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A VLSI chip is fabricated by integrating several rectangular circuit blocks on a 2D silicon floor. The circuit blocks are assumed to be placed isothetically and the netlist attached to each block is given. For wire routing, the terminals belonging to the same net are to be electrically interconnected using conducting paths. A staircase channel is an empty polygonal region on the silicon floor bounded by two monotonically increasing (or decreasing) staircase paths from one corner of the floor to its diagonally opposite corner. The staircase paths are defined by the boundaries of the blocks. In this paper, the problem of determining a monotone staircase channel on the floorplan is considered such that the number of distinct nets whose terminals lie on both sides of the channel, is minimized. Two polynomial-time algorithms are presented based on the network flow paradigm. First, the simple two-terminal net model is considered, i.e., each net is assumed to connect exactly two blocks, for which an O(n×k) time algorithm is proposed, where n and k are respectively the number of blocks and nets on the floor. Next, the algorithm is extended to the more realistic case of multi-terminal net problem. The time complexity of the latter algorithm is O((n+k)×T), where T is the total number of terminals attached to all nets in the floorplan. Solutions to these problems are useful in modeling the repeater block placement that arises in interconnect-driven floorplanning for deep-submicron VLSI physical design. It is also an important problem in context to the classical global routing, where channels are used as routing space on silicon. [ABSTRACT FROM AUTHOR]

Given a set of n colored points in IR2 with a total of m (3 ≤ m ≤ n) colors, the problem of identifying the smallest color-spanning object of some predefined shape is studied in this paper. We shall consider two different shapes: (i) corridor and (ii) rectangle of arbitrary orientation. Our proposed algorithm for identifying the smallest color-spanning corridor is simple and runs in O(n2log n) time using O(n) space. A dynamic version of the problem is also studied, where new points may be added, and the narrowest color-spanning corridor at any instance can be reported in O(mn(α(n))2log m) time. Our algorithm for identifying the smallest color-spanning rectangle of arbitrary orientation runs in O(n3log m) time and O(n) space. [ABSTRACT FROM AUTHOR]

In the unbounded version of the range assignment problem for all-to-all communication in 1D, a set of n radio-stations are placed arbitrarily on a line; the objective is to assign ranges to these radio-stations such that each of them can communicate with the others (using at most n - 1 hops) and the total power consumption is minimum. A simple incremental algorithm for this problem is proposed which produces optimum solution in O(n3) time and O(n2) space. This is an improvement in the running time by a factor of n over the best known existing algorithm for the same problem. [ABSTRACT FROM AUTHOR]

Given a convex polygon with n vertices, we study the problem of identifying a triangle with its smallest side as large as possible among all the triangles that can be drawn inside the polygon. We show that at least one of the vertices of such a triangle must coincide with a vertex of the polygon. We also propose an O (n 2) time algorithm to compute such a triangle inside the given convex polygon. [ABSTRACT FROM AUTHOR]

A new type of range searching problem, called the partial enclosure range searching problem, is introduced in this paper. Given a set of geometric objects S and a query region Q , our goal is to identify those objects in S which intersect the query region Q by at least a fixed proportion of their original size. Two variations of this problem are studied. In the first variation, the objects in S are axis-parallel line segments and the goal is to count the total number of members of S so that their intersection with Q is at least a given proportion of their size. Here, Q can be an axis-parallel rectangle or a parallelogram of arbitrary orientation. In the second variation, S is a polygon and Q is an axis-parallel rectangle. The problem is to report the area of the intersection between the polygon S and a query rectangle Q. [ABSTRACT FROM AUTHOR]

In this article, we study approximation algorithms for the problem of computing minimum dominating set for a given set of unit disks in . We first present a simple time 5-factor approximation algorithm for this problem, where is the size of the output. The best known 4-factor and 3-factor approximation algorithms for the same problem run in time and respectively [M. De, G. K. Das, P. Carmi and S. C. Nandy, Approximation algorithms for a variant of discrete piercing set problem for unit disks, Int. J. of Computational Geometry and Appl., 22(6):461-477, 2013]. We show that the time complexity of the in-place 4-factor approximation algorithm for this problem can be improved to . A minor modification of this algorithm produces a -factor approximation algorithm in time. The same techniques can be applied to have a 3-factor and a -factor approximation algorithms in time and respectively. Finally, we propose a very important shifting lemma, which is of independent interest, and it helps to present -factor approximation algorithm for the same problem. It also helps to improve the time complexity of the proposed PTAS for the problem. [ABSTRACT FROM AUTHOR]

In this paper, we consider constant factor approximation algorithms for a variant of the discrete piercing set problem for unit disks. Here a set of points P is given; the objective is to choose minimum number of points in P to pierce the unit disks centered at all the points in P. We first propose a very simple algorithm that produces 12-approximation result in O(n log n) time. Next, we improve the approximation factor to 4 and then to 3. The worst case running time of these algorithms are O(n8 log n) and O(n15 log n) respectively. Apart from the space required for storing the input, the extra work-space requirement for each of these algorithms is O(1). Finally, we propose a PTAS for the same problem. Given a positive integer k, it can produce a solution with performance ratio (1+1/k)2 in nO(k) time. [ABSTRACT FROM AUTHOR]

Quality of Surveillance (QoSv) is a generic measure of the coverage offered by a given network of sensors over the field it monitors. In this work, we consider it to be the length of the longest straight line segment that does not pass through the sensing zone of any sensor. It can be seen as the longest length that an intruder may travel along a straight line, before being detected. An easy to implement algorithm for computing the QoSv in a rectangular field $\mathcal{R}$ containing a set of n identical sensors is proposed. The time and space complexities of our algorithm are O(n2log n) and O(n) respectively. It is also shown that a minor modification of the algorithm runs in O(n2) time and space. [ABSTRACT FROM AUTHOR]

This paper deals with an important problem of mobile communication. The objective is to place k base stations of equal range on the boundary of a convex polygonal region P such that each point inside P is covered by at least one base station. We name this problem as region-cover(k) problem. A simplified form of this problem is the vertex-cover(k) problem, where the objective is to communicate with only the vertices of P instead of covering the entire polygon. We first present efficient algorithms for vertex-cover(2) and region-cover(2) problems, where the base stations are to be installed on a pair of specified edges. The time complexity of these algorithms are O(n log n) and O(n2) respectively, where n is the number of vertices in the polygon P. Next, we consider the case where k ≥ 3. We first concentrate on a restricted version of the vertex-cover(k) and region-cover(k) problems, where all the k base stations are to be installed on the same edge of P. Our proposed algorithm for the restricted vertex-cover(k) problem produces optimum result in O(min(n2,nk log n)) time, whereas the algorithm for the restricted region-cover(k) problem produces an (1 + ∊)-factor approximation result in $O((n + k){\rm log}(n + k) + n{\rm log}(\lceil \frac{1}{\epsilon} \rceil)$ time. Finally, we propose efficient heuristic algorithm for the unrestricted version of the region-cover(k) problem for k ≥ 3. Experimental results demonstrate that our proposed algorithm runs fast and produces near optimum solutions. [ABSTRACT FROM AUTHOR]