Digital Signal Processing

Contains summary of research and reports on sixteen research projects.U.S. Navy - Office of Naval Research (Contract N00014-75-C-0852)National Science Foundation FellowshipNATO FellowshipU.S. Navy - Office of Naval Research (Contract N00014-75-C-0951)National Science Foundation (Grant ECS79-15226)U.S. Navy - Office of Naval Research (Contract N00014-77-C-0257)Bell LaboratoriesNational Science Foundation (Grant ECS80-07102)Schlumberger-Doll Research Center FellowshipHertz Foundation FellowshipGovernment of Pakistan ScholarshipU.S. Navy - Office of Naval Research (Contract N00014-77-C-0196)U.S. Air Force (Contract F19628-81-C-0002)Hughes Aircraft Company Fellowshi

Tooling design and microwave curing technologies for the manufacturing of fiber-reinforced polymer composites in aerospace applications

The increasing demand for high-performance and quality polymer composite materials has led to international research effort on pursuing advanced tooling design and new processing technologies to satisfy the highly specialized requirements of composite components used in the aerospace industry. This paper reports the problems in the fabrication of advanced composite materials identified through literature survey, and an investigation carried out by the authors about the composite manufacturing status in China’s aerospace industry. Current tooling design technologies use tooling materials which cannot match the thermal expansion coefficient of composite parts, and hardly consider the calibration of tooling surface. Current autoclave curing technologies cannot ensure high accuracy of large composite materials because of the wide range of temperature gradients and long curing cycles. It has been identified that microwave curing has the potential to solve those problems. The proposed technologies for the manufacturing of fiber-reinforced polymer composite materials include the design of tooling using anisotropy composite materials with characteristics for compensating part deformation during forming process, and vacuum-pressure microwave curing technology. Those technologies are mainly for ensuring the high accuracy of anisotropic composite parts in aerospace applications with large size (both in length and thickness) and complex shapes. Experiments have been carried out in this on-going research project and the results have been verified with engineering applications in one of the project collaborating companies

An Aggregate MapReduce Data Block Placement Strategy for Wireless IoT Edge Nodes in Smart Grid

Big data analytics has simplified processing complexity of large dataset in a distributed environment. Many state-of-the-art platforms i.e. smart grid has adopted the processing structure of big data and manages a large volume of data through MapReduce paradigm at distribution ends. Thus, whenever a wireless IoT edge node bundles a sensor dataset into storage media, MapReduce agent performs analytics and generates output into the grid repository. This practice has efficiently reduced the consumption of resources in such a giant network and strengthens other components of the smart grid to perform data analytics through aggregate programming. However, it consumes an operational latency of accessing large dataset from a central repository. As we know that, smart grid processes I/O operations of multi-homing networks, therefore, it accesses large datasets for processing MapReduce jobs at wireless IoT edge nodes. As a result, aggregate MapReduce at wireless IoT edge node produces a network congestion and operational latency problem. To overcome this issue, we propose Wireless IoT Edge-enabled Block Replica Strategy (WIEBRS), that stores in-place, partition-based and multi-homing block replica to respective edge nodes. This reduces the delay latency of accessing datasets for aggregate MapReduce and increases the performance of the job in the smart grid. The simulation results show that WIEBRS effective decreases operational latency with an increment of aggregate MapReduce job performance in the smart grid

UNL-based machine translation scheme for Bangla locative case constructs

Case structure plays a vital role in grammatical structure of any language during language translation. This paper presents an in-depth analysis of Bangla locative case constructs based on UNL (Universal Networking Language) machine translation scheme. A set of analysis rules have been defined to convert various Bangla locative case sentences into UNL expressions that can later be converted to any native language using language independent deconversion rules. We have demonstrated five different analysis rules and illustrated how each of them can effectively convert Bangla sentences to UNL expression

An efficient conversion approach of the bangla infinite verb sentence into unl

This paper presents conversion procedure of Bangla infinite verb sentences to assimilate them into an interlingua representation called Universal Networking Language (UNL). It focuses on the analysis of infinite verbs and develops the morphological rules to resolve morphological analysis between the infinite and finite verbs. This paper also develops semantic rules to perform semantic analysis between the words in a sentence for the EnConverter to convert infinite verb sentence into UNL expression. Finally, we have shown the conversion procedures of a Bangla infinite verb sentence into UNL

Machine Learning Approach for Prediction of Crimp in Cotton Woven Fabrics

The interlacements of yarns in woven fabrics cause the yarn to follow a wavy path that produces crimp. Off-loom width of the fabric is determined by the percentage of the induced crimp. Therefore, the final width of the fabric will be less or surplus than required if crimp percentage is not precisely measured. Both excessive or recessive fabric width is unwanted and leads to huge loss of cost (profit), manufacturing time, energy (electricity) and ultimately loss of competition. Crimp percentage in yarns is determined by physically measuring the extra yarn length or by predicting it based on fabric structural parameters. Existing methods are mainly post-production, time and resource intensive that require specialized skills and tangible fabric samples. The proposed framework applies supervised machine learning for crimp prediction to cater for the limitations of the existing techniques. The framework has been cross-validated and has prediction accuracy (R2) of 0.86 and 0.79 for warp and weft yarn crimp respectively. It has prediction accuracy (R2) for warp and weft yarns crimp of 0.99 and 0.81 respectively for the unseen industrial dataset. The proposed prediction model shows better performance when compared with an existing standard system