Numerically Stable and Efficient Implementation of a Continuous-Discrete Multiple-Model Estimator

This paper deals with the problem of implementing adaptive radar tracking filters based on continuous-time models of target motion and on discrete-time models of measurement process. The particular difficulties addressed include: nonlinear and non-stationary target movement models with uncertain parameters, and low data rate due to a rotating radar antenna. The proposed tracking filter relies basically on the continuous-discrete variant of the extended Kalman filter (EKF), the probabilistic data association (PDA) technique and the interacting multiplemodel (IMM) state estimation scheme. Numerical properties of the algorithm are discussed and a software implementation is developed using the open-source BLAS library. Several design concepts are combined to assure numerical stability, convergence and efficiency of the estimator

COMPACT: biometric dataset of face images acquired in uncontrolled indoor environment

Biometric databases are important components that help to improve state-of-the-art recognition performance. The availability of more and more difficult data attracts the researchers' attention, who systematically develop novel recognition algorithms and increase identification accuracy. Surprisingly, most of the popular face datasets, like LFW or IJBA are not fully unconstrained. The majority of the available images were not acquired on-the-move, which reduces the amount of blur caused by motion or incorrect focusing. Therefore, in this paper, the COMPACT database for studying less-cooperative face recognition is introduced. The dataset consists of high-resolution images of 108 subjects acquired in a fully automated manner as people go through the recognition gate. This ensures that the collected data contains the real world degradation factors: different distances, expressions, occlusions, pose variations and motion blur. Additionally, the authors conducted a series of experiments that verify face recognition performance on the collected data

IMAGE AND VIDEO PROCESSING WITH FPGA SUPPORT USED FOR BIOMETRIC AS WELL AS OTHER APPLICATIONS

Paper presents the recent research in DMCS. The image processing and biometric research projects are presented. One of the key elements is an image acquisition and processing. The most recent biometric research projects are in the area of authentication in uncooperative scenarios and utilizing many different biometric traits (multimodal biometric systems). Also the recent research on the removal of geometric distortion from live video streams using FPGA and GPU hardware is presented together with preliminary performance results

RECENT RESEARCH IN VLSI, MEMS AND POWER DEVICES WITH PRACTICAL APPLICATION TO THE ITER AND DREAM PROJECTS

Several MEMS (Micro Electro-Mechanical Systems) devices have been analysed and simulated. The new proposed model of SiC MPS (Merged PIN-Schottky) diodes is in full agreement with the real MPS devices. The real size DLL (Dynamic Lattice Liquid) simulator as well as the research on modelling and simulation of modern VLSI devices with practical applications have been presented. In the basis of experience in the field of ATCA (Advanced Telecommunications Computing Architecture) based systems a proof-of-concept DAQ (data acquisition) system for ITER (International Thermonuclear Experimental Reactor) have been proposed

COMPACT: biometric dataset of face images acquired in uncontrolled indoor environment

Biometric databases are important components that help improve the performanceof state-of-the-art recognition applications. The availability of more andmore challenging data is attracting the attention of researchers, who are systematicallydeveloping novel recognition algorithms and increasing the accuracyof identification. Surprisingly, most of the popular face datasets (like LFW orIJBA) are not fully unconstrained. The majority of the available images werenot acquired on-the-move, which reduces the amount of blurring that is causedby motion or incorrect focusing. Therefore, the COMPACT database for studyingless-cooperative face recognition is introduced in this paper. The datasetconsists of high-resolution images of 108 subjects acquired in a fully automatedmanner as people go through the recognition gate. This ensures that the collecteddata contains real-world degradation factors: different distances, expressions,occlusions, pose variations, and motion blur. Additionally, the authorsconducted a series of experiments that verified the face-recognition performanceon the collected data

COMPACT: biometric dataset of face images acquired in uncontrolled indoor environment

Biometric databases are important components that help improve the performanceof state-of-the-art recognition applications. The availability of more andmore challenging data is attracting the attention of researchers, who are systematicallydeveloping novel recognition algorithms and increasing the accuracyof identification. Surprisingly, most of the popular face datasets (like LFW orIJBA) are not fully unconstrained. The majority of the available images werenot acquired on-the-move, which reduces the amount of blurring that is causedby motion or incorrect focusing. Therefore, the COMPACT database for studyingless-cooperative face recognition is introduced in this paper. The datasetconsists of high-resolution images of 108 subjects acquired in a fully automatedmanner as people go through the recognition gate. This ensures that the collecteddata contains real-world degradation factors: different distances, expressions,occlusions, pose variations, and motion blur. Additionally, the authorsconducted a series of experiments that verified the face-recognition performanceon the collected data

Contactless person identification based on double-sided 3D scan of hand geometry

This paper presents person identification algorithm based on 3D hand geometry. The algorithm comprises the following phases of 3D hand scan processing: segmentation, feature extraction and comparison, which the authors explain in detail in the paper. The authors present results of algorithm performance tested on publicly available DMCSv1 database which contains 1400 samples of 3D hand scans of left and right hand acquired from 35 individuals. Obtained values of equal error rate are 16% for the left hand scans, 17% for the right hand scan sand the values of rank-1 accuracy are 85% and 82% for the left and right hand scans, respectively

Long-term safety and efficacy of benralizumab in patients with severe, uncontrolled asthma: 1-year results from the BORA phase 3 extension trial

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