Application of Sigma Point Particle Filter Method for Passive State Estimation in Underwater

Bearings-only tracking (BOT) plays a vital role in underwater surveillance. In BOT, measurement is tangentially related to state of the system. This measurement is also corrupted with noise due to turbulent underwater environment. Hence state estimation process using BOT becomes nonlinear. This necessitates the use of nonlinear filtering algorithms in place of traditional linear filters like Kalman filter. In general, these nonlinear filters utilize the assumption of measurements being corrupted with Gaussian noise for state estimation. The measurements cannot be always corrupted with Gaussian noise because of the highly unstable sea environment. These problems indicate the necessity for development of nonlinear non-Gaussian filters like particle filter (PF) for underwater tracking. However, PF suffers from severe problems like sample degeneracy and impoverishment and also it is tedious to select an appropriate technique for resampling. To overcome these difficulties in PF implementation, the strategy of combining PF with another filter like unscented Kalman filter is proposed for target’s state estimation. The detailed analysis of the same is presented in comparison with other particle filter combinations using the simulation results obtained in Matlab

Purification and properties of phage P22-induced lysozyme

Phage P22 induces a lysozyme in Salmonella typhimurium cells toward the later stage of its multiplication. P22 lysozyme has been purified about 1000-fold starting from the lysate of C1 (clear plaque-forming mutant of phage P22)-infected cells. The enzyme has an optimum pH between 7 and 8 and its activity is dependent on the ionic strength of the assay medium. Salts like MgCl2, NaCl, and KCl are inhibitory to the lysozyme. Gram-negative cells act as better substrates for the lysozyme than do gram-positive cells. The enzyme has a molecular weight of about 2 × 104 and rapidly looses its activity at temperatures higher than 40°. The properties of P22 lysozyme have been compared with those of λ and T4 lysozymes. All three lysozymes have more or less the same molecular weight and have similar properties although P22 and T4 lysozymes seem to be closer than P22 and λ lysozymes

Dynamic Time Slice Calculation for Round Robin Process Scheduling Using NOC

Process scheduling means allocating a certain amount of CPU time to each of the user processes.  One of the popular scheduling algorithms is the “Round Robin” algorithm, which allows each and every process to utilize the CPU for short time duration.  Processes which finish executing during the time slice are removed from the ready queue.  Processes which do not complete execution during the specified time slice are removed from the front of the queue, and placed at the rear end of the queue. This paper presents an improvisation to the traditional round robin scheduling algorithm, by proposing a new method. The new method represents the time slice as a function of the burst time of the waiting process in the ready queue. Fixing the time slice for a process is a crucial factor, because it subsequently influences many performance parameters like turnaround time, waiting time, response time and the frequency of context switches.  Though the time slot is fixed for each process, this paper explores the fine-tuning of the time slice for processes which do not complete in the stipulated time allotted to them

A NOVEL APPROACH IN IMAGE WATERMARKING WITH DISCRETE WAVELET TRANSFORM

In this paper we present a new watermarking scheme for still image data. Most of the recent work in watermarking can be grouped into two categories: spatial domain methods and frequency domain methods. We introduce a novel approach of watermarking which involves embedding the watermark in the discrete wavelet domain. We make use of a multi resolution data fusion approach in which the image and watermark are both transformed into the discrete wavelet domain. The resulting image pyramids are then fused according to a series of combination. After watermark insertion, inverse DWT is applied to the sub-bands with modified coefficients to obtain the watermarked image. For watermark extraction, a threshold-based decoder is designed. Embedding and extraction process are characterized with parameters and genetic algorithm is used for parameter optimization. Optimization is to maximize the values of peak signal-to-noise ratio of the watermarked image and normalized cross correlation of the extracted watermark. The performance of the proposed scheme is compared with the existing schemes and significant improvement is observed

A Heterogeneous, Highly Efficient, and Reusable Mo-Al2O3 Composites Based Organocatalyst for One Pot Three-Component Mannich Reaction for the Synthesis of β-aminocarbonyl Compounds

We have successfully developed a new Mo-Al2O3/p-TSA organocatalyst which has improved the yield of the Mannich reaction. This protocol has the advantage of mild conditions, high yield, easy work-up, environmentally friendly procedure, and the solid catalyst was recycled. The catalyst was analysed by XRD, SEM, and EDS techniques. The synthesized Mo-Al2O3composites were used in the presence of p-toluene sulfonic acid (p-TSA) conducts the one-pot three-component Mannich reaction and various β-amino ketones prepared with good yields and characterized by IR, 1H, and 13C NMR Techniques

Closest Keyword Search in Dynamic Multidimensional Data Sets

Adding text to databases opens up many different innovations and functionalities that can be made feasible for keyword-based quests. The application in question focuses on search results that are keyword-marked and that are located in a geographical area. For these datasets, our main goal is to locate groups of points that satisfy search queries. Our team's recommendation is a process we call Projection and Multi Scale Hashing that combines random projection and hashing to provide great scalability and efficiency. This example illustrates how to present algorithms in both an exact and approximate manner. Analyses that take into account experimental and analytical studies show that, with regard to overall efficiency, multi-dimensional hashing offers up to 65 times better results. A point in a dynamic connection multi-dimensional feature space is a typical way to classify an object, and we often describe various objects as a point in a multi-dimensional feature space. In other words, for example, images are described using feature vectors that are comprised of colour components, and a textual description of the image is typically correlated with it (such as tags or keywords)

Spammers Detection on Twitter by Automated Multi Level Detection System

Twitter is one of the most well known micro-blogging administrations, which is commonly used to share news and updates through short messages confined to 280 characters. In any case, its open nature and huge client base are every now and again misused via robotized spammers, content polluters, and other not well expected clients to carry out different cyber violations, for example, cyber bullying, trolling, rumor dissemination, and stalking. Likewise, various methodologies have been proposed by specialists to address these issues. Nonetheless, the majority of these methodologies depend on client portrayal and totally dismissing shared communications. In this examination, we present a hybrid methodology for recognizing mechanized spammers by amalgamating network based features with other feature classifications, to be specific metadata-, content-, and association based features. The curiosity of the proposed methodology lies in the portrayal of clients dependent on their communications with their supporters given that a client can dodge features that are identified with his/her very own exercises, yet sidestepping those dependent on the devotees is troublesome. Nineteen distinct features, including six recently characterized features and two re-imagined features, are distinguished for learning three classifiers, in particular, irregular woods, choice tree, Bayesian system, and example pre-handling on a genuine dataset that involves generous clients and spammers. The separation intensity of various feature classifications is additionally broke down, and cooperation and network based features are resolved to be the best for spam identification, though metadata-based features are demonstrated to be the least compelling