A hybrid LBG/lattice vector quantizer for high quality image coding

It is well known that a vector quantizer is an efficient coder offering a good trade-off between quantization distortion and bit rate. The performance of a vector quantizer asymptotically approaches the optimum bound with increasing dimensionality. A vector quantized image suffers from the following types of degradations: (1) edge regions in the coded image contain staircase effects, (2) quasi-constant or slowly varying regions suffer from contouring effects, and (3) textured regions lose details and suffer from granular noise. All three of these degradations are due to the finite size of the code book, the distortion measures used in the design, and due to the finite training procedure involved in the construction of the code book. In this paper, we present an adaptive technique which attempts to ameliorate the edge distortion and contouring effects

Integration of Fuzzy with Incremental Import Vector Machine for Intrusion Detection

IDM design and implementation remain a difficult undertaking and an unsolved research topic. Multi-dimensional irrelevant characteristics and duplicate information are included in the network dataset. To boost the effectiveness of IDM, a novel hybrid model is developed that combines Fuzzy Genetic Algorithms with Increment Import Vector Machines (FGA-I2VM), which works with huge amounts of both normal and aberrant network data with high detecting accuracy and low false alarm rates. The algorithms chosen for IDM in this stage are machine learning algorithms, which learn, find, and adapt patterns to changing situations over time. Pre-processing is the most essential stage in any IDM, and feature selection is utilized for pre-processing, which is the act of picking a collection or subset of relevant features for the purpose of creating a solution model. Information Gain (IG) is utilized in this FGA-I2VM model to pick features from the dataset for I2VM classification. To train the I2VM classifier, FGA uses three sets of operations to produce a new set of inhabitants with distinct patterns: cross over operation, selection, and finally mutation. The new population is then put into the Import Vector Machine, a strong classifier that has been used to solve a wide range of pattern recognition issues. FGA are quick, especially considering their capacity to discover global optima. Another advantage of FGA is their naturally parallel nature of assessing the individuals within a population. As a classifier, I2VM has self-tuning properties that allow patterns to attain global optimums. The FGA-efficacy I2VM model’s is complemented by information gain, which improves speed and detection accuracy while having a low computing cos

Biodiversity assessment of sea cage farming sites in Gulf of Mannar, Tamil Nadu, India

Marine fin and shellfish farming is an age old practice across the globe to augment the seafood production and to supplement the protein rich food. The open sea cages are particularly advantageous for maintaining the brood stock of potential species like cobia (Rachycentron canadum) and silver pompano (Trachinotus blochii) for captive breeding. A large congregation of various bioresources are also found to be associated with cage farming (Imelda et al., 2010). In the present study, an attempt was made to understand the biodiversity of the cage farming area at Mandapam in Gulf of Mannar, India

Action Sequencing Using Visual Permutations

Humans can easily reason about the sequence of high level actions needed to complete tasks, but it is particularly difficult to instil this ability in robots trained from relatively few examples. This work considers the task of neural action sequencing conditioned on a single reference visual state. This task is extremely challenging as it is not only subject to the significant combinatorial complexity that arises from large action sets, but also requires a model that can perform some form of symbol grounding, mapping high dimensional input data to actions, while reasoning about action relationships. This paper takes a permutation perspective and argues that action sequencing benefits from the ability to reason about both permutations and ordering concepts. Empirical analysis shows that neural models trained with latent permutations outperform standard neural architectures in constrained action sequencing tasks. Results also show that action sequencing using visual permutations is an effective mechanism to initialise and speed up traditional planning techniques and successfully scales to far greater action set sizes than models considered previously.Comment: This paper has been accepted for publication at IEEE RA-

Anticancer effects and lysosomal acidification in A549 cells by astaxanthin from Haematococcus lacustris

Astaxanthin (AXN) is known to have health benefits by epidemiological studies. Therefore, it is of interest to assess the effect of AXN (derived from indigenous unicellular green alga Haematococcus lacustris) to modulate cell cycle arrest, lysosomal acidification and eventually apoptosis using in vitro in A549 lung cancer cells. Natural extracts of astaxanthin were obtained by standardized methods as reported earlier and characterized by standard HPLC and MS. Treatment of A549 cells with AXN (purified fraction) showed significant reduction in cell viability (about 50%) as compared to crude extract at 50µM concentration. Thus, we show the anticancer effects and lysosomal acidification in A549 cells by Astaxanthin from Haematococcus lacustris for further consideration. Together, our results demonstrated the anticancer potential of AXN from Haematococcus lacustris, which is found to be mediated via its ability to induce cell cycle arrest, lysosomal acidification and apoptotic induction.</jats:p

Ecology and biodiversity of open sea cage farm at Mandapam, India

Farming of marine fin and shellfish species is an age old practice across the globe to augment the seafood demand and to supplement the rich protein food. Fin fish mariculture in India has been a recent initiative and the Central Marine Fisheries Research Institute was successful in developing captive breeding and seed production technique for cobia (Rachycentron canadum) and silver pompano (Trachinotus blochii). The open sea cages are particularly advantageous for maintaining the brood stock of potential species for captive breeding. However, the ecology of the cage farming sites is of paramount importance and should be conducive for the survival, health and growth of fishes that are farmed. In the present study, an attempt was made to understand the ecology and biodiversity of the cage farming area at Mandapam in Gulf of Mannar, south-east coast of India

Lawsonia intracellularis ELISA: A New Test at the ISU-VDL

Porcine Proliferative Enteropathy (PPE) is a common disease of swine which is caused by the intracellular bacterium, Lawsonia intracellularis. The performance of a recently available commercial ELISA for L. intracellularis was evaluated by comparison with an immuno-peroxidase monolayer assay (IPMA) and found to have at least 97% correlation. The same assay when conducted at different laboratories showed 100% agreement in results. Examination of serum samples from various cases submitted to the Iowa State University Veterinary Diagnostic laboratory indicated that 87% of the herds examined were sero-positive for L. intracellularis. Therefore, the Enzyme-Linked Immunosorbent Assay (ELISA) for Lawsonia intracellularis is a useful and reliable test which allows veterinary practitioners and producers to obtain same day results on swine serum samples submitted

Production and partial purification of α-amylase producing Streptomyces sp. SNAJSM6 isolated from seaweed Sargassum myriocystum J. Agardh

1245-1251The bacteria Streptomyces sp. was isolated from the seaweed Sargassum myriocystum that was collected from Kilakarai, Ramanathapuram District, Tamil Nadu. Among the isolates, the SNAJSM6 produced the maximum content of extracellular α-amylase, which was conformed by starch hydrolysis and identified as Streptomyces sp. SNAJSM6. The culture optimization studies for augmented enzyme production in the Streptomyces sp. were conducted under different physico-chemical parameters, such as pH, temperature, and carbon and nitrogen sources for α-amylase production. The maximum α-amylase production was recorded on the third day of incubation (56U/ml) by Streptomyces sp. SNAJSM6. The Streptomyces sp. optimized α-amylase enzyme production was recorded at pH 7.0 and temperature 30 °C, which was 1.86 fold higher than the unoptimized culture broth of the Streptomyces sp. The Streptomyces sp. α-amylase enzyme was partially purified and its molecular weight was found to be 44 kDa by using SDS-PAGE. Among the isolates that were screened, Streptomyces sp. SNAJSM6 is a potential candidate for maximum production of α-amylase. Further, it showed excellent antibacterial activity against selected human pathogens