribution distances are used to complete the attribution. The experimental results show that the method proposed in this paper can effectively solve the problem of character segmentation of historical Tibetan documents and achieve a better character segmentation effect, which also provides a reference for the relevant document character segmentation.

Retracted] FH‐YOLOv4 with Constrained Aspect Ratio Loss for Video Face Detection and Public Safety

Video face detection is a crucial first step in many facial recognition and face analysis systems. It should serve postprocessing steps as much as possible while satisfying high‐accuracy real‐time detection. In this paper, we first introduce the constrained aspect ratio loss (CARLoss) for better facial boxes regression and incorporate it into the modified FH‐YOLOv4, then the IoU Tracker‐based video face image deduplication algorithm is proposed on the detection level. Extensive experiments and comparative tests show the effectiveness of our method.

Surgical face masks as a source of emergent pollutants in aquatic systems: Analysis of their degradation product effects in Danio rerio through RNA-Seq.

Surgical face masks are the most popularised and effective personal equipment for protecting public health during the COVID-19 pandemic. They are composed of plastic polymer fibres with a large amount of inorganic and organic compounds that can be released into aquatic environments through degradation processes. This source of microplastics and inorganic and organic substances could potentially impact aquatic organisms. In this study, the toxicogenomic effects of face masks at different stages of degradation in water were analysed in zebrafish larvae (Danio rerio) through RNA-Seq. Larvae were exposed for 10 days to three treatments: 1) face mask fragments in an initial stage of degradation (poorly degraded masks -PDM- products) with the corresponding water; 2) face mask fragments in an advanced stage of degradation (highly degraded masks -HDM- products) with the corresponding water; and 3) water derived from HDM (W-HDM). Transcriptome analyses revealed that the three treatments provoked the down-regulation of genes related to reproduction, especially the HDM products, suggesting that degradation products derived from face masks could act as endocrine disruptors. The affected genes are involved in different steps of reproduction, including gametogenesis, sperm-egg recognition and binding or fertilisation. Immune-related genes and metabolic processes were also differentially affected by the treatments.

Recognizing human activities in Industry 4.0 scenarios through an analysis-modeling- recognition algorithm and context labels

Activity recognition technologies only present a good performance in controlled conditions, where a limited number of actions are allowed. On the contrary, industrial applications are scenarios with real and uncontrolled conditions where thousands of different activities (such as transporting or manufacturing craft products), with an incredible variability, may be developed. In this context, new and enhanced human activity recognition technologies are needed. Therefore, in this paper, a new activity recognition technology, focused on Industry 4.0 scenarios, is proposed. The proposed mechanism consists of different steps, including a first analysis phase where physical signals are processed using moving averages, filters and signal processing techniques, and an atomic recognition step where Dynamic Time Warping technologies and k-nearest neighbors solutions are integrated; a second phase where activities are modeled using generalized Markov models and context labels are recognized using a multi-layer perceptron; and a third step where activities are recognized using the previously created Markov models and context information, formatted as labels. The proposed solution achieves the best recognition rate of 87% which demonstrates the efficacy of the described method. Compared to the state-of-the-art solutions, an improvement up to 10% is reported.

An Integrated Model to Improve Medication Reconciliation in Oncology: Prospective Interventional Study.

BackgroundAccurate medication reconciliation reduces the risk of drug incompatibilities and adverse events that can occur during transitions in care. Community pharmacies (CPs) are a crucial part of the health care system and could be involved in collecting essential information on conventional and supplementary drugs used at home.ObjectiveThe aim of this paper was to establish an alliance between our cancer institute, Istituto Romagnolo per lo Studio dei Tumori (IRST), and CPs, the latter entrusted with the completion of a pharmacological recognition survey. We also aimed to integrate the national information technology (IT) platform of CPs with the electronic medical records of IRST.MethodsCancer patients undergoing antiblastic treatments were invited to select a CP taking part in the study and to complete the pharmacological recognition step. The information collected by the pharmacist was sent to the electronic medical records of IRST through the new IT platform, after which the oncologist performed the reconciliation process.ResultsA total of 66 CPs completed surveys for 134 patients. An average of 5.9 drugs per patient was used at home, with 12 or more used in the most advanced age groups. Moreover, 60% (80/134) of the patients used nonconventional products or critical foods. Some potential interactions between nonconventional medications and cancer treatments were reported.ConclusionsIn the PROF-1 (Progetto di Rete in Oncologia con le Farmacie di comunità della Romagna) study, an alliance was created between our cancer center and CPs to improve medication reconciliation, and a new integrated IT platform was validated.Trial RegistrationClinicalTrials.gov NCT04796142; https://clinicaltrials.gov/ct2/show/NCT04796142

A REAL – TIME SYSTEM FOR RECOGNITION OF AMERICAN SIGN LANGUAGE BY USING DEEP LEARINING

Every day we see many people, who are facing illness like deaf, dumb etc. There are not as many technologies which help them to interact with each other. They face difficulty in interacting with others. Sign language is used by deaf and hard hearing people to exchange information between their own community and with other people. Computer recognition of sign language deals from sign gesture acquisition and continues till text/speech generation. Sign gestures can be classified as static and dynamic. However static gesture recognition is simpler than dynamic gesture recognition but both recognition systems are important to the human community. The ASL American sign language recognition steps are described in this survey. There are not as many technologies which help them to interact with each other. They face difficulty in interacting with others. Image classification and machine learning can be used to help computers recognize sign language, which could then be interpreted by other people. Earlier we have Glove-based method in which the person has to wear a hardware glove, while the hand movements are getting captured. It seems a bit uncomfortable for practical use. Here we use visual based method. Convolutional neural networks and mobile ssd model have been employed in this paper to recognize sign language gestures. Preprocessing was performed on the images, which then served as the cleaned input. Tensor flow is used for training of images. A system will be developed which serves as a tool for sign language detection. Tensor flow is used for training of images. Keywords: ASL recognition system, convolutional neural network (CNNs), classification, real time, tensor flow

Plant Viruses Can Alter Aphid-Triggered Calcium Elevations in Infected Leaves.

Alighting aphids probe a new host plant by intracellular test punctures for suitability. These induce immediate calcium signals that emanate from the punctured sites and might be the first step in plant recognition of aphid feeding and the subsequent elicitation of plant defence responses. Calcium is also involved in the transmission of non-persistent plant viruses that are acquired by aphids during test punctures. Therefore, we wanted to determine whether viral infection alters calcium signalling. For this, calcium signals triggered by aphids were imaged on transgenic Arabidopsis plants expressing the cytosolic FRET-based calcium reporter YC3.6-NES and infected with the non-persistent viruses cauliflower mosaic (CaMV) and turnip mosaic (TuMV), or the persistent virus, turnip yellows (TuYV). Aphids were placed on infected leaves and calcium elevations were recorded by time-lapse fluorescence microscopy. Calcium signal velocities were significantly slower in plants infected with CaMV or TuMV and signal areas were smaller in CaMV-infected plants. Transmission tests using CaMV-infected Arabidopsis mutants impaired in pathogen perception or in the generation of calcium signals revealed no differences in transmission efficiency. A transcriptomic meta-analysis indicated significant changes in expression of receptor-like kinases in the BAK1 pathway as well as of calcium channels in CaMV- and TuMV-infected plants. Taken together, infection with CaMV and TuMV, but not with TuYV, impacts aphid-induced calcium signalling. This suggests that viruses can modify plant responses to aphids from the very first vector/host contact.

Multiple Collaborative Supervision Pattern Recognition Method within Social Organizations Based on Data Clustering Algorithm

This paper proposes a multiple collaborative supervision pattern recognition method within social organizations based on data clustering algorithm to realize diversified supervision within social organizations and improve the effect of the said pattern recognition. Firstly, the characteristics and functions of social organizations are analyzed, and the definition of social organizations is given. Further, this paper studies the meaning and characteristics of social organization supervision, analyzes the failure of internal supervision of social organizations, and then determines the internal governance elements of social organizations. In addition, the basic steps of pattern recognition are given. Finally, multiple collaborative supervision patterns recognition within social organizations is realized based on data clustering algorithm. Experiments show that this method can improve the recognition accuracy of multiple collaborative supervision patterns and reduce the recognition time.

Fragment-Sized and Bidentate (Immuno)Proteasome Inhibitors Derived from Cysteine and Threonine Targeting Warheads.

Constitutive- and immunoproteasomes are part of the ubiquitin–proteasome system (UPS), which is responsible for the protein homeostasis. Selective inhibition of the immunoproteasome offers opportunities for the treatment of numerous diseases, including inflammation, autoimmune diseases, and hematologic malignancies. Although several inhibitors have been reported, selective nonpeptidic inhibitors are sparse. Here, we describe two series of compounds that target both proteasomes. First, benzoxazole-2-carbonitriles as fragment-sized covalent immunoproteasome inhibitors are reported. Systematic substituent scans around the fragment core of benzoxazole-2-carbonitrile led to compounds with single digit micromolar inhibition of the β5i subunit. Experimental and computational reactivity studies revealed that the substituents do not affect the covalent reactivity of the carbonitrile warhead, but mainly influence the non-covalent recognition. Considering the small size of the inhibitors, this finding emphasizes the importance of the non-covalent recognition step in the covalent mechanism of action. As a follow-up series, bidentate inhibitors are disclosed, in which electrophilic heterocyclic fragments, i.e., 2-vinylthiazole, benzoxazole-2-carbonitrile, and benzimidazole-2-carbonitrile were linked to threonine-targeting (R)-boroleucine moieties. These compounds were designed to bind both the Thr1 and β5i-subunit-specific residue Cys48. However, inhibitory activities against (immuno)proteasome subunits showed that bidentate compounds inhibit the β5, β5i, β1, and β1i subunits with submicromolar to low-micromolar IC50 values. Inhibitory assays against unrelated enzymes showed that compounds from both series are selective for proteasomes. The presented nonpeptidic and covalent derivatives are suitable hit compounds for the development of either β5i-selective immunoproteasome inhibitors or compounds targeting multiple subunits of both proteasomes.

Symbolic Learning using Brain Programming for the Recognition of Leukemia Images

In this work, we propose an approach of symbolic learning for the recognition of leukemia images. Image recognition for cancer detection is often a subjective problem due to different interpretations by experts of the medical area. Feature extraction is acritical step in image recognition, and current automatic approaches are unintelligible since they need to be adapted to different image domains. We propose the paradigm of brain programming as a symbolic learning approach to address aspects involved in the derivation of knowledge that allows us to recognize subtypes of leukemia in color images. Experimental results provide evidence that the multi-class recognition task is achieved through the solutions discovered from multiples runs of the bioinspired model.

Trigger primitive generation algorithm in the CMS barrel muon chambers during HL-LHC

This contribution presents an update on the Analytical Method (AM) algorithm for trigger primitive (TP) generation in the CMS Drift Tube (DT) chambers during the High Luminosity LHC operation (HL-LHC or LHC phase 2). The algorithm has been developed and validated both in software with an emulation approach, and through hardware implementation tests. The algorithm is mainly divided into the following steps: a grouping (pattern recognition) step that finds the path of a given muon, a fitting step to extract the track parameters (position and bending angle), and a correlation step that matches the information from the different super-layers and with signal from the resistive plate chambers. Agreement between the software emulation and the firmware implementation has been verified using different data samples, including a sample of real muons collected during 2016 data taking. In this contribution, an update of the grouping step using a pseudo-Bayes classifier will be discussed.

Early transcriptomic response of the mycoparasite Sphaerodes mycoparasitica to the mycotoxigenic Fusarium graminearum 3-ADON, the cause of Fusarium head blight

Mycoparasites are an assemblage of biotrophic and necrotrophic fungi that occur on plant pathogenic fungal hosts. Biotrophic mycoparasites are often overlooked in transcriptomic-based biocontrol studies. Sphaerodes mycoparasitica (S.m.) is a specific biotrophic mycoparasite of plant pathogenic Fusarium graminearum (F.g.), a devastating Fusarium head blight (FHB) disease in small-grain cereals. To understand the biotrophic mycoparasitism comprehensively, we performed Illumina RNA-Seq transcriptomic study on the fungus–fungus interaction in vitro. The aim is to identify the transcript-level mechanism related to the biotrophic S.m. mycoparasitism, particularly its ability to effectively control the F.g. 3-ADON chemotype. A shift in the transcriptomic profile of the mycoparasite was triggered in response to its interaction with F.g. during recognition (1.5 days) and colonization (3.5 days) steps. RNA-Seq analysis revealed ~ 30% of annotated transcripts with "function unknown". Further, 14 differentially expressed genes functionally linked to the biotrophic mycoparasitism were validated by quantitative real-time PCR (qPCR). The gene expression patterns of the filamentous haemagglutinin/adhesin/attachment factor as well as cell wall-degrading glucanases and chitinases were upregulated by host interaction. Besides, mycoparasitism-associated antioxidant resistance genes encoding ATP-binding cassette (ABC) transporter(s) and glutathione synthetase(s) were upregulated. However, the thioredoxin reductase was downregulated which infers that this antioxidant gene can be used as a resistance marker to assess S.m. antifungal and antimycotoxigenic activities. The interactive transcriptome of S. mycoparasitica provides new insights into specific mycoparasitism and will contribute to future research in controlling FHB.Graphic

Hydrogel Microparticles for Fluorescence Detection of miRNA in Mix-Read Bioassay.

Herein we describe the development of a mix-read bioassay based on a three-dimensional (3D) poly ethylene glycol—(PEG)-hydrogel microparticles for the detection of oligonucleotides in complex media. The key steps of hydrogels synthesis and molecular recognition in a 3D polymer network are elucidated. The design of the DNA probes and their density in polymer network were opportunely optimized. Furthermore, the diffusion into the polymer was tuned adjusting the polymer concentration and consequently the characteristic mesh size. Upon parameters optimization, 3D-PEG-hydrogels were synthetized in a microfluidic system and provided with fluorescent probe. Target detection occurred by double strand displacement assay associated to fluorescence depletion within the hydrogel microparticle. Proposed 3D-PEG-hydrogel microparticles were designed for miR-143-3p detection. Results showed 3D-hydrogel microparticles with working range comprise between 10−6–10−12 M, had limit of detection of 30 pM and good specificity. Moreover, due to the anti-fouling properties of PEG-hydrogel, the target detection occurred in human serum with performance comparable to that in buffer. Due to the approach versatility, such design could be easily adapted to other short oligonucleotides detection.

Detection optimization of license plate targets based on AdvancedEAST

As an important core in the intelligent traffic management system, the technology and application of license plate recognition have become research focus. Detecting the accurate location of a license plate from a vehicle image is considered to be the most crucial step of license plate recognition, which greatly affects the recognition rate and speed of the whole system. Nevertheless, due to the low accuracy of license plate detection in natural scenes, further investigations are still needed in this field in order to make the detection process very efficient. In this paper, We have studied and implemented a convolutional neural network license plate detection algorithm based on transfer learning. According to the invention, new energy license plates and ordinary license plates are adopted as the research objects. The text detection model AdvancedEAST is trained on the license plate images through the transfer learning method, and experiments are carried out on the self-built license plate dataset. The experimental results show that the algorithm can better adapt to light complexity, low resolution, target interference, license plate tilt and other complex conditions. The license plate positioning algorithm has high accuracy in natural scenes, and it is superior to the traditional license plate detection methods.