Abstract

Using simulations and data, we localize a quiet source in the presence of an interferer. The SWellEx-96 Event S59 consists of a submerged source towed along an isobath over a 65 min duration with an interferer traversing the source track. This range independent, multi-frequency scenario includes mismatch, non-stationary noise, and operational uncertainty. Mismatch is defined as a misalignment between the actual source field observed at the array and the modeled replica vector. The noise process changes likely with time. This is modelled as a heteroscedastic Gaussian process, meaning that the noise variance is non-stationary across snapshots. Sparse Bayesian learning (SBL) has been applied previously to the matched field processing application [Gemba et al, J. Acoust. Soc. Am., 141:3411-3420, 2017]. Results demonstrate that SBL exhibits desirable robustness properties and improved localization …

Abstract

Given a topology of local parity-check constraints, a maximally recoverable code (MRC) can correct all erasure patterns that are information-theoretically correctable. In a grid-like topology, there are a local constraints in every column forming a column code, b local constraints in every row forming a row code, and h global constraints in an (m × n) grid of codeword. Recently, Gopalan et al. initiated the study of MRCs under grid-like topology, and derived a necessary and sufficient condition, termed as the regularity condition, for an erasure pattern to be recoverable when a = 1, h = 0. In this paper, we consider MRCs for product topology ( h = 0). First, we construct a certain bipartite graph based on the erasure pattern satisfying the regularity condition for product topology (any a, b, h = 0) and show that there exists a complete matching in this graph. We then present an alternate direct proof of the sufficient condition when a = 1, h = 0. We later extend our technique to study the topology for a = 2, h = 0, and characterize a subset of recoverable erasure patterns in that case. For both a = 1, 2, our method of proof is uniform, i.e., by constructing tensor product G col ⊗ G row of generator matrices of column and row codes such that certain square sub-matrices retain full rank. The full-rank condition is proved by resorting to the matching identified earlier and also another set of matchings in erasure sub-patterns.

Abstract

Index coding for broadcast channels allows each receiver or client to retrieve its demanded message from its side information and the transmitted codeword. In general, a client may have to observe the entire codeword to decode its demanded message. However, downloading or querying the codeword symbols might involve costs at a client - such as network utilization costs and storage. Traditional index coding does not consider this client perspective, and as a result, for these codes the number of codeword symbols queried by a client per decoded message symbol, which we refer to as locality, could be large. In this paper we study a `client aware' approach to index coding by viewing the problem as a trade-off between the achievable broadcast rate and locality, where the objective is to minimize the rate for a given value of locality and vice versa. We first consider the minimum possible locality 1 and show that the …

Abstract

Most reinforcement learning algorithms are inefficient for learning multiple tasks in complex robotic systems, where different tasks share a set of actions. In such environments a compound policy may be learnt with shared neural network parameters, which performs multiple tasks concurrently. However such compound policy may get biased towards a task or the gradients from different tasks negate each other, making the learning unstable and sometimes less data efficient. In this paper, we propose a new approach for simultaneous training of multiple tasks sharing a set of common actions in continuous action spaces, which we call as DiGrad (Differential Policy Gradient). The proposed framework is based on differential policy gradients and can accommodate multi-task learning in a single actor-critic network. We also propose a simple heuristic in the differential policy gradient update to further improve the learning. The proposed architecture was tested on 8 link planar manipulator and 27 degrees of freedom (DoF) Humanoid for learning multi-goal reachability tasks for 3 and 2 end effectors respectively. We show that our approach supports efficient multi-task learning in complex robotic systems, outperforming related methods in continuous action spaces.

Abstract

Performing dual arm coordinated (reachability)tasks in humanoid robots require complex planning strategies and this complexity increases further, in case of humanoids with articulated torso. These complex strategies may not be suitable for online motion planning. This paper proposes a faster way to accomplish dual arm coordinated tasks using methodology based on Reinforcement Learning. The contribution of this paper is twofold. Firstly, we propose DiGrad (Differential Gradients), a new RL framework for multi-task learning in manipulators. Secondly, we show how this framework can be adopted to learn dual arm coordination in a 27 degrees of freedom (DOF)humanoid robot with articulated spine. The proposed framework and methodology are evaluated in various environments and simulation results are presented. A comparative study of DiGrad with its parent algorithm in different settings is also presented

Abstract

The concept of discrete-time higher order sliding mode (DHOSM) has received increased attention. However, because of the dearth of knowledge on bound of the external disturbance in the system, an adaptive control law is desirable. This brief presents a technique to design a DHOSM control with adaptive switching gains where the bound on the disturbance is considered to be unknown.

Abstract

One of the major issues in an interconnected power system is the low damping of inter-area oscillations. A proportional-derivative (PD) compensator is a popular control method which is extensively being used as an active damping control in various domains such as robotics, smart structure, etc. However, existence of noise in wide-area signal makes an ideal PD compensator unrealisable in power systems. In this endeavour, a new methodology christened as time-delayed control is proposed where artificial delay is used to provide derivative action which acts as a damping component of wide-area controller. The delay margin is computed for the closed loop system using Razumikhin approach. It is observed that the proposed controller is able to damp the oscillations adequately with reduced sensitivity to the noise in the signals. Further, the proposed controller is able to provide robustness against uncertainties arising from the system dynamics and inherent delay in communication channel.

Abstract

In this paper, a new Adaptive Sliding Mode Control (ASMC) framework is proposed for the tracking control of a class of uncertain nonlinear systems where system states are present explicitly in the upper bound of the overall (or lumped) system uncertainty. Conventional ASMC strategies presume that either the overall (or lumped) uncertainty of the system or its time derivative is norm bounded by a constant. However, such assumption restricts the evaluation of the states a priori for the class of systems that has been considered in this paper. The proposed ASMC law does not presume such constant upper bound on the system uncertainties and, rather, exploits the unique structure of the uncertainty bound to design the control law. Moreover, the adaptive law of the proposed ASMC alleviates the overestimation-underestimation problems of switching gain which is commonly observed in the existing ASMC laws. Simulation results using a two-link manipulator demonstrate improved control performance of the proposed controller in comparison to the conventional ASMC.

Abstract

Preablative stimulated thyroglobulin (ps‑Tg) is an important investigation in the follow‑up of patients with Differentiated thyroid cancer(DTC) after surgery. Levels of ps‑Tg >2–10 ng/ml have been suggested to predict metastasis to cervical and extracervical sites. There is still debate on the need for routine iodine whole‑body scan (131I WBS) in the management of low‑to‑intermediate‑risk DTC patients. Objective: We analyzed our data of patients with DTC who underwent total thyroidectomy to discuss the predictability of ps‑Tg on metastatic disease on the 131I WBS. Materials and Methods: Retrospective analysis of patient records. Results: One hundred and seventeen patients with DTC (95 papillary thyroid cancer [71 had classic histology, 8 had tall cell variant, 16 had follicular variant] and 22 follicular thyroid cancer [18 minimally invasive, 2 hurtle cell, and 2 widely invasive cancers]) had undergone total thyroidectomy. All these patients underwent ps‑Tg assessment and an 131I WBS. About 65% of them went on to have radioiodine ablation along with a posttherapy 131I WBS. We divided the cohort into four groups based on their ps‑Tg levels: Group 1 (ps‑Tg <1), Group 2 (ps‑Tg 1–1.9), Group 3 (ps‑Tg 2–5), and Group 4 (ps‑Tg >5). None of the patients in Group 1, 7% of those combined in Groups 2 and 3 (2 out of 28 patients), and 26% (12 out of 47) of those in Group 4 had either cervical or extracervical metastasis. Those with extracervical metastatic disease to lungs and bones had a mean (standard deviation) ps‑Tg value of 436 (130) and median of 500 ng/ml and those with cervical metastatic disease had a mean Tg value of 31 (64) and median 6.6 ng/ml. Conclusions: A ps‑Tg value in the absence of anti‑Tgantibodies <1 ng/ml reliably excludes metastatic disease in DTC, while a value >5 ng/ml has a 26% risk of having either cervical or extracervical metastasis.

Abstract

Efficient discovery of interesting patterns using parallel algorithms is an actively studied topic in data mining. A key research issue related to this topic is data segmentation, which influences the overall computational requirements of an algorithm. This paper makes an effort to address this issue in correlated pattern mining. Two novel data segmentation techniques, ‘database segmentation’ and ‘transaction segmentation,’ have been introduced to discover the patterns efficiently. The former technique involves segmenting the database into multiple sub-databases such that each sub-database can be mined independently. The latter technique involves segmenting a transaction into multiple sub-transactions such that each sub-transaction can be processed as an individual transaction. The proposed techniques are algorithm independent, and therefore, can be incorporated into any parallel algorithm to find correlated patterns effectively. In this paper, we introduce map-reduce based pattern-growth algorithm by incorporating the above mentioned techniques. Experimental results demonstrate that the proposed algorithm is memory and runtime efficient and highly scalable as well.