Error Correction For Automotive Telematics Systems

One benefit of data communication over the voice channel of the cellular network is to reliably transmit real-time high priority data in case of life critical situations. An important implementation of this use-case is the pan-European eCall automotive standard, which has already been deployed since 2018. This is the first international standard for mobile emergency call that was adopted by multiple regions in Europe and the world. Other countries in the world are currently working on deploying a similar emergency communication system, such as in Russia and China. Moreover, many experiments and road tests are conducted yearly to validate and improve the requirements of the system. The results have proven that the requirements are unachievable thus far, with a success rate of emergency data delivery of only 70%. The eCall in-band modem transmits emergency information from the in-vehicle system (IVS) over the voice channel of the circuit switch real time communication system to the public safety answering point (PSAP) in case of a collision. The voice channel is characterized by the non-linear vocoder which is designed to compress speech waveforms. In addition, multipath fading, caused by the surrounding buildings and hills, results in severe signal distortion and causes delays in the transmission of the emergency information. Therefore, to reliably transmit data over the voice channels, the in-band modem modulates the data into speech-like (SL) waveforms, and employs a powerful forward error correcting (FEC) code to secure the real-time transmission. In this dissertation, the Turbo coded performance of the eCall in-band modem is first evaluated through the adaptive white Gaussian noise (AWGN) channel and the adaptive multi-rate (AMR) voice channel. The modulation used is biorthogonal pulse position modulation (BPPM). Simulations are conducted for both the fast and robust eCall modem. The results show that the distortion added by the vocoder is significantly large and degrades the system performance. In addition, the robust modem performs better than the fast modem. For instance, to achieve a bit error rate (BER) of 10^{-6} using the AMR compression rate of 7.4 kbps, the signal-to-noise ratio (SNR) required is 5.5 dB for the robust modem while a SNR of 7.5 dB is required for the fast modem. On the other hand, the fading effect is studied in the eCall channel. It was shown that the fading distribution does not follow a Rayleigh distribution. The performance of the in-band modem is evaluated through the AWGN, AMR and fading channel. The results are compared with a Rayleigh fading channel. The analysis shows that strong fading still exists in the voice channel after power control. The results explain the large delays and failure of the emergency data transmission to the PSAP. Thus, the eCall standard needs to re-evaluate their requirements in order to consider the impact of fading on the transmission of the modulated signals. The results can be directly applied to design real-time emergency communication systems, including modulation and coding

Detecting and locating electronic devices using their unintended electromagnetic emissions

Electronically-initiated explosives can have unintended electromagnetic emissions which propagate through walls and sealed containers. These emissions, if properly characterized, enable the prompt and accurate detection of explosive threats. The following dissertation develops and evaluates techniques for detecting and locating common electronic initiators. The unintended emissions of radio receivers and microcontrollers are analyzed. These emissions are low-power radio signals that result from the device\u27s normal operation. In the first section, it is demonstrated that arbitrary signals can be injected into a radio receiver\u27s unintended emissions using a relatively weak stimulation signal. This effect is called stimulated emissions. The performance of stimulated emissions is compared to passive detection techniques. The novel technique offers a 5 to 10 dB sensitivity improvement over passive methods for detecting radio receivers. The second section develops a radar-like technique for accurately locating radio receivers. The radar utilizes the stimulated emissions technique with wideband signals. A radar-like system is designed and implemented in hardware. Its accuracy tested in a noisy, multipath-rich, indoor environment. The proposed radar can locate superheterodyne radio receivers with a root mean square position error less than 5 meters when the SNR is 15 dB or above. In the third section, an analytic model is developed for the unintended emissions of microcontrollers. It is demonstrated that these emissions consist of a periodic train of impulses. Measurements of an 8051 microcontroller validate this model. The model is used to evaluate the noise performance of several existing algorithms. Results indicate that the pitch estimation techniques have a 4 dB sensitivity improvement over epoch folding algorithms --Abstract, page iii

Novel Hilbert Huang transform techniques for bearing fault detection

Bearings are commonly used in rotary machinery; while up to half of machinery malfunctions could be related to bearing defects. A reliable bearing fault detection technique becomes vital to a wide array of industries to recognize an incipient bearing defect to prevent machinery performance degradation, malfunction, and unexpected breakdown. Many signal processing techniques have been suggested in literature to extract fault-related signatures for bearing fault detection, but most of them are not robust in real-world bearing health condition monitoring when signal properties vary with time. Vibration signals generated from bearings can be either stationary or nonstationary. If bearing defect-related signature is stationary, it is relatively easy to analyze using these classical data analysis techniques. However, bearing nonstationary signals are much more complex to analyze using these classical signal processing techniques, especially when slippage has occurred. Reliable fault detection still remains a challenging task, especially when bearing defect-related features are nonstationary. Two alternative approaches are proposed in this work for bearing fault detection: The first technique is based on analytical normality test, named Normalized Hilbert Haung Transform (NHHT). The second technique is based on information domain analysis, named enhanced Hilbert Haung Transform (eHHT). In the proposed NHHT technique, a novel strategy based on d’Agostino-Pearson normality analysis is suggested to demodulate feature functions and highlight feature characteristics for bearing fault detection. In the proposed eHHT, a novel strategy is proposed to enhance feature extraction based on the analysis of correlation and mutual information. The effectiveness of the proposed techniques is verified by a series of experimental tests corresponding to different bearing health conditions. Their robustness in bearing fault diagnostic is examined by the use of data sets from a different experimental setup

Investigation of the impact of high frequency transmitted speech on speaker recognition

Thesis (MScEng)--Stellenbosch University, 2002.Some digitised pages may appear illegible due to the condition of the original hard copy.ENGLISH ABSTRACT: Speaker recognition systems have evolved to a point where near perfect performance can be obtained under ideal conditions, even if the system must distinguish between a large number of speakers. Under adverse conditions, such as when high noise levels are present or when the transmission channel deforms the speech, the performance is often less than satisfying. This project investigated the performance of a popular speaker recognition system, that use Gaussian mixture models, on speech transmitted over a high frequency channel. Initial experiments demonstrated very unsatisfactory results for the base line system. We investigated a number of robust techniques. We implemented and applied some of them in an attempt to improve the performance of the speaker recognition systems. The techniques we tested showed only slight improvements. We also investigates the effects of a high frequency channel and single sideband modulation on the speech features of speech processing systems. The effects that can deform the features, and therefore reduce the performance of speech systems, were identified. One of the effects that can greatly affect the performance of a speech processing system is noise. We investigated some speech enhancement techniques and as a result we developed a new statistical based speech enhancement technique that employs hidden Markov models to represent the clean speech process.AFRIKAANSE OPSOMMING: Sprekerherkenning-stelsels het 'n punt bereik waar nabyaan perfekte resultate verwag kan word onder ideale kondisies, selfs al moet die stelsel tussen 'n groot aantal sprekers onderskei. Wanneer nie-ideale kondisies, soos byvoorbeeld hoë ruisvlakke of 'n transmissie kanaal wat die spraak vervorm, teenwoordig is, is die resultate gewoonlik nie bevredigend nie. Die projek ondersoek die werksverrigting van 'n gewilde sprekerherkenning-stelsel, wat gebruik maak van Gaussiese mengselmodelle, op spraak wat oor 'n hoë frekwensie transmissie kanaal gestuur is. Aanvanklike eksperimente wat gebruik maak van 'n basiese stelsel het nie goeie resultate opgelewer nie. Ons het 'n aantal robuuste tegnieke ondersoek en 'n paar van hulle geïmplementeer en getoets in 'n poging om die resultate van die sprekerherkenning-stelsel te verbeter. Die tegnieke wat ons getoets het, het net geringe verbetering getoon. Die studie het ook die effekte wat die hoë-frekwensie kanaal en enkel-syband modulasie op spraak kenmerkvektore, ondersoek. Die effekte wat die spraak kenmerkvektore kan vervorm en dus die werkverrigting van spraak stelsels kan verlaag, is geïdentifiseer. Een van die effekte wat 'n groot invloed op die werkverrigting van spraakstelsels het, is ruis. Ons het spraak verbeterings metodes ondersoek en dit het gelei tot die ontwikkeling van 'n statisties gebaseerde spraak verbeteringstegniek wat gebruik maak van verskuilde Markov modelle om die skoon spraakproses voor te stel

Advanced time-varying approaches for modeling the multipath channel in wireless network

This dissertation proposes the use of advanced time-varying approaches for modeling the dynamics of the multipath channel in wireless communication networks. These advanced time-varying approaches include linear Kalman innovation models in observable block companion form, and neural network-based models. The e˙ectiveness of these type of models is evaluated through three case studies. The first case study involves the identification of a linear time-varying Kalman innovation model, for describing measured received signal strength (RSSI) as a function of the speed of the link in an indoor multipath wireless channel. Results for this first case study show that the model exhibits both accuracy and robustness. The second case study evaluates the suitability of using a linear time-varying Kalman innovation model of the RSSI, for secret key generation in the physical layer of multipath wireless channels. It was found that the residuals of the Kalman model, due to their significant randomness, exhibit a notable potential for secret key generation; indeed, improved values of maximum channel capacity for secret key generation were achieved. At last, the third case study includes the identification of a neural network-based autoregressive moving average with exogenous inputs (NN-ARMAX) model and of a neural network-based autoregressive with exogenous inputs (NN-ARX) model, for describing traÿc in a 4G-LTE network. Both models showed similar performance, but the NN-ARMAX has the advantage that it can be converted to a linear time-varying Kalman innovation model, and thus can be used for the implementation of advanced strategies for controlling the operation of the network