Sound Processing for Autonomous Driving

Nowadays, a variety of intelligent systems for autonomous driving have been developed, which have already shown a very high level of capability. One of the prerequisites for autonomous driving is an accurate and reliable representation of the environment around the vehicle. Current systems rely on cameras, RADAR, and LiDAR to capture the visual environment and to locate and track other traffic participants. Human drivers, in addition to vision, have hearing and use a lot of auditory information to understand the environment in addition to visual cues. In this thesis, we present the sound signal processing system for auditory based environment representation. Sound propagation is less dependent on occlusion than all other types of sensors and in some situations is less sensitive to different types of weather conditions such as snow, ice, fog or rain. Various audio processing algorithms provide the detection and classification of different audio signals specific to certain types of vehicles, as well as localization. First, the ambient sound is classified into fourteen major categories consisting of traffic objects and actions performed. Additionally, the classification of three specific types of emergency vehicles sirens is provided. Secondly, each object is localized using a combined localization algorithm based on time difference of arrival and amplitude. The system is evaluated on real data with a focus on reliable detection and accurate localization of emergency vehicles. On the third stage the possibility of visualizing the sound source on the image from the autonomous vehicle camera system is provided. For this purpose, a method for camera to microphones calibration has been developed. The presented approaches and methods have great potential to increase the accuracy of environment perception and, consequently, to improve the reliability and safety of autonomous driving systems in general

Fast and Sensitive Determination of Bioflavonoids Using a New Analytical System Based on Label-Free Silver Triangular Nanoplates

Optical sensors based on silver triangular nanoplates (AgTNPs) are insufficiently studied as probes for the spectrophotometric determination of biologically active compounds. In the present article, an interaction between label-free AgTNPs and bioflavonoids in the presence of silver(I) ions was assessed to outline the possibilities of AgTNPs as a colorimetric probe for the fast and sensitive determination of bioflavonoids. It is shown that the interaction was accompanied by a bathochromic shift of the local surface plasmon resonance band of nanoparticles and an increase in its intensity. Seven bioflavonoids differing in their structure were tested. The influence of the structure of analytes and the main external factors on the analytical signal is discussed in detail. It was found that the detection limits of bioflavonoids in the selected optimal conditions increased in the series morin −1, respectively. Chrysin, naringenin, and naringin were found not to affect the spectral characteristics of AgTNPs. The suggested approach was applied for the spectrophotometric determination of flavonoids in pharmaceuticals and onion peel

End-to-End Train Horn Detection for Railway Transit Safety

The train horn sound is an active audible warning signal used for warning commuters and railway employees of the oncoming train(s), assuring a smooth operation and traffic safety, especially at barrier-free crossings. This work studies deep learning-based approaches to develop a system providing the early detection of train arrival based on the recognition of train horn sounds from the traffic soundscape. A custom dataset of train horn sounds, car horn sounds, and traffic noises is developed to conduct experiments and analysis. We propose a novel two-stream end-to-end CNN model (i.e., THD-RawNet), which combines two approaches of feature extraction from raw audio waveforms, for audio classification in train horn detection (THD). Besides a stream with a sequential one-dimensional CNN (1D-CNN) as in existing sound classification works, we propose to utilize multiple 1D-CNN branches to process raw waves in different temporal resolutions to extract an image-like representation for the 2D-CNN classification part. Our experiment results and comparative analysis have proved the effectiveness of the proposed two-stream network and the method of combining features extracted in multiple temporal resolutions. The THD-RawNet obtained better accuracies and robustness compared to those of baseline models trained on either raw audio or handcrafted features, in which at the input size of one second the network yielded an accuracy of 95.11% for testing data in normal traffic conditions and remained above a 93% accuracy for the considerable noisy condition of-10 dB SNR. The proposed THD system can be integrated into the smart railway crossing systems, private cars, and self-driving cars to improve railway transit safety

A Three-Reagent “Green” Paper-Based Analytical Device for Solid-Phase Spectrometric and Colorimetric Determination of Dihydroquercetin

Microfluidic paper-based analytical devices (µPADs) represent one of the promising green analytical strategies for low-cost and simple determination of various analytes. The actual task is the development of such devices for quantitation of antioxidants, e.g., flavonoids. In this paper, possibilities of a novel three-reagent µPAD including silver nitrate, 4-nitrophenyldiazonium tetrafluoroborate, and iron(III) chloride as reagents are assessed with respect to the determination of dihydroquercetin. It is shown that all the three reagents produce different colorimetric responses that can be detected by a mini-spectrophotometer–monitor calibrator or by a smartphone. The method is applicable to direct measuring high contents of dihydroquercetin (the linearity range is 0.026–1 mg mL−1, and the limit of detection is 7.7 µg mL−1), which is favorable for many dietary supplements. The analysis of a food supplement was possible with the relative standard deviations of 9–26%, which is satisfactory for quantitative and semiquantitative determinations. It was found that plotting a calibration graph in 3D space of the three reagents’ responses allows us to distinguish dihydroquercetin from its close structural analogue, quercetin

A Three-Reagent “Green” Paper-Based Analytical Device for Solid-Phase Spectrometric and Colorimetric Determination of Dihydroquercetin

Microfluidic paper-based analytical devices (µPADs) represent one of the promising green analytical strategies for low-cost and simple determination of various analytes. The actual task is the development of such devices for quantitation of antioxidants, e.g., flavonoids. In this paper, possibilities of a novel three-reagent µPAD including silver nitrate, 4-nitrophenyldiazonium tetrafluoroborate, and iron(III) chloride as reagents are assessed with respect to the determination of dihydroquercetin. It is shown that all the three reagents produce different colorimetric responses that can be detected by a mini-spectrophotometer–monitor calibrator or by a smartphone. The method is applicable to direct measuring high contents of dihydroquercetin (the linearity range is 0.026–1 mg mL−1, and the limit of detection is 7.7 µg mL−1), which is favorable for many dietary supplements. The analysis of a food supplement was possible with the relative standard deviations of 9–26%, which is satisfactory for quantitative and semiquantitative determinations. It was found that plotting a calibration graph in 3D space of the three reagents’ responses allows us to distinguish dihydroquercetin from its close structural analogue, quercetin

Research into the Peculiarities of the Individual Traction Drive Nonlinear System Oscillatory Processes

Auto-oscillations may occur in moving vehicles in the area where the tire interacts with the support base. The parameters of such oscillations depend on the sliding velocity in the contact patch. As they negatively affect the processes occurring in the electric drive and the mechanical transmission, reducing their energy efficiency, such processes can cause failures in various elements. This paper aims to conduct a theoretical study into the peculiarities of oscillatory processes in the nonlinear system and an experimental study of the auto-oscillation modes of an individual traction drive. It presents the theoretical basis used to analyze the peculiarities of oscillation processes, including their onset and course, the results of simulation mathematical modeling and the experimental studies into the oscillation phenomena in the movement of the vehicle towards the supporting base. The practical value of this study lies in the possibility to use the results in the development of algorithms for the exclusion of auto-oscillation phenomena in the development of vehicle control systems, as well as to use the auto-oscillation processes onset and course analysis methodology to design the electric drive of the driving wheels