RFID signal acquisition and identification

RFID is one of the fastest growing technologies grouped under Automatic Identification (auto ID). RFID tags are very low cost and used for identification of objects. RFID is a global technology that is used in industries, medical, wall mart, airport baggage, Libraries, Smart cards, even in every transported object has its own RFID tag. Therefore, concern of security and privacy should be there to prevent unauthorized access. A method is proposed to prevent cloning and counterfeiting of tags based on RF Fingerprinting. RF fingerprinting of a tag is based upon physical attributed such as an electromagnetic (EM) signal of the tag. By capturing the EM signal of RFID tags a method known as Dynamic wavelet fingerprinting is applied to generate fingerprint images of signals. Our proposed method consists of four stages: Namely Real time data acquiring by use of a CRO, Dynamic wavelet fingerprinting (DWFP) of the signal, Feature extraction, and Classification. Feature is extracted such as Eccentricity, perimeter, centroid , extent, area and orientation. Ann classifier is used which is a one vs. One classifier. To improve the performance of classification multi-feature based serial feature fusion technique has been proposed, which shows a significant improvement in classification performance. RF fingerprint allows prevention of unauthorized access, identification and detecting cloning of sensitive devices. To identify tags and to detect counterfeit RF fingerprinting can be used. The cost of the tag does not increase and can be used in existing tag with only requirement of softwar

Discovery of Unconventional Patterns for Sequence Analysis: Theory and Algorithms

The biology community is collecting a large amount of raw data, such as the genome sequences of organisms, microarray data, interaction data such as gene-protein interactions, protein-protein interactions, etc. This amount is rapidly increasing and the process of understanding the data is lagging behind the process of acquiring it. An inevitable first step towards making sense of the data is to study their regularities focusing on the non-random structures appearing surprisingly often in the input sequences: patterns. In this thesis we discuss three incarnations of the pattern discovery task, exploring three types of patterns that can model different regularities of the input dataset. While mask patterns have been designed to model short repeated biological sequences, showing a high conservation of their content at some specific positions, permutation patterns have been designed to detect repeated patterns whose parts maintain their physical adjacency but not their ordering in all the pattern occurrences. Transposons, instead, model mobile sequences in the input dataset, which can be discovered by comparing different copies of the same input string, detecting large insertions and deletions in their alignment

New Algorithms for Text Fingerprinting

Abstract. Let s = s1..sn be a text (or sequence) on a finite alphabet Σ. A fingerprint in s is the set of distinct characters contained in one of its substrings. Fingerprinting a text consists of computing the set F of all fingerprints of all its substrings and being able to efficiently answer several questions on this set. A given fingerprint f ∈ F is represented by a binary array, F, of size |Σ | named a fingerprint table. A fingerprint, f ∈ F, admits a number of maximal locations 〈i, j 〉 in S, that is the alphabet of si..sj is f and si−1, sj+1, if defined, are not in f. The set of maximal locations is L, |L | ≤ n|Σ|. We present new algorithms and a new data structure for the three problems: (1) compute F; (2) given F, answer if F represents a fingerprint in F; (3) given F, find all maximal locations of F in s. These problems are respectively solved in O(n+|L | log |Σ|), Θ(|Σ|), and Θ(|Σ | + K) time- where K is the number of maximal locations of F.

New Algorithms for Text Fingerprinting- extended abstract-

Abstract: Let s = s1... sn be a text (or sequence) on a finite alphabet Σ. A fingerprint in s is the set of distinct characters contained in one of its substrings. Fingerprinting a text consists of computing the set F of all fingerprints of all its substrings and being able to efficiently answer several questions on this set. A given fingerprint f ∈ F is represented by a binary array, F, of size |Σ | named a fingerprint table. A fingerprint, f ∈ F, admits a number of maximal locations (i, j) in S, that is the alphabet of si... sj is f and si−1, sj+1, if defined, are not in f. The total number of maximal locations is L ≤ n|Σ | + 1. We present new algorithms and a new data structure for the three problems: (1) compute F; (2) given F, answer if F represents a fingerprint in F; (3) given F, find all maximal locations of F in s. These problems are respectively solved in O((L + n) log |Σ|), Θ(|Σ|), and Θ(|Σ | + K) time- where K is the number of maximal locations of F.