Partitioning Technique Research Articles

FPGA-SPICE: A Simulation-Based Architecture Evaluation Framework for FPGAs

In this paper, we developed a simulation-based architecture evaluation framework for field-programmable gate arrays (FPGAs), called FPGA-SPICE, which enables automatic layout-level estimation and electrical simulations of FPGA architectures. FPGA-SPICE can automatically generate Verilog and SPICE netlists based on realistic FPGA configurations and a high-level eTtensible Markup Language-based FPGA architectural description language. The outputted Verilog netlists can be used to generate layouts of full FPGA fabrics through a semicustom design flow. SPICE simulation decks can be generated at three levels of complexity, namely, full-chip-level, grid-level, and component-level, providing different tradeoff between accuracy and simulation time. In order to enable such level of analysis, we presented two SPICE netlist partitioning techniques: loads extraction and parasitic net activity estimation. Electrical simulations showed that averaged over the selected benchmarks, the grid-/component-level approach can achieve $6.1\times /7.5\times $ execution speed-up with 9.9%/8.3% accuracy loss, respectively, compared to the full-chip level simulation. FPGA-SPICE was showcased through three different case studies: 1) an area breakdown analysis for static random access memory-based FPGAs, showing that configuration memories are a dominant factor; 2) a power breakdown comparison to analytical models, analyzing the source of accuracy loss; and 3) a robustness evaluation against process corners, studying their impact on energy consumption of full FPGA fabrics.

Cache control techniques to provide QoS on real systems

In a multicore environment, the operating system often coschedules programs on different cores simultaneously to improve utilization of system resources. As a result, the programs compete with each other for the shared last level cache, resulting in cache interference and reduced performance. Providing Quality of Service or QoS guarantees for performance of applications running in such environments has been an important area of research in the past decade. We propose a system to provide such guarantees using hardware support provided in modern processors. Our system supports specifying performance targets for high-priority programs and performance constraints for low-priority programs. The system strives to meet the given performance targets while ensuring that the performance constraints are always met. While previous works have explored cache partitioning and rate-based techniques to provide QoS, we have used direct cache control support provided in modern processors. We have explored two cache control techniques—core level control and application page level control. We have carefully selected a wide variety of workloads and performed extensive experiments on real systems. We have obtained on average 95.2% accuracy, with as high as 99.4% in some cases.

Randomized Parameterized Algorithms for the Kidney Exchange Problem

In order to increase the potential kidney transplants between patients and their incompatible donors, kidney exchange programs have been created in many countries. In the programs, designing algorithms for the kidney exchange problem plays a critical role. The graph theory model of the kidney exchange problem is to find a maximum weight packing of vertex-disjoint cycles and chains for a given weighted digraph. In general, the length of cycles is not more than a given constant L (typically 2 ≤ L ≤ 5), and the objective function corresponds to maximizing the number of possible kidney transplants. In this paper, we study the parameterized complexity and randomized algorithms for the kidney exchange problem without chains from theory. We construct two different parameterized models of the kidney exchange problem for two cases L = 3 and L ≥ 3, and propose two randomized parameterized algorithms based on the random partitioning technique and the randomized algebraic technique, respectively.

Enhanced Security in Cloud Using Data Partitioning Technique

Cloud computing is now one of most popular technology in the computer science. Due to the invention of cloud, the perception of everyone has changes with infrastructure, deployment and development of software. The cloud provide various type of services like software as a service (SaaS),platform as a service (PaaS), and infrastructure as a service (IaaS).The sharing of data is the most important facility of the cloud. The emphasis of this paper is to provide a second layer of security to existing cloud storage solutions. The data is split into number of block and individual blocks are encrypt with the help of respective encryption keys within an image file and store the same on local hardware storage.

Psychosocial distress in oncology: using the distress thermometer for assessing risk classes.

Cancer patients often suffer for psychological distress, which can compromise their quality of life. Our study aimed to recognize risk classes for the development of psychosocial distress. Three hundred seventy-two adult cancer patients were assessed by the Distress Thermometer (DT) and Problem List at the National Cancer Research Centre "Giovanni Paolo II" of Bari. We also compiled a socio-medical and clinical-medical record survey ad hoc for collecting socio-demographic information and clinical variables. To examine the interplay among the different variables and distinguish internally homogeneous subgroups of patients with diverse risks of distress, the RECursive Partitioning and Amalgamation (RECPAM) technique was used. Most of patients were female and the most frequent diagnosis was breast cancer, followed by gastro-intestinal cancer and hematological cancer. Distress was present in 43% of the sample, with a total of 156 patients with a DT > 5. The RECPAM analysis identified three distinct and homogeneous patient subgroups (RECPAM classes) with different risks of distress: diagnosis, marital status, and Eastern Cooperative Oncology Group Performance Status. The use of the distress thermometer allows clinicians to identify patients with certain characteristics that may increase the risk of developing psychosocial distress. This evaluation can allow timely psychological intervention and improve the patient's therapeutic program.

Enhanced microalgal protein extraction and purification using sustainable microwave-assisted multiphase partitioning technique

A green and effective method of microwave-assisted three phase partitioning (MWTPP) was developed for the extraction and purification of proteins from Chlorella vulgaris microalgae species. Five types of salts were investigated and ammonium sulphate was selected as the best to form a three phase system with t-butanol as the solvent. The optimal conditions for maximum protein recovery are as follow: ammonium sulphate concentration 30%, ratio of slurry to t-butanol 1:1, microwave time 120 s, microwave duty cycle 80%, microwave power 100 W and microalgae biomass concentration 0.5 %w/w. Under the optimized conditions, the protein recovery yield from microalgae was found to be 63.2% with a separation efficiency of 67.2%. Compared to the TPP process and initial MWTPP process, the recovery yield was enhanced by 2.54 and 1.49 times, respectively. The FESEM characterization of the microalgae after MWTPP process also showed the potential effect of microwaves to disrupt the cell wall and release the biomolecules for ease of recovery. MWTPP represents an efficient, economic and environmentally friendly technique for the extraction of bioactive compounds from microalgae.

Integrated Approach of Airgap Insertion for Circuit Timing Optimization

Airgap technology enables air to be introduced in inter-metal dielectric (IMD). Airgap between certain wires reduces coupling capacitance due to the reduced permittivity; this can be utilized to decrease circuit delay. We propose an integrated approach of airgap insertion with the goal of circuit timing optimization. It consists of three sub-problems. We first select the layers that employ airgap, called airgap layers, that maximize total negative slack (TNS) improvement; this yields TNS improvement of 7% to 15% and worst negative slack (WNS) improvement of 2% to 8%, compared to a simple assumption of airgap layers. Second, we reassign the layers of wires such that more wires on critical paths can be placed in airgap layers. This is formulated as integer linear programming (ILP), and a more practical heuristic algorithm is also proposed. It provides an additional 17% TNS improvement and 6% WNS improvement. Finally, we perform airgap insertion through ILP formulation, where a number of design rules are modeled with linear constraints. To reduce the heavy runtime of ILP, a layout partitioning technique is also applied. It implements a feasible airgap mask in a manageable time where the amount of inserted airgap is close to the optimal solution.

A simple approximation algorithm for the diameter of a set of points in an Euclidean plane.

Approximation algorithms with linear complexities are required in the treatments of big data, however, present algorithms cannot output the diameter of a set of points with arbitrary accuracy and near-linear complexity. By introducing the partition technique, we introduce a very simple approximation algorithm with arbitrary accuracy ε and a complexity of O(N + ε−1 log ε−1) for the cases that all points are located in an Euclidean plane. The error bounds are proved strictly, and are verified by numerical tests. This complexity is better than existing algorithms, and the present algorithm is also very simple to be implemented in applications.

RTOS Solution for NoC-Based COTS MPSoC Usage in Mixed-Criticality Systems

Multi-core system on chip (MPSoC) is a clear trend for the future embedded systems. Network-on-chip (NoC) is the most scalable interconnection, allowing to have hundreds of cores on the same chip. On the other hand, safety critical applications (e.g., avionic) require the system to present a set of strict guarantees, which are difficult to achieve for MPSoC-based systems. In this work we target Commercial Off-The-Shelf (COTS) NoC-based MPSoC for the mixed criticality systems, in the safety critical field. The focus of the proposed work is on the issue of contention on the NoC and the related temporal interference. As the main contribution, we propose a partitioning technique to enable the usage of COTS NoC-based MPSoC for the mixed criticality systems, enabling an unbounded number of levels of criticality to be deployed. The proposed technique exploits the deterministic routing algorithm of the NoC and it is suitable for any NoC-based MPSoC which meets a set of fairly common characteristics. The partitioning technique is intended to have a purely software implementation as a module of a real-time operating system, which will allow easier certification as well. The proposed approach overcomes the concept of strict network partitioning between regions, as it implements traffic isolation allowing partitions to overlap. As a further contribution this paper describes the cost of proposed solution in terms of the network connectivity reduction. A set of rules to enable an efficient usage of the proposed solution has been provided.

An efficient two-stage method for optimal sensor placement using graph-theoretical partitioning and evolutionary algorithms

This paper presents a two-stage optimal sensor placement method for modal identification of structures. At the first stage, using a graph theoretical technique, the structure is partitioned into equal substructures. At the second stage, a preset number of triaxial sensors are proportionately allocated to the substructures. The location of sensors is determined using an evolutionary optimization algorithm, which optimizes the triaxial modal assurance criterion of the structure. The first stage leads to the even distribution of the sensors. This stage not only improves the mode shape visualization as the secondary criterion but also accelerates the optimization process by space reduction. Here, various graph-theoretical methods including k-means, k-means++, and spectral partitioning are examined as the partitioning techniques. In addition, a dynamic version for quantum-inspired evolutionary optimization algorithm (DQEA) is proposed and applied to find the placement of triaxial sensors, along with the standard version of quantum-inspired evolutionary algorithm and genetic algorithm. In order to examine the efficiency of the methods, the bridge model of the University of Central Florida, USA, is considered as the benchmark structure. The results show that the proposed method efficiently satisfies both criteria. Moreover, the introduced optimization algorithm (DQEA) outperforms other algorithms.

A Conditional Inference Tree Model for Predicting Sleep-Related Breathing Disorders in Patients With Chiari Malformation Type 1: Description and External Validation.

The aim of this study is to generate and validate supervised machine learning algorithms to detect patients with Chiari malformation (CM) 1 or 1.5 at high risk of the development of sleep-related breathing disorders (SRBD) using clinical and neuroradiological parameters. We prospectively included two independent datasets. A training dataset (n = 90) was used to obtain the best model, whereas a second dataset was used to validate it (n = 74). In both cohorts, the same clinical, neuroradiological, and sleep studies were carried out. We used two supervised machine learning approaches, multiple logistic regression (MLR) and the unbiased recursive partitioning technique conditional inference tree (URP-CTREE), to detect patients at high risk of SRBD. We then compared the accuracy, sensitivity, and specificity of the two prediction models. Age (odds ratio [OR] 1.1 95% confidence interval [CI] 1.05-1.17), sex (OR 0.19 95% CI 0.05-0.67), CM type (OR 4.36 95% CI 1.14-18.5), and clivus length (OR 1.14 95% CI 1.01-1.31) were the significant predictor variables for a respiratory disturbance index (RDI) cutoff that was ≥ 10 events/h using MLR. The URP-CTREE model predicted that patients with CM-1 who were age 52 years or older and males with CM-1 who were older than 29 years had a high risk of SRBD. The accuracy of predicting patients with an RDI ≥ 10 events/h was similar in the two cohorts but in the URP-CTREE model, specificity was significantly greater when compared to MLR in both study groups. Both MLR and URP-CTREE predictive models are useful for the diagnosis of SRBD in patients with CM. However, URP-CTREE is easier to apply and interpret in clinical practice.

An image thresholding approach based on Gaussian mixture model

Image thresholding is an important technique for partitioning the image into foreground and background in image processing and analysis. It is difficult for traditional thresholding methods to get satisfactory performance on the noisy and uneven grayscale images. In this paper, we propose an image thresholding approach based on Gaussian mixture model (GMM) to solve this problem. GMM assumes that image is a mixture of two unknown parameters’ Gaussian distributions, which corresponds to foreground and background, respectively. Based on this assumption, we adopt expectation maximization algorithm with a simple initialization strategy to estimate the statistical parameters and utilize Bayesian criteria to generate the binary map. Furthermore, we calculate the posterior probabilities in consideration of neighborhood effect to achieve good performance on noisy and uneven grayscale images. Experimental results conducted on the synthetic and real images demonstrate the effectiveness of the proposed method.

Vibration analyses of general thin and moderately thick laminated composite curved beams with variable curvatures and general boundary conditions

An exact semianalytical method for vibration analysis of general thin and moderately thick laminated composite curved beams with variable curvatures and general boundary conditions is presented. In the framework of the first-order shear deformation theory, the method combines the variational principle and multilevel partition technique. As one of the innovation points, the general boundary conditions are enforced by using the virtual boundary spring technology. Each of the fundamental beam unknowns is then invariantly expanded as Jacobi polynomials. The convergence study and numerical verifications of the laminated composite curved beams with various boundary conditions are carried out.

Combination of wavelet transform and singular value decomposition-based contrast enhancement technique for target detection in UAV reconnaissance thermal images

ABSTRACTIn Aerial surveillance, thermal images acquired by unmanned aerial vehicle (UAV) are greatly affected due to various external interferences, which results in a low contrast image. Widely used conventional contrast enhancement methods such as histogram equalization and dynamic range partitioning techniques suffer from severe brightness changes and reduced sharpness, which in turn fail to preserve the edge details of the image. Thus for efficient target detection, it is essential to develop effective thermal infrared image contrast and edge enhancement technique. In this paper, wavelet transform (WT) and singular value decomposition (SVD)-based image enhancement technique is attempted for the target detection using thermal images captured by UAV. The discrete wavelet transform (DWT), stationary wavelet transform (SWT) and SVD are used for texture feature enhancement, edge enhancement and illumination correction, respectively. The experimental results show that the proposed technique yields higher entropy (6.7485), EMEE (2.1212), MSSIM (0.8719) and lower AMBE (21.9049) values when compared to other existing techniques.

A²I: abstract² interpretation

The fundamental idea of Abstract 2 Interpretation (A 2 I), also called meta-abstract interpretation, is to apply abstract interpretation to abstract interpretation-based static program analyses. A 2 I is generally meant to use abstract interpretation to analyse properties of program analysers. A 2 I can be either offline or online. Offline A 2 I is performed either before the program analysis, such as variable packing used by the Astrée program analyser, or after the program analysis, such as in alarm diagnosis. Online A 2 I is performed during the program analysis, such as Venet’s cofibred domains or Halbwachs et al.’s and Singh et al.’s variable partitioning techniques for fast polyhedra/numerical abstract domains. We formalize offline and online meta-abstract interpretation and illustrate this notion with the design of widenings and the decomposition of relational abstract domains to speed-up program analyses. This shows how novel static analyses can be extracted as meta-abstract interpretations to design efficient and precise program analysis algorithms.

Dynamic Software Updates to Enhance Security and Privacy in High Availability Energy Management Applications in Smart Cities

The Internet of Things (IoT) and Industrial Internet of Things (IIoT) trends, where high connectivity is envisioned, are giving rise to new applications, services, and paradigms, such as smart cities. Due to this connectivity and information sharing features, security, and privacy protection mechanisms need to be implemented, which may become obsolete at some future time. Software updates are, then, crucial. However, software updates requiring system shutdown and restarts might not be acceptable from the business and service point of view when high availability is demanded. In this paper, a mixed-criticality software architecture and design for a building energy management system, built upon the Cetratus runtime framework, is presented, where partitioning techniques are employed to ensure temporal and spatial isolation. Through this framework, software updates are dynamically accomplished, without the need for system shutdown and restarts. A live patching example is also presented, where customers privacy is enhanced by means of homomorphic encryption.

Techniques, Tools and Applications of Graph Analytic

Graphs have acute significance because of poly-tropic nature and have wide spread real world big data appli-cations, e.g., search engines, social media, knowledge discovery, network systems, etc. Major challenge is to develop efficient systems to store, process and analyze large graphs generated by these applications. Graph analytic is important research area in big data graphs dealing with efficient extraction of useful knowledge and interesting patterns from rapidly growing big data streams. Tremendously huge and complex data of graph applications requires specially designed graph databases having special data structures and effective features for data modeling and querying. The manipulation of large size of data requires effective scalable and distributed computational techniques for efficient graph partitioning and communication. Researchers have proposed different analytical techniques, storage structures, and processing models. This study provides insight of different graph analytical techniques and compares existing graph storage and computational technologies. This work also assesses the perfor-mance, strengths and limitations of various graph databases and processing models.

LiteTE: Lightweight, Communication-Efficient Distributed-Memory Triangle Enumerating

Distributed-memory triangle enumerating has attracted considerable interests due to its potential capability to process huge graphs quickly. However, existing algorithms suffer from low speed due to high communication cost and load imbalance. To solve the problems, we propose LiteTE, a lightweight, communication-efficient triangle enumerating scheme. To reduce communication cost, LiteTE proposes several techniques, including a graph partitioning method to fully leverage the large memory of commodity servers and the high bandwidth of modern networks and a fast broadcast algorithm to effectively utilize the bidirectional bandwidth of cables and the aggregate bandwidth of clusters. To reduce load imbalance, LiteTE proposes three-level techniques, including a codesign technique of graph partitioning and partition-level load balance, a decentralized dynamic node-level load balance technique, and a chunk-based lock-free work-stealing technique, all of which are lightweight and incur no or hardly any communication cost. The experimental results show that LiteTE reduces communication cost and load imbalance considerably and achieves much better performance in metrics, such as setup time, runtime, scalability, and load balance than the state-of-the-art algorithms. On a small-scale cluster, LiteTE enumerates the 15 trillion triangles in a graph of 92 billion edges in 15 min, while other algorithms fail to complete.

Antitubercular Activity of the Fungus Gliocladium sp. MR41 Strain.

Tuberculosis (TB) is a leading cause of death worldwide from infectious diseases and its inadequate treatment has led to emergence of resistant strains. The emergence of these strains renders the search for new drugs for the treatment of TB. The aim of this study was the evaluation of the anti-TB activity of the extract from fungus Gliocladium sp. MR41, and bioassay-guided fractionation and identification of majority compounds was carried out. Fungal strain culture was lyophilized and extracted by maceration in Ethyl Acetate (EtOAc). This extract was fractionated by liquid-liquid partitioning and chromatographic techniques, and the compounds were identified by their spectroscopic data. Furthermore, the EtOAc extract, fractions, and pure compounds were tested on Mycobacterium tuberculosis using the Microplate Alamar Blue Assay. From the bioactive AcetoNitrile Fraction (AcNF; MIC = 3.13 µg/mL) of the EtOAc extract, four compounds were isolated: ergosterol (1), ergosterol-5, 8-peroxide (2), 1, 6-di-O-acetyl-2,3,4,5-tetrahydroxy-hexane (3), and allitol (4). Only 2 exhibited potent activities against M. tuberculosis (MIC = 0.78 µg/mL). Additionally, this is the first report, to our knowledge, of polyols 3 and 4 from this fungus.

Improved Lattice-Based Signcryption in the Standard Model

Signcryption is a basic cryptographic primitive that simultaneously captures the functions of encryption and signature. To realize comprehensive information security against quantum computing attacks, lattice-based signcryption schemes have been successively proposed. However, the performance of signcryption schemes should be improved in the lattice setting. An efficient lattice-based signcryption scheme in the standard model is proposed in this paper. Under the ring learning with errors (RLWE) assumption and the ideal short integer solution (ISIS) assumption, the proposed signcryption scheme achieves indistinguishability against adaptive chosen ciphertext attacks (IND-CCA2) and existential unforgeability under an adaptive chosen-message attack (EUF-ACMA). Our scheme not only reduces the communication and computational overhead but also realizes a new design that combines the partitioning technique with the idea of tag-based key encapsulation. The performance analysis results show that our scheme is more efficient than previous lattice-based signcryption schemes in the standard model.