Boundary Control by Boundary Observer for Hyper-redundant Robots

The control problem of a class of hyper-redundant arms with continuum elements, with boundary measuring and control is discussed. First, the dynamic model of the continuum arm is presented. The measuring systems are based on the film sensors that are placed at the terminal sub-regions of the arm. The observers are proposed in order to reconstruct the full state of the arm. A back-stepping method is used to design a boundary control algorithm. Numerical simulations of the arm motion toward an imposed position are presented. An experimental platform shows the effectiveness of the proposed methods

Prime Number Sieving—A Systematic Review with Performance Analysis

The systematic generation of prime numbers has been almost ignored since the 1990s, when most of the IT research resources related to prime numbers migrated to studies on the use of very large primes for cryptography, and little effort was made to further the knowledge regarding techniques like sieving. At present, sieving techniques are mostly used for didactic purposes, and no real advances seem to be made in this domain. This systematic review analyzes the theoretical advances in sieving that have occurred up to the present. The research followed the PRISMA 2020 guidelines and was conducted using three established databases: Web of Science, IEEE Xplore and Scopus. Our methodical review aims to provide an extensive overview of the progress in prime sieving—unfortunately, no significant advancements in this field were identified in the last 20 years

Fractional Order Model Identification of a Person with Parkinson’s Disease for Wheelchair Control

The paper focuses on the design of an intelligent interface that compensates for the incapacity of a person with Parkinson’s disease to drive a wheelchair. The fractional order model that defines a person with Parkinson’s disease is investigated. An identification technique based on the analysis of the frequency behavior of the movement of a wheelchair driven by a with Parkinson’s disease person on the test trajectory is proposed and a delay time crossover model with fractional order exponent β=1.5 is inferred. The fractional dynamic model of the “disabled man-wheelchair” system is discussed and a control system is proposed to compensate for the inability of the wheelchair driver. The conditions that ensure the stability of the closed loop control system are inferred. An experimental technique for analyzing movement performance is developed and a quality index is proposed to evaluate these experiments. The values of this index on the tests performed on Parkinson’s patients are analyzed and discussed

FPGA based co-design of a speed fuzzy logic controller applied to an autonomous car

This paper invests in FPGA technology to control the speed of an autonomous car using fuzzy logic. For that purpose, we propose a co-design based on a novel fuzzy controller IP. It was developed using the hardware language VHDL and driven by the Zynq processor through an SDK software design written in C. The proposed IP acts according to the ambient temperature and the presence or absence of an obstacle and its distance from the car. The partitioning of the co-design tasks divides them into hardware and software parts. The simulation results of the fuzzy IP and those of the complete co-design implementation on a Xilinx Zynq board showed the effectiveness of the proposed controller to meet the target constraints and generate suitable PWM signals. The proposed hardware architecture based on 6-LUT blocks uses 11 times fewer logic resources than other previous similar designs. Also, it can be easily updated when new constraints on the system are to be considered, which makes it suitable for many related applications. Fuzzy computing was accelerated thanks to the use of digital signal processing blocks that ensure parallel processing. Indeed, a complete execution cycle takes only 7 us

IoT and AI-Based Application for Automatic Interpretation of the Affective State of Children Diagnosed with Autism

In the context in which it was demonstrated that humanoid robots are efficient in helping children diagnosed with autism in exploring their affective state, this paper underlines and proves the efficiency of a previously developed machine learning-based mobile application called PandaSays, which was improved and integrated with an Alpha 1 Pro robot, and discusses performance evaluations using deep convolutional neural networks and residual neural networks. The model trained with MobileNet convolutional neural network had an accuracy of 56.25%, performing better than ResNet50 and VGG16. A strategy for commanding the Alpha 1 Pro robot without its native application was also established and a robot module was developed that includes the communication protocols with the application PandaSays. The output of the machine learning algorithm involved in PandaSays is sent to the humanoid robot to execute some actions as singing, dancing, and so on. Alpha 1 Pro has its own programming language—Blockly—and, in order to give the robot specific commands, Bluetooth programming is used, with the help of a Raspberry Pi. Therefore, the robot motions can be controlled based on the corresponding protocols. The tests have proved the robustness of the whole solution

Antibiotics Used for COVID-19 In-Patients from an Infectious Disease Ward

Background: although the prevalence of bacterial co-infections for COVID-19 patients is very low, most patients receive empirical antimicrobial therapy. Furthermore, broad spectrum antibiotics are preferred to narrow spectrum antibiotics. Methods: in order to estimate the excess of antibiotic prescriptions for patients with COVID-19, and to identify the factors that were correlated with the unjustified antibiotic usage, we conducted an observational (cohort) prospective study in patients hospitalized with COVID-19 at the National Institute for Infectious Diseases “Prof. Dr. Matei Bals”, Bucharest, on an infectious disease ward, from November 2021 to January 2022. To evaluate the prevalence of bacterial co-infection in these patients, all positive microbiology results and concomitant suspected or confirmed bacterial co-infections, as documented by the treating doctor, were recorded. The patients were grouped in two categories: patients who received antibiotics and those who did not receive antibiotics, justified or not. Results: from the 205 patients enrolled in the study, 83 (40.4%) received antibiotics prior to being admitted to the hospital. 84 patients (41.0%) received antibiotics during their hospitalization; however, only 32 patients (15.6%) had signs and symptoms suggestive of an infection, 19 (9.3%) presented pulmonary consolidation on the computed tomography (CT) scan, 20 (9.7%) patients had leukocytosis, 29 (14.1%) had an increased procalcitonin level and only 22 (10.7%) patients had positive microbiological tests. It was observed that patients treated with antibiotics were older [70 (54–76) vs. 65 (52.5–71.5), p = 0.023, r = 0.159], had a higher Charlson index [4 (2–5) vs. 2 (1–4), p = 0.007, r = 0.189], had a severe/critical COVID-19 disease more frequently [61 (72.6%) vs. 38 (31.4%), p 2 = 39.563] and required more oxygen [3 (0–6) vs. 0 (0–2), p < 0.001, r = 0.328]. Conclusion: empirical antibiotic treatment recommendation should be reserved for COVID-19 patients that also had other clinical or paraclinical changes, which suggest a bacterial infection. Further research is needed to better identify patients with bacterial co-infection that should receive antibiotic treatment

Predicting Catheter Ablation Outcome in Persistent Atrial Fibrillation via Multivariate Analyses of ECG Fibrillatory Wave Amplitude

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Noninvasive Prediction of Catheter Ablation Outcome in Persistent Atrial Fibrillation by Fibrillatory Wave Amplitude Computation in Multiple ECG Leads

International audienceBackground. Catheter ablation (CA) of persistent atrial fibrillation (AF) is challenging and reported results are perfectible. Improving patient selection for the procedure could enhance its success rate while avoiding the risks associated with ablation for patients with low odds of success. CA outcome can be predicted noninvasively by atrial fibrillatory wave (f-wave) amplitude, but previous works have mostly focused on manual measures in single ECG leads only.Aims. The present work aims at assessing the long-term prediction ability of f-wave amplitude when computed in multiple ECG leads.Methods and Results. Sixty-two persistent AF patients (52 males, 61.5±10.4 years) referred to CA were enrolled in this study. During an average follow-up of 14±8 months, 47 patients had no AF recurrence after ablation. A standard one-minute 12-lead ECG was acquired before the ablation procedure for each patient. F-wave amplitudes in different ECG leads were computed by a noninvasive signal processing algorithm and combined into a multivariate prediction model based on logistic regression. A lead selection approach relying on the Wald index pointed to I, V1, V2 and V5 as the most relevant ECG leads to predict jointly CA outcome using f-wave amplitudes, reaching an AUC of 0.854 and improving on single-lead amplitude-based predictors.Conclusion. Analyzing the f-wave amplitude simultaneously in several ECG leads can significantly improve CA long-term outcome prediction in persistent AF over predictors based on single-lead measures

ECG Spatiotemporal Complexity Predicts Catheter Ablation Outcome of Persistent Afib

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