Software-based Approaches Research Articles

Landscape Analysis for the Specimen Data Refinery

This report reviews the current state-of-the-art applied approaches on automated tools, services and workflows for extracting information from images of natural history specimens and their labels. We consider the potential for repurposing existing tools, including workflow management systems; and areas where more development is required. This paper was written as part of the SYNTHESYS+ project for software development teams and informatics teams working on new software-based approaches to improve mass digitisation of natural history specimens.

Combined software and hardware fault injection vulnerability detection

Fault injection is a well-known method to test the robustness and security vulnerabilities of software. Software-based and hardware-based approaches have been used to detect fault injection vulnerabilities. Software-based approaches typically rely upon simulations that can provide broad and rapid coverage, but may not correlate with genuine hardware vulnerabilities. Hardware-based experiments are indisputable in their results, but rely upon expensive expert knowledge and manual testing yielding ad hoc and extremely limited results. Further, there is very limited connection between software-based simulation results and hardware-based experiments. This work bridges software-based and hardware-based fault injection vulnerability detection by contrasting results of both approaches. This demonstrates that: not all software-based vulnerabilities can be reproduced in hardware; prior conjectures on the fault model for electromagnetic pulse attacks may not be accurate; and that there is a co-relation between software-based and hardware-based approaches. Further, combining both approaches can yield a vastly more accurate and efficient approach to detecting genuine fault injection vulnerabilities.

A review on presentation attack detection system for fake fingerprint

Fingerprint recognition systems are susceptible to artificial spoof fingerprint attacks, like molds manufactured from polymer, gelatin or Play-Doh. Presentation attack is an open issue for fingerprint recognition systems. In a presentation attack, synthetic fingerprint which is reproduced from a real user is submitted for authentication. Different sensors are used to capture the live and fake fingerprint images. A liveness detection system has been designed to defeat different classes of spoof attacks by differentiating the features of live and fake fingerprint images. In the past few years, many hardware- and software-based approaches are suggested by researchers. However, the issues still remain challenging in terms of robustness, effectiveness and efficiency. In this paper, we explore all kinds of software-based solution to differentiate between real and fake fingerprints and present a comprehensive survey of efforts in the past to address this problem.

Compensating for Fingertip Size to Render Tactile Cues More Accurately.

Fingertip haptic feedback offers advantages in many applications, including robotic teleoperation, gaming, and training. However, fingertip size and shape vary significantly across humans, making it difficult to design fingertip interfaces and rendering techniques suitable for everyone. This article starts with an existing data-driven haptic rendering algorithm that ignores fingertip size, and it then develops two software-based approaches to personalize this algorithm for fingertips of different sizes using either additional data or geometry. We evaluate our algorithms in the rendering of pre-recorded tactile sensations onto rubber casts of six different fingertips as well as onto the real fingertips of 13 human participants. Results on the casts show that both approaches significantly improve performance, reducing force error magnitudes by an average of 78% with respect to the standard non-personalized rendering technique. Congruent results were obtained for real fingertips, with subjects rating each of the two personalized rendering techniques significantly better than the standard non-personalized method.

Improving Neutron-Gamma Discrimination with Stilbene Organic Scintillation Detector Using Blind Nonnegative Matrix and Tensor Factorization Methods

In order to perform highly qualified neutron-gamma discrimination in mixed radiation field, we investigate the application of blind source separation methods based on nonnegative matrix and tensor factorization algorithms as new and robust neutron-gamma discrimination software-based approaches. These signal processing tools have allowed to recover original source components from real-world mixture signals which have been recorded at the output of the stilbene scintillation detector. The computation of the performance index of separability of each tested nonnegative algorithm has allowed to select Second-Order NMF algorithm and NTF-2 model as the most efficient techniques for discriminating neutrons and gammas. Furthermore, the neutron-gamma discrimination is highlighted through the computation of the cross-correlation function. The performance of the blind source separation methods has been quantified through the obtained results that prove a good neutron-gamma separation.

FPGA-Assisted DPI Systems: 100 Gbit/s and Beyond

Carrying out deep packet inspection (DPI) in aggregated network connections remains a continuous requirement even though the line rate reaches and exceeds 100 Gb/s. The increasing packet-arrival rate necessitates efficient solutions for on-the-fly packet parsing, packet classification, and distribution for parallelized, software-based payload inspection. Inspection complexity and real-time processing are competing requirements. The deep analysis capabilities of software-based approaches can be enhanced by hardware-based support on time-critical packet parsing and classification. Moreover, some payload inspection tasks can be carried out in hardware as well, further reducing the resources spent on software-based solutions. This paper aims at presenting the state-of-the-art and describing a set of best practices in field programmable gate arrays (FPGA)-based packet processing, which can be applied for DPI-related tasks at 100 Gb/s and beyond. Accordingly, we provide an architectural view of the DPI systems throughout the paper. Besides summarizing the limitations of hardware- and software-based solutions for the three processing phases within a DPI system (packet parsing, packet classification, and payload inspection), this paper reveals the possible trade-offs for choosing the different technical approaches. These limitations include operating frequency, bus size, available memory, on-chip physical resources for hardware-based implementations, and CPU time for software-based solutions.

A taxonomy of software-based and hardware-based approaches for energy efficiency management in the Hadoop

Apache Hadoop framework supports the storing and processing of big data datasets using simple programming models. Energy management has been recognized as one of the major issues in Hadoop, and many types of research have been conducted in this scope. However, despite the importance of this issue, there is no inclusive study about energy efficiency in Hadoop. In this paper, the techniques of energy efficiency in Hadoop are classified into two main categories. Moreover, the benefits and drawbacks of these methods and a systematic study of the conducted research are provided and examined in this paper. Another aim is to provide the visions for the descriptions of open issues and recommendations for future research.

Digital pathology imaging and computer-aided diagnostics as a novel tool for standardization of evaluation of aganglionic megacolon (Hirschsprung disease) histopathology.

Based on a recently introduced immunohistochemical panel (Bachmann et al. 2015) for aganglionic megacolon (AM), also known as Hirschsprung disease, histopathological diagnosis, we evaluated whether the use of digital pathology and 'machine learning' could help to obtain a reliable diagnosis. Slides were obtained from 31 specimens of 27 patients immunohistochemically stained for MAP2, calretinin, S100β and GLUT1. Slides were digitized by whole slide scanning. We used a Definiens Developer Tissue Studios as software for analysis. We configured necessary parameters in combination with 'machine learning' to identify pathological aberrations. A significant difference between AM- and non-AM-affected tissues was found for calretinin (AM 0.55% vs. non-AM 1.44%) and MAP2 (AM 0.004% vs. non-AM 0.07%) staining measurements and software-based evaluations. In contrast, S100β and GLUT1 staining measurements and software-based evaluations showed no significant differences between AM- and non-AM-affected tissues. However, no difference was found in comparison of suction biopsies with resections. Applying machine learning via an ensemble voting classifier, we achieved an accuracy of 87.5% on the test set. Automated diagnosis of AM by applying digital pathology on immunohistochemical panels was successful for calretinin and MAP2, whereas S100β and GLUT1 were not effective in diagnosis. Our method suggests that software-based approaches are capable of diagnosing AM. Our future challenge will be the improvement of efficiency by reduction of the time-consuming need for large pre-labelled training data. With increasing technical improvement, especially in unsupervised training procedures, this method could be helpful in the future.

Selection and evaluation of potential reference genes for gene expression analysis in greenbug (Schizaphis graminum Rondani)

In order to precisely assess gene expression level, a suitable internal reference gene must be chosen to quantify real-time reverse transcription polymerase chain reaction (RT-qPCR) data. For greenbug, Schizaphis graminum, a suitable reference gene for assessing the level of transcriptional expression of target genes has yet to be explored. In our study, eight reference genes, elongation fator 1 beta (Ef1β), TATA box binding protein (TBP), alpha-tubulin (α-TUB), 18S ribosomal (18S), 28S ribosomal (28S), glyceraldehyde-3-phosphate (GAPDH), actin (ACT), and ribosomal protein L18 (RPL18) were evaluated in S. graminum at different developmental stages, tissues, and insecticide treatments. To further explore whether these genes are suitable to serve as internal control, three software-based approaches (geNorm, BestKeeper, and NormFinder), ΔCt method, and one web-based comprehensive tool (RefFinder) were employed to analyze and rank the tested genes. The optimal number of reference genes was determined using the geNorm program, and the suitability of particular reference genes was empirically validated according to normalized gene expression data of three target genes, heat shock protein gene (HSP70), cytocrome P450 gene (SgraCYP18A1), and glutathione S-transferase (GST). We found that the most suitable reference genes varied considerably under different experimental conditions. For developmental stages, α-TUB and 28S were the optimal reference genes; for different tissues, 18S and ACT were suitable reference genes; for insecticide treatments, 28S and α-TUB were suitable for normalizations of expression data. In addition, 28S and α-TUB were the suitable reference gene as they had the most stable expression among different developmental stages, tissues and insecticide treatments. This should be useful for the selection of the suitable reference genes to obtain reliable RT-qPCR data in the gene expression of S. graminum.

Software for Small-scale Robotics: A Review

In recent years, a large number of relatively advanced and often ready-to-use robotic hardware components and systems have been developed for small-scale use. As these tools are mature, there is now a shift towards advanced applications. These often require automation and demand reliability, efficiency and decisional autonomy. New software tools and algorithms for artificial intelligence (AI) and machine learning (ML) can help here. However, since there are many software-based control approaches for small-scale robotics, it is rather unclear how these can be integrated and which approach may be used as a starting point. Therefore, this paper attempts to shed light on existing approaches with their advantages and disadvantages compared to established requirements. For this purpose, a survey was conducted in the target group. The software categories presented include vendor-provided software, robotic software frameworks (RSF), scientific software and in-house developed software (IHDS). Typical representatives for each category are described in detail, including SmarAct precision tool commander, MathWorks Matlab and national instruments LabVIEW, as well as the robot operating system (ROS). The identified software categories and their representatives are rated for end user satisfaction based on functional and non-functional requirements, recommendations and learning curves. The paper concludes with a recommendation of ROS as a basis for future work.

Intel MPX Explained

Memory-safety violations are the primary cause of security and reliability issues in software systems written in unsafe languages. Given the limited adoption of decades-long research in software-based memory safety approaches, as an alternative, Intel released Memory Protection Extensions (MPX)---a hardware-assisted technique to achieve memory safety. In this work, we perform an exhaustive study of Intel MPX architecture along three dimensions: (a) performance overheads, (b) security guarantees, and (c) usability issues. We present the first detailed root cause analysis of problems in the Intel MPX architecture through a cross-layer dissection of the entire system stack, involving the hardware, operating system, compilers, and applications. To put our findings into perspective, we also present an in-depth comparison of Intel MPX with three prominent types of software-based memory safety approaches. Lastly, based on our investigation, we propose directions for potential changes to the Intel MPX architecture to aid the design space exploration of future hardware extensions for memory safety.

Intel MPX Explained

Memory-safety violations are the primary cause of security and reliability issues in software systems written in unsafe languages. Given the limited adoption of decades-long research in software-based memory safety approaches, as an alternative, Intel released Memory Protection Extensions (MPX)---a hardware-assisted technique to achieve memory safety. In this work, we perform an exhaustive study of Intel MPX architecture along three dimensions: (a) performance overheads, (b) security guarantees, and (c) usability issues. We present the first detailed root cause analysis of problems in the Intel MPX architecture through a cross-layer dissection of the entire system stack, involving the hardware, operating system, compilers, and applications. To put our findings into perspective, we also present an in-depth comparison of Intel MPX with three prominent types of software-based memory safety approaches. Lastly, based on our investigation, we propose directions for potential changes to the Intel MPX architecture to aid the design space exploration of future hardware extensions for memory safety. A complete version of this work appears in the 2018 proceedings of the ACM on Measurement and Analysis of Computing Systems.

Purpose, processes, partnerships, and products: four Ps to advance participatory socio-environmental modeling.

Including stakeholders in environmental model building and analysis is an increasingly popular approach to understanding ecological change. This is because stakeholders often hold valuable knowledge about socio-environmental dynamics and collaborative forms of modeling produce important boundary objects used to collectively reason about environmental problems. Although the number of participatory modeling (PM) case studies and the number of researchers adopting these approaches has grown in recent years, the lack of standardized reporting and limited reproducibility have prevented PM's establishment and advancement as a cohesive field of study. We suggest a four-dimensional framework (4P) that includes reporting on dimensions of (1) the Purpose for selecting a PM approach (the why); (2) the Process by which the public was involved in model building or evaluation (the how); (3) the Partnerships formed (the who); and (4) the Products that resulted from these efforts (the what). We highlight four case studies that use common PM software-based approaches (fuzzy cognitive mapping, agent-based modeling, system dynamics, and participatory geospatial modeling) to understand human-environment interactions and the consequences of ecological changes, including bushmeat hunting in Tanzania and Cameroon, agricultural production and deforestation in Zambia, and groundwater management in India. We demonstrate how standardizing communication about PM case studies can lead to innovation and new insights about model-based reasoning in support of ecological policy development. We suggest that our 4P framework and reporting approach provides a way for new hypotheses to be identified and tested in the growing field of PM.

Modern endoscopic imaging tools for urothelial carcinoma of the urinary bladder

Cystoscopy is the gold standard for the initial diagnosis of urothelial carcinoma in the urinary bladder (UCB). White light exhibits significant limitations in its ability to detect flat lesions or carcinoma in situ. Photodynamic Diagnosis (PDD) and Narrow Band Imaging (NBI) are established techniques which may improve the detection rates of UCB and reduce the risk of recurrence. Multiple novel tools have been developed in order to improve the diagnostic accuracy of endoscopic procedures, including Digital Contrast Enhancement Technology, Confocal Laser Endomicroscopy (CLE) and Optical Coherence Tomography (OCT). In future, spectral analysis of urothelial lesions may be achieved by Raman spectroscopy, thus providing information about biological alterations in the tissue. Furthermore, molecular imaging may allow specific targeting by fluorescent antibodies or small molecule agents, thus enabling differential diagnosis of suspicious lesions. Software-based approaches have been developed to incorporate computer-aided diagnosis and improve digital documentation of endoscopic findings. This study reviews current and future developments in the field of modern endoscopy of the urinary bladder and discusses the different approaches.

An FPGA-based dynamically reconfigurable platform for emulation of permanent faults in ASICs

FPGA-based emulation of permanent faults in ASICs can considerably improve the fault simulation time compared to traditional software-based approaches. Moreover, a hardware-based solution provides realistic behavior during fault emulation which is often required in safety-critical systems' validation. Previous emulation approaches not only suffers from considerable area (for instrumentation) and reconfiguration (for fault injection) overheads but also provides limited coverage of the target faults (and fault sites). The latter is due to difficulties in establishing a fault model equivalence when the ASIC structural netlist is passed through the design automation phases of an FPGA. This paper presents a novel approach for fast emulation of permanent faults in ASICs on state-of-the-art dynamically reconfigurable SRAM-based FPGAs while achieving fault model equivalence. Our proposed approach leverages localized run-time in-place Look Up Table (LUT) reconfigurations to avoid the time-consuming bitstream generation process for every ASIC fault. Moreover, the speed of fault injection is enhanced by direct LUT configuration data modification inside a bitstream frame. This results in 17 and 4 times improvements in fault injection speeds over vendor-provided LUT modification libraries and existing partial bitstream based approaches respectively. However, this improvement is achieved at an average 1.2 and 1.1 times degradation in area and delay metrics for the considered mapped circuits which is affordable considering the benefits in terms of the emulation speed.

An FPGA-Based Unscented Kalman Filter for System-On-Chip Applications

The demand for fast and accurate state estimation in embedded systems has been increasing lately, due at least in part to mobile robotics such as an unmanned aerial vehicle (UAV). The desire to maintain high performance but with compact form factors leads to implementation issues, particularly with more complex systems. A hardware-based approach using field-programmable gate arrays may be able to alleviate these issues but tends to have a more complicated development process than traditional software-based approaches. In order to simplify development and promote portability between embedded applications, a hardware/software codesign of the unscented Kalman filter is presented. An example implementation ( $N = 18$ ) of the hardware IP core only is presented using the Zynq-7000 XC7Z045 with synthesis, power, and timing results for the 1, 2, 5, and 10 processing-element cases.

The effect of X-ray micro computed tomography image resolution on flow properties of porous rocks.

The study of digital rock physics has seen significant advances due to the development of X-ray micro computed tomography scanning devices. One of the advantages of using such a device is that the pore structure of rock can be mapped down to the micrometre level in three dimensions. However, in providing such high-resolution images (low voxel size), the resulting file sizes are necessarily large (of the order of gigabytes). Lower image resolution (high voxel size) produces smaller file sizes (of the order of hundreds of megabytes), but risks losing significant details. This study describes the effect of the image resolution obtained by means of hardware-based and software-based approach. Four samples of porous rock were scanned using a SkyScan 1173 High Energy Micro-CT. We found that acquisition using increased pixel binning of the camera (hardware-based resizing) significantly affects the calculated physical properties of the samples. By contrast, voxel resizing by means of a software-based approach during the reconstruction process yielded less effect on the porosity and specific surface area of the samples. However, the decreasing resolution of the image obtained by both the hardware-based and the software-based approaches affects the permeability significantly. We conclude that simulating fluid flow through the pore space using the Lattice Boltzmann method to calculate the permeability has a significant dependency on the image resolution.

IIoTEED: An Enhanced, Trusted Execution Environment for Industrial IoT Edge Devices

With the advent of the Internet of Things (IoT), security has emerged as a major design goal for smart connected devices. This explosion in connectivity created a larger attack surface area. Software-based approaches have been applied for security purposes; however, these methods must be extended with security-oriented technologies that promote hardware as the root of trust. The ARM TrustZone can enable trusted execution environments (TEEs), but existing solutions disregard real-time needs. Here, the authors demonstrate why TrustZone is becoming a reference technology for securing IoT edge devices, and how enhanced TEEs can help meet industrial IoT applications real-time requirements.

Feature Fusion for Fingerprint Liveness Detection: a Comparative Study

Spoofing attacks carried out using artificial replicas are a severe threat for fingerprint-based biometric systems and, thus, require the development of effective countermeasures. One possible protection method is to implement software modules that analyze fingerprint images to tell live from fake samples. Most of the static software-based approaches in the literature are based on various image features, each with its own strengths, weaknesses, and discriminative power. Such features can be seen as different and often complementary views of the object in analysis, and their fusion is likely to improve the classification accuracy. This paper aims at assessing the potential of these feature fusion approaches in the area of fingerprint liveness detection by analyzing different features and different methods for their aggregation. Experiments on publicly available benchmarks show the effectiveness of feature fusion methods, which improve the accuracy of those based on individual features and are competitive with respect to the alternative methods, such as the ones based on convolutional neural networks.

UMH

In this article, we describe how to ease memory management between a Central Processing Unit (CPU) and one or multiple discrete Graphic Processing Units (GPUs) by architecting a novel hardware-based Unified Memory Hierarchy (UMH). Adopting UMH, a GPU accesses the CPU memory only if it does not find its required data in the directories associated with its high-bandwidth memory, or the NMOESI coherency protocol limits the access to that data. Using UMH with NMOESI improves performance of a CPU-multiGPU system by at least 1.92 × in comparison to alternative software-based approaches. It also allows the CPU to access GPUs modified data by at least 13 × faster.