When implementing an appropriate windowing, the interference from a Cognitive Radio (CR) system to licensed systems (primary users) will be significantly reduced. Consequently, power allocated to subcarriers can be increased, especially subcarriers having far spectral distance to primary user bands can be allocated full of its maximum possible power. In this paper, we propose a new class of sub-optimal subcarrier power allocation algorithm that significantly reduces complexity of OFDMA-based CR systems. Two sub-optimal proposals, called Pre-set Filling Range (PFR) and Maximum Filling Range (MFR) are studied. Investigations show that this new power allocating algorithm allows CR systems obtain high throughput while retaining low complexity.

Abstract:
We propose a Cooperative Rate Adaptation (CRA) MAC protocol based on the standard CSMA/CA protocolused in IEEE 802.11 wireless networks. The proposal provides cooperative error recovery, namely cooperativepacket retransmission, while using rate adaptation. The transmission rate is selected based onthe transmission rate history of the neighbours. The cooperating partner is selected based on a fuzzy logicselection algorithm. Three inputs are considered in the fuzzy system: the average transmission rate of aneighbour, the erroneous packet ratio and the acknowledged packet ratio of a neighbour. The output of thefuzzy system is the partnership probability of a neighbour. In this paper, the protocol is compared to thenon-cooperative rate adaptation scheme RBAR (Receiver Based Auto Rate) and to the cooperative rateadaptation scheme CRBAR (Cooperative Relay Based Auto Rate). The simulation results show that theproposed protocol improves the delay and packet delivery ratio while contributing to the transmission opportunityfairness among the terminals, regardless of their channel conditions.

Abstract:
Many studies in heuristic search suggest that the accuracy of the heuristic used has a positive impact on improving the performance of the search. In another direction, historical research perceives that the performance of heuristic search algorithms, such as A* and IDA*, can be improved by requiring the heuristics to be consistent -- a property satisfied by any perfect heuristic. However, a few recent studies show that inconsistent heuristics can also be used to achieve a large improvement in these heuristic search algorithms. These results leave us a natural question: which property of heuristics, accuracy or consistency/inconsistency, should we focus on when building heuristics? While there are studies on the heuristic accuracy with the assumption of consistency, no studies on both the inconsistency and the accuracy of heuristics are known to our knowledge. In this study, we investigate the relationship between the inconsistency and the accuracy of heuristics with A* search. Our analytical result reveals a correlation between these two properties. We then run experiments on the domain for the Knapsack problem with a family of practical heuristics. Our empirical results show that in many cases, the more accurate heuristics also have higher level of inconsistency and result in fewer node expansions by A*.

Abstract:
Three versions of the motif search problem have been proposed in the literature: Simple Motif Search (SMS), (l, d)-motif search (or Planted Motif Search (PMS)), and Edit-distance-based Motif Search (EMS). In this paper we focus on PMS. Two kinds of algorithms can be found in the literature for solving the PMS problem: exact and approximate. An exact algorithm identifies the motifs always and an approximate algorithm may fail to identify some or all of the motifs. The exact version of PMS problem has been shown to be NP-hard. Exact algorithms proposed in the literature for PMS take time that is exponential in some of the underlying parameters. In this paper we propose a generic technique that can be used to speedup PMS algorithms.We present a speedup technique that can be used on any PMS algorithm. We have tested our speedup technique on a number of algorithms. These experimental results show that our speedup technique is indeed very effective. The implementation of algorithms is freely available on the web at http://www.engr.uconn.edu/rajasek/PMS4.zip webcitePattern search in biological sequences has numerous applications and hence a large amount of research has been done to identify patterns. Motifs are fundamental functional elements in proteins vital for understanding gene function, human disease, and may serve as therapeutic drug targets. Three versions of the motif search problem have been identified by researchers: Simple Motif Search (SMS), Planted Motif Search (PMS) - also known as (l, d)-motif search, and Edit-distance-based Motif Search (EMS) (see e.g., [1]).PMS problem takes as input n sequences of length m each and two integers l and d. The problem is to identify a string M of length l such that M occurs in each of the n sequences with a Hamming distance of at most d. For example, if the input sequences are GCGCGAT, CACGTGA, and CGGTGCC; l = 3 and d = 1, then GGT is a motif of interest.EMS is the same as PMS, except that edit distance is used instead of

Abstract:
Background Identification of DNA/Protein motifs is a crucial problem for biologists. Computational techniques could be of great help in this identification. In this direction, many computational models for motifs have been proposed in the literature. Methods One such important model is the motif model. In this paper we describe a motif search web tool that predominantly employs this motif model. This web tool exploits the state-of-the art algorithms for solving the motif search problem. Results The online tool has been helping scientists identify many unknown motifs. Many of our predictions have been successfully verified as well. We hope that this paper will expose this crucial tool to many more scientists. Availability and requirements Project name: PMS - Panoptic Motif Search Tool. Project home page: http://pms.engr.uconn.edu or http://motifsearch.com. Licence: PMS tools will be readily available to any scientist wishing to use it for non-commercial purposes, without restrictions. The online tool is freely available without login.

Abstract:
An exact-match overlap graph of $n$ given strings of length $\ell$ is an edge-weighted graph in which each vertex is associated with a string and there is an edge $(x,y)$ of weight $\omega = \ell - |ov_{max}(x,y)|$ if and only if $\omega \leq \lambda$, where $|ov_{max}(x,y)|$ is the length of $ov_{max}(x,y)$ and $\lambda$ is a given threshold. In this paper, we show that the exact-match overlap graphs can be represented by a compact data structure that can be stored using at most $(2\lambda -1 )(2\lceil\log n\rceil + \lceil\log\lambda\rceil)n$ bits with a guarantee that the basic operation of accessing an edge takes $O(\log \lambda)$ time. Exact-match overlap graphs have been broadly used in the context of DNA assembly and the \emph{shortest super string problem} where the number of strings $n$ ranges from a couple of thousands to a couple of billions, the length $\ell$ of the strings is from 25 to 1000, depending on DNA sequencing technologies. However, many DNA assemblers using overlap graphs are facing a major problem of constructing and storing them. Especially, it is impossible for these DNA assemblers to handle the huge amount of data produced by the next generation sequencing technologies where the number of strings $n$ is usually very large ranging from hundred million to a couple of billions. In fact, to our best knowledge there is no DNA assemblers that can handle such a large number of strings. Fortunately, with our compact data structure, the major problem of constructing and storing overlap graphs is practically solved since it only requires linear time and and linear memory. As a result, it opens the door of possibilities to build a DNA assembler that can handle large-scale datasets efficiently.

Abstract:
We study on what conditions on $B_k,$ \ a linear transformation of rank $r$ \label{form} T(A)=\sum_{k=1}^r\tr(AB_k)U_k where $U_k,\ k=1,2,..., r$ are linear independent and all positive definite; is positive definite preserving. We give some first results for this question. For the case of rank one and two, the necessary and sufficient conditions are given. We also give some sufficient conditions for the case of rank $r.$

Abstract:
Detection of rare events happening in a set of DNA/protein sequences could lead to new biological discoveries. One kind of such rare events is the presence of patterns called motifs in DNA/protein sequences. Finding motifs is a challenging problem since the general version of motif search has been proven to be intractable. Motifs discovery is an important problem in biology. For example, it is useful in the detection of transcription factor binding sites and transcriptional regulatory elements that are very crucial in understanding gene function, human disease, drug design, etc. Many versions of the motif search problem have been proposed in the literature. One such is the -motif search (or Planted Motif Search (PMS)). A generalized version of the PMS problem, namely, Quorum Planted Motif Search (qPMS), is shown to accurately model motifs in real data. However, solving the qPMS problem is an extremely difficult task because a special case of it, the PMS Problem, is already NP-hard, which means that any algorithm solving it can be expected to take exponential time in the worse case scenario. In this paper, we propose a novel algorithm named qPMS7 that tackles the qPMS problem on real data as well as challenging instances. Experimental results show that our Algorithm qPMS7 is on an average 5 times faster than the state-of-art algorithm. The executable program of Algorithm qPMS7 is freely available on the web at http://pms.engr.uconn.edu/downloads/qPMS？7.zip. Our online motif discovery tools that use Algorithm qPMS7 are freely available at http://pms.engr.uconn.edu or http://motifsearch.com.

Abstract:
Many facets of the motif search problem have been identified in the literature. One of them is (？, d)-motif search (or Planted Motif Search (PMS)). The PMS problem has been well investigated and shown to be NP-hard. Any algorithm for PMS that always finds all the (？, d)-motifs on a given input set is called an exact algorithm. In this paper we focus on exact algorithms only. All the known exact algorithms for PMS take exponential time in some of the underlying parameters in the worst case scenario. But it does not mean that we cannot design exact algorithms for solving practical instances within a reasonable amount of time. In this paper, we propose a fast algorithm that can solve the well-known challenging instances of PMS: (21, 8) and (23, 9). No prior exact algorithm could solve these instances. In particular, our proposed algorithm takes about 10 hours on the challenging instance (21, 8) and about 54 hours on the challenging instance (23, 9). The algorithm has been run on a single 2.4GHz PC with 3GB RAM. The implementation of PMS5 is freely available on the web at http://www.pms.engr.uconn.edu/downloads/PMS5.zip webcite.We present an efficient algorithm PMS5 that uses some novel ideas and combines them with well-known algorithm PMS1 and PMSPrune. PMS5 can tackle the large challenging instances (21, 8) and (23, 9). Therefore, we hope that PMS5 will help biologists discover longer motifs in the futures.The discovery of patterns in DNA, RNA, and protein sequences has led to the solution of many vital biological problems. For instance, the identification of patterns in nucleic acid sequences has resulted in the determination of open reading frames, identification of promoter elements of genes, identification of intron/exon splicing sites, identification of SH RNAs, location of RNA degradation signals, identification of alternative splicing sites, etc. In protein sequences, patterns have proven to be extremely helpful in domain identification, location of protease cl