etd@IISc Community:http://hdl.handle.net/2005/12017-11-22T06:12:55Z2017-11-22T06:12:55ZSecuring Multiprocessor Systems-on-ChipBiswas, Arnab Kumarhttp://hdl.handle.net/2005/25542016-09-08T07:08:11Z2016-09-07T18:30:00ZTitle: Securing Multiprocessor Systems-on-Chip
Authors: Biswas, Arnab Kumar
Abstract: With Multiprocessor Systems-on-Chips (MPSoCs) pervading our lives, security issues are emerging as a serious problem and attacks against these systems are becoming more critical and sophisticated. We have designed and implemented different hardware based solutions to ensure security of an MPSoC. Security assisting modules can be implemented at different abstraction
levels of an MPSoC design. We propose solutions both at circuit level and system level of abstractions. At the VLSI circuit level abstraction, we consider the problem of presence of noise voltage in input signal coming from outside world. This noise voltage disturbs the normal circuit operation inside a chip causing false logic reception. If the disturbance is caused
intentionally the security of a chip may be compromised causing glitch/transient attack. We propose an input receiver with hysteresis characteristic that can work at voltage levels between 0.9V and 5V. The circuit can protect the MPSoC from glitch/transient attack. At the system level, we propose solutions targeting Network-on-Chip (NoC) as the on-chip communication medium. We survey the possible attack scenarios on present-day MPSoCs and investigate a new attack scenario, i.e., router attack targeted toward NoC enabled MPSoC. We propose different monitoring-based countermeasures against routing table-based router attack in an MPSoC having multiple Trusted Execution Environments (TEEs). Software attacks, the most common type of attacks, mainly exploit vulnerabilities like buffer overflow. This is possible if proper access control to memory is absent in the system. We propose four hardware based mechanisms to implement Role Based Access Control (RBAC) model in NoC based MPSoC.2016-09-07T18:30:00ZWho Spoke What And Where? A Latent Variable Framework For Acoustic Scene AnalysisSundar, Harshavardhanhttp://hdl.handle.net/2005/25692016-09-15T11:46:20Z2016-09-14T18:30:00ZTitle: Who Spoke What And Where? A Latent Variable Framework For Acoustic Scene Analysis
Authors: Sundar, Harshavardhan
Abstract: Speech is by far the most natural form of communication between human beings. It is intuitive, expressive and contains information at several cognitive levels. We as humans, are perceptive to several of these cognitive levels of information, as we can gather the information pertaining to the identity of the speaker, the speaker's gender, emotion, location, the language, and so on, in addition to the content of what is being spoken. This makes speech based human machine interaction (HMI), both desirable and challenging for the same set of reasons. For HMI to be natural for humans, it is imperative that a machine understands information present in speech, at least at the level of speaker identity, language, location in space, and the summary of what is being spoken.
Although one can draw parallels between the human-human interaction and HMI, the two differ in their purpose. We, as humans, interact with a machine, mostly in the context of getting a task done more efficiently, than is possible without the machine. Thus, typically in HMI, controlling the machine in a specific manner is the primary goal. In this context, it can be argued that, HMI, with a limited vocabulary containing specific commands, would suffice for a more efficient use of the machine.
In this thesis, we address the problem of ``Who spoke what and where", in the context of a machine understanding the information pertaining to identities of the speakers, their locations in space and the keywords they spoke, thus considering three levels of information - speaker identity (who), location (where) and keywords (what). This can be addressed with the help of multiple sensors like microphones, video camera, proximity sensors, motion detectors, etc., and combining all these modalities. However, we explore the use of only microphones to address this issue. In practical scenarios, often there are times, wherein, multiple people are talking at the same time. Thus, the goal of this thesis is to detect all the speakers, their keywords, and their locations in mixture signals containing speech from simultaneous speakers. Addressing this problem of ``Who spoke what and where" using only microphone signals, forms a part of acoustic scene analysis (ASA) of speech based acoustic events.
We divide the problem of ``who spoke what and where" into two sub-problems: ``Who spoke what?" and ``Who spoke where". Each of these problems is cast in a generic latent variable (LV) framework to capture information in speech at different levels. We associate a LV to represent each of these levels and model the relationship between the levels using conditional dependency.
The sub-problem of ``who spoke what" is addressed using single channel microphone signal, by modeling the mixture signal in terms of LV mass functions of speaker identity, the conditional mass function of the keyword spoken given the speaker identity, and a speaker-specific-keyword model. The LV mass functions are estimated in a Maximum likelihood (ML) framework using the Expectation Maximization (EM) algorithm using Student's-t Mixture Model (tMM) as speaker-specific-keyword models. Motivated by HMI in a home environment, we have created our own database. In mixture signals, containing two speakers uttering the keywords simultaneously, the proposed framework achieves an accuracy of 82 % for detecting both the speakers and their respective keywords.
The other sub-problem of ``who spoke where?" is addressed in two stages. In the first stage, the enclosure is discretized into sectors. The speakers and the sectors in which they are located are detected in an approach similar to the one employed for ``who spoke what" using signals collected from a Uniform Circular Array (UCA). However, in place of speaker-specific-keyword models, we use tMM based speaker models trained on clean speech, along with a simple Delay and Sum Beamformer (DSB). In the second stage, the speakers are localized within the active sectors using a novel region constrained localization technique based on time difference of arrival (TDOA). Since the problem being addressed is a multi-label classification task, we use the average Hamming score (accuracy) as the performance metric. Although the proposed approach yields an accuracy of 100 % in an anechoic setting for detecting both the speakers and their corresponding sectors in two-speaker mixture signals, the performance degrades to an accuracy of 67 % in a reverberant setting, with a $60$ dB reverberation time (RT60) of 300 ms. To improve the performance under reverberation, prior knowledge of the location of multiple sources is derived using a novel technique derived from geometrical insights into TDOA estimation. With this prior knowledge, the accuracy of the proposed approach improves to 91 %. It is worthwhile to note that, the accuracies are computed for mixture signals containing more than 90 % overlap of competing speakers.
The proposed LV framework offers a convenient methodology to represent information at broad levels. In this thesis, we have shown its use with three different levels. This can be extended to several such levels to be applicable for a generic analysis of the acoustic scene consisting of broad levels of events. It will turn out that not all levels are dependent on each other and hence the LV dependencies can be minimized by independence assumption, which will lead to solving several smaller sub-problems, as we have shown above. The LV framework is also attractive to incorporate prior knowledge about the acoustic setting, which is combined with the evidence from the data to derive the information about the presence of an acoustic event. The performance of the framework, is dependent on the choice of stochastic models, which model the likelihood function of the data given the presence of acoustic events. However, it provides an access to compare and contrast the use of different stochastic models for representing the likelihood function.2016-09-14T18:30:00ZSpectral And Temporal Zero-Crossings-Based Signal AnalysisShenoy, Ravi Rhttp://hdl.handle.net/2005/26602017-09-20T09:24:26Z2017-09-19T18:30:00ZTitle: Spectral And Temporal Zero-Crossings-Based Signal Analysis
Authors: Shenoy, Ravi R
Abstract: We consider real zero-crossing analysis of the real/imaginary parts of the spectrum, namely, spectral zero-crossings (SZCs). The two major contributions are to show that: (i) SZCs provide enable temporal localization of transients; and (b) SZCs are suitable for modeling transient signals. We develop a spectral dual of Kedem’s result linking temporal zero-crossing rate (ZCR) to the spectral centroid. The key requirement is stationarity, which we achieve through random-phase modulations of the time-domain signal. Transient signals are not amenable to modelling in the time domain since they are bursts of energy localized in time and lack structure. We show that the spectrum of transient signals have a rich modulation structure, which leads to an amplitude-modulation – frequency-modulation (AM-FM) model of the spectrum.
We generalize Kedem’s arc-cosine formula for lags greater than one. For the specific case of a sinusoid in white Gaussian noise, He and Kedem devised an iterative filtering algorithm, which leads to a contraction mapping. An autoregressive filter of order one is employed and the location of the pole is the parameter that is updated based on the filtered output. We use the higher-order property, which relates the autocorrelation to the expected ZCR of the filtered process, between lagged ZCR and higher-lag autocorrelation to develop an iterative higher-order autoregressive-filtering scheme, which stabilizes the ZCR and consequently provides robust estimates of the autocorrelation at higher lags.
Next, we investigate ZC properties of critically sampled outputs of a maximally decimated M-channel power complementary analysis filterbank (PCAF) and derive the relationship between the ZCR of the input Gaussian process at lags that are integer multiples of M in terms of the subband ZCRs. Based on this result, we propose a robust autocorrelation estimator for a signal consisting of a sum of sinusoids of fixed amplitudes and uniformly distributed random phases. Robust subband ZCRs are obtained through iterative filtering and the subband variances are estimated using the method-of-moments estimator. We compare the performance of the proposed estimator with the sample auto-correlation estimate in terms of bias, variance, and mean-squared error, and show through simulations that the performance of the proposed estimator is better than the sample auto- correlation for medium to low SNR.
We then consider the ZC statistics of the real/imaginary parts of the discrete Fourier spectrum. We introduce the notion of the spectral zero-crossing rate (SZCR) and show that, for transients, it gives information regarding the location of the transient. We also demonstrate the utility of SZCR to estimate interaural time delay between the left and right head-related impulse responses. The accuracy of interaural time delay plays a vital role in binaural synthesis and a comparison of the performance of the SZCR estimates with that of the cross-correlation estimates illustrate that spectral zeros alone contain enough information for accurately estimating interaural time delay. We provide a mathematical formalism for establishing the dual of the link between zero-crossing rate and spectral centroid. Specifically, we show that the expected SZCR of a stationary spectrum is a temporal centroid. For a deterministic sequence, we obtain the stationary spectrum by modulating the sequence with a random phase unit amplitude sequence and then computing the spectrum. The notion of a stationary spectrum is necessary for deriving counterparts of the results available in temporal zero-crossings literature. The robustness of location information embedded in SZCR is analyzed in presence of a second transient within the observation window, and also in the presence of additive white Gaussian noise. A spectral-domain iterative filtering scheme based on autoregressive filters is presented and improvement in the robustness of the location estimates is demonstrated. As an application, we consider epoch estimation in voiced speech signals and show that the location information is accurately estimated using spectral zeros than other techniques.
The relationship between temporal centroid and SZCR also finds applications in frequency-domain linear prediction (FDLP), which is used in audio compression. The prediction coefficients are estimated by solving the Yule-Walker equations constructed from the spectral autocorrelation. We use the relationship between the spectral autocorrelation and temporal centroid to obtain the spectral autocorrelation directly by time-domain windowing without explicitly computing the spectrum. The proposed method leads to identical results as the standard FDLP method but with reduced computational load.
We then develop a SZCs-based spectral-envelope and group-delay (SEGD) model, which finds applications in modelling of non-stationary signals such as Castanets. Taking into account the modulation structure and spectral continuity, local polynomial regression is performed to estimate the GD from the real spectral zeros. The SE is estimated based on the phase function computed from the estimated GD. Since the GD estimate is parametric, the degree of smoothness can be controlled directly. Simulation results based on synthetic transient signals are presented to analyze the noise-robustness of the SE-GD model. Applications to castanet modeling, transient compression, and estimation of the glottal closure instants in speech are shown.2017-09-19T18:30:00ZDesign And Control of Power Converters for Renewable Energy SystemsAbhijit, Khttp://hdl.handle.net/2005/26632017-09-20T09:58:57Z2017-09-19T18:30:00ZTitle: Design And Control of Power Converters for Renewable Energy Systems
Authors: Abhijit, K
Abstract: Renewable energy sources normally require power converters to convert their energy into standardized regulated ac output. The motivation for this thesis is to design and control power converters for renewable energy systems to ensure very good power quality, efficiency and reliability. The renewable energy sources considered are low voltage dc sources such as photovoltaic (PV) modules. Two transformer-isolated power circuit topologies with input voltage of less than 50V are designed and developed for low and medium power applications. Various design and control issues of these converters are identified and new solutions are proposed.
For low power rating of a few hundred watts, a line-frequency transformer interfaced inverter is developed. In the grid connected operation, it is observed that this topology injects considerable lower order odd and even harmonics in the grid current. The reasons for this are identified. A new current control method using adaptive harmonic compensation technique and a proportional-resonant-integral (PRI) controller is proposed. The proposed current controller is designed to ensure that the grid current harmonics are within the limits set by the IEEE 1547-2003 standard.
Phase-locked loops (PLLs) are used for grid synchronization of power converters in grid-tied operation and for closed-loop control reference generation. Analysis and design of synchronous reference frame PLL (SRF-PLL) and second-order generalized integrator (SOGI) based PLLs considering unit vector distortion under the possible non-ideal grid conditions of harmonics, unbalance, dc offsets and frequency deviations are proposed and validated. Both SRF-PLL and SOGI-PLL are low-complexity PLLs. The proposed designs achieve fastest settling time for these PLLs for a given worst-case input condition. The harmonic distortion and dc offsets in the resulting unit vectors are limited to be well within the limits set by the IEEE 1547-2003 standard. The proposed designs can be used to achieve very good performance using conventional low-complexity PLLs without the requirement of advanced PLLs which can be computationally intensive.
A high-frequency (HF) transformer interfaced ac link inverter with a lossless snubber is developed medium power level in the order of few kilowatts. The HF transformer makes the topology compact and economical compared to an equally rated line frequency transformer. A new synchronized modulation method is proposed to suppress the possible over-voltages due to current commutation in the leakage inductance of the HF transformer. The effect of circuit non-ideality of turn-on delay time is analyzed. The proposed modulation mitigates the problem of spurious turn-on that can occur due to the turn-on delay time. The HF inverter, rectifier and snubber devices have soft switching with this modulation. A new reliable start-up method is proposed for this inverter topology without any additional start- up circuitry. This solves the problems of over-voltages and inrush currents during start-up.
The overall research work reported in the thesis shows that it is possible to have compact, reliable and high performance power converters for renewable energy conversion systems. It is also shown that high control performance and power quality can be achieved using the proposed control techniques of low implementation complexity.2017-09-19T18:30:00Z