Timing based source separation

Ph.D

Hybrid solutions to instantaneous MIMO blind separation and decoding: narrowband, QAM and square cases

Future wireless communication systems are desired to support high data rates and high quality transmission when considering the growing multimedia applications. Increasing the channel throughput leads to the multiple input and multiple output and blind equalization techniques in recent years. Thereby blind MIMO equalization has attracted a great interest.Both system performance and computational complexities play important roles in real time communications. Reducing the computational load and providing accurate performances are the main challenges in present systems. In this thesis, a hybrid method which can provide an affordable complexity with good performance for Blind Equalization in large constellation MIMO systems is proposed first. Saving computational cost happens both in the signal sep- aration part and in signal detection part. First, based on Quadrature amplitude modulation signal characteristics, an efficient and simple nonlinear function for the Independent Compo- nent Analysis is introduced. Second, using the idea of the sphere decoding, we choose the soft information of channels in a sphere, and overcome the so- called curse of dimensionality of the Expectation Maximization (EM) algorithm and enhance the final results simultaneously. Mathematically, we demonstrate in the digital communication cases, the EM algorithm shows Newton -like convergence.Despite the widespread use of forward -error coding (FEC), most multiple input multiple output (MIMO) blind channel estimation techniques ignore its presence, and instead make the sim- plifying assumption that the transmitted symbols are uncoded. However, FEC induces code structure in the transmitted sequence that can be exploited to improve blind MIMO channel estimates. In final part of this work, we exploit the iterative channel estimation and decoding performance for blind MIMO equalization. Experiments show the improvements achievable by exploiting the existence of coding structures and that it can access the performance of a BCJR equalizer with perfect channel information in a reasonable SNR range. All results are confirmed experimentally for the example of blind equalization in block fading MIMO systems

Autoregressive models for text independent speaker identification in noisy environments

The closed-set speaker identification problem is defined as the search within a set of persons for the speaker of a certain utterance. It is reported that the Gaussian mixture model (GMM) classifier achieves very high classification accuracies (in the range 95% - 100%) when both the training and testing utterances are recorded in sound proof studio, i.e., there is neither additive noise nor spectral distortion to the speech signals. However, in real life applications, speech is usually corrupted by noise and band-limitation. Moreover, there is a mismatch between the recording conditions of the training and testing environments. As a result, the classification accuracy of GMM-based systems deteriorates significantly. In this thesis, we propose a two-step procedure for improving the speaker identification performance under noisy environment. In the first step, we introduce a new classifier: vector autoregressive Gaussian mixture (VARGM) model. Unlike the GMM, the new classifier models correlations between successive feature vectors. We also integrate the proposed method into the framework of the universal background model (UBM). In addition, we develop the learning procedure according to the maximum likelihood (ML) criterion. Based on a thorough experimental evaluation, the proposed method achieves an improvement of 3 to 5% in the identification accuracy. In the second step, we propose a new compensation technique based on the generalized maximum likelihood (GML) decision rule. In particular, we assume a general form for the distribution of the noise-corrupted utterances, which contains two types of parameters: clean speech-related parameters and noise-related parameters. While the clean speech related parameters are estimated during the training phase, the noise related parameters are estimated from the corrupted speech in the testing phase. We applied the proposed method to utterances of 50 speakers selected from the TIMIT database, artificially corrupted by convolutive and additive noise. The signal to noise ratio (SNR) varies from 0 to 20 dB. Simulation results reveal that the proposed method achieves good robustness against variation in the SNR. For utterances corrupted by covolutive noise, the improvement in the classification accuracy ranges from 70% for SNR = 0 dB to around 4% for SNR = 10dB, compared to the standard ML decision rule. For utterances corrupted by additive noise, the improvement in the classification accuracy ranges from 1% to 10% for SNRs ranging from 0 to 20 dB. The proposed VARGM classifier is also applied to the speech emotion classification problem. In particular, we use the Berlin emotional speech database to validate the classification performance of the proposed VARGM classifier. The proposed technique provides a classification accuracy of 76% versus 71% for the hidden Markov model, 67% for the k-nearest neighbors, 55% for feed-forward neural networks. The model gives also better discrimination between high-arousal emotions (joy, anger, fear), low arousal emotions (sadness, boredom), and neutral emotions than the HMM. Another interesting application of the VARGM model is the blind equalization of multi input multiple output (MIMO) communication channels. Based on VARGM modeling of MIMO channels, we propose a four-step equalization procedure. First, the received data vectors are fitted into a VARGM model using the expectation maximization (EM) algorithm. The constructed VARGM model is then used to filter the received data. A Baysian decision rule is then applied to identify the transmitted symbols up to a permutation and phase ambiguities, which are finally resolved using a small training sequence. Moreover, we propose a fast and easily implementable model order selection technique. The new equalization algorithm is compared to the whitening method and found to provide less symbol error probability. The proposed technique is also applied to frequency-flat slow fading channels and found to provide a more accurate estimate of the channel response than that provided by the blind de-convolution exploiting channel encoding (BDCC) method and at a higher information rate

An Approach to Guide Users Towards Less Revealing Internet Browsers

When browsing the Internet, HTTP headers enable both clients and servers send extra data in their requests or responses such as the User-Agent string. This string contains information related to the sender’s device, browser, and operating system. Previous research has shown that there are numerous privacy and security risks result from exposing sensitive information in the User-Agent string. For example, it enables device and browser fingerprinting and user tracking and identification. Our large analysis of thousands of User-Agent strings shows that browsers differ tremendously in the amount of information they include in their User-Agent strings. As such, our work aims at guiding users towards using less exposing browsers. In doing so, we propose to assign an exposure score to browsers based on the information they expose and vulnerability records. Thus, our contribution in this work is as follows: first, provide a full implementation that is ready to be deployed and used by users. Second, conduct a user study to identify the effectiveness and limitations of our proposed approach. Our implementation is based on using more than 52 thousand unique browsers. Our performance and validation analysis show that our solution is accurate and efficient. The source code and data set are publicly available and the solution has been deployed

A selective list of acronyms and abbreviations

A glossary of acronyms, abbreviations, initials, code words, and phrases used at the John F. Kennedy Space Center is presented. The revision contains more than 12,100 entries

Psr1p interacts with SUN/sad1p and EB1/mal3p to establish the bipolar spindle

Regular Abstracts - Sunday Poster Presentations: no. 382During mitosis, interpolar microtubules from two spindle pole bodies (SPBs) interdigitate to create an antiparallel microtubule array for accommodating numerous regulatory proteins. Among these proteins, the kinesin-5 cut7p/Eg5 is the key player responsible for sliding apart antiparallel microtubules and thus helps in establishing the bipolar spindle. At the onset of mitosis, two SPBs are adjacent to one another with most microtubules running nearly parallel toward the nuclear envelope, creating an unfavorable microtubule configuration for the kinesin-5 kinesins. Therefore, how the cell organizes the antiparallel microtubule array in the first place at mitotic onset remains enigmatic. Here, we show that a novel protein psrp1p localizes to the SPB and plays a key role in organizing the antiparallel microtubule array. The absence of psr1+ leads to a transient monopolar spindle and massive chromosome loss. Further functional characterization demonstrates that psr1p is recruited to the SPB through interaction with the conserved SUN protein sad1p and that psr1p physically interacts with the conserved microtubule plus tip protein mal3p/EB1. These results suggest a model that psr1p serves as a linking protein between sad1p/SUN and mal3p/EB1 to allow microtubule plus ends to be coupled to the SPBs for organization of an antiparallel microtubule array. Thus, we conclude that psr1p is involved in organizing the antiparallel microtubule array in the first place at mitosis onset by interaction with SUN/sad1p and EB1/mal3p, thereby establishing the bipolar spindle.postprin

Removal of antagonistic spindle forces can rescue metaphase spindle length and reduce chromosome segregation defects

Regular Abstracts - Tuesday Poster Presentations: no. 1925Metaphase describes a phase of mitosis where chromosomes are attached and oriented on the bipolar spindle for subsequent segregation at anaphase. In diverse cell types, the metaphase spindle is maintained at a relatively constant length. Metaphase spindle length is proposed to be regulated by a balance of pushing and pulling forces generated by distinct sets of spindle microtubules and their interactions with motors and microtubule-associated proteins (MAPs). Spindle length appears important for chromosome segregation fidelity, as cells with shorter or longer than normal metaphase spindles, generated through deletion or inhibition of individual mitotic motors or MAPs, showed chromosome segregation defects. To test the force balance model of spindle length control and its effect on chromosome segregation, we applied fast microfluidic temperature-control with live-cell imaging to monitor the effect of switching off different combinations of antagonistic forces in the fission yeast metaphase spindle. We show that spindle midzone proteins kinesin-5 cut7p and microtubule bundler ase1p contribute to outward pushing forces, and spindle kinetochore proteins kinesin-8 klp5/6p and dam1p contribute to inward pulling forces. Removing these proteins individually led to aberrant metaphase spindle length and chromosome segregation defects. Removing these proteins in antagonistic combination rescued the defective spindle length and, in some combinations, also partially rescued chromosome segregation defects. Our results stress the importance of proper chromosome-to-microtubule attachment over spindle length regulation for proper chromosome segregation.postprin

Multi-Agent Modeling for Integrated Process Planning and Scheduling

Multi-agent systems have been used for modelling various problems in the social, biological and technical domain. When comes to technical systems, especially manufacturing systems, agents are most often applied in optimization and scheduling problems. Traditionally, scheduling is done after creation of process plans. In this paper, agent methodology is used for integration of these two functions. The proposed multi-agent architecture provides simultaneous performance of process planning and scheduling and it consists of four intelligent agents: part and job agents, machine agent, and optimization agent. Verification and feasibility of a proposed approach is conducted using agent based simulation in AnyLogic software