Efficiently solving quantified bit-vector formulas

In recent years, bit-precise reasoning has gained importance in hardware and software verification. Of renewed interest is the use of symbolic reasoning for synthesising loop invariants, ranking functions, or whole program fragments and hardware circuits. Solvers for the quantifier-free fragment of bit-vector logic exist and often rely on SAT solvers for efficiency. However, many techniques require quantifiers in bit-vector formulas to avoid an exponential blow-up during construction. Solvers for quantified formulas usually flatten the input to obtain a quantified Boolean formula, losing much of the word-level information in the formula. We present a new approach based on a set of effective word-level simplifications that are traditionally employed in automated theorem proving, heuristic quantifier instantiation methods used in SMT solvers, and model finding techniques based on skeletons/templates. Experimental results on two different types of benchmarks indicate that our method outperforms the traditional flattening approach by multiple orders of magnitude of runtime.     

Benchmarking of the 3-D CAD-based Discrete Ordinates code “ATTILA” for dose rate calculations against experiments and Monte Carlo calculations

Shutdown dose rate (SDDR) inside and around the diagnostics ports of ITER is performed at PPPL/UCLA using the 3-D, FEM, Discrete Ordinates code, ATTILA, along with its updated FORNAX transmutation/decay gamma library. Other ITER partners assess SDDR using codes based on the Monte Carlo (MC) approach (e.g. MCNP code) for transport calculation and the radioactivity inventory code FISPACT or other equivalent decay data libraries for dose rate assessment. To reveal the range of discrepancies in the results obtained by various analysts, an extensive experimental and calculation benchmarking effort has been undertaken to validate the capability of ATTILA for dose rate assessment. On the experimental validation front, the comparison was performed using the measured data from two SDDR experiments performed at the FNG facility, Italy. Comparison was made to the experimental data and to MC results obtained by other analysts. On the calculation validation front, the ATTILA's predictions were compared to other results at key locations inside a calculation benchmark whose configuration duplicates an upper diagnostics port plug (UPP) in ITER. Both serial and parallel version of ATTILA-7.1.0 are used in the PPPL/UCLA analysis performed with FENDL-2.1/FORNAX databases. In the FNG 1st experimental, it was shown that ATTILA's dose rates are largely over estimated (by ～30–60%) with the ANSI/ANS-6.1.1 flux-to-dose factors whereas the ICRP-74 factors give better agreement (10–20%) with the experimental data and with the MC results at all cooling times. In the 2nd experiment, there is an under estimation in SDDR calculated by both MCNP and ATTILA based on ANSI/ANS-6.1.1 for cooling times up to ～4 days after irradiation. Thereafter, an over estimation is observed (～5–10% with MCNP and ～10–15% with ATTILA). As for the calculation benchmark, the agreement is much better based on ICRP-74 1996 data. The divergence among all dose rate results at ～11 days cooling time is no more than 15% among all participants.     

Determination of the optical properties of a two-layer tissue model by detecting photons migrating at progressively increasing depths

We have investigated a method for solving the inverse problem of determining the optical properties of a two-layer turbid model. The method is based on deducing the optical properties (OPs) of the top layer from the absolute spatially resolved reflectance that results from photon migration within only the top layer by use of a multivariate calibration model. Then the OPs of the bottom layer are deduced from relative frequency-domain (FD) reflectance measurements by use of the two-layer FD diffusion model. The method was validated with Monte Carlo FD reflectance profiles and experimental measurements of two-layer phantoms. The results showed that the method is useful for two-layer models with interface depths of >5 mm; the OPs were estimated, within a relatively short time (<1 min), with a mean error of <10% for the Monte Carlo reflectance profiles and with errors of <25% for the phantom measurements.     

An effective DeepWINet CNN model for off-line text-independent writer identification

Pattern Analysis and Applications - Writer identification based on handwriting recognition is considered one of the most common research areas in pattern recognition and biometrics. It has...     

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%.

     

Design Guidelines for the Noise Optimization of a 0.18 μm CMOS Low-Noise Amplifier

This paper presents the design considerations for the noise optimization of fully integrated tuned low-noise amplifiers (LNA) based on the four noise parameters and two-port noise theory. Specifically, this paper provides the design guidelines for a 0.18 μm CMOS tuned LNA. These guidelines give a useful indication to the design tradeoffs associated with noise figure, power dissipation and gate overdrive voltage for the LNA designed using this technology. As a case study, a 10 GHz LNA has been designed using 0.18 μm CMOS technology for a wireless LAN application. The amplifier has a 2.4 dB noise figure with a −13 dBm third-order input intercept point, while drawing 5 mW from a 1.8 V power supply. The results show that the proposed theoretical contours of constant noise figure which relate the gate overdrive voltage and power dissipation can accurately predict the noise performance of a 0.18 μm CMOS LNA design Ahmed A. Youssef received the B.Sc. (Hon.) and M.Sc. degrees both in electrical engineering from Ain Shams University, Cairo, Egypt, in 1998 and 2002, respectively. Since 2003, he has been with the University of Calgary, AB, Canada, where he is currently working toward the Ph.D. degree in RF integrated circuits and systems. Mr. Youssef has joined the Wireless Research Center at TRLab, Alberta, Canada as a research associate in 2004. His research interests include the analog high speed integrated circuit for the wireless LAN applications. Mr. Youssef is the recipient of the Mobinil Telecommunication Inc. Pre-master Fellowship in 1999. He also received the Young Scientist award at the Maastricht General Assembly of the International Union of Radio Science in 2002 and an Honorable Mention at 2003 in the Symposium of the Microelectronics Research & Development in Canada, Montreal. Mr. Youssef received the Gordon Lewis Hedberg Doctoral Scholarship in 2005.     

A new scalable multicast routing algorithm for interactive real-time applications

Quality of service (QoS) provisioning generally assumes more than one QoS measure that implies that QoS routing can be categorized as an instance of routing subject to multiple constraints: delay jitter, bandwidth, cost, etc. We study the problem of constructing multicast trees to meet the QoS requirements of real-time interactive applications where it is necessary to provide bounded delays and bounded delay variation among the source and all destinations while keeping overall cost of the multicast tree low. The main contribution of our work is a new strategy for constructing multiconstrained multicast trees. We first derive mathematically a new delay-variation estimation scheme and prove its efficiency. Thereafter, we propose a simple and competitive (in terms of running time) heuristic algorithm, for delay and delay variation constrained routing problem based on the proposed delay-variation estimation scheme and using the Extended Prim-Dijkstra tradeoffs’ algorithm. Our contribution also extends previous works in providing some properties and analyses of delay bounded paths satisfying delay variation constraints. Extensive simulation results show that our algorithm outperforms DVDMR in terms of multicast delay variation with the same time complexity as DVDMR.     

Polymeric nanofibers containing solid nanoparticles prepared by electrospinning and their applications

Generally, polymer solution or sol–gel is used to produce electrospun nanofibers via the electrospinning technique. In the utilized sol–gel, the metallic precursor should be soluble in a proper solvent since it has to hydrolyze and polycondensate in the final solution; this strategy straitens the applications of the electrospinning process and limits the category of the electrospinnable materials. In this study, we are discussing electrospinning of a colloidal solution process as an alternative strategy. We have utilized many solid nanopowders and different polymers as well. All the examined colloids have been successfully electrospun. According to the SEM and FE SEM analyses for the obtained nanofiber mats, the polymeric nanofibers could imprison the small nanoparticles; however, the big size ones were observed attaching the nanofiber mats. Successfully, the proposed strategy could be exploited to prepare polymeric nanofibers incorporating metal nanoparticles which might have interesting properties compared with the pristine. For instance, PCL/Ti nanofiber mats exhibited good bioactivity compared with pristine PCL. The proposed strategy can be considered as an innovated methodology to prepare a new class of the electrospun nanofiber mats which cannot be obtained by the conventional electrospinning technique.     

Dynamic tensile strength of polyurea-bonded steel/E-glass composite joints

The dynamic tensile strengths of E-glass composite/polyurea and polyurea/steel interfaces within the E-glass composite/polyurea/AL-6XN stainless steel joint were measured using a laser spallation technique. Values of 370?±?20?MPa were obtained for the polyurea/composite interface while a much higher value of 486?±?20?MPa was obtained for the steel/polyurea interface. Because of the transient nature of the stress pulse, the strain rate changes continuously as the interface stress builds up. A peak strain rate of 5?×?10⁵?s^?1 was estimated. The effect of moisture on the tensile strength of the E-glass/polyurea interface was also examined. The effect was found to be minimal, with the tensile strength stabilizing at 320?±?25?MPa after 30?days of exposure to a 90%RH, 50?°C environment. When comparing the strengths of corresponding interfaces in an epoxy-bonded joint from a previous study, it was concluded that polyurea results in a much stronger and durable joint.