Moving Vehicle Detection and Recognition Technology based on Artificial Intelligence

The existing target detection and recognition technology has the problem of fuzzy features of moving vehicles, which leads to poor detection effect. A moving car detection and recognition technology based on artificial intelligence is designed. The point operation is adopted to enhance the high frequency information of the image, increase the image contrast, and delineate the video image tracking target. The motion vector similarity is used to predict the moving target area in the next frame of the image. The texture features of the moving car are extracted by artificial intelligence, and the center moment is calculated by the gray histogram distribution curve, the edge feature extraction algorithm is used to set the detection and recognition mode. Experimental results: under complex conditions, this design technology, compared with the other two kinds of moving vehicle detection and recognition technology, detected three more moving vehicles, which proved that the application prospect of the moving vehicle detection and recognition technology integrated with artificial intelligence is broader.

Sensitivity and Specificity of CD19.CAR-T Cell Detection by Flow Cytometry and PCR

Chimeric-antigen-receptor-T (CAR-T) cells are currently revolutionizing the field of cancer immunotherapy. Therefore, there is an urgent need for CAR-T cell monitoring by clinicians to assess cell expansion and persistence in patients. CAR-T cell manufacturers and researchers need to evaluate transduction efficiency and vector copy number for quality control. Here, CAR expression was analyzed in peripheral blood samples from patients and healthy donors by flow cytometry with four commercially available detection reagents and on the gene level by quantitative polymerase chain reaction (qPCR). Flow cytometric analysis of CAR expression showed higher mean CAR expression values for CD19 CAR detection reagent and the F(ab’)2 antibody than Protein L and CD19 Protein. In addition, the CD19 CAR detection reagent showed a significantly lower median background staining of 0.02% (range 0.007–0.06%) when compared to the F(ab’)2 antibody, CD19 protein and Protein L with 0.80% (range 0.47–1.58%), 0.65% (range 0.25–1.35%) and 0.73% (range 0.44–1.23%). Furthermore, flow cytometry-based CAR-T cell frequencies by CD19 CAR detection reagent showed a good correlation with qPCR results. In conclusion, quality control of CAR-T cell products can be performed by FACS and qPCR. For the monitoring of CAR-T cell frequencies by FACS in patients, CAR detection reagents with a low background staining are preferable.

CD19.CAR-T Cell Analysis Using Flow Cytometry and PCR

Introduction: After approval of CD19.CAR-T cell therapy by both FDA and EMA, chimeric antigen receptor T (CAR-T) cell therapy has been established as a new and innovative therapy method for patients with relapsed/refractory acute lymphoblastic leukemia (ALL) and non-Hodgkin lymphoma (NHL). Because of this, there is great need for reliable methods for CAR-T cell monitoring by clinicians to further assess cell expansion, distribution and persistence in patients. This is both necessary for CAR-T cell manufacturing sites and researchers to assess transduction efficiency and vector copy numbers per cell. Therefore, we analyzed CAR expression of CD19.CAR-T cells using flow cytometry as well as quantitative polymerase chain reaction (qPCR). Methods: In this study, four different commercially available CD19.CAR-T cell staining reagents (Protein L, CD19 protein, F(ab`)2 antibody, and CD19.CAR-T cell detection reagent) were used to evaluate CAR-T cell products and peripheral blood samples of both patients and healthy donors by flow cytometry. Furthermore, qPCR was performed with data comparison using flow cytometry results. Results: Flow cytometric analysis of CAR expression showed higher mean CAR expression values for CD19 CAR detection reagent and the F(ab')2 antibody than Protein L and CD19 Protein. Moreove, the CD19 CAR detection reagent showed a significantly lower median background staining of 0.02% (range 0.007-0.06%) when compared to F(ab')2 antibody, CD19 protein and Protein L with 0.80% (range 0.47-1.58%.), 0.68% (range 0.30-1.38%) and 0.73% (range 0.44-1.23%). Furthermore, flow cytometry-based CAR-T cell frequencies by CD19 CAR detection reagent showed a good correlation with qPCR results. Conclusion: CAR-T staining was successfully performed with all tested commercially available CAR detection reagents. Evaluation of manufactured CAR-T cells as well as quality control was comparably done using FACS and qPCR. Because of lower frequencies of CAR-T cells in the patient samples, CAR-T cell staining reagents with a low background staining should be preferably used. Disclosures Sauer: Abbvie: Consultancy, Speakers Bureau; Pfizer: Consultancy, Speakers Bureau; Matterhorn Biosciences AG: Consultancy, Other: DSMB/SAB Member; Takeda: Consultancy, Other: DSMB/SAB Member. Schubert: Gilead: Consultancy. Müller-Tidow: Janssen: Consultancy, Research Funding; Bioline: Research Funding; Pfizer: Research Funding. Schmitt: TolerogenixX: Current holder of individual stocks in a privately-held company; Novartis: Other: Travel grants, Research Funding; MSD: Membership on an entity's Board of Directors or advisory committees; Kite Gilead: Other: Travel grants; Apogenix: Research Funding; Hexal: Other: Travel grants, Research Funding; Bluebird Bio: Other: Travel grants. Schmitt: Hexal: Other: Travel grant; Therakos/Mallinckrodt: Research Funding; Jazz Pharmaceuticals: Other: Travel grant; TolerogenixX Ltd: Current Employment.

Object detection and used car price predicting analysis system (ucpas) using machine learning technique

The highly interesting research area that noticed in the last few years is object detection and find out the prediction based on the features that can be benefited to consumers and the industry. In this paper, we understand the concept of object detection like the car detection, to look into the price of a second-hand car using automatic machine learning methods. We also understand the concept of object detection categories. Nowadays, the most challenging task is to determine what is the listed price of a used car on the market, Possibility of various factors that can drive a used car price. The main objective of this paper is to develop machine learning models which make it possible to accurately predict the price of a second-hand car according to its parameter or characteristics. In this paper, implementation techniques and evaluation methods are used on a Car dataset consisting of the selling prices of various models of car across different cities of India. The outcome of this experiment shows that clustering with linear regression and Random Forest model yield the best accuracy outcome. The machine learning model produces a satisfactory result within a short duration of time compared to the aforementioned self.

2D-to-3D Projection for Monocular and Multi-View 3D Object Detection in Outdoor Scenes

In this article, we introduce the task of multi-view RGB-based 3D object detection as an end-to-end optimization problem. In a multi-view formulation of the 3D object detection problem, several images of a static scene are used to detect objects in the scene. To address the 3D object detection problem in a multi-view formulation, we propose a novel 3D object detection method named ImVoxelNet. ImVoxelNet is based on a fully convolutional neural network. Unlike existing 3D object detection methods, ImVoxelNet works directly with 3D representations and does not mediate 3D object detection through 2D object detection. The proposed method accepts multi-view inputs. The number of monocular images in each multi-view input can vary during training and inference; actually, this number might be unique for each multi-view input. Moreover, we propose to treat a single RGB image as a special case of a multi-view input. Accordingly, the proposed method can also accept monocular inputs with no modifications. Through extensive evaluation, we demonstrate that the proposed method successfully handles a variety of outdoor scenes. Specifically, it achieves state-of-the-art results in car detection on KITTI (monocular) and nuScenes (multi-view) benchmarks among all methods that accept RGB images. The proposed method operates in real-time, which makes it possible to integrate it into the navigation systems of autonomous devices. The results of this study can be used to address tasks of navigation, path planning, and semantic scene mapping.