Abstract

In this paper we describe the mechanical design, gait and control for a modular hexapod robot which makes it compliant to terrain while climbing slopes up and down. The robot uses a unique electronically actuated 2DOF universal spine similar to the spine of the Snake and the four legged animals like leopard, tiger etc. By controlling the amount of stiffness of the spine we were able to make the robot compliant to the surface. To our knowledge this is the first modular hexapod robot which achieves controllable compliance by both mechanical design and electronically actuating the robots spinal backbone. This design was biologically inspired based on the structure of a caterpillar with legs. Also we present the hardware and software architecture of the robot in this paper.

Abstract

This paper proposes a new framework for data association to solve the problem of SLAM. The proposed framework has specific relevance to range scanner based EKF-SLAM. The resulting data representation enables semantic reasoning on a spatial level which reduces the misassociation of closely spaced data from different spatial configurations through the use of convex polygons to represent data from similar spatial configurations. The data representation is especially effective for association when revisiting previously mapped regions efficiently. The spatial data representation also builds an occupancy grid for the entire map. We also provide a means of clustering range scan data using an adaptive threshold to be able to divide data at various ranges into clusters and dense data clustering to get more accurate data.

Abstract

Here in this paper we present a snake robot which can autonomously change its gait depending on the terrain. Also we present the design and construction details for the same. Here a custom simulator for the robot and an API for the same was developed which reduced the design and development time. For controlling the Snake robot manually a data glove using accelerometers was developed. The Snake robot can also be operated in autonomous mode where an over camera is used for sensing the terrain and the snake autonomously travels from one part of the terrain to another by switching its gait

Abstract

In this work, the authors present the idea of inexact decision making, and its application to threshold voting of Hamming weights, used in bus coding schemes, N-modular redundancy (NMR), median filtering, and other pattern matching applications. Decision circuits can be tweaked, to perform in an inexact manner, in order to optimize in terms of delay and power, but still maintaining high system accuracy. One such circuit identified by the authors is the threshold voting of Hamming weights. A majority voter is a special case of this family of circuits. The proposed inexact voter consumes up to 8 times less power than the exact voter, with negligible reduction in system accuracy and performance. The leakage power is also reduced by a factor of 3. The inexact voter allows for a higher frequency of operation, by reducing the critical path delay by a factor of up to 3.4. The results obtained validate the application of inexact decision making, with respect to threshold voters.

Abstract

In this work the authors propose a data coding protocol that leads to power reduction for block data transfer in off-chip buses. I/O pads driving off-chip buses contribute to a major portion of power dissipation in chips. Also, block data transfer is preferred in most systems like caches, DMA etc. In this proposed work, the prior knowledge of the block of data to be transmitted, when it is stored in the buffer, is exploited in a serial fashion to reduce transitions on every bus line. Statistical analysis shows up to 31.9% reduction in transitions. Benchmark results show that it leads to 29% reduction in power consumption. The technique provides added error detection on the lines of parity bit technique, with similar average error detection capability.

Abstract

Making the experience of e-learning more effective requires interactive and collaborative systems to be adaptive and customizable. Specialized learning systems tend to be monolithic and difficult to extend. We present an alternative approach, where we synthesize a customizable learning environment from existing tools (Trac, SVN, reST, SQLite). The system presents the student not just with content, but an immersive experience that allows both individual and group annotations, versioning of the student's work, custom querying, and a uniform markup language to store content. We report the motivation and design of such an environment. We demonstrate the use of this system and its ability to plug into other environments by showcasing a custom interactive workbook, built for teaching and learning the principles of programming.

Abstract

We present here three different approaches to the problem of modeling mathematically the concept of a non-deterministic mechanism. Each of these three approaches leads to a mathematical definition. We then show that all the three mathematical concepts are equivalent to one another. This insight gives us the option of approaching the wp-formalism of Dijkstra from a different viewpoint that is easier to understand and to teach.

Abstract

The objective of this work is to characterize certain important features of excitation of speech, namely, detecting the regions of glottal activity and estimating the strength of excita-tion in each glottal cycle. The proposed method is based on the assumption that the excitation to the vocal-tract system can be ap- proximated by a sequence of impulses of varying strengths. The effect due to an impulse in the time-domain is spread uniformly across the frequency-domain including at zero-frequency. We propose the use of a zero-frequency resonator to extract the characteristics of excitation source from speech signals by filtering out most of the time-varying vocal-tract information. The regions of glottal activity and the strengths of excitation estimated from the speech signal are in close agreement with those observed from the simultaneously recorded electro-glotto-graph signals. The performance of the proposed glottal activity detection is evaluated under different noisy environments at varying levels of degradation.

Abstract

The Graphics Processing Units (GPUs) provide highcomputation power at a low cost and is an important computeaccelerator with a massively multithreaded architecture. Inthis paper, we present fast implementations of common graphoperations like breadth-first search, st-connectivity, single-sourceshortest path, all-pairs shortest path, minimum spanning tree,and maximum flow for undirected graphs on the GPU using theCUDA programming model. Our implementations exhibit highperformance, especially on large graphs. We use two data-parallelprogramming methodologies for these algorithms. One is aniterative, mask-based approach that processes valid data elementslike vertices and edges using independent threads for each.The other is a divide-and-conquer approach that reduces theproblem into smaller problems that are handled later using thesame approach. Parallel algorithms for such problems have beenreported in the literature before, especially on supercomputers.The massively multithreaded model of the GPU makes it possibleto adopt the data-parallel approach even to irregular algorithmslike graph algorithms, usingO(V)orO(E)simultaneous threads.The algorithms and the underlying techniques presented in thispaper are likely to be applicable to many irregular algorithms.We show the impact of our implementations on random, scale-free, and real-life graphs of up to millions of vertices on high-end and low-end GPUs. The availability and spread of GPUs todesktops and laptops make them ideal candidates to accelerategraph operations over the CPU-only implementations. Practicalimplementations of basic operations go a long way in realizingtheir potential.

Abstract

We present efficient implementations of two primitives for datamapping and distribution on the massively multithreaded architec-ture of the GPUs in this paper. Thesplitprimitive distributes el-ements of a list according to their category. Split is an importantoperation for data mapping and is used to build data structures,distribute work load, etc., in a massively parallel environment.Thegather/scatterprimitive performs fast, distributed data move-ment. Efficient data movement is critical to high performance onthe GPUs as suboptimal memory accesses can pay heavy penalties.The split we implement is a generalization of the binary split [Blel-loch 1990] and is implemented using the shared memory and theatomic operations available on them. The split performance scaleslogarithmically with the number of categories, linearly with the listlength, and linearly with the number of cores on the GPU. Thismakes it useful for applications that deal with large data sets. Wealso present a variant of split that partitions the indexes of records.This facilitates the use of the GPU as a coprocessor for split or sort,with the actual data movement handled separately. We can computethe split indexes for a list of 32 million records in 180 millisecondsfor a 32-bit key and in 800 ms for a 96-bit key. The instantaneouslocality of memory references play a critical role in data movementon the current GPU memory architectures. For scatter and gatherinvolving large records, we use collective data movement in whichmultiple threads cooperate on individual records to improve the in-stantaneous locality. The split, gather, and their combinations findmany applications and expect our primitives will be used by fu-ture GPU programmers. We show sorting of 16 million 128-byterecords in 379 milliseconds with 4-byte keys and in 556 ms with8-byte keys