Hrishikesh Sharma

Image Analysis-Based Automatic Detection of Transmission Towers using Aerial Imagery

Uninterrupted electricity transmission is a critical utility service for any nation. A major component of nation-wide infrastructure carrying electricity are the transmission towers. To give uninterrupted supply, timely maintenance of towers is a must. Due to vastness of power grid, fault detection via aerial inspection and imaging is emerging as a popular method. In this paper, we attend to the problem of automatic detection of towers in specific images. We present a four-stage algorithm for such detection. For a porous, cage like object structure that of a tower, we use gradient density and a novel feature called cluster density to detect pylon blocks. The algorithm was tested against image data captured for many towers along two different power grid corridors. The algorithm demonstrated missed detection of \(<\) 1 % and complete absence of false positives, which is very encouraging. We believe that our result is far more useful in tower detection, than available previous works.

A design methodology for optimally folded, pipelined architectures in VLSI applications using projective space lattices

Semi-parallel, or folded, VLSI architectures are used whenever hardware resources need to be saved. Most recent applications that are based on Projective Geometry (PG) based balanced bipartite graphs also fall in this category. Many of these applications are actively being researched upon, especially in the area of coding theory and matrix computations. Almost all these applications need bipartite graphs of the order of tens of thousands in practice, whose nodes represent parallel processing. To reduce its implementation cost, reducing amount of hardware resources is an important engineering objective. In this paper, we provide a high-level, top-down design methodology to design optimal semi-parallel architectures for applications, whose Data Flow Graph (DFG) is based on PG bipartite graph. Unlike many other folding schemes, the topology of connections between physical elements nodesdoes not change at runtime in this methodology. Hence the folding scheme achieves the best possible throughput, in lack of any overhead of shuffling data across memories while scheduling another computation on the same processing unit. Another advantage is the ease of implementation. To lessen the throughput loss due to folding, we also incorporate a multi-tier pipelining strategy in the design methodology. A C++-based synthesis tool has been developed and tested for automatic generation of RTL models, and is publicly available. A specific high-performance design of a low-density parity check (LDPC) decoder based on this methodology was worked out in past, and has been patent pending.

A Lattice-Theoretic Approach for Segmentation of Truss-Like Porous Objects in Outdoor Aerial Scenes.

Remote video surveillance of vast outdoor systems for structural health monitoring using e.g. drones is gaining rapid popularity. Many such systems are designed as truss structures, due to well-known mechanical reasons. A truss structure has interstices inherently porous, and hence no closed region or contour really represents useful properties or features of just foreground or just background. In this paper, we present a novel approach to segment and detect porous objects of truss-like structures in videos. Our approach is primarily based on modeling of such objects as composite shapes, organized in a structure called geometric lattices. We define a novel feature called shape density to classify and segment the truss region. The segmented region is then analyzed for various surveillance goals such as bending. The algorithm was tested against video data captured for many transmission towers along two different power grid corridors. We believe that our algorithm will be very useful for analysis of truss-like structures in many a outdoor vision applications.

OPTIMAL FOLDING OF DATA FLOW GRAPHS BASED ON FINITE PROJECTIVE GEOMETRY USING VECTOR SPACE PARTITIONING

A number of computations exist, especially in area of error-control coding and matrix computations, whose underlying data flow graphs are based on finite projective-geometry (PG)-based balanced bipartite graphs. Many of these applications of finite projective geometry are actively being researched upon, especially in coding theory. Almost all these applications need large bipartite graphs, whose nodes represent parallel computations. To reduce its implementation cost, reducing amount of system/hardware resources during design is an important engineering objective. In this context, we present a scheme to reduce resource utilization while designing systems modeled using PG-based graphs. In such systems, the number of processing units is equal to the number of vertices, each performing an atomic computation. We present a novel way of partitioning the vertex set assigned to various atomic computations, into blocks. Each block of partition is then assigned to a processing unit. A processing unit performs the computations corresponding to the vertices in the block assigned to it in a sequential fashion, thus creating the effect of folding the overall computation. The symmetric properties of projective space lattices enable us to develop a conflict-free communication schedule. We employed the technique of coset decomposition of a finite field for partitioning. The folding scheme achieves the best possible throughput, in lack of any overhead of shuffling data across memories while scheduling another computation on the same processing unit. We first provide a scheme for a finite projective space of dimension five, and the corresponding schedules. This specific scheme is then generalized for arbitrary finite projective spaces. Both the folding schemes have been verified by both simulation as well as hardware prototyping. For example, a semi-parallel decoder architecture for a new class of expander codes was designed and implemented using this scheme, with potential deployment in DVD-R/CD-ROM drives.

An efficient backtracking-based approach to turn-constrained path planning for aerial mobile robots

Many important classes of civilian applications of Unmanned Aerial Vehicles, such as the class of remote monitoring of long linear infrastructures e.g. power grid, gas pipeline etc. entail usage of fixed-wing vehicles. Such vehicles are known to be constrained with restricted angular movement. Similarly, mobile robots such as car robots or tractor-trailer robots are also known to entail such constraint. The algorithms known so far require a lot of preprocessing for turn constraint. In this paper, we introduce a novel algorithm for turn angle- constrained path planning. The proposed algorithm uses a greedy backtracking strategy to satisfy the constraint, which minimizes the amount of backtracking involved. By further constructing an efficient depth-first brute-force algorithm for path planning and comparing against its performance, we see an improvement in convergence performance by a factor of at least 10x. Further, compared to recent LIAN suite of path-planning algorithm, our algorithm exhibits much reduced discretization offset/error with respect to shortest path length. We believe that this algorithm will form an useful stepping stone towards evolution of better path planning algorithm for specific mobile robots such as UAVs.

A Novel Cognitive Cycle for Fault Diagnosis in Infrastructural Systems

Abstract Remote sensing techniques are being increasingly used for periodic structural health monitoring of vast infrastructures such as power transmission systems. The current efforts concentrate on analysis of visual and other signals captured from the sensing devices, to diagnose the faults. Such data collection and analysis is expensive in terms of both computational overheads as well as towards robotic maneuvering of the data collection platform, such as a UAV. In this paper, we model the data gathering platform as an intelligent situated agent, and propose to autonomously control its data gathering and analysis activities through a cognitive cycle, to optimize the cost of efforts in identifying the faults that may exist. In this context, we explore use of less expensive qualitative reasoning with the background knowledge expressed as a Qualitative Bayesian Network (QBN). We introduce a reactive, economical planning algorithm around QBN that controls the sequence of data collection and analysis, much like how human inspectors do. We substantiate our claims with the results of simulation of the corresponding cognitive cycle.

Optimal communication scheduling for iterative decoding of irregular codes

Many important and newer classes of error-correction codes, such as LDPC, expander, repeat-accumulate or polar codes have a bipartite graph representation of their computation. Decoders for such codes are practically implemented using iterative decoding over such bipartite graphs. The iterative decoding progresses as per various communication schedules between the nodes on both sides of the graph. The schedules are designed to be optimal typically in the latency or the throughput of decoding process. Designing optimal schedules for irregular or partial regular codes, which have found way in communication standards such as DVB-S2 and WiMAX, is a challenging problem. In our work, we have tried to design VLSI decoding schemes having optimal communication schedule for such codes. We employed edge-coloring based greedy approach for communication scheduling during the decoding process for such codes. The communication throughput of such decoder systems is provably optimal. As such, it is well-known that the irregular-graph based codes can asymptotically achieve the Shannon limit on erasure channels. Hence we are hopeful that irregular graphs will be used in many other practical error correction systems in future, for which usage of such optimal communication scheduling will lead to efficient design of decoders.