Discrete Wavelet Transform: Architectures, Design and Performance Issues

Due to the demand for real time wavelet processors in applications such as video compression [1], Internet communications compression [2], object recognition [3], and numerical analysis, many architectures for the Discrete Wavelet Transform (DWT) systems have been proposed. This paper surveys the different approaches to designing DWT architectures. The types of architectures depend on whether the application is 1-D, 2-D, or 3-D, as well as the style of architecture: systolic, semi-systolic, folded, digit-serial, etc. This paper presents an overview and evaluation of the architectures based on the criteria of latency, control, area, memory, and number of multipliers and adders. This paper will give the reader an indication of the advantages and disadvantages of each design.     

Design and Implementation of a Flexible Manufacturing Control System Using Neural Network

Design and implementation of a sequential controller based on the concept of artificial neural networks for a flexible manufacturing system are presented. The recurrent neural network (RNN) type is used for such a purpose. Contrary to the programmable controller, an RNN-based sequential controller is based on a definite mathematical model rather than depending on experience and trial and error techniques. The proposed controller is also more flexible because it is not limited by the restrictions of the finite state automata theory. Adequate guidelines of how to construct an RNN-based sequential controller are presented. These guidelines are applied to different case studies. The proposed controller is tested by simulations and real-time experiments. These tests prove the successfulness of the proposed controller performances. Theoretical as well as experimental results are presented and discussed indicating that the proposed design procedure using Elman's RNN can be effective in designing a sequential controller for event-based type manufacturing systems. In addition, the simulation results assure the effectiveness of the proposed controller to outperform the effect of noisy inputs.     

Structural Characterization of Different Asphaltenes of Kuwaiti Origin

Asphaltenes obtained by precipitation from crude Kuwaiti oils have been analyzed by proton magnetic resonance (¹H-NMR), carbon-13 nuclear magnetic resonance (¹³C-NMR) and Infrared (IR) spectral techniques. The molecular weight and elemental analysis were also determined. These combined analytical data were used for the characterization of these Kuwaiti oils. The asphaltenes molecular weights range from approximately 4200-6500 with an H/C ratio of 0.91-1.1 with an average 45-71% aromatic carbons. The average side chain length was of 4-6 carbons. It can also be concluded that the asphaltenes under investigation contain 5-9 sets of condensed aromatic rings joined together by bridges of alkyl chains or other hetero atoms and the average number of each of these sets of condensed aromatic rings is nearly 7. There are a number of alicyclic rings and condensed alicyclic rings in asphaltene. The IR spectra showed main molecular groups including OH, NH, SH, C=O and aliphatic and aromatic C-H's.     

Novel thermally and mechanically stable hydrogel for enzyme immobilization of penicillin G acylase via covalent technique

κ‐Carrageenan hydrogel crosslinked with protonated polyethyleneimine (PEI⁺) and glutaraldehyde (GA) was prepared and evaluated as a novel biocatalytic support for covalent immobilization of penicillin G acylase (PGA). The method of modification of the carrageenan biopolymer is clearly illustrated using a schematic diagram and was verified by FTIR, elemental analysis, DSC, and INSTRON using the compression mode. Results showed that the gels' mechanical strength was greatly enhanced from 3.9 kg/cm² to 16.8 kg/cm² with an outstanding improvement in the gels thermal stability. It was proven that, the control gels were completely dissolved at 35°C, whereas the modified gels remained intact at 90°C. The DSC thermogram revealed a shift in the endothermic band of water from 62 to 93°C showing more gel‐crosslinking. FTIR revealed the presence of the new functionality, aldehydic carbonyl group, at 1710 cm^?1 for covalent PGA immobilization. PGA was successfully immobilized as a model industrial enzyme retaining 71% of its activity. The enzyme loading increased from 2.2 U/g (control gel) to 10 U/g using the covalent technique. The operational stability showed no loss of activity after 20 cycles. The present support could be a good candidate for the immobilization of industrial enzymes rich in amino groups, especially the thermophilic ones. © 2008 Wiley Periodicals, Inc. J Appl Polym Sci, 2008     

Direct sequential evaluation of optimal orthonormal eigenvectors of the discrete Fourier transform matrix by constrained optimization

The recent emergence of the discrete fractional Fourier transform has spurred research activity aiming at generating Hermite–Gaussian-like (HGL) orthonormal eigenvectors of the discrete Fourier transform (DFT) matrix F. By exploiting the unitarity of matrix F – resulting in the orthogonality of its eigenspaces pertaining to the distinct eigenvalues – the problem decouples into finding orthonormal eigenvectors for each eigenspace separately. A Direct Sequential Evaluation by constrained Optimization Algorithm (DSEOA) is contributed for the generation of optimal orthonormal eigenvectors for each eigenspace separately. This technique is direct in the sense that it does not require the generation of initial orthonormal eigenvectors as a prerequisite for obtaining the final optimal ones. The resulting eigenvectors are optimal in the sense of being as close as possible to samples of the Hermite–Gaussian functions. The technique is found to be numerically robust because total pivoting is allowed in performing the QR matrix decomposition step. The DSEOA is proved to be theoretically equivalent to each of the Gram–Schmidt algorithm (GSA) and the sequential orthogonal Procrustes algorithm (SOPA). However the three techniques are algorithmically quite distinct. An extensive comparative simulation study shows that the DSEOA is by far the most numerically robust technique among all sequential algorithms thus paying off for its relatively long computation time.     

Biodegradation of low‐density polyethylene/thermoplastic starch foams before and after electron beam irradiation

Foamy low‐density polyethylene/plasticized starch (LDPE/PLST) blends at different compositions were produced in the presence of azodicarbonamide (ACA) compound as foaming agent. The LDPE/PLST blends before and after electron beam irradiation were investigated in terms of mechanical properties, bulk density, and structure morphology. Moreover, the biodegradability of these materials was evaluated by the soil burial test for 2 months, in which the buried sheets were also examined by scanning electron microscopy (SEM). The results showed that the increase of PLST content from 24 to 30% was accompanied by a decrease in the yield and break stresses of 10 and 20% for the unirradiated blends without the foaming agent, respectively. Further decrease in these mechanical parameters was observed after the foaming process. The bulk density, void fraction, cell size measurements as well as the examination by SEM illustrate clearly the cell growth of the foam structure. The soil burial test and SEM micrographs indicate the growth of microorganisms overall the blend sheets and that the blend was completely damaged after two months of burying. © 2007 Wiley Periodicals, Inc. J Appl Polym Sci 2007     

Recognizing arabic handwritten characters using deep learning and genetic algorithms

Automated techniques for Arabic content recognition are at a beginning period contrasted with their partners for the Latin and Chinese contents recognition. There is a bulk of handwritten Arabic archives available in libraries, data centers, historical centers, and workplaces. Digitization of these documents facilitates (1) to preserve and transfer the country’s history electronically, (2) to save the physical storage space, (3) to proper handling of the documents, and (4) to enhance the retrieval of information through the Internet and other mediums. Arabic handwritten character recognition (AHCR) systems face several challenges including the unlimited variations in human handwriting and the leakage of large and public databases. In the current study, the segmentation and recognition phases are addressed. The text segmentation challenges and a set of solutions for each challenge are presented. The convolutional neural network (CNN), deep learning approach, is used in the recognition phase. The usage of CNN leads to significant improvements across different machine learning classification algorithms. It facilitates the automatic feature extraction of images. 14 different native CNN architectures are proposed after a set of try-and-error trials. They are trained and tested on the HMBD database that contains 54,115 of the handwritten Arabic characters. Experiments are performed on the native CNN architectures and the best-reported testing accuracy is 91.96%. A transfer learning (TF) and genetic algorithm (GA) approach named “HMB-AHCR-DLGA” is suggested to optimize the training parameters and hyperparameters in the recognition phase. The pre-trained CNN models (VGG16, VGG19, and MobileNetV2) are used in the later approach. Five optimization experiments are performed and the best combinations are reported. The highest reported testing accuracy is 92.88%.

     

Impact of graphene oxide nanoparticles and carbon black on the gamma radiation sensitization of acrylonitrile–butadiene rubber seal materials

Seals prepared from acrylonitrile–butadiene rubber (NBR) are primarily used in nuclear services. Nevertheless, at relatively high ionizing radiation doses, NBR seal materials may undergo radiation-induced degradation processes, leading to adverse effects on the sealing ability life. Herein, to strengthen the functional characteristics of NBR seals against radiation, graphene oxide (GO) nanoparticles were prepared and characterized by transmission electron microscopy, X-ray diffraction (XRD), Fourier transform infrared (FTIR), and ultraviolet/visible spectroscopies. Various NBR/GO composites fabricated with different ratios of GO nanoparticles and in the presence or absence of carbon black (CB) were investigated via cross-linking density, scanning electron microscopy, XRD, FTIR, and mechanical and thermal stability analyses. The synergistic effect of the simultaneous presence of GO and CB on the NBR seal sensitization to gamma radiation up to a dose of 1 MGy was studied. The physicomechanical properties were enhanced by adding GO nanosheets up to 3 phr and by incorporating 35 phr of a CB with GO until 5 phr. Further, the application of γ-irradiation resulted in an overall enhancement in the mechanical, physical, and thermal stability of the prepared composites up to 0.5 and 1 MGy with GO nanosheets in the absence or presence of CB particles, respectively. The mechanical measurements indicated significant increments by loading with GO nanosheets in the absence and presence of CB as well as by irradiation. The tensile strength elevated up to about 121%, 336%, and 366% by adding 3 phr GO, 3 GO:35 CB phr, and 5 GO:35 CB phr, respectively.     

On dual-phase-lag thermoelasticity theory with memory-dependent derivative

A new mathematical model of generalized thermoelasticity with memory-dependent derivatives for the dual-phase-lag heat conduction law is constructed. The governing equations of the new model are applied to a half-space subjected to ramp-type heating. Laplace transforms technique is used. The solution is obtained for different types of functions representing the thermal shock and for different values of the parameter of the time fraction derivative of the model. The effects of time-delay and arbitrary kernel function on elastic material are studied and represented graphically. The predictions of the theory are discussed and compared with dynamic classical coupled theory.     

Formal Verification Successes at Motorola

Formal tools are either too labor intensive or are completely impractical for industrial-size problems. This paper describes two formal verification tools used within Motorola, Versys2 and CBV, that challenge this assertion. The two tools are being used in current design verification flows and have shown that it is possible to seamlessly integrate formal tools into existing design flows.