Software Approach Research Articles

Study on ultrasonic-assisted drilling of Ti6Al4V using 3-flute drill in the finite element simulation

Continuous chip is one of the major problems during drilling Ti6A14V, and chip breaking is dependent on many factors such as drilling parameters, tool geometries and type of drill bits used. This paper attempts to analyze the effect of various drilling parameters such as feed rate, spindle speed on performance characteristics such as chip morphology, thrust force, temperature, and tool wear in conventional drilling and ultrasonic-assisted drilling of Ti6A14V using twist drill bit and 3-flute drill bit in order to optimize the chip breakability of Ti6A14V. The twist and 3-flute drill bit are utilized to establish the finite element models to simulate the drilling process with Lagrangian approach in DEFORM-3D software. The results of the simulations not only reveal obvious varying regular pattern of thrust force, temperature, tool wear depth, chip thickness and damage with the increasing of feed rates, spindle speeds, which confirm the capability and advantage of finite element model of the drilling process, but also provide a more profound knowledge about the drilling mechanism including the effect of 3-flute drill bit in ultrasonic-assisted drilling on chip breakability and tool wear.

Numerical simulation of wind flow around building complex with different software approaches

Numerical simulation of wind flow around 4-building complex in hot climate conditions is described in order to assess a wind comfort in the courtyard area. The solution of the air flow around the multiply building complex geometry including the using of various numeral simulation software packages is presented in this article. The article also provides the subsequent comparison of the results obtained from specialized and simplified software packages (ANSYS FLUENT, Autodesk Flow design). Use of the numeral simulation in conditions of technogenic relief and complicated construction is relevant at the present time. This method allows significantly reducing the costs of undoing mistakes and to speed up the problem solving. An explicit demonstration of the results is also a very important criterion for choosing the methods and numeral simulation software. Use of modern methods of CFD-data visualization in real time can greatly simplify the interpretation of simulation results for non-professional users.

Estimated delamination of an elastic-plastic liner in a high-pressure metal composite vessel

To carry out delamination analysis in high-pressure metal composite vessels, a software approach is proposed in which the dynamic calculation algorithm is implemented in software systems. The algorithm is built on three points: the introduction of technological deviations (at the level of permissible defects), the determination of a three-dimensional stress-strain state, and a real-time solution. As a calculation program, LS-DYNA was selected in a dynamic formulation with the structure divided into 3D solid elements and unilateral constraint on the contact surface of the metal (liner) and composite shells was introduced. A geometrically and physically nonlinear problem is solved. The moment of liner delamination from the composite shell is determined by the change in the liner stress-strain state and by the liner deformable surface in time presented visually.

Evolutionary Cost-cognizant Test Case Selection and Prioritization for Object-Oriented Programs

Test case prioritization (TCP) is a software testing technique that finds an ideal ordering of test cases for regression testing, so that testers can obtain the maximum benefit of their test suite, even if the testing process is stop at some arbitrary point. The recent trend of software development uses OO paradigm. This paper proposed a cost-cognizant TCP approach for object-oriented software that uses path-based integration testing. Path-based integration testing will identify the possible execution path and extract these paths from the Java System Dependence Graph (JSDG) model of the source code using forward slicing technique. Afterward evolutionary algorithm (EA) was employed to prioritize test cases based on the severity detection per unit cost for each of the dependent faults. The proposed technique was known as Evolutionary Cost-Cognizant Regression Test Case Prioritization (ECRTP) and being implemented as regression testing approach for experiment.

Head Motion Correction Based on Filtered Backprojection in Helical CT Scanning.

Head motion may unexpectedly occur during a CT scan. It thereby results in motion artifacts in a reconstructed image and may lead to a false diagnosis or a failure of diagnosis. To alleviate this motion problem, as a hardware approach, increasing the gantry rotation speed or using an immobilization device is usually considered. These approaches, however, cannot completely resolve the motion problem. Hence, motion estimation (ME) and compensation for it have been explored as a software approach instead. In this paper, adopting the latter approach, we propose a head motion correction algorithm in helical CT scanning, based on filtered backprojection (FBP). For the motion correction, we first introduce a new motion-compensated (MC) reconstruction scheme based on FBP, which is applicable to helical scanning. We then estimate the head motion parameters by using an iterative nonlinear optimization algorithm, or the L-BFGS. Note here that an objective function for the optimization is defined on reconstructed images in each iteration, which are obtained by using the proposed MC reconstruction scheme. Using the estimated motion parameters, we then obtain the final MC reconstructed image. Using numerical and physical phantom datasets along with simulated head motions, we demonstrate that the proposed algorithm can provide significantly improved quality to MC reconstructed images by alleviating motion artifacts.

Identification of Circular RNAs Regulating Islet β-Cell Autophagy in Type 2 Diabetes Mellitus.

This study is to identify the circular RNA (circRNA) expression profile that is functionally related to pancreatic islet β-cell autophagy and their potential regulation mechanisms in type 2 diabetes mellitus (T2DM). T2DM rat model was constructed by administration of high-fat and high-sugar diet. β-cells were isolated from islets by flow cytometry. CircRNA expression profile in β-cells was detected by circRNA microarrays, and the differentially expressed circRNAs were identified and validated by qRT-PCR. MicroRNA (miRNA) target prediction software and multiple bioinformatic approaches were used to construct a map of circRNA-miRNA interactions for the differentially expressed circRNAs. A total of 825 differentially expressed circular transcripts were identified in T2DM rats compared with control rats, among which 388 were upregulated and 437 were downregulated. Ten circRNAs were identified to have significant differences by qRT-PCR. GO analysis enriched terms such as organelle membrane and protein binding and the top enriched pathways for the circRNAs included MAPK signaling pathway. The differentially expressed circRNAs might involve in MAPK signaling pathway, apoptosis, and Ras signaling pathway. We speculate that these circRNAs, especially rno_circRNA_008565, can regulate the autophagy of islet β-cells via interactions with miRNA. Dysregulation of several circRNAs may play a role in T2DM development, and rno_circRNA_008565 may be a potential regulator of β-cell autophagy.

Generalized Architecture for Reconfigurable Hardware Security Incorporating PKI

Software-based techniques represent the dominant paradigm for the implementation of network security. Due to the rapidly growing and dynamic nature of contemporary networks security threats are becoming challenging and the overheads of a purely software approach are mounting to enormous proportions. Higher scales of hardware integration have also produced novel types of hardware threats and attacks which cannot be addressed by a purely software approach. Hence the interest in hardware security approaches is increasing. In this paper, we propose a general architecture for hardware-based security that can address the custom security needs of the users. The architecture incorporates the security model adopted in the Public Key Infrastructure (PKI) model for security.

An Increase in Kinematic Freedom in the Lokomat Is Related to the Ability to Elicit a Physiological Muscle Activity Pattern: A Secondary Data Analysis Investigating Differences Between Guidance Force, Path Control, and FreeD.

Background: Robot-assisted gait therapy is a fast-growing field in pediatric neuro-rehabilitation. Understanding how these constantly developing technologies work is a prerequisite for shaping clinical application. For the Lokomat, two new features are supposed to increase patients' movement variability and should enable a more physiological gait pattern: Path Control and FreeD. This work provides a secondary data analysis of a previously published study, and looks at surface electromyography (sEMG) during Guidance Force walking and six sub-conditions of Path Control and FreeD.Objective: The aim was to evaluate different levels of kinematic freedom on the gait pattern of pediatric patients by modulating settings of Path Control and FreeD.Methods: Fifteen patients (mean age 16 ± 2 years) with neurological gait disorders completed the measurements. We analyzed sEMG amplitudes and the correlation of sEMG patterns with normative data of five leg muscles during walking conditions with increasing kinematic freedom in the Lokomat. The new outcome measure of inter-step similarity is introduced as a proxy for walking task complexity.Results: Within Path Control, sub-conditions showed significantly higher sEMG amplitudes in a majority of muscles with increasing kinematic freedom, and correlations with the norm pattern increased with increasing kinematic freedom. FreeD sub-conditions generally showed low or even negative correlations with the norm pattern and a lower inter-step similarity compared to Guidance Force.Conclusions: In general, this work highlights that the new hard- and software approaches of the Lokomat influence muscle activity differently. An increase of kinematic freedom of the walking condition led to an increase in muscular effort (Path Control) or to a higher step variability (FreeD) which can be interpreted as an increased task complexity of this condition. The inter-step similarity could be a helpful tool for the therapist to estimate the patient's state of strain.

Hardware Acceleration of SVM classifier using Zynq SoC FPGA

Support Vector Machines (SVM) is one of the most commonly used the state-of-the-art supervised machine learning algorithm for various classification problems. It provides high accuracy rate compared to other classification algorithms. However, When SVM is modelled only using Software, it is a time consuming algorithm due to its high computational complexity. This makes the algorithm to be not suitable for embedded real time applications. We propose a new hardware software co-design approach to achieve the real time performance by accelerating the computationally intensive classifier part of the algorithm as a custom hardware Intellectual Property (IP) core. In this paper, a novel Support Vector Machine (SVM) linear classifier is modelled as a custom hardware Intellectual Property (IP) core using High Level Synthesis (HLS). The developed IP core is optimized for latency and hardware resource utilization by applying various directives of HLS tool. The synthesis results of the IP core for Skin segmentation dataset is reported. The proposed hardware software co-design approach is implemented in real time on Zynq-7000 XC7Z020 System on Chip (SoC) field programmable gate arrays (FPGA). A detailed comparative results of proposed hardware software co-design approach and the complete software approach is reported in this work for Iris and Breast cancer dataset. A promising result of 18x speedup is achieved using SVM classifier hardware IP compared to is software counterpart.

A fully pipelined FPGA accelerator for scale invariant feature transform keypoint descriptor matching

The scale invariant feature transform (SIFT) algorithm is considered a classical feature extraction algorithm within the field of computer vision. SIFT keypoint descriptor matching is a computationally intensive process due to the amount of data consumed. In this work, we designed a novel fully pipelined hardware accelerator architecture for SIFT keypoint descriptor matching. The accelerator core was implemented and tested on a field programmable gate array (FPGA). The proposed hardware architecture is able to properly handle the memory bandwidth necessary for a fully-pipelined implementation and hits the roofline performance model, achieving the potential maximum throughput. The fully pipelined matching architecture was designed based on the consine angle distance method. Our architecture was optimized for 16-bit fixed-point operations and implemented on hardware using a Xilinx Zynq-based FPGA development board. Our proposed architecture shows a noticeable reduction of area resources compared with its counterparts in literature, while maintaining high throughput by alleviating memory bandwidth restrictions. The results show a reduction in consumed device resources of up to 91% in LUTs and 79% of BRAMs. Our hardware implementation is 15.7 × faster than the comparable software approach.

A psychoacoustic application for the adjustment of electrical hearing thresholds in cochlear implant patients.

ObjectiveFitting cochlear implants, especially the precise determination of electrical hearing thresholds, is a time-consuming and complex task for patients as well as audiologists. Aim of the study was to develop a method that enables cochlear implant (CI) patients to determine their electrical hearing thresholds precisely and independently. Applicability and impact of this method on speech perception in noise at soft speech levels were evaluated.MethodAn adaptive psychoacoustic procedure for precise hearing threshold determination (precT) was implemented using MatLab (MathWorks) and a graphical user interface was created. Sound signals were calibrated with a CIC4-Implant-Decoder. Study design: A prospective study including 15 experienced adult cochlear implant users was conducted. Electrical hearing thresholds were determined using the automated precT procedure (auto-precT). Speech perception in noise at 50 dB SPL presentation levels was measured for three conditions: (P1) T-levels kept at the previously established T-levels; (P2) T-levels set to the hearing thresholds determined using auto-precT application; (P3) T-levels set 10 cu below the values determined with auto-precT.ResultsAll subjects were able to perform the auto-precT application independently. T-levels were altered on average by an absolute value of 10.5 cu using auto-precT. Median speech reception thresholds were significantly improved from 2.5 dB SNR (P1) to 1.6 dB SNR (P2, p = 0.02). Speech perception was lowest using the globally lowered T-levels, median 2.9 dB SNR (P3, not significant compared to P1 and P2).ConclusionThe applicability of the developed auto-precT application was confirmed in the present clinical study. Patients benefited from adjusting previously established T-levels to the threshold levels determined by the auto-precT application. The integration of the application in the clinical fitting routine as well as a remote fitting software approach is recommended. Furthermore, future possibilities of auto-precT include the implementation of the application on tablets or smart phones.

Trace aware random testing for distributed systems

Distributed and concurrent applications often have subtle bugs that only get exposed under specific schedules. While these schedules may be found by systematic model checking techniques, in practice, model checkers do not scale to large systems. On the other hand, naive random exploration techniques often require a very large number of runs to find the specific interactions needed to expose a bug. In recent years, several random testing algorithms have been proposed that, on the one hand, exploit state-space reduction strategies from model checking and, on the other, provide guarantees on the probability of hitting bugs of certain kinds. These existing techniques exploit two orthogonal strategies to reduce the state space: partial-order reduction and bug depth. Testing algorithms based on partial order techniques, such as RAPOS or POS, ensure non-redundant exploration of independent interleavings among system events by imposing an equivalence relation on schedules and ideally exploring only one schedule from each equivalence class. Techniques based on bug depth, such as PCT, exploit the empirical observation that many bugs are exposed by the clever scheduling of a small number of key events. They bias the sample space of schedules to only cover all executions of small depth, rather than the much larger space of all schedules. At this point, there is no random testing algorithm that combines the power of both approaches. In this paper, we provide such an algorithm. Our algorithm, trace-aware PCT (taPCTCP), extends and unifies several different algorithms in the random testing literature. It samples the space of low-depth executions by constructing a schedule online, while taking dependencies among events into account. Moreover, the algorithm comes with a theoretical guarantee on the probability of sampling a trace of low depth---the probability grows exponentially with the depth but only polynomially with the number of racy events explored. We further show that the guarantee is optimal among a large class of techniques. We empirically compare our algorithm with several state-of-the-art random testing approaches for concurrent software on two large-scale distributed systems, Zookeeper and Cassandra, and show that our approach is effective in uncovering subtle bugs and usually outperforms related random testing algorithms.

Challenging the multimodal ultrasound with GPU-based research ultrasound platforms

Old-school ultrasound was all about discrete modalities (B-mode, Doppler). Today, we prefer to combine multiple methods and advanced processing algorithms to extract more diagnostic data out of the raw RF ultrasound echoes. Thus, high-speed acquisition and processing of channel data have become a standard feature of contemporary research systems. I will present our developments in the programmable and scalable ultrasound systems featuring GPU-based processing. The implementation of multimodal methods is enabled through a high-performance data streaming and software processing approach. We will look into GPU processing and how challenging real-time realization of the Software Defined Ultrasound paradigm can be. We will also discuss the many new opportunities and open-source tools the software approach nevertheless opens up, which can be used for both standard ultrasound processing and advanced post-processing (e.g., machine learning).The case-studies presented show how the platforms can be used as versatile research tools, as well as demonstrators for commercial introduction of the technology. A sneak preview of a 3-D-ready research system will conclude my talk.

Development of Custom Interface Driver between USRP and SoC devices for SDR Applications

Global Navigation Satellite System (GNSS) is used to provide position based on satellite constellation. The GNSS Software defined radio (SDR) is a software approach which is more flexible than Hardware receivers. In GNSS SDR, most of the RF frontends are interfaced with a host-Personal Computer (PC). The host-PC based GNSS-SDR systems suffer from computational complexity and latency between IQ samples of GNSS signals. There are several RF front ends available to capture the real time signals. Among all these, USRP RF frontends are flexible for the prototype development. Earlier the host-PC was interfaced with the USRP device for the SDR implementation. In this work the, USRP N210 is interfaced with Zynq SoC device for GNSS SDR applications. This approach will introduce parallelism, which in turn leads to reduction in latency between IQ samples. A custom Ethernet driver to interface the USRP N210 with Zynq SoC device is proposed. The experimental results showcase that the proposed SoC based approach is suitable for GNSS-SDR prototype.

Process Scheduling in Heterogeneous Multicore System using Agent based Graph Coloring Algorithm

In any heterogeneous multicore system, there are numerous amount of processors with different platform and all the processing units are fabricated on a common single unit preferably on a System on Chip. As there is a tremendous amount of parallelism encompassed in a multicore system, proper utilization of the cores is a big challenge in the current era. Hence a more automated software approach is required like an agent based graph coloring algorithm to find the free processor and schedule the tasks on the respective cores. Predominantly the entire process of scheduling the tasks on multicore system is based on arrival time of process. This paper incorporates the scheduling on the linux 2.6.11 kernel and GEMS simulator for multicore implementation. The core utilization in this type of agent scheduling is 50% more than the existing scheduling mechanism.

Fingerprint Detection Technique using SURF, PHOG & PCA Feature Extraction Process

The biometric way of identifying a person are wildly spared around many industries and organizations. The identification techniques followed for the biometric are mostly common in using fingerprint detection for individual identification. Basically password based security systems are cracked through many techniques, which makes many problem to the organization using password for security purpose. The spoof fingerprint way of identifying a person is becoming very famous in providing security to the users. The research work focuses on proposing a novel approach in merging fingerprint features all together in one static software approach. The features identified from the fingerprints are extracted using histogram equations in initial step of fingerprint security system. The Gabor wavelet transformation techniques is one of the images processing technique used for identifying features. The features are maintained carefully with applying dynamic score level integration. The efficiency of proposed work is checked with LivDet 2011 dataset. The rate of classification shows 9.625% and error rate is 2.27%.

Practical considerations for kriging groundwater surfaces

AbstractKriging is an accepted method of characterizing the groundwater elevation surface at sites where the water level data are available but where there may be insufficient additional data necessary for groundwater flow modeling. Groundwater surface interpolation via kriging is readily performed using commercial, state of the practice software, but some practitioners may not be able to justify such efforts because the process is not validated within studies documented in the peer‐reviewed literature. This paper describes the available kriging software and literature studies and then uses a case study to compare practical groundwater surface modeling to the studies available in the scientific literature. The literature review shows that the state of the practice as represented by the commercial software approach is consistent with the literature. Specifically, cokriging with groundwater elevation as the primary variable with trend removal and ground‐surface elevation as the secondary variable is an appropriate point of departure in practice. The literature review‐based summary of variogram model parameters (model type, nugget, sill, range, and trend model) was not useful as a quality‐control step to assess the reasonableness of variogram parameters identified by the standard practice of software‐assisted iteration when applied to the case‐study data set. The literature review indicated that groundwater elevation kriging has been performed using as few as 10 data points but a comparison of the case‐study simulated groundwater elevations and groundwater gradient magnitudes and directions indicated that the 30‐well threshold more commonly found in the literature was an appropriate minimum at the study site.

Ultrasonic image processing based on fusion super-resolution reconstruction of familiar models

Ultrasound image technology is to measure the energy and time of arrival of the reflected echo after the pulse acoustic signal is sent out by the ultrasonic wave. Usually, the distance between the ultrasonic source and the reflector is measured. Super-resolution image reconstruction aims to recover high-resolution images from one or more low-resolution images. Super-resolution image reconstruction is a software approach to solve the problem of low-resolution images by overcoming the limitations of hardware. In this paper, an image super-resolution reconstruction method based on sparse representation model and multi-feature fusion is proposed. Sparse dictionary is used to learn and reconstruct the luminance details of ultrasonic images, and edge interpolation is used to improve the edge clarity. Experimental results show that the proposed method is superior to Bicubic interpolation, SCSR and JOR in PSNR, and is 0.35 dB higher than RAISR. On the FSIM index, this method is also slightly better than other comparison methods, which can get better reconstruction results. Visual effects and numerical evaluation results of reconstructed images are better than several comparison methods.

Revelation of genomic breed composition in a crossbred cattle of India with the help of Bovine50K BeadChip

The aim of the present study was to assess the population structure and admixture levels in the Vrindavani composite population in India by using Bovine50KSNP BeadChip data. Genotypic data were generated for randomly selected animals (n = 72) of Vrindavani population and the data for parental breeds i.e., Hariana (n = 10), Holstein-Friesian (n = 63), Jersey (n = 28) and Brown Swiss (n = 22) were retrieved from a public repository. The indices of population structure were calculated using PLINK software and R-program. The merged dataset was analysed for assessing admixture levels and population stratification using three different approaches i.e., principal component analysis (PCA), multi-dimensional scaling (MDS) approach and the model-based approach in STRUCTURE software. The average minor allele frequency (MAF) value for Vrindavani population was estimated to be 0.235. Vrindavani population was found to possess an average ancestry of 39.5, 22.9, 26.9, and 10.7% inheritance levels from Holstein Friesian, Jersey, Hariana and Brown Swiss cattle breeds, respectively.

Model-Driven Architecture Based Software Development for Epidemiological Surveillance Systems.

Epidemiological surveillance systems enable collection, analysis and dissemination of information on the monitored disease to different stakeholders. It may be done manually or using a software. Given the poor performances of manual systems, the software approach is generally adopted. Epidemiological surveillance systems are based on existing softwares, softwares developed from scratch given the specifications or softwares provided by a vendor. These solutions are not always suitable because epidemiological surveillance systems evolve quickly (new drugs, new treatment protocols, etc.), leading to software updates, which can take time (while waiting for a new version) and be expensive. In this article, we present the use of the Model-Driven Architecture (MDA) approach to model and generate epidemiological surveillance systems. The result is a complete MDA based methodology and tool to develop epidemiological surveillance systems. The tool was used to model and generate softwares that are now used for epidemiological surveillance of tuberculosis in Cameroon.