Abstract

Consider an interference channel consisting of transmitters and receivers with AWGN noise and complex channel gains, and with files in the system. The one-shot for this channel is the maximum number of receivers which can be served simultaneously with vanishing probability of error as the grows large, under a class of schemes known as\textit {one-shot} schemes. Consider that there exists transmitter and receiver side caches which can store fractions and of the library respectively. Recent work for this cache-aided interference channel setup shows that, using a carefully designed prefetching (caching) phase, and a one-shot coded delivery scheme combined with a proper choice of beamforming coefficients at the transmitters, we can achieve a of , where and which was shown to be almost optimal. The existing scheme involves splitting the file into subfiles (the parameter is called the\textit {subpacketization}), where can be extremely large (in fact, with constant cache fractions, it becomes exponential in , for large ). In this work, our first contribution is a scheme which achieves the same of with a smaller subpacketization than prior schemes. Our second contribution is a new coded caching scheme for the interference channel based on projective geometries over finite fields which achieves a one-shot of , with a subpacketization (for some prime power ) that is\textit {subexponential} in , for small constant cache fraction at the receivers. To the best of our knowledge, this is the first coded caching scheme with subpacketization …

Abstract

An index code is said to be locally decodable if each receiver can decode its demand using its side information and by querying only a subset of the transmitted codeword symbols instead of observing the entire codeword. Local decodability can be a beneficial feature in some communication scenarios, such as when the receivers can afford to listen to only a part of the transmissions because of limited availability of power. The locality of an index code is the ratio of the maximum number of codeword symbols queried by a receiver to the message length. In this paper we analyze the optimum locality of linear codes for the family of index coding problems whose min-rank is one less than the number of receivers in the network. We first derive the optimal trade-off between the index coding rate and locality with vector linear coding when the side information graph is a directed cycle. We then provide the optimal trade-off …

Abstract

We consider the standard broadcast setup with a single server broadcasting information to a number of clients, each of which contains local storage (called cache) of some size, which can store some parts of the available files at the server. The centralized coded caching framework, consists of a caching phase and a delivery phase, both of which are carefully designed in order to use the cache and the channel together optimally. In prior literature, various combinatorial structures have been used to construct coded caching schemes. In this work, we propose a binary matrix model to construct the coded caching scheme. The ones in such a caching matrix indicate uncached subfiles at the users. Identity submatrices of the caching matrix represent transmissions in the delivery phase. Using this model, we then propose several novel constructions for coded caching based on the various types of combinatorial designs …

Abstract

Monocular SLAM is a well-studied problem and has shown significant progress in recent years, but still, challenges remain in creating a rich semantic description of the scene. Feature-based visual SLAMs are vulnerable to erroneous pose estimates due to insufficient tracking of mapped points or motion induced errors such as in large or in-place rotations. We present a new SLAM framework in which we use monocular edge based SLAM [1], along with category level models, to localize objects in the scene as well as improve the camera trajectory. In monocular SLAM systems, the camera track tends to break in conditions with abrupt motion which leads to reduction in the number of 2D point correspondences. In order to tackle this problem, we propose the first most principled formulation of its kind which integrates object category models in the core SLAM back-end to jointly optimize for the camera trajectory, object poses along with its shape and 3D structure. We show that our joint optimization is able to recover a better camera trajectory in such cases, as compared to Edge SLAM. Moreover, this method gives a better visualization incorporating object representations in the scene along with the 3D structure of the base SLAM system, which makes it useful for augmented reality (AR) applications

Abstract

In this paper, we present a novel control law for longitudinal speed control of autonomous vehicles. The key contributions of the proposed work include the design of a control law that reactively integrates the longitudinal surface gradient of the road into its operation. In contrast to the existing works, we found that integrating the path gradient into the control framework improves the speed tracking efficacy. Since the control law is implemented over a shrinking domain scheme, it minimizes the integrated error by recomputing the control inputs at every discretized step and consequently provides less reaction time. This makes our control law suitable for motion planning frameworks that are operating at high frequencies. Furthermore, our work is implemented using a generalized vehicle model and can be easily extended to other classes of vehicles. The performance of gradient aware - shrinking domain based controller is implemented and tested on an electric car. Results from the tests show the robustness of our control law for speed tracking efficiency on terrain with varying gradient while also considering stringent time constraints imposed by the planning framework.

Abstract

Multi-agent learning provides a potential framework for learning and simulating traffic behaviors. This paper proposes a novel architecture to learn multiple driving behaviors in a traffic scenario. The proposed architecture can learn multiple behaviors independently as well as simultaneously. We take advantage of the homogeneity of agents and learn in a parameter sharing paradigm. To further speed up the training process asynchronous updates are employed into the architecture. While learning different behaviors simultaneously, the given framework was also able to learn cooperation between the agents, without any explicit communication. We applied this framework to learn two important behaviors in driving: 1) Lane-Keeping and 2) Over-Taking. Results indicate faster convergence and learning of a more generic behavior, that is scalable to any number of agents. When compared the results with existing approaches, our results indicate equal and even better performance in some cases.

Abstract

Coded Caching is a promising solution to reduce the peak traffic in broadcast networks by prefetching the popular content close to end users and using coded transmissions. One of the chief issues of most coded caching schemes in literature is the issue of large subpacketization, i.e., they require each file to be divided into a large number of subfiles. In this work, we present a coded caching scheme using line graphs of bipartite graphs in conjunction with projective geometries over finite fields. The presented scheme achieves a rate Θ(K/log q K) (K being the number of users, q is some prime power) with subexponential subpacketization q O((logq K)^2) when cached fraction is upper bounded by a constant (M/N ≤ 1/q α , for some positive integer α). Compared to earlier schemes, the presented scheme has a lower subpacketization (albeit possessing a higher rate). We also present a new subpacketization …

Abstract

Multi-agent learning provides a potential framework for learning and simulating traffic behaviors. This paper proposes a novel architecture to learn multiple driving behaviors in a traffic scenario. The proposed architecture can learn multiple behaviors independently as well as simultaneously. We take advantage of the homogeneity of agents and learn in a parameter sharing paradigm. To further speed up the training process asynchronous updates are employed into the architecture. While learning different behaviors simultaneously, the given framework was also able to learn cooperation between the agents, without any explicit communication. We applied this framework to learn two important behaviors in driving: 1) Lane-Keeping and 2) Over-Taking. Results indicate faster convergence and learning of a more generic behavior, that is scalable to any number of agents. When compared the results with existing approaches, our results indicate equal and even better performance in some cases.

Abstract

Robots with joint or link compliance are gaining importance due to their inherent safety. In this paper four different designs of compliant shanks of an 8 degrees of freedom (DoF) biped robot are compared to get the least energy consumed during walk. Different leg and pelvis trajectories of the biped during walk are generated by varying the gait parameters. The deflection of the compliant links during walk is modeled using a finite element method (FEM). Simulations are carried out in which the optimal walking trajectory that consumes least energy is found by using a genetic algorithm (GA). Balance is ensured during walk by ensuring that the trajectory always satisfies the zero moment point (ZMP) criteria. The optimal trajectories obtained from the simulations were then experimentally evaluated for a compliant link biped robot. The motion of the different joints of the biped during the experiment was tracked using a vision system and the performance of the rigid link and compliant link bipeds compared. The best compliant link design was finally chosen from the four different designs.

Abstract

Coded Caching is a recent technique that optimizes the use of a multi-client broadcast channel by the use of local storage available at the clients and by using coded transmissions to serve multiple clients at once. While large gains in the rate of communication are obtained using coded caching, most existing schemes require that the files at the server be divisible into a large number of parts. In particular, most known coded caching schemes require subpacketization $ F={e^{Oleft ({{K^{frac {1}{r}}}}right)}} $, where K is the number of clients and r is some constant positive integer. While few schemes having subpacketization linear in K are known in literature, unfortunately such schemes require large number of users to exist or offer little gain in rate. In this work, we propose a class of coded caching schemes based on projective geometries over finite fields, generalizing recent results. Our construction achieves subexponential (in K) subpacketization, ie, $ F={q^{O …