Mayank Dave

Hybrid technique for robust and imperceptible multiple watermarking using medical images

This paper presents a secure multiple watermarking method based on discrete wavelet transform (DWT), discrete cosine transforms (DCT) and singular value decomposition (SVD). For identity authentication purpose, the proposed method uses medical image as the image watermark, and the personal and medical record of the patient as the text watermark. In the embedding process, the cover medical image is decomposed up to second level of DWT coefficients. Low frequency band (LL) of the host medical image is transformed by DCT and SVD. The watermark medical image is also transformed by DCT and SVD. The singular value of watermark image is embedded in the singular value of the host image. Furthermore, the text watermark is embedding at the second level of the high frequency band (HH) of the host image. In order to enhance the security of the text watermark, encryption is applied to the ASCII representation of the text watermark before embedding. Results are obtained by varying the gain factor, size of the text watermark, and medical image modalities. Experimental results are provided to illustrate that the proposed method is able to withstand a variety of signal processing attacks such as JPEG, Gaussian, Salt-and-Pepper, Histogram equalization etc. The performance of the proposed technique is also evaluated by using the benchmark software Checkmark and the technique is found to be robust against the Checkmark attacks such as Collage, Trimmed Mean, Hard and Soft Thresholding, Wavelet Compression, Mid Point, Projective, and Wrap etc.

Robust and Imperceptible Dual Watermarking for Telemedicine Applications

In this paper, the effects of different error correction codes on the robustness and imperceptibility of discrete wavelet transform and singular value decomposition based dual watermarking scheme is investigated. Text and image watermarks are embedded into cover radiological image for their potential application in secure and compact medical data transmission. Four different error correcting codes such as Hamming, the Bose, Ray-Chaudhuri, Hocquenghem (BCH), the Reed–Solomon and hybrid error correcting (BCH and repetition code) codes are considered for encoding of text watermark in order to achieve additional robustness for sensitive text data such as patient identification code. Performance of the proposed algorithm is evaluated against number of signal processing attacks by varying the strength of watermarking and covers image modalities. The experimental results demonstrate that this algorithm provides better robustness without affecting the quality of watermarked image.This algorithm combines the advantages and removes the disadvantages of the two transform techniques. Out of the three error correcting codes tested, it has been found that Reed–Solomon shows the best performance. Further, a hybrid model of two of the error correcting codes (BCH and repetition code) is concatenated and implemented. It is found that the hybrid code achieves better results in terms of robustness. This paper provides a detailed analysis of the obtained experimental results.

Multilevel Encrypted Text Watermarking on Medical Images Using Spread-Spectrum in DWT Domain

This paper presents a secure multilevel watermarking scheme in which the encrypted text acts as a watermark. The algorithm is based on secure spread-spectrum technique for digital images in discrete wavelet transform (DWT) domain. Potential application of the proposed watermarking scheme is successfully demonstrated for embedding various medical watermarks in text format at different subband decomposition levels depending upon their performance requirements. In the embedding process, the cover CT Scan image is decomposed up to third level of DWT coefficients. Different text watermarks such as personal and medical record of the patient, diagnostic/image codes and doctor code/signature are embedded into the selective coefficients of the second and third level DWT for potential telemedicine applications. Selection of DWT coefficients for embedding is done by column wise thresholding of coefficients values. Also, encryption is applied to the ASCII representation of the text and the encoded text watermark is embedded. The algorithm correctly extracts the embedded watermarks without error and is robust against numerous known attacks without much degradation of the medical image quality of the watermarked image.

Network Partitioning Recovery Mechanisms in WSANs: a Survey

Wireless sensor and actor networks (WSANs) are more promising and most addressing research field in the area of wireless sensor networks in recent scenario. It composed of possibly a large number of tiny, autonomous sensor devices and resources rich actor nodes equipped with wireless communication and computation capabilities. Actors collect sensors’ information and respond collaboratively to achieve an application specific mission. Since actors have to coordinate their operation, a strongly connected inter-actor network would be required at all the time in the network. Actor nodes may fail for many reasons (i.e. due of battery exhaustion or hardware failure due to hash environment etc.) and failures may convert connected network into disjoint networks. This can hinder sometimes not only the performance of network but also degrade the usefulness and effectiveness of the network. Thus, having a partitioning detection and connectivity restoration procedure at the time of failure occurs in the network is crucial for WSANs. In this paper, we review the present network partitioning recovery approaches and provide an overall view of this study by summarizing previous achievements.

Acoustic modeling problem for automatic speech recognition system: conventional methods (Part I)

In automatic speech recognition (ASR) systems, the speech signal is captured and parameterized at front end and evaluated at back end using the statistical framework of hidden Markov model (HMM). The performance of these systems depend critically on both the type of models used and the methods adopted for signal analysis. Researchers have proposed a variety of modifications and extensions for HMM based acoustic models to overcome their limitations. In this review, we summarize most of the research work related to HMM-ASR which has been carried out during the last three decades. We present all these approaches under three categories, namely conventional methods, refinements and advancements of HMM. The review is presented in two parts (papers): (i) An overview of conventional methods for acoustic phonetic modeling, (ii) Refinements and advancements of acoustic models. Part I explores the architecture and working of the standard HMM with its limitations. It also covers different modeling units, language models and decoders. Part II presents a review on the advances and refinements of the conventional HMM techniques along with the current challenges and performance issues related to ASR.

A Hybrid Timer Based Single Node Failure Recovery Approach for WSANs

The inter-actor connectivity is a very crucial issue to maintain network operation in the wireless sensor and actor networks. Most of the applications have been proposed for harsh environments where the backbone actor nodes are prone to failure or get damaged due to their battery power exhaustion or get physically damaged. Such failures can partition the network due to failure of the cut-vertex node and eventually decrease the network performance or even sometimes make the network useless. Currently, a few approaches have been proposed to restore the partitioned network due to failure of the cut-vertex node but without considering the recovery node capabilities. This paper proposes a localized hybrid timer based cut-vertex node failure recovery approach called distributed prioritized connectivity restoration algorithm (DPCRA) to handle such partitions and restore connectivity with the help of a small number of nodes. The main idea is to proactively identify whether the failure of an actor node causes partition or not in the network. If partition occurs the designated failure handlers (FHs) detect that partition and repair it locally using minimum information stored in each actor node. In case first designated node is unable to start the recovery process within a permissible reaction time the next designated FH could start the recovery process. The main strength of our paper is the use of multiple backup nodes for the guaranteed partitioned recovery. The experimental simulation shows that DPCRA outperforms other existing state-of-the-art approaches in terms of the number of participated repairing nodes and their total moving distance for the recovery to restore the disconnected partitions.

A novel technique for digital image watermarking in spatial domain

Information technology has eased the duplication, manipulation and distribution of digital data in recent times which has resulted in the demand for safe ownership of digital images. A very crucial concern for the content owners and distributors is copyright protection and content authentication. The solution to these problems is Digital Watermarking. The watermarking is the process of embedding a signal in to other signal robustly and invisibly at the same time, the embedded signal is called watermark and the other signal is called cover or host signal. In this paper we presents a brief overview of digital image watermarking techniques in spatial and frequency domain, proposed a watermarking algorithm in spatial domain by using the Least Significant Bit (LSB) method. Every technique has some advantages and disadvantages but the LSB method is to very good compromise between performance, robustness, computational cost and quality of embedding.

Performance evaluation of sequentially combined heterogeneous feature streams for Hindi speech recognition system

State-of-the-art automatic speech recognition (ASR) systems follow a well established statistical paradigm, that of parameterization of speech signals (a.k.a. feature extraction) at front-end and likelihood evaluation of feature vectors at back-end. For feature extraction, Mel-frequency cepstral coefficients (MFCC) and perceptual linear prediction (PLP) are the two dominant signal processing methods, which have been used mainly in ASR. Although the effects of both techniques have been analyzed individually, it is not known whether any combination of the two can produce an improvement in the recognition accuracy or not. This paper presents an investigation on the possibility to integrate different types of features such as MFCC, PLP and gravity centroids to improve the performance of ASR in the context of Hindi language. Our experimental results show a significant improvement in case of such few combinations when applied to medium size lexicons in typical field conditions.

A Framework For Handling Local Broadcast Storm Using Probabilistic Data Aggregation In VANET

Data aggregation is used to combine correlated data items from different vehicles before redistributing to other vehicles in the vehicular ad hoc networks (VANET). The number of retransmissions and the communication overhead can be reduced considerably by using aggregation. It is a prerequisite for applications that require periodic dissemination of information into a large region so that, drivers can be informed well in advance and can take alternative route in case of traffic congestion. Dissemination of information to vehicles through broadcasting creates a broadcast storm problem in VANET. In this paper a novel framework is proposed for handling the local broadcast storm problem using probabilistic data aggregation which reduces the bandwidth consumption and hence improves the information dissemination. This system exploits the knowledge base and stores the decisions for aggregation and is based on a flexible and extensible set of criteria. These criteria’s can be application specific and can enable a dynamic fragmentation of the road according to the various application requirements. The framework is evaluated for VANET based traffic information system through simulation for strictly limited bandwidth and local broadcast problem. The results demonstrate that completely structure-free probabilistic data aggregation reduces the bandwidth consumption by eliminating the local broadcast problem.

Antecedence Graph Approach to Checkpointing for Fault Tolerance in Mobile Agent Systems

The flexibility offered by mobile agents is quite noticeable in distributed computing environments. However, the greater flexibility of the mobile agent paradigm compared to the client/server computing paradigm comes at an additional threats since agent systems are prone to failures originating from bad communication, security attacks, agent server crashes, system resources unavailability, network congestion, or even deadlock situations. In such events, mobile agents either get lost or damaged (partially or totally) during execution. In this paper, we propose parallel checkpointing approach based on the use of antecedence graphs for providing fault tolerance in mobile agent systems. During normal computation message transmission, the dependency information among mobile agents is recorded in the form of antecedence graphs by participating mobile agents of mobile agent group. When a checkpointing procedure begins, the initiator concurrently informs relevant mobile agents, which minimizes the identifying time. The proposed scheme utilizes the checkpointed information for fault tolerance which is stored in form of antecedence graphs. In case of failures, using checkpointed information, the antecedence graphs and message logs are regenerated for recovery and then normal operation continued. Moreover, compared with the existing schemes, our algorithm involves the minimum number of mobile agents during the identifying and checkpoiting procedure, which leads to the improvement of the system performance. In addition, the proposed algorithm is a domino-free checkpointing algorithm, which is especially desirable for mobile agent systems. Quantitative analysis and experimental simulation show that our algorithm outperforms other coordinated checkpointing schemes in terms of the identifying time and the number of blocked mobile agents and then can provide a better system performance. The main contribution of the proposed checkpointing scheme is the enhancement of graph-based approach in terms of considerable improvement by reducing message overhead, execution, and recovery times.