Hand Gesture of Recognition Pattern Analysis by Image Treatment Techniques

The goal of this project is to write a program in the C++ language that can recognize motions made by a subject in front of a camera. To do this, in the first place, a sequence of distance images has been obtained using a depth camera. Later, these images are processed through a series of blocks into which the program has been divided; each of them will yield a numerical or logical result, which will be used later by the following blocks. The blocks into which the program has been divided are three; the first detects the subject’s hands, the second detects if there has been movement (and therefore a gesture has been made), and the last detects the type of gesture that has been made accomplished. On the other hand, it intends to present to the reader three unique techniques for acquiring 3D images: stereovision, structured light, and flight time, in addition to exposing some of the most used techniques in image processing, such as morphology and segmentation.

Advances in Computer Recognition, Image Processing and Communications

This Special Issue aimed to gather high-quality advancements in theoretical and practical aspects of computer recognition, pattern recognition, image processing and machine learning (shallow and deep), including, in particular, novel implementations of these techniques in the areas of modern telecommunications and cybersecurity [...]

Changes in the Sensitization Pattern to Alternaria alternata Allergens in Patients Treated with Alt a 1 Immunotherapy

Alternaria alternata is the most important allergenic fungus, with up to 20% of allergic patients affected. The sensitization profile of patients sensitized to A. alternata and how it changes when treated with immunotherapy is not known. Our objective is to determine the allergen recognition pattern of allergic patients to A. alternata and to study its association to the parameters studied in a clinical trial recently published. Sera of 64 patients from the clinical trial of immunotherapy with native major allergen Alt a 1 were analyzed by immunoblotting; 98. 4% of the patients recognized Alt a 1. The percentage of recognition for Alt a 3, Alt a 4, and/or Alt a 6, Alt a 7, Alt a 8, Alt a 10 and/or Alt a 15 was 1.6%, 21.9%, 12.5%, 12.5%, and 12.5% respectively. Of the 64 patients, 45 (70.3%) only recognized Alt a 1 among the allergens present in the A. alternata extract. Immunotherapy with Alt a 1 desensitizes treated patients, reducing their symptoms and medication consumption through the elimination of Alt a 1 sensitization, which is no longer present in the immunoblotting of some patients. There may be gender differences in the pattern of sensitization to A. alternata allergens, among others.

Effect on Neural Pattern Classifier for Intelligent Gas Sensor by Increasing Number of Hidden Layer

Neural networks are used to solve complex problem viz., speech and image recognition, pattern recognition (Pattern classification), computer vision etc. Pattern classification by using Back Propagation algorithm for an intelligent gas sensor application is presented. The classifier is trained using published data of thick film tin oxide sensor array. Its superior classification and learning performance is demonstrated for discrimination of alcohols and alcoholic beverages by increasing number of hidden layer. The new model proposed in this article give steep and monotone learning curve and better classification efficiency. Keywords: Neural Network classifier, Back Propagation Algorithm, system error, classification efficiency, learning curve

The benefits of impossible tests: Assessing the role of error-correction in the pretesting effect

Relative to studying alone, guessing the meanings of unknown words can improve later recognition of their meanings, even if those guesses were incorrect – the pretesting effect (PTE). The error-correction hypothesis suggests that incorrect guesses produce error signals that promote memory for the meanings when they are revealed. The current research sought to test the error-correction explanation of the PTE. In three experiments, participants studied unfamiliar Finnish-English word pairs by either studying each complete pair or by guessing the English translation before its presentation. In the latter case, the participants also guessed which of two categories the word belonged to. Hence, guesses from the correct category were semantically closer to the true translation than guesses from the incorrect category. In Experiment 1, guessing increased subsequent recognition of the English translations, especially for translations that were presented on trials in which the participants’ guesses were from the correct category. Experiment 2 replicated these target recognition effects while also demonstrating that they do not extend to associative recognition performance. Experiment 3 again replicated the target recognition pattern, while also examining participants’ metacognitive recognition judgments. Participants correctly judged that their memory would be better after small than after large errors, but incorrectly believed that making any errors would be detrimental, relative to study-only. Overall, the data are inconsistent with the error-correction hypothesis; small, within-category errors produced better recognition than large, cross-category errors. Alternative theories, based on elaborative encoding and motivated learning, are considered.

AIoT-enabled Rehabilitation Recognition System—Exemplified by Hybrid Lower-Limb Exercises

Ubiquitous health management (UHM) is vital in the aging society. The UHM services with artificial intelligence of things (AIoT) can assist home-isolated healthcare in tracking rehabilitation exercises for clinical diagnosis. This study combined a personalized rehabilitation recognition (PRR) system with the AIoT for the UHM of lower-limb rehabilitation exercises. The three-tier infrastructure integrated the recognition pattern bank with the sensor, network, and application layers. The wearable sensor collected and uploaded the rehab data to the network layer for AI-based modeling, including the data preprocessing, featuring, machine learning (ML), and evaluation, to build the recognition pattern. We employed the SVM and ANFIS methods in the ML process to evaluate 63 features in the time and frequency domains for multiclass recognition. The Hilbert-Huang transform (HHT) process was applied to derive the frequency-domain features. As a result, the patterns combining the time- and frequency-domain features, such as relative motion angles in y- and z-axis, and the HHT-based frequency and energy, could achieve successful recognition. Finally, the suggestive patterns stored in the AIoT-PRR system enabled the ML models for intelligent computation. The PRR system can incorporate the proposed modeling with the UHM service to track the rehabilitation program in the future.

Snapshop-An Image Based Search Application

E-commerce has been growing rapidly over the past few years, Peoples uses them to buy and sell products. Since In these offline stores, they face many problems such as inability. For many online customers, image recognition of clothing and to identify the style, the color, and, in fact, it is a challenge to the sophistication of the fashion industry. In e-commerce, the online platform primarily offers text-based search capabilities. They can search many product searches, but they cannot manage searches based on product features, for example, colors or t-shirt patterns. Often, it is difficult for the user to make this determination features when searching for a product. Furthermore, an increasing number of consumers are depending on social media to make purchasing decisions. Consumers are trying to discover what is going on right now and are looking for the same things. This brings us to the need for a visual commerce platform, or a plan, which recommends products based on users, provided that the product images. The database uses a flexible neural network. You extract data using this deep neural network of image recognition, pattern matching and are very effective in testing fabric prediction.

Protective Immunity Induced by an Eimeria tenella Whole Sporozoite Vaccine Elicits Specific B-Cell Antigens

This study investigated protection against Eimeria tenella following the vaccination of chicks with 5.3 × 106 E. tenella whole-sporozoites emulsified in the nanoparticle adjuvant IMS 1313 N VG Montanide™ (EtSz-IMS1313). One-day-old specific pathogen-free (SPF) chicks were subcutaneously injected in the neck with EtSz-IMS1313 on the 1st and 10th days of age. Acquired immunity was assayed through a challenge with 3 × 104 homologous sporulated oocysts at 21 days of age. The anticoccidial index (ACI) calculated for every group showed the effectiveness of EtSz-IMS1313 as a vaccine with an ACI of 186; the mock-injected control showed an ACI of 18 and the unimmunized, challenged control showed an ACI of −28. In a comparison assay, antibodies from rabbits and SPF birds immunized with EtSz-IMS1313 recognized almost the same polypeptides in the blotting of E. tenella sporozoites and merozoites. However, rabbit antisera showed the clearest recognition pattern. Polypeptides of 120, 105, 94, 70, 38, and 19 kDa from both E. tenella life cycle stages were the most strongly recognized by both animal species. The E. tenella zoite-specific IgG antibodies from the rabbits demonstrated the feasibility for successful B cell antigen identification.

Recognition for lateral faces using Neural Networks

Face recognition is most difficult and complicated technique. Recognition of lateral faces is very difficult compare with normal face recognition. Pattern recognition is mostly used in this system to recognise the lateral face patterns (LFP). Neural network is used to find the patterns and lateral face recognition can be done by this technique. After the many researches face recognition becomes difficulty for the various techniques based on their parameters. In this paper, the amalgamative lateral face recognition(ALFR) which is merged with machine learning and neural network features can be done by using synthetic dataset consists of 200 lateral faces. Performance shows the improved results of proposed technique.

Identification of NY-ESO-1157–165 Specific Murine T Cell Receptors With Distinct Recognition Pattern for Tumor Immunotherapy

New York esophageal squamous cell carcinoma 1 (NY-ESO-1) is a promising target for T-cell receptor-engineered T cell (TCR-T) therapy, and targeting the human leukocyte antigen (HLA)-A2 restricted NY-ESO-1157−165 epitope has yielded remarkable clinical benefits in the treatment of multiple advanced malignancies. Herein, we report the identification of two NY-ESO-1157−165 epitope-specific murine TCRs obtained from HLA-A*0201 transgenic mice. NY-ESO-1157−165 specific TCRs were isolated after vaccinating HLA-A2 transgenic mice with epitope peptides. HZ6 and HZ8 TCRs could specifically bind to NY-ESO-1157−165/HLA-A2 and were capable of cytokine secretion with engineered Jurkat T cells and primary T cells upon recognition with K562 target cells expressing the single-chain trimer (SCT) of NY-ESO-1157−165/HLA-A2. The reactivity profiles of the HZ6 and HZ8 TCRs were found to be distinct from one another when co-cultured with K562 target cells carrying alanine-substituted NY-ESO-1157−165 SCTs. The binding characterization revealed that the recognition pattern of the HZ6 TCR to NY-ESO-1157−165/HLA-A2 was substantially different from the widely used 1G4 TCR. These findings would broaden the understanding of immunogenicity of the NY-ESO-1157−165, and the two identified TCRs may serve as promising candidates for the future development of TCR-T therapy for tumors.