Fast Inverse Motion Compensation Algorithms for MPEG and for Partial DCT Information

In prior work, we developed a fast inverse motion compensation method that can be implemented directly on the DCT domain representation derived from the compressed bitstreams conforming to MPEG, H.261, and H.263 standards. That work was restricted to compressed-domain representations wherein the motion-vectors have integer pel accuracy. Here, we extend this work to fractional-pel accurate motion-vectors. We also extend the prior work to speed up the inverse motion compensation process in the DCT domain by explicitly exploiting the sparseness of the DCT domain representation. Using partial DCT information, we show that the DCT domain method has substantially lower operation count than the conventional spatial domain approach which requires decompression followed by inverse motion-compensation.     

Engineering plastics from lignin. VI. Structure–property relationships of PEG-containing polyurethane networks

Hydroxypropyl lignin-based thermosetting polyurethanes were synthesized with excess hexamethylene diisocyanate (HDI) and tolylene diisocyanate (TDI) by solution casting. Four polyethylene glycols (PEG) of molecular weight 400, 600, 1000, and 4000 were mixed with lignin polyol to incorporate different proportions of soft segment into the network prior to crosslinking. Neither thermal nor mechanical and limited small angle x-ray scattering (SAXS) analysis provided distinct evidence for phase separation and microstructure formation. The study examines the effect of the soft segment in relation to chain length and weight contribution on the thermal and mechanical properties of the final networks. A significant sensitivity of glass transition temperature (T_g), of swelling in DMF, and of the mechanical properties to soft segment content was observed. Some of this sensitivity must, however, be attributed to differences in crosslink density since the polyol to diisocyanate weight ratio was kept constant throughout the synthesis series. The magnitude of the change of the different properties was found to be influenced by both glycol content and glycol molecular weight. The T_g of the network decreased from 105°C to as low as 38°C (HDI), and from 158°C to 70°C (TDI), with incorporation of up to 17.8% glycol, and it was greater with lower molecular weight glycols than with higher ones at any weight fraction. Swelling in DMF increased as expected with soft segment content. Mechanical properties were affected most if HDI and lower molecular weight glycols were used. The uniformity in structure, reduction in brittleness, and considerable improvement in mechanical properties with inclusion of minor PEG contents indicates that lignin-based network polyurethanes can be synthesized with controllable performance characteristics.     

Intelligent Machine Learning with Metaheuristics Based Sentiment Analysis and Classification

Sentiment Analysis (SA) is one of the subfields in Natural Language Processing (NLP) which focuses on identification and extraction of opinions that exist in the text provided across reviews, social media, blogs, news, and so on. SA has the ability to handle the drastically-increasing unstructured text by transforming them into structured data with the help of NLP and open source tools. The current research work designs a novel Modified Red Deer Algorithm (MRDA) Extreme Learning Machine Sparse Autoencoder (ELMSAE) model for SA and classification. The proposed MRDA-ELMSAE technique initially performs preprocessing to transform the data into a compatible format. Moreover, TF-IDF vectorizer is employed in the extraction of features while ELMSAE model is applied in the classification of sentiments. Furthermore, optimal parameter tuning is done for ELMSAE model using MRDA technique. A wide range of simulation analyses was carried out and results from comparative analysis establish the enhanced efficiency of MRDA-ELMSAE technique against other recent techniques.     

Use of poultry litter for biodegradation of soil contaminated with 2,4- and 2,6-dinitrotoluene

Pseudomonas sp. and Pseudomonas putida can utilize dinitrotoluene (DNT) as N-source after the enzymatic removal of nitro groups from the aromatic ring. Addition of nutrients is known to stimulate the biodegradation process. Poultry litter has consortia of microorganisms (including Pseudomonas) along with many nutrients. The objective of this research was to study the biodegradation of 2,4- and 2,6-DNT contaminated soil (from Badger Army Ammunition Plant) using poultry litter. Complete biodegradation of both 2,4- and 2,6-DNT in the soil was observed after 1-day interaction with poultry litter. No degradation was observed using autoclaved litter.     

Hydroxy Propylated Lignin-Isocyanate Combinations as Bonding Agents for Wood and Cellulosic Fibers

Lignin and hydroxy propylated lignin derivatives were reacted with diisocyanates in the presence of cellulose fibers and wood particles. The attachment of lignin and lignin derivates to hydroxyl-rich surfaces of lignocellulosic materials proved to be a convenient way for increasing strength properties of reconstituted fiber and particle materials. Results indicate that hydroxypropylated lignin derivatives are capable of contributing equal or even greater strength increases to relignified fiber composites than do monomeric or polymeric diisocyanates alone. With reinforced fiber mats, it was found that strength properties were virtually unaffected up to a level of approximately 50% replacement of diisocyanates by lignin-based materials. This beneficial contribution by lignin to bonding properties could not be verified for reconstituted wood products, the strength and swelling properties of which suffered in relation to lignin content. Thftse differences are explained with differences in method of adhesive preparation and application. Lignins were obtained from the spent pulping liquor of the Kraft process, and by alkaline extraction of steam exploded Aspen wood chips. These lignins were, for the most part, reacted with propylene oxide.     

Artificial neural network a tool for predicting failure strength of composite tensile coupons using acoustic emission technique

A series of 18 tensile coupons were monitored with an acoustic emission (AE) system, while loading them up to failure. AE signals emitted due to different failure modes in tensile coupons were recorded. Amplitude, duration, energy, counts, etc., are the effective parameters to classify the different failure modes in composites, viz., matrix crazing, fiber cut, and delamination, with several subcategories such as matrix splitting, fiber/matrix debonding, fiber pullout, etc. Back propagation neural network was generated to predict the failure load of tensile specimens. Three different networks were developed with the amplitude distribution data of AE collected up to 30%, 40%, and 50% of the failure loads, respectively. Amplitude frequencies of 12 specimens in the training set and the corresponding failure loads were used to train the network. Only amplitude frequencies of six remaining specimens were given as input to get the output failure load from the trained network. The results of three independent networks were compared, and we found that the network trained with more data was having better prediction performance.     

Effect of argon dilution on the pre-ignition oxidation kinetics of benzene

Experimental and modelling studies on the high-temperature oxidation of benzene have been done to study the effect of argon dilution on its pre-ignition thermochemical kinetics. The ignition delay times of stoichiometric C₆H₆/O₂ mixtures are measured behind reflected shock waves at a total pressure of 4 bar with varying argon dilution (0–95%) for a wide range of temperatures (650–2200 K). A kinetic scheme with 60 elementary reactions among 23 reacting species is proposed which is able to predict the experimental results obtained in this as well as earlier studies. The model was subjected to a sensitivity analysis to identify the significant reactions. The effects of diluent concentration on the reaction rates and energy release history in the vicinity of the induction times are reported. Correlations are deduced to predict the ignition delay times. The thermal and kinetic effects of argon concentration on the ignition activation energy and induction times are examined.