PMSS: A programmable memory system and scheduler for complex memory patterns

HPC industry demands more computing units on FPGAs, to enhance the performance by using task/data parallelism. FPGAs can provide its ultimate performance on certain kernels by customizing the hardware for the applications. However, applications are getting more complex, with multiple kernels and complex data arrangements, generating overhead while scheduling/managing system resources. Due to this reason all classes of multi threaded machines–minicomputer to supercomputer–require to have efficient hardware scheduler and memory manager that improves the effective bandwidth and latency of the DRAM main memory. This architecture could be a very competitive choice for supercomputing systems that meets the demand of parallelism for HPC benchmarks. In this article, we proposed a Programmable Memory System and Scheduler (PMSS), which provides high speed complex data access pattern to the multi threaded architecture. This proposed PMSS system is implemented and tested on a Xilinx ML505 evaluation FPGA board. The performance of the system is compared with a microprocessor based system that has been integrated with the Xilkernel operating system. Results show that the modified PMSS based multi-accelerator system consumes 50% less hardware resources, 32% less on-chip power and achieves approximately a 19x speedup compared to the MicroBlaze based system.     

Self and static interference mitigation scheme for coexisting wireless networks

High density of coexisting networks in the Industrial, Scientific and Medical (ISM) band leads to static and self interferences among different communication entities. The inevitability of these interferences demands for interference avoidance schemes to ensure reliability of network operations. This paper proposes a novel Diversified Adaptive Frequency Rolling (DAFR) technique for frequency hopping in Bluetooth piconets. DAFR employs intelligent hopping procedures in order to mitigate self interferences, weeds out the static interferer efficiently and ensures sufficient frequency diversity. We compare the performance of our proposed technique with the widely used existing frequency hopping techniques, namely, Adaptive Frequency Hopping (AFH) and Adaptive Frequency Rolling (AFR). Simulation studies validate the significant improvement in goodput and hopping diversity of our scheme compared to other schemes and demonstrate its potential benefit in real world deployment.     

In-focus electron microscopy of frozen-hydrated biological samples with a Boersch phase plate

We report the implementation of an electrostatic Einzel lens (Boersch) phase plate in a prototype transmission electron microscope dedicated to aberration-corrected cryo-EM. The combination of phase plate, C_s corrector and Diffraction Magnification Unit (DMU) as a new electron-optical element ensures minimal information loss due to obstruction by the phase plate and enables in-focus phase contrast imaging of large macromolecular assemblies. As no defocussing is necessary and the spherical aberration is corrected, maximal, non-oscillating phase contrast transfer can be achieved up to the information limit of the instrument. A microchip produced by a scalable micro-fabrication process has 10 phase plates, which are positioned in a conjugate, magnified diffraction plane generated by the DMU. Phase plates remained fully functional for weeks or months. The large distance between phase plate and the cryo sample permits the use of an effective anti-contaminator, resulting in ice contamination rates of <0.6 nm/h at the specimen. Maximal in-focus phase contrast was obtained by applying voltages between 80 and 700 mV to the phase plate electrode. The phase plate allows for in-focus imaging of biological objects with a signal-to-noise of 5-10 at a resolution of 2-3 nm, as demonstrated for frozen-hydrated virus particles and purple membrane at liquid-nitrogen temperature.     

Estimation of Defects Based on Defect Decay Model: ED^{3}M

An accurate prediction of the number of defects in a software product during system testing contributes not only to the management of the system testing process but also to the estimation of the product's required maintenance. Here, a new approach called ED³M is presented that computes an estimate of the total number of defects in an ongoing testing process. ED³M is based on estimation theory. Unlike many existing approaches the technique presented here does not depend on historical data from previous projects or any assumptions about the requirements and/or testers' productivity. It is a completely automated approach that relies only on the data collected during an ongoing testing process. This is a key advantage of the ED³M approach, as it makes it widely applicable in different testing environments. Here, the ED³M approach has been evaluated using five data sets from large industrial projects and two data sets from the literature. In addition, a performance analysis has been conducted using simulated data sets to explore its behavior using different models for the input data. The results are very promising; they indicate the ED³M approach provides accurate estimates with as fast or better convergence time in comparison to well-known alternative techniques, while only using defect data as the input.     

Structural study of titanium oxide films synthesized by ion beam-assisted deposition

The application of titanium dioxide (TiO(2)) films as surgical implant coatings for antibiotic attachment depends crucially on their available surface area and thus their surface morphology and crystallinity. Here, we report our fabrication of high Wenzel ratio TiO(2) films targeted to increase the film surface area using the ion beam-assisted deposition (IBAD) technique at high-deposition temperatures (approximately 610 degrees C). The modulation of the films' surface morphology was accomplished by varying the chemical identity of the concurrent ion beams bombarded on the films during the e-beam evaporation process. Atomic force microscopy (AFM) and scanning electron microscopy (SEM) were employed to investigate the surface morphology of the as-deposited films. X-ray diffractometry (XRD) revealed that these nanocrystalline films primarily consist of anatase phase TiO(2). Wenzel ratio, the ratio of the actual surface area to the projected area, of IBAD films prepared with argon, oxygen, and nitrogen ion beams was measured to be 1.52, 1.31 and 1.49, respectively. The effect of the differences in chemical reactivity and ion size of these three type ion beams are discussed to explain the present results.     

Towards a standard in assessment of bone cutting for total knee replacement

Arthroplasty outcome is influenced by the 'quality' of bone preparation for implant insertion. Surgeons face increasing choices of technique and instrumentation, yet clinical scoring methods assess the overall outcome and patient satisfaction but not the bone cuts directly. 'Quality management' of bone reshaping is needed to evaluate different bone cutting methods and computer assisted orthopaedic surgery (CAOS) systems. Analyses and experiments in this study were formulated for measurement and computation of four quantitative characteristics of bone preparation 'quality' and produced a highly condensed index for each. These represented (a) surface finish of cuts, (b) implant fit/looseness possible with the cut shape, (c) implant location/misalignment, and (d) accuracy of individual planar cuts. Assessment of synthetic bone cuts verified the robustness of the method for wide application in arthroplasty intraoperatively, in vitro and for comparing navigation systems.     

Alpha Fusion Adversarial Attack Analysis Using Deep Learning

The deep learning model encompasses a powerful learning ability that integrates the feature extraction, and classification method to improve accuracy. Convolutional Neural Networks (CNN) perform well in machine learning and image processing tasks like segmentation, classification, detection, identification, etc. The CNN models are still sensitive to noise and attack. The smallest change in training images as in an adversarial attack can greatly decrease the accuracy of the CNN model. This paper presents an alpha fusion attack analysis and generates defense against adversarial attacks. The proposed work is divided into three phases: firstly, an MLSTM-based CNN classification model is developed for classifying COVID-CT images. Secondly, an alpha fusion attack is generated to fool the classification model. The alpha fusion attack is tested in the last phase on a modified LSTM-based CNN (CNN-MLSTM) model and other pre-trained models. The results of CNN models show that the accuracy of these models dropped greatly after the alpha-fusion attack. The highest F1 score before the attack was achieved is 97.45 And after the attack lowest F1 score recorded is 22%. Results elucidate the performance in terms of accuracy, precision, F1 score and Recall.     

Robust Multimodal Biometric System Based on Feature Level Fusion of Optimiseddeepnet Features

Wireless Personal Communications - Multimodal biometric systems combine feature knowledge from multiple traits to overcome shortcomings of unimodal systems. However, most of the traditional...