etd@IISc Community:http://hdl.handle.net/2005/12014-08-14T12:35:51Z2014-08-14T12:35:51ZA Workload Based Lookup Table For Minimal Power Operation Under Supply And Body Bias ControlSreejith, Khttp://hdl.handle.net/2005/10302011-01-25T20:30:31Z2011-01-24T18:30:00ZTitle: A Workload Based Lookup Table For Minimal Power Operation Under Supply And Body Bias Control
Authors: Sreejith, K
Abstract: Dynamic Voltage Scaling (DVS) and Adaptive body bias (ABB) techniques respectively try to reduce the dynamic and static power components of an integrated circuit. Ideally, the two techniques can be combined to find the optimal operating voltages (VDD and VBB) to minimize power consumption. A combination of the DVS and ABB may warrant the circuit to operate at voltages (supply and body bias) different from the values specified by the two methods working independently. Also, this VDD and VBB values for minimal power consumption varies with the workload of the circuit. The workload can be used as an index to select the optimal VDD/VBB values to minimize the total power consumption. This paper examines the optimal voltages for minimal power operation for typical data path circuits like adders and multiply-accumulate (MAC) units across various process, voltage, and temperature conditions and under different workloads. In addition, a workload based look up table to minimize the power consumption is also proposed. Simulation results for an adder and a multiply-accumulate circuit block indicate a power saving of 12-30% over standard DVS scheme.2011-01-24T18:30:00ZWhy only two ears? Some indicators from the study of source separation using two sensorsJoseph, Jobyhttp://hdl.handle.net/2005/552006-03-03T04:06:56Z2005-02-10T05:12:41ZTitle: Why only two ears? Some indicators from the study of source separation using two sensors
Authors: Joseph, Joby
Abstract: In this thesis we develop algorithms for estimating broadband source signals from a mixture using only two sensors. This is motivated by what is known in the literature as cocktail party effect, the ability of human beings to listen to the desired source from a mixture of sources with at most two ears. Such a study lets us, achieve a better understanding of the auditory pathway in the brain and confirmation of the results from physiology and psychoacoustics, have a clue to search for an equivalent structure in the brain which corresponds to the modification which improves the algorithm, come up with a benchmark system to automate the evaluation of the systems like 'surround sound', perform speech recognition in noisy environments. Moreover, it is possible that, what we learn about the replication of the functional units in the brain may help us in replacing those using signal processing units for patients suffering due to the defects in these units.
There are two parts to the thesis. In the first part we assume the source signals to be broadband and having strong spectral overlap. Channel is assumed to have a few strong multipaths. We propose an algorithm to estimate all the strong multi-paths from each source to the sensors for more than two sources with measurement from two sensors. Because the channel matrix is not invertible when the number of sources is more than the number of sensors, we make use of the estimates of the multi-path delays for each source to improve the SIR of the sources. In the second part we look at a specific scenario of colored signals and channel being one with a prominent direct path. Speech signals as the sources in a weakly reverberant room and a pair of microphones as the sensors satisfy these conditions. We consider the case with and without a head like structure between the microphones. The head like structure we used was a cubical block of wood. We propose an algorithm for separating sources under such a scenario. We identify the features of speech and the channel which makes it possible for the human auditory system to solve the cocktail party problem. These properties are the same as that satisfied by our model. The algorithm works well in a partly acoustically treated room, (with three persons speaking and two microphones and data acquired using standard PC setup) and not so well in a heavily reverberant scenario.
We see that there are similarities in the processing steps involved in the algorithm and what we know of the way our auditory system works, especially so in the regions before the auditory cortex in the auditory pathway. Based on the above experiments we give reasons to support the hypothesis about why all the known organisms need to have only two ears and not more but may have more than two eyes to their advantage. Our results also indicate that part of pitch estimation for individual sources might be occurring in the brain after separating the individual source components. This might solve the dilemma of having to do multi-pitch estimation. Recent works suggest that there are parallel pathways in the brain up to the primary auditory cortex which deal with temporal cue based processing and spatial cue based processing. Our model seem to mimic the pathway which makes use of the spatial cues.2005-02-10T05:12:41ZWavelet Based Denoising Techniques For Improved DOA Estimation And Source LocalisationSathish, Rhttp://hdl.handle.net/2005/12012011-05-16T04:39:49Z2011-05-15T18:30:00ZTitle: Wavelet Based Denoising Techniques For Improved DOA Estimation And Source Localisation
Authors: Sathish, R2011-05-15T18:30:00ZWavelet Based Algorithms For Spike Detection In Micro Electrode Array RecordingsNabar, Nisseem Shttp://hdl.handle.net/2005/7452010-07-13T20:31:09Z2010-07-12T18:30:00ZTitle: Wavelet Based Algorithms For Spike Detection In Micro Electrode Array Recordings
Authors: Nabar, Nisseem S
Abstract: In this work, the problem of detecting neuronal spikes or action potentials (AP) in noisy recordings from a Microelectrode Array (MEA) is investigated. In particular, the spike detection algorithms should be less complex and with low computational complexity so as to be amenable for real time applications. The use of the MEA is that it allows collection of extracellular signals from either a single unit or multiple (45) units within a small area. The noisy MEA recordings then undergo basic filtering, digitization and are presented to a computer for further processing. The challenge lies in using this data for detection of spikes from neuronal firings and extracting spatiotemporal patterns from the spike train which may allow control of a robotic limb or other neuroprosthetic device directly from the brain. The aim is to understand the spiking action of the neurons, and use this knowledge to devise efficient algorithms for Brain Machine Interfaces (BMIs).
An effective BMI will require a realtime, computationally efficient implementation which can be carried out on a DSP board or FPGA system. The aim is to devise algorithms which can detect spikes and underlying spatio-temporal correlations having computational and time complexities to make a real time implementation feasible on a specialized DSP chip or an FPGA device. The time-frequency localization, multiresolution representation and analysis properties of wavelets make them suitable for analysing sharp transients and spikes in signals and distinguish them from noise resembling a transient or the spike. Three algorithms for the detection of spikes in low SNR MEA neuronal recordings are proposed:
1. A wavelet denoising method based on the Discrete Wavelet Transform (DWT) to suppress the noise power in the MEA signal or improve the SNR followed by standard thresholding techniques to detect the spikes from the denoised signal.
2. Directly thresholding the coefficients of the Stationary (Undecimated) Wavelet Transform (SWT) to detect the spikes.
3. Thresholding the output of a Teager Energy Operator (TEO) applied to the signal on the discrete wavelet decomposed signal resulting in a multiresolution TEO framework.
The performance of the proposed three wavelet based algorithms in terms of the accuracy of spike detection, percentage of false positives and the computational complexity for different types of wavelet families in the presence of colored AR(5) (autoregressive model with order 5) and additive white Gaussian noise (AWGN) is evaluated. The performance is further evaluated for the wavelet family chosen under different levels of SNR in the presence of the colored AR(5) and AWGN noise.
Chapter 1 gives an introduction to the concept behind Brain Machine Interfaces (BMIs), an overview of their history, the current state-of-the-art and the trends for the future. It also describes the working of the Microelectrode Arrays (MEAs). The generation of a spike in a neuron, the proposed mechanism behind it and its modeling as an electrical circuit based on the Hodgkin-Huxley model is described. An overview of some of the algorithms that have been suggested for spike detection purposes whether in MEA recordings or Electroencephalographic (EEG) signals is given.
Chapter 2 describes in brief the underlying ideas that lead us to the Wavelet Transform paradigm. An introduction to the Fourier Transform, the Short Time Fourier Transform (STFT) and the Time-Frequency Uncertainty Principle is provided. This is followed by a brief description of the Continuous Wavelet Transform and the Multiresolution Analysis (MRA) property of wavelets. The Discrete Wavelet Transform (DWT) and its ﬁlter bank implementation are described next. It is proposed to apply the wavelet denoising algorithm pioneered by Donoho, to first denoise the MEA recordings followed by standard thresholding technique for spike detection.
Chapter 3 deals with the use of the Stationary or Undecimated Wavelet Transform (SWT) for spike detection. It brings out the differences between the DWT and the SWT. A brief discussion of the analysis of non-stationary time series using the SWT is presented. An algorithm for spike detection based on directly thresholding the SWT coefficients without any need for reconstructing the denoised signal followed by thresholding technique as in the first method is presented.
In chapter 4 a spike detection method based on multiresolution Teager Energy Operator is discussed. The Teager Energy Operator (TEO) picks up localized spikes in signal energy and thus is directly used for spike detection in many applications including R wave detection in ECG and various (alpha, beta) rhythms in EEG. Some basic properties of the TEO are discussed followed by the need for a multiresolution approach to TEO and the methods existing in literature.
The wavelet decomposition and the subsampled signal involved at each level naturally lends it to a multiresolution TEO framework at the same time significantly reducing the computational complexity due the subsampled signal at each level. A wavelet-TEO algorithm for spike detection with similar accuracies as the previous two algorithms is proposed. The method proposed here differs significantly from that in literature since wavelets are used instead of time domain processing.
Chapter 5 describes the method of evaluation of the three algorithms proposed in the previous chapters. The spike templates are obtained from MEA recordings, resampled and normalized for use in spike trains simulated as Poisson processes. The noise is modeled as colored autoregressive (AR) of order 5, i.e AR(5), as well as Additive White Gaussian Noise (AWGN). The noise in most human and animal MEA recordings conforms to the autoregressive model with orders of around 5. The AWGN Noise model is used in most spike detection methods in the literature. The performance of the proposed three wavelet based algorithms is measured in terms of the accuracy of spike detection, percentage of false positives and the computational complexity for different types of wavelet families. The optimal wavelet for this purpose is then chosen from the wavelet family which gives the best results. Also, optimal levels of decomposition and threshold factors are chosen while maintaining a balance between accuracy and false positives. The algorithms are then tested for performance under different levels of SNR with the noise modeled as AR(5) or AWGN. The proposed wavelet based algorithms exhibit a detection accuracy of approximately 90% at a low SNR of 2.35 dB with the false positives below 5%. This constitutes a significant improvement over the results in existing literature which claim an accuracy of 80% with false positives of nearly 10%. As the SNR increases, the detection accuracy increases to close to 100% and the false alarm rate falls to 0.
Chapter 6 summarizes the work. A comparison is made between the three proposed algorithms in terms of detection accuracy and false positives. Directions in which future work may be carried out are suggested.2010-07-12T18:30:00Z