Improved subject-independent acoustic-to-articulatory inversion

In subject-independent acoustic-to-articulatory inversion, the articulatory kinematics of a test subject are estimated assuming that the training corpus does not include data from the test subject. The training corpus in subject-independent inversion (SII) is formed with acoustic and articulatory kinematics data and the acoustic mismatch between training and test subjects is then estimated by an acoustic normalization using acoustic data drawn from a large pool of speakers called generic acoustic space (GAS). In this work, we focus on improving the SII performance through better acoustic normalization and adaptation. We propose unsupervised and several supervised ways of clustering GAS for acoustic normalization. We perform an adaptation of acoustic models of GAS using the acoustic data of the training and test subjects in SII. It is found that SII performance significantly improves (similar to 25% relative on average) over the subject-dependent inversion when the acoustic clusters in GAS correspond to phonetic units (or states of 3-state phonetic HMMs) and when the acoustic model built on GAS is adapted to training and test subjects while optimizing the inversion criterion. (C) 2014 Elsevier B.V. All rights reserved.

Leveraging coherent distributed space-time codes for noncoherent communication in relay networks via training

For point to point multiple input multiple output systems, Dayal-Brehler-Varanasi have proved that training codes achieve the same diversity order as that of the underlying coherent space time block code (STBC) if a simple minimum mean squared error estimate of the channel formed using the training part is employed for coherent detection of the underlying STBC. In this letter, a similar strategy involving a combination of training, channel estimation and detection in conjunction with existing coherent distributed STBCs is proposed for noncoherent communication in Amplify-and-Forward (AF) relay networks. Simulation results show that the proposed simple strategy outperforms distributed differential space-time coding for AF relay networks. Finally, the proposed strategy is extended to asynchronous relay networks using orthogonal frequency division multiplexing.

Reverse Channel Training For Reciprocal Mimo Systems With Spatial Multiplexing

This paper investigates the problem of designing reverse channel training sequences for a TDD-MIMO spatial-multiplexing system. Assuming perfect channel state information at the receiver and spatial multiplexing at the transmitter with equal power allocation to them dominant modes of the estimated channel, the pilot is designed to ensure an stimate of the channel which improves the forward link capacity. Using perturbation techniques, a lower bound on the forward link capacity is derived with respect to which the training sequence is optimized. Thus, the reverse channel training sequence makes use of the channel knowledge at the receiver. The performance of orthogonal training sequence with MMSE estimation at the transmitter and the proposed training sequence are compared. Simulation results show a significant improvement in performance.

Joint evaluation of multiple speech patterns for speech recognition and training

We are addressing the novel problem of jointly evaluating multiple speech patterns for automatic speech recognition and training. We propose solutions based on both the non-parametric dynamic time warping (DTW) algorithm, and the parametric hidden Markov model (HMM). We show that a hybrid approach is quite effective for the application of noisy speech recognition. We extend the concept to HMM training wherein some patterns may be noisy or distorted. Utilizing the concept of ``virtual pattern'' developed for joint evaluation, we propose selective iterative training of HMMs. Evaluating these algorithms for burst/transient noisy speech and isolated word recognition, significant improvement in recognition accuracy is obtained using the new algorithms over those which do not utilize the joint evaluation strategy.

Cluster based training for scaling non-linear support vector machines

Support Vector Machines(SVMs) are hyperplane classifiers defined in a kernel induced feature space. The data size dependent training time complexity of SVMs usually prohibits its use in applications involving more than a few thousands of data points. In this paper we propose a novel kernel based incremental data clustering approach and its use for scaling Non-linear Support Vector Machines to handle large data sets. The clustering method introduced can find cluster abstractions of the training data in a kernel induced feature space. These cluster abstractions are then used for selective sampling based training of Support Vector Machines to reduce the training time without compromising the generalization performance. Experiments done with real world datasets show that this approach gives good generalization performance at reasonable computational expense.

Stability Training and Measurement System for Sportsperson (P84)

Balance and stability are very important for everybody and especially for sports-person who undergo extreme physical activities. Balance and stability exercises not only have a great impact on the performance of the sportsperson but also play a pivotal role in their rehabilitation. Therefore, it is very essential to have knowledge about a sportsperson’s balance and also to quantify the same. In this work, we propose a system consisting of a wobble board, with a gyro enhanced orientation sensor and a motion display for visual feedback to help the sportsperson improve their stability. The display unit gives in real time the orientation of the wobble board, which can help the sportsperson to apply necessary corrective forces to maintain neutral position. The system is compact and portable. We also quantify balance and stability using power spectral density. The sportsperson is made stand on the wobble board and the angular orientation of the wobble board is recorded for each 0.1 second interval. The signal is analized using discrete Fourier transforms. The power of this signal is related to the stability of the subject. This procedure is used to measure the balance and stability of an elite cricket team. Representative results are shown below: Table 1 represents power comparison of two subjects and Table 2 represents power comparison of left leg and right leg of one subject. This procedure can also be used in clinical practice to monitor improvement in stability dysfunction of sportsperson with injuries or other related problems undergoing rehabilitation.

Characteristics of yeasts isolated from phenol- and catechol-adapted activated sludges

Phenol- and catechol-adapted sludges contained large numbers of the yeasts, Candida tropicalis and Trichosporon cutaneum. Both were able to grow on a variety of aromatic compounds and utilized phenol and catechol at a high rate. This property was inducible. The feasibility of using these yeasts for removing phenols from waste waters is suggested.

Minimum enclosing spheres formulations for support vector ordinal regression

We present two new support vector approaches for ordinal regression. These approaches find the concentric spheres with minimum volume that contain most of the training samples. Both approaches guarantee that the radii of the spheres are properly ordered at the optimal solution. The size of the optimization problem is linear in the number of training samples. The popular SMO algorithm is adapted to solve the resulting optimization problem. Numerical experiments on some real-world data sets verify the usefulness of our approaches for data mining.

A course for teaching design research methodology

Design research informs and supports practice by developing knowledge to improve the chances of producing successful products.Training in design research has been poorly supported. Design research uses human and natural/technical sciences, embracing all facets of design; its methods and tools are adapted from both these traditions. However, design researchers are rarely trained in methods from both the traditions. Research in traditional sciences focuses primarily on understanding phenomena related to human, natural, or technical systems. Design research focuses on supporting improvement of such systems, using understanding as a necessary but not sufficient step, and it must embrace methods for both understanding reality and developing support for its improvement. A one-semester, postgraduate-level, credited course that has been offered since 2002, entitled Methodology for Design Research, is described that teaches a methodology for carrying out research into design. Its steps are to clarify research success; to understand relevant phenomena of design and how these influence success; to use this to envision design improvement and develop proposals for supporting improvement; to evaluate support for its influence on success; and, if unacceptable, to modify, support, or improve the understanding of success and its links to the phenomena of design. This paper highlights some major issues about the status of design research and describes how design research methodology addresses these. The teaching material, model of delivery, and evaluation of the course on methodology for design research are discussed.

Optimal Weighted Antenna Selection For Imperfect Channel Knowledge From Training

Receive antenna selection (AS) reduces the hardware complexity of multi-antenna receivers by dynamically connecting an instantaneously best antenna element to the available radio frequency (RF) chain. Due to the hardware constraints, the channels at various antenna elements have to be sounded sequentially to obtain estimates that are required for selecting the ``best'' antenna and for coherently demodulating data. Consequently, the channel state information at different antennas is outdated by different amounts. We show that, for this reason, simply selecting the antenna with the highest estimated channel gain is not optimum. Rather, the channel estimates of different antennas should be weighted differently, depending on the training scheme. We derive closed-form expressions for the symbol error probability (SEP) of AS for MPSK and MQAM in time-varying Rayleigh fading channels for arbitrary selection weights, and validate them with simulations. We then derive an explicit formula for the optimal selection weights that minimize the SEP. We find that when selection weights are not used, the SEP need not improve as the number of antenna elements increases, which is in contrast to the ideal channel estimation case. However, the optimal selection weights remedy this situation and significantly improve performance.

A Training-Based Iterative Detection/Channel Estimation Scheme for Large Non-Orthogonal STBC MIMO Systems

In this paper, we propose a training-based channel estimation scheme for large non-orthogonal space-time block coded (STBC) MIMO systems.The proposed scheme employs a block transmission strategy where an N-t x N-t pilot matrix is sent (for training purposes) followed by several N-t x N-t square data STBC matrices, where Nt is the number of transmit antennas. At the receiver, we iterate between channel estimation (using an MMSE estimator) and detection (using a low-complexity likelihood ascent search (LAS) detector) till convergence or for a fixed number of iterations. Our simulation results show that excellent bit error rate and nearness-to-capacity performance are achieved by the proposed scheme at low complexities. The fact that we could show such good results for large STBCs (e.g., 16 x 16 STBC from cyclic division algebras) operating at spectral efficiencies in excess of 20 bps/Hz (even after accounting for the overheads meant for pilot-based channel estimation and turbo coding) establishes the effectiveness of the proposed scheme.

Optimal Receive Antenna Selection in Time-Varying Fading Channels with Practical Training Constraints

Hardware constraints, which motivate receive antenna selection, also require that various antenna elements at the receiver be sounded sequentially to obtain estimates required for selecting the `best' antenna and for coherently demodulating data thereafter. Consequently, the channel state information at different antennas is outdated by different amounts and corrupted by noise. We show that, for this reason, simply selecting the antenna with the highest estimated channel gain is not optimum. Rather, a preferable strategy is to linearly weight the channel estimates of different antennas differently, depending on the training scheme. We derive closed-form expressions for the symbol error probability (SEP) of AS for MPSK and MQAM in time-varying Rayleigh fading channels for arbitrary selection weights, and validate them with simulations. We then characterize explicitly the optimal selection weights that minimize the SEP. We also consider packet reception, in which multiple symbols of a packet are received by the same antenna. New suboptimal, but computationally efficient weighted selection schemes are proposed for reducing the packet error rate. The benefits of weighted selection are also demonstrated using a practical channel code used in third generation cellular systems. Our results show that optimal weighted selection yields a significant performance gain over conventional unweighted selection.

A Novel, Balanced, and Energy-Efficient Training Method for Receive Antenna Selection

In receive antenna selection (AS), only signals from a subset of the antennas are processed at any time by the limited number of radio frequency (RF) chains available at the receiver. Hence, the transmitter needs to send pilots multiple times to enable the receiver to estimate the channel state of all the antennas and select the best subset. Conventionally, the sensitivity of coherent reception to channel estimation errors has been tackled by boosting the energy allocated to all pilots to ensure accurate channel estimates for all antennas. Energy for pilots received by unselected antennas is mostly wasted, especially since the selection process is robust to estimation errors. In this paper, we propose a novel training method uniquely tailored for AS that transmits one extra pilot symbol that generates accurate channel estimates for the antenna subset that actually receives data. Consequently, the transmitter can selectively boost the energy allocated to the extra pilot. We derive closed-form expressions for the proposed scheme's symbol error probability for MPSK and MQAM, and optimize the energy allocated to pilot and data symbols. Through an insightful asymptotic analysis, we show that the optimal solution achieves full diversity and is better than the conventional method.

Training-Symbol Embedded, High-Rate Complex Orthogonal Designs for Relay Networks

Distributed Space-Time Block Codes (DSTBCs) from Complex Orthogonal Designs (CODs) (both square and non-square CODs other than the Alamouti design) are known to lose their single-symbol ML decodable (SSD) property when used in two-hop wireless relay networks using the amplify and forward protocol. For such a network, a new class of high rate, training-symbol embedded (TSE) SSD DSTBCs are proposed from TSE-CODs. The constructed codes include the training symbols within the structure of the code which is shown to be the key point to obtain high rate along with the SSD property. TSE-CODs are shown to offer full-diversity for arbitrary complex constellations. Non-square TSE-CODs are shown to provide better rates (in symbols per channel use) compared to the known SSD DSTBCs for relay networks when the number of relays is less than 10. Importantly, the proposed DSTBCs do not contain zeros in their codewords and as a result, antennas of the relay nodes do not undergo a sequence of switch on and off transitions within every codeword use. Hence, the proposed DSTBCs eliminate the antenna switching problem.

On the improvement of diversity-multiplexing gain tradeoff in a training based TDD-simo system

This paper investigates the diversity-multiplexing gain tradeoff (DMT) of a time-division duplex (TDD) single-input multiple-output (SIMO) system with perfect channel state information (CSI) at the receiver (CSIR) and partial CSI at the transmitter (CSIT). The partial CSIT is acquired through a training sequence from the receiver to the transmitter. The training sequence is chosen in an intelligent manner based on the CSIR, to reduce the training length by a factor of r, the number of receive antennas. We show that, for the proposed training scheme and a given channel coherence time, the diversity order increases linearly with r for nonzero multiplexing gain. This is a significant improvement over conventional orthogonal training schemes.