Realistic Ultrasonic Environment Simulation Using Conditional Generative Adversarial Networks

Abstract

Recently, realistic data augmentation using neural networks especially generative neural networks (GAN) has achieved outstanding results. The communities main research focus is visual image processing. However, automotive cars and robots are equipped with a large suite of sensors to achieve a high redundancy. In addition to others, ultrasonic sensors are often used due to their low-costs and reliable near field distance measuring capabilities. Hence, Pattern recognition needs to be applied to ultrasonic signals as well. Machine Learning requires extensive data sets and those measurements are time-consuming, expensive and not flexible to hardware and environmental changes. On the other hand, there exists no method to simulate those signals deterministically. We present a novel approach for synthetic ultrasonic signal simulation using conditional GANs (cGANs). For the best of our knowledge, we present the first realistic data augmentation for automotive ultrasonics. The performance of cGANs allows us to bring the realistic environment simulation to a new level. By using setup and environmental parameters as condition, the proposed approach is flexible to external influences. Due to the low complexity and time effort for data generation, we outperform other simulation algorithms, such as finite element method. We verify the outstanding accuracy and realism of our method by applying a detailed statistical analysis and comparing the generated data to an extensive amount of measured signals.

Spectrum of Advancements and Developments in Multidisciplinary Domains for Generative Adversarial Networks (GANs)

“…Bass, drums, guitar, piano and strings Proposed three models known as call jamming model, composer model and hybrid model for music generation Yu et al [41] 2019 Simultaneous generation of lyrics-conditioned melody and association alignment between syllables of given lyrics by using conditional deep Lstm generator and discriminator Deep generative model for generation of melody and notes of predicted melody Weather forecasting Chen et al [42] 2018 Scenario generation used for weather forecasting, however errors become more pronounced when the typhoons move into deep sea Advantage: generates wind patterns and weather forecasts based on historic data Ruttgers et al [43] 2018 Predict track of typhoons by using satellite image. If information about surface pressure, velocity and sea surface temperature are added the results can become more accurate Advantage: predict the typhoon center as well as the movement of clouds with certain margins for error Sports Jiao et al [44] 2018 Distinguishes correct performed golf swings Achieved accuracy and precision both in identification as well as classification of golf swings Deverall et al [45] 2017 Conditional GAN for designing athletic shoes based on google gnet Achieved shoes categorization according to their physical attributes as well as functional type Internet of things (IoT) Wang et al [46] 2018 Use of Bayesian methods for Radio Frequency (RF) sensing for IoT Advantage: overcome limitation of limited data availability by introducing an offline stage Zhao et al [47] 2018 Individual identity authentication by applying open-categorical classification model based on gan (occ-gan) Advantage: better results are achieved than other methods like one-class support vector machine (oc-svm) and one-versus-rest support vector machine (ovr-svm) Genetic engineering Dizaji et al [48] 2018 Gene expression profiling by using semi-supervised GAN for expression inference Use landmark genes instead of whole gene expressions Simulation and modeling Hassouni et al [49] 2018 Generating realistic simulation environments that simulates daily activities of users Advantage: generate realistic sensory data that related to daily activities of users Pöpperl et al [50] 2019 Synthetic ultrasonic signal simulation using conditional gans (cgans) Advantage: real like data augmentation for automotive ultrasonic and also adaptive to external influences Market prediction and forecasting Tian et al [51] 2019 A technique for predicting the consumption of energy Advantage: outperforms the standard approaches i.e. information diffusion technology (idt), the heuristic mega-trend-diffusion (hmtd) technology and the bootstrap technique Advantage: scalable to perform forecast for demand of electricity and the traffic supply Luo et al [52] 2018 A technique for predicting the prices of the crude oil using adap- [55] 2019 Double P-buried layers MISFET (DP-MISFET) is proposed Simulated and characteristics are analysed by the Sentaurus TCAD tool Road network generation and path planning Albert et al [56] 2018 Novel techniq...…”

“…The simulator can easily be trained and tested by the GAN based approach and assessment demonstrated same level of performance on synthetic data as delivered real dataset. In [50] the authors presented a novel using conditional GANs (cGANs) method for synthetic ultrasonic signal simulation and as per the authors prerogative, it is the foremost data augmentation technique for automotive ultrasonics which is also adaptive to external influences. The performance of cGANs will surely uplift realistic environment simulation to a new horizon.…”