Vector quantization using information theoretic concepts

The process of representing a large data set with a smaller number of vectors in the best possible way, also known as vector quantization, has been intensively studied in the recent years. Very efficient algorithms like the Kohonen self-organizing map (SOM) and the Linde Buzo Gray (LBG) algorithm have been devised. In this paper a physical approach to the problem is taken, and it is shown that by considering the processing elements as points moving in a potential field an algorithm equally efficient as the before mentioned can be derived. Unlike SOM and LBG this algorithm has a clear physical interpretation and relies on minimization of a well defined cost function. It is also shown how the potential field approach can be linked to information theory by use of the Parzen density estimator. In the light of information theory it becomes clear that minimizing the free energy of the system is in fact equivalent to minimizing a divergence measure between the distribution of the data and the distribution of the processing elements, hence, the algorithm can be seen as a density matching method.     

On the feasibility of exploiting P2P systems to launch DDoS attacks

We show that malicious nodes in a peer-to-peer (P2P) system may impact the external Internet environment, by causing large-scale distributed denial of service (DDoS) attacks on nodes not even part of the overlay system. This is in contrast to attacks that disrupt the normal functioning, and performance of the overlay system itself. We demonstrate the significance of the attacks in the context of mature and extensively deployed P2P systems with representative and contrasting membership management algorithms—Kad, a DHT-based file-sharing system, and ESM, a gossip-based video broadcasting system. We then present an evaluation study of three possible mitigation schemes and discuss their strength and weakness. These schemes include (i) preferring pull-based membership propagation over push-based; (ii) corroborating membership information through multiple sources; and (iii) bounding multiple references to the same network entity. We evaluate the schemes through both experiments on PlanetLab with real and synthetic traces, and measurement of the real deployments. Our results show the potential of the schemes in enhancing the DDoS resilience of P2P systems, and also reveal the weakness in the schemes and regimes where they may not be sufficient.     

XML structural delta mining: Issues and challenges

Recently, there is an increasing research efforts in XML data mining. These research efforts largely assumed that XML documents are static. However, in reality, the documents are rarely static. In this paper, we propose a novel research problem called XML structural delta mining. The objective of XML structural delta mining is to discover knowledge by analyzing structural evolution pattern (also called structural delta) of history of XML documents. Unlike existing approaches, XML structural delta mining focuses on the dynamic and temporal features of XML data. Furthermore, the data source for this novel mining technique is a sequence of historical versions of an XML document rather than a set of snapshot XML documents. Such mining technique can be useful in many applications such as change detection for very large XML documents, efficient XML indexing, XML search engine, etc. Our aim in this paper is not to provide a specific solution to a particular mining problem. Rather, we present the vision of the mining framework and present the issues and challenges for three types of XML structural delta mining: identifying various interesting structures, discovering association rules from structural deltas, and structural change pattern-based classification.     

Edge magnitude based multilevel thresholding using Cuckoo search technique

Multilevel thresholding technique is popular and extensively used in the field of image processing. In this paper, a multilevel threshold selection is proposed based on edge magnitude of an image. The gray level co-occurrence matrix (second order statistics) of the image is used for obtaining multilevel thresholds by optimizing the edge magnitude using Cuckoo search technique. New theoretical formulation for objective functions is introduced. Key to our success is to exploit the correlation among gray levels in an image for improved thresholding performance. Apart from qualitative improvements the method also provides us optimal threshold values. Results are compared with histogram (first order statistics) based between-class variance method for multilevel thresholding. It is observed that the results of our proposed method are encouraging both qualitatively and quantitatively.     

An ant-swarm inspired dynamic multiresolution data dissemination protocol for wireless sensor networks

One of the main concerns of wireless sensor networks (WSNs) is to deliver useful information from data sources to users at a minimum power consumption due to constraints that sensor nodes must operate on limited power sources for extended time. In particular, achieving power-efficiency and multihop communication in WSN applications is a major issue. This paper continues on the investigation of a recently proposed Minimum-power Multiresolution Data Dissemination (MMDD) problem for WSNs (whose solution is considered here as a benchmark). We propose an ant-inspired solution to this problem. To the best of our knowledge, no attempts have been made so far in this direction. We have evaluated the performance of our proposed solution by conducting a variety of experiments and have found our solution to be promising in terms of total energy consumption in data dissemination.     

Block Truncation Coding based effective watermarking scheme for image authentication with recovery capability

Due to the advancement of photo-editing software, powerful computers and high resolution capturing devices, it has become tough to prevent the digital image from tampering. So, in these days just by looking a digital image we cannot say whether it is a genuine or not. This is why digital image authentication, as well as restoration, has become the essential issues, especially when it is utilized in medical science, evidence of court, and forensic science. This paper proposes an effective self-embedding fragile watermarking technique for the digital image authentication as well as recovery. The watermark is generated by quantization, and block truncation coding (BTC) of each 2 × 2 non-overlapping block and embedded in three least significant bits (LSBs) of the corresponding mapped block. The recovery bits are derived from most significant bits (MSBs) of the host image, and the authentication bits are derived from recovery bits, the spatial location of pixels and watermark keys. Even if tempering rate is 50%, the reconstruction of tampered image is achieved with high peak signal-to-noise ratio (PSNR) and normalized correlation coefficient (NCC). The experimental results demonstrate that the proposed scheme not only outperforms high-quality recovery fidelity but also negotiate the blocking artifacts additionally it improves the accuracy of tamper localization due to the use of very small size blocks.

     

Snowmelt runoff modelling in a Himalayan basin with the aid of satellite data

The Himalayan basins have runoff contributions from rainfall as well as from snow and ice. In the present study a snowmelt runoff model (SRM) was applied to estimate the streamflow for Satluj basin located in the western Himalayan region. This model uses the direct input of remotely sensed snow-cover area (SCA) data for calibration and simulation. The SCA in the basin was determined using remote sensing data from the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) and the Moderate Resolution Imaging Spectrometer (MODIS) onboard the Terra-Aqua satellite. In addition, daily precipitation and temperature data, as well as a Shuttle Radar Topography Mission digital elevation model (SRTM-DEM), were used to prepare the area elevation curves. The model was calibrated using the dataset for a period of 3 years (1996–1997, 1997–1998 and 1998–1999) and model parameters for streamflow routing were optimized. Using the optimized parameters, streamflow simulations were made for four years of data (i.e. 2000–2003 and 2004–2005). The accuracy of the streamflow verification was determined using different criteria such as shape of the outflow hydrograph, efficiency and difference in volume. The seasonal temperature lapse rates (TLRs) estimated from land surface temperature (LST) maps were used in the model and considerable improvement in simulation was observed. It was found that the overall efficiency increased when using varying TLRs.     

Accuracy assessment and trend analysis of MODIS-derived data on snow-covered areas in the Sutlej basin,Western Himalayas

Snow is important for hydrological studies and is a variable very sensitive to climatic variations. In the present study, the variability of snow-covered areas (SCAs) obtained through Moderate Resolution Imaging Spectroradiometer (MODIS) snow data products was analysed using the Mann–Kendall test and Sen’s slope estimator in the Sutlej basin, Western Himalayas, India. However, due to the limitations of long time-series snow cover data, the study has been carried out for a time period from 2000 to 2009. Before trend analysis, the estimated SCA was validated using the ground-based snowfall data. A simple linear regression test was applied to analyse the relationship between the variation in SCA and snowfall. The relationship between the mean annual snowfall and SCA indicated a highly significant correlation (R² = 0.95). In order to have a better insight into the relationship, the regression test was also carried out for six elevation zones. The coefficient of determination (R²) varied from 0.78 at the 1500–2000 m asl zone to 0.96 at the 3000–3500 m asl zone. The trend analysis indicated reduction in SCA with significant negative behaviour for annual, winter, and post-monsoon seasons and for November and December months. The negative trend was observed for an elevation of <2500 m asl in the basin. Moreover, during the same period (2000–2009), the temperature (T_max and T_min) increased while there was a decrease in snowfall. The trend analysis of temperature from 1984 to 2009 revealed positive trends with significant trend in T_min as determined by using the Mann–Kendall statistical test. The reduction in SCA was, therefore, attributed to the increasing trends in temperature, particularly T_min, associated with reduction in snowfall. These SCA variations have significant implications for water resources managers in the area as some of these observed trends, if continue, may result in changes in hydrological/ecological balance of the Sutlej basin.     

Network traffic analysis using singular value decomposition and multiscale transforms

The present work integrates the multiscale transform provided by the wavelets and singular value decomposition (SVD) for the detection of anomaly in self-similar network data. The algorithm proposed in this paper uses the properties of singular value decomposition (SVD) of a matrix whose elements are local energies of wavelet coefficients at different scales. Unlike existing techniques, our method determines both the presence (i.e., the time intervals in which anomaly occurs) and the nature of anomaly (i.e., anomaly of bursty type, long or short duration, etc.) in network data. It uses the diagonal, left and right singular matrices obtained in SVD to determine the number of scales of self-similarity, location and scales of anomaly in data, respectively. Our simulation work on different data sets demonstrates that the method performs better than the existing anomaly detection methods proposed for self-similar data.