Automatic discrimination between front and back ensemble locations in HRTF-convolved binaural recordings of music

One of the greatest challenges in the development of binaural machine audition systems is the disambiguation between front and back audio sources, particularly in complex spatial audio scenes. The goal of this work was to develop a method for discriminating between front and back located ensembles in binaural recordings of music. To this end, 22, 496 binaural excerpts, representing either front or back located ensembles, were synthesized by convolving multi-track music recordings with 74 sets of head-related transfer functions (HRTF). The discrimination method was developed based on the traditional approach, involving hand-engineering of features, as well as using a deep learning technique incorporating the convolutional neural network (CNN). According to the results obtained under HRTF-dependent test conditions, CNN showed a very high discrimination accuracy (99.4%), slightly outperforming the traditional method. However, under the HRTF-independent test scenario, CNN performed worse than the traditional algorithm, highlighting the importance of testing the algorithms under HRTF-independent conditions and indicating that the traditional method might be more generalizable than CNN. A minimum of 20 HRTFs are required to achieve a satisfactory generalization performance for the traditional algorithm and 30 HRTFs for CNN. The minimum duration of audio excerpts required by both the traditional and CNN-based methods was assessed as 3 s. Feature importance analysis, based on a gradient attribution mapping technique, revealed that for both the traditional and the deep learning methods, a frequency band between 5 and 6 kHz is particularly important in terms of the discrimination between front and back ensemble locations. Linear-frequency cepstral coefficients, interaural level differences, and audio bandwidth were identified as the key descriptors facilitating the discrimination process using the traditional approach.

Automation of an automobile internal combustion engine bench calibration tests

Introduction (problem statement and relevance). The article presents the work on the automation of an internal combustion engine (ICE) calibration tests results on a motor stand. The relevance of the article is due to the high labor intensity of such tests, the complexity of documentation and decisionmaking based on the results of the work.Purpose of the study. This work is part of a comprehensive methodology, the purpose of which is to reduce the duration of tests and improve the calibration results quality of the vehicle’s power plant. The entire methodology description as a whole is also given in the publication.Methodology and research methods. The achievement of this goal is ensured with the help of special systems – INCA-FLOW (test automation) and ASCMO (processing results and optimization), produced by Bosch/ETAS. The approbation of the technique was carried out on a motor stand in the MADI training box in relation to the problem of forming an ignition timing map.Scientific novelty and results. As a result of the methodology application, a 4.8 times reduction in the motor tests duration takes place if 2 people work in manual mode at the test bench without interruption.At the same time, the variance of the adequacy of Sad of the torque empirical model Mk turned out to be, on average, 1.5 times less if the model was built according to the automated tests results. The obtained data indicated an improvement in the quality of measurements in the transition to automated test methods.From a scientific point of view, the most original part of the work is the application of the “Gaussian process” method to build empirical models. This method provides more accurate results than, for example, the traditional method of least squares.The practical significance of the work lies in the ability to considerably reduce routine actions on a motor stand, and the additional time spent on developing and testing a test scenario (program) is compensated for by the fact that scenario models can be used in the future for other similar tests. The proposed methodology makes it possible to cover a significant part of the internal combustion engine calibration tests. For example, you can apply it if you possess the preliminary information about the test object (basing on which you can draw up an experiment plan) and the engine is to be prepared either for a car road tests or tests under special conditions.

Designing simulation model for minimizing coal train unloading time: A case study of Kereta Api Logistik Company

The increase in coal production every year has influence the transport volume of coal trains of Kereta Api Logistik Company is getting higher. This increase causes the current number of train unloading equipment to be unable to keep up with this increase and has an impact on the poor performance of train unloading, which is indicated by the unachieved of train’s waiting time target. The coal train unloading system is a very complex system and many uncertainties occur, so the appropriate method to use is discrete event simulation. The simulation model is designed using the Simulation Arena software. The results of the simulation method are 4 alternative scenarios will be selected by the Bonferonni test. Scenario 4 has the highest reduction in train unloading operating time, which is 30.7%. The results of this study recommend the addition of a tool with a combination of 1 unit of Gantry Crane integrated with coal traveling hopper and 5 units of Dump Truck and for management, this recommendation can reduce high overtime costs every month and increase coal transport capacity so that coal transport profits will increase.

User Interface Analysis of Gapura Universitas Multimedia Nusantara Website

The user interface (UI) of the Gapura site is proven to have various problems such as a poor visual hierarchy, UI that confuses its users, and UI that are considered unattractive by users. These things result in the poor feedback of its users. This study aims to examine the problems in the Gapura site by using the guidelines of the e-book published by UXPin, "Web UI Best Practice". The series of tests that will be conducted are blur test, scenario test, questionnaire and survey. After that, a prototype will be built according to the results of the tests with the aim of improving the UI Gapura site. The results of the prototypes made show that while there are still mistakes regarding the visual hierarchy of the prototype, the prototype was proven to be more usable by the users, and received better feedback than the Gapura site. Thus, it can be concluded that the changes applied in the prototype has made the UI of Gapura better.

Assessment of Nanobag as a New Safety System in the Frontal Sled Test

Objective: The future mobility challenges leads to considering new safety systems to protect vehicle passengers in non-standard and complex seating configurations. The objective of this study is to assess the performance of a brand new safety system called nanobag and to compare it to the traditional airbag performance in the frontal sled test scenario. Methods: The nanobag technology is assessed in the frontal crash test scenario and compared with the standard airbag by numerical simulation. The previously identified material model is used to assemble the nanobag numerical model. The paper exploits an existing validated human body model to assess the performance of the nanobag safety system. Using both the new nanobag and the standard airbag, the sled test numerical simulations with the variation of human bodies are performed in 30 km/h and 50 km/h frontal impacts. Results: The sled test results for both the nanobag and the standard airbag based on injury criteria shows a good and acceptable performance of the nanobag safety system compared to the traditional airbag. Conclusion: The results show that the nanobag system has its performance compared to the standard airbag, which means that thanks to the design, the nanobag safety system has a high potential and extended application for multi-directional protection against impact.

Design, Control, Modeling, and Simulation of Mechanical Ventilator for Respiratory Support

In the early stage of the 21st century, humankind is facing high medical risks. To the best of our knowledge, there is currently no efficient way to stop chains of infections, and hence citizens suffer significantly increasing numbers of diseases. The most important factor in this scenario is the lack of necessary equipment to cure disease and maintain our living. Once breath cannot be guaranteed, humans find themselves in a dangerous state. This study aimed to design, control, model, and simulate mechanical ventilator that is open-source structure, lightweight, and portable, which is proper for patients to cure themselves at home. In the scope of this research, the hardware platform for the mechanical design, implementation of control rules, and some trials of both simulations and experiments are presented as our methodology. The proposed design of ventilator newly features the bioinspired mechanism, finger-like actuator, and flow rate-based control. Firstly, the approximate evaluation of the lung model is presented with some physiological characteristics. Owing to this investigation, the control scheme was established to adapt to the biological body. Moreover, it is essential for the model to be integrated to determine the appropriate performance of the closed-loop system. Derived from these theoretical computations, the innovative concept of mechanical design was demonstrated using the open-source approach, and the real-world model was constructed. In order to estimate the driving torque, the hardware modeling was conducted using mathematical expressions. To validate the proposed approach, the overall system was evaluated using Matlab/Simulink, and experiments with the proposed platform were conducted in two situations: 20 lpm as a reference flow rate for 4 seconds and 45 lpm for 2.5 seconds, corresponding to normal breath and urgent breath. From the results of this study, it can be clearly observed that the system’s performance ensures that accurate airflow is provided, although the desired airflow fluctuates. Based on the test scenario in hardware, the RMS (root-mean-square) values of tracking errors in airflow for both cases were 1.542 and 1.767. The proposed design could deal with changes in airflow, and this machine could play a role as a proper, feasible, and robust solution to support human living.

Discrimination of SARS-CoV-2 Infections From Other Viral Respiratory Infections by Scent Detection Dogs

Background: Testing of possibly infected individuals remains cornerstone of containing the spread of SARS-CoV-2. Detection dogs could contribute to mass screening. Previous research demonstrated canines' ability to detect SARS-CoV-2-infections but has not investigated if dogs can differentiate between COVID-19 and other virus infections.Methods: Twelve dogs were trained to detect SARS-CoV-2 positive samples. Three test scenarios were performed to evaluate their ability to discriminate SARS-CoV-2-infections from viral infections of a different aetiology. Naso- and oropharyngeal swab samples from individuals and samples from cell culture both infected with one of 15 viruses that may cause COVID-19-like symptoms were presented as distractors in a randomised, double-blind study. Dogs were either trained with SARS-CoV-2 positive saliva samples (test scenario I and II) or with supernatant from cell cultures (test scenario III).Results: When using swab samples from individuals infected with viruses other than SARS-CoV-2 as distractors (test scenario I), dogs detected swab samples from SARS-CoV-2-infected individuals with a mean diagnostic sensitivity of 73.8% (95% CI: 66.0–81.7%) and a specificity of 95.1% (95% CI: 92.6–97.7%). In test scenario II and III cell culture supernatant from cells infected with SARS-CoV-2, cells infected with other coronaviruses and non-infected cells were presented. Dogs achieved mean diagnostic sensitivities of 61.2% (95% CI: 50.7–71.6%, test scenario II) and 75.8% (95% CI: 53.0–98.5%, test scenario III), respectively. The diagnostic specificities were 90.9% (95% CI: 87.3–94.6%, test scenario II) and 90.2% (95% CI: 81.1–99.4%, test scenario III), respectively.Conclusion: In all three test scenarios the mean specificities were above 90% which indicates that dogs can distinguish SARS-CoV-2-infections from other viral infections. However, compared to earlier studies our scent dogs achieved lower diagnostic sensitivities. To deploy COVID-19 detection dogs as a reliable screening method it is therefore mandatory to include a variety of samples from different viral respiratory tract infections in dog training to ensure a successful discrimination process.

Route Optimization Tool (RoOT) for distribution of vaccines and health products

Ensuring the delivery and availability of health products, including temperature-sensitive vaccines, is vital to saving lives in low- and middle-income countries (LMICs). In many LMICs routes are hand drawn by logisticians and are adjusted based on vehicle availability and product quantities. Easy-to-use real-time supply chain tools are needed to create or adjust routes for available vehicles and road conditions. Having more efficient and optimized distribution is especially critical for COVID-19 vaccine distribution. Route Optimization Tool (RoOT) works best for planning routes for 50 health facilities or less, in two minutes. We develop RoOT using a variant of a Vehicle Routing and Scheduling Algorithm (VeRSA) that is coded in Python but reads and writes Excel files to make data input and using outputs easier. RoOT can be used for routine operations or in emergency situations, such as delivery of new COVID-19 vaccine. The tool has a user-centric design with easy dropdown menus and the ability to optimize on time, risk, or combination of both. RoOT is an open-source tool for optimal routing of health products. It provides optimized routes faster than most commercial software and is tailored to meet the needs of government stakeholders We trained supply chain logisticians in Mozambique on using RoOT, and their feedback validates that RoOT is a practical tool to improve planning and efficient distribution of health products, especially vaccines. We also illustrate how RoOT can be adapted for an emergency situation by using a test scenario of a cyclone. Currently, RoOT does not allow multi-day routes, and is designed for trips that can be completed within twenty-four hours. Areas for future development include multi-day routing and integration with mapping software to facilitate distance calculations and visualization of routes.

Robust Control Design for Autonomous Vehicles Using Neural Network-Based Model-Matching Approach

In this paper, a novel neural network-based robust control method is presented for a vehicle-oriented problem, in which the main goal is to ensure stable motion of the vehicle under critical circumstances. The proposed method can be divided into two main steps. In the first step, the model matching algorithm is proposed, which can adjust the nonlinear dynamics of the controlled system to a nominal, linear model. The aim of model matching is to eliminate the effects of the nonlinearities and uncertainties of the system to increase the performances of the closed-loop system. The model matching process results in an additional control input, which is computed by a neural network during the operation of the control system. Furthermore, in the second step, a robust H∞ is designed, which has double purposes: to handle the fitting error of the neural network and ensure the accurate tracking of the reference signal. The operation and efficiency of the proposed control algorithm are investigated through a complex test scenario, which is performed in the high-fidelity vehicle dynamics simulation software, CarMaker.