Pravesh Biyani

Joint Classification and Pairing of Human Chromosomes

We reexamine the problems of computer-aided classification and pairing of human chromosomes, and propose to jointly optimize the solutions of these two related problems. The combined problem is formulated into one of optimal three-dimensional assignment with an objective function of maximum likelihood. This formulation poses two technical challenges: 1) estimation of the posterior probability that two chromosomes form a pair and the pair belongs to a class and 2) good heuristic algorithms to solve the three-dimensional assignment problem which is NP-hard. We present various techniques to solve these problems. We also generalize our algorithms to cases where the cell data are incomplete as often encountered in practice.

Globally optimal classification and pairing of human chromosomes

We investigate globally optimal algorithms for automated classification and pairing of human chromosomes. Even in cases where the cell data are incomplete as often encountered in practice, we can still formulate the problem as a transportation problem, and hence find the globally optimal solution in polynomial time. In addition, we propose a technique of homologue pairing via maximum-weight graph matching. It obtains the globally optimal solution by forming all homologue pairs simultaneously under a maximum likelihood criterion, rather than finding one pair at a time as in existing heuristic algorithms. After the optimal homologue pairing, chromosome classification can also be done by maximum-weight graph matching. This new graph theoretical approach to chromosome pairing and classification is more robust than the transportation algorithm, because many attributes of a chromosome have less variations within a cell than between different cells.

Impulse denoising for hyper-spectral images

In this work we propose a technique to remove sparse impulse noise from hyperspectral images. Our algorithm accounts for the spatial redundancy and spectral correlation of such images. The proposed method is based on the recently introduced Blind Compressed Sensing (BCS) framework, i.e. it empirically learns the spatial and spectral sparsifying dictionaries while denoising the images. The BCS framework differs from existing CS techniques that employ fixed sparsifying basis; BCS also differs from prior dictionary learning studies which learn the dictionary in an offline training phase. Our proposed formulation has shown over 5dB improvement in PSNR over other techniques.

Co-operative Alien Noise Cancellation in Upstream VDSL: A New Decision Directed Approach

Alien noise in the vectored very-high-speed digital subscriber line (VDSL) system is part of the additive noise at the receiver and exhibits strong correlation among users. We present a per-tone co-operative alien noise cancellation (CoMAC) algorithm for the upstream (US) VDSL that can be applied subsequent to any self far-end-crosstalk (FEXT) mitigation strategy. CoMAC operates by predicting the noise seen by a given user based on the error samples from the remaining users. These errors are conveniently obtained after slicing the self-FEXT canceled signal of all the vectored users. We show that if the estimation of these errors is accurate, the proposed alien canceler achieves the Cramer-Rao lower bound (CRLB). In practice, the seamless rate adaptation (SRA) operation, which enables increased bit rate by increasing the bit-loading per-tone, can cause decision errors in any decision directed strategy. We also analyze the impact of these decision errors - an issue not addressed in the literature. We propose a strategy for bit-loading during the SRA operation by formulating a max-min optimization problem and demonstrate a possibility of a guaranteed (minimum) improvement in the per-user rate. Simulations indicate that performance of the algorithm can exceed the minimum value significantly in practical situations.

Cooperative MIMO for alien noise cancellation in upstream VDSL

We present cooperative MIMO for alien noise cancellation (CoMAC): a per-tone, blind, low-complexity, linear, and adaptive noise whitening algorithm for alien crosstalk mitigation in upstream vectored VDSL systems. CoMAC directly acts on the residual errors of the vectored users after self-FEXT cancellation and frequency domain equalization, and thus, leverages the inherent alien-crosstalk-induced spatial correlation across users. CoMAC employs a lowcomplexity recursion scheme derived from the optimal MMSE noise whitener to non-disruptively initialize, engage, and adapt the noise canceller while the vectored users operate in data mode. Assuming reliable transmit symbol estimation at its input, we show that CoMAC achieves the Cramer-Rao lower bound. Further, the SNR improvements accruing from CoMAC can be translated into substantial rate improvements for upstream vectored VDSL.

Dynamic Programming Based Multi-User Resource Allocation for Partial Crosstalk Cancellation in VDSL

This letter deals with multi-user computational resource allocation for partial crosstalk cancellation in the vectored VDSL2 systems. We re-look at the multi-user rate optimization problem and provide a dynamic programming based optimal algorithm with deterministic computational complexity, which is quadratic in the problem size.

A* algorithm based power minimization for discontinuous operations in G.fast

To enable energy efficiency, G.fast standards define discontinuous operations (DO) where a set of users can remain inactive while others transmit during a time domain duplex (TDD) frame. In this work, we investigate energy efficient discontinuous operations (DO) by scheduling users to time slots, such that the total energy consumption is minimized while satisfying the individual data rate constraints. Since the user-slot assignment problem is NP complete in nature, we propose the use of weighted A* algorithm that achieves reasonable performance with limited computational resources. The main insight of this work is that on using our user-slot assignment algorithm and by only precoding during the normal operations, we achieve the same energy efficiency as achieved by precoding strategies like discontinuous vectoring [1] while satisfying the provisions of the G.fast standard.

On low complexity per-tone common mode sensor based alien noise cancellation for downstream VDSL

For VDSL systems, alien noise cancellation using an additional common mode sensor at the customer premises equipment (CPE) receiver can be done by combining the differential-mode (DM) signal with the common-mode (CM) signal passed through a long linear filter. Frequency domain per-tone cancellation offers a low complexity approach to the problem but suffers from loss in cancellation performance due to approximations in the per-tone model. We analyze this loss and show that it is possible to minimize it by a post-training “delay” adjustment. We also address the problem of training such a noise canceller during data mode in the presence of a much stronger useful data signal in DM since noise events may not occur during modem train-up. We propose an algorithm based on the per-tone approach which is capable of fast convergence during data mode for intermittent alien noise sources, analyze its convergence behaviour and demonstrate the usefulness of the pertone approach and the proposed training algorithm over existing time domain methods.

A fast converging method for common mode sensor based impulse noise cancellation for downstream VDSL

Impulse noise cancellation using an additional common mode sensor at the customer premises equipment (CPE) receiver is akin to an interference cancellation problem in a SIMO receiver. However, the common mode (CM)-differential mode (DM) cross-correlation for impulse noise signal needs to be estimated during showtime in the presence of a much stronger DM useful data signal. Existing works on this topic rely on the repetitive nature of impulse noise and use a large number of DMT symbols for estimation of the canceler and are therefore not suitable for handling transient noise events. We propose an iterative decision-directed method based on alternating minimization which can provide partial cancellation of the impulse noise using a single DMT symbol (useful for transient noise) and much faster convergence using multiple DMT symbols as compared to existing methods (useful for repetitive impulse noise) and demonstrate its efficacy via simulation.

TrafficKarma: Estimating Effective Traffic Indicators using Public Data

TrafficKarma optimizes the use of publicly available information to monitor and estimate traffic data in order to map and visualize it for its effective use. This is achieved by using state of the art optimization and machine learning techniques coupled with insightful visualization of the data. Traffic Karma can be used by authorities in transportation, traffic police and various agencies that needs current(online) or past(statistical) information about traffic. This paper will concisely explain the features of the application proposed, strategy, data sources and methodology, system architecture and the data analysis techniques used.