Modular Algorithm Research Articles

A CAD approach for pre-layout optimal PDN design and its post-layout verification

Design of power distribution network (PDN) draws great attention to integrated circuit (IC) designers as it directly affects the circuit performance. The challenge is to immune the circuit from the noise arising due to PDN by means of effective placement of decoupling capacitance (decap) with optimal power, delay, energy and area utilization. Our work involves extraction of circuit parameters in the pre-layout stage and estimating decoupling capacitance as an early prediction activity based on dynamic power dissipation. We propose a new modular pre-layout PDN (MPLPDN) design approach, which modularizes the circuit and involves a computer aided design (CAD) methodology to investigate the changes in noise, power and delay parameter based on predictive decap allocation across the modules. A pruning methodology has been adopted to choose the best combination of the modules. The modularization algorithm is applied in a bottom up approach i.e. the lower level modules are combined at first and the top level module is created at the last stage. The experimental results based on pre and post layout simulation of application circuits show that MPLPDN achieves a considerable PDN noise suppression with a negligible increase in power, delay, energy and area. The comparison between pre-layout and post-layout simulation results also establishes the correctness of our early prediction.

Variable step size P&O MPPT algorithm for optimal power extraction of multi-phase PMSG based wind generation system

To eradicate limitations of conventional fixed step-size perturb and observe (P&O) maximum power point tracking (MPPT) based on the variable speed - wind energy conversion system (VS-WECS), this article proposes a fast and an efficient MPPT modular sectors algorithm. The variable-step perturb and observe (VS-PO) MPPT algorithm is implemented to divide the power-speed (P-ω) curve into modular sectors each with specific step-size. The modular sectors are established by comparing a special synthesized ratio, that associates with the theoretical power, with a specified ratio corresponding the required power accuracy. Hence, the proper step-size is selected according to the operating sector location. For the sectors near the maximum power point (MPP), a small step-size is applied. Otherwise, a large step-size is used. To calculate the theoretical power, a wind speed estimation algorithm is utilized for evaluating the wind speed. The system configuration involves a 1.5 MW five-phase permanent magnet synchronous generator (PMSG), and a back-to-back frequency converter with the dc-link capacitor for the grid integration. The simulation results illustrate the VS-PO superiority over both the conventional P&O (CPO) and modified P&O (MPO) techniques. The VS-PO improves the initial speed tracking and minimizes the steady state oscillations. The performance of the proposed control schemes is validated using MATLAB/SIMULINK environment.

Correlated Extra Reductions Defeat Fixed Window Exponentiation

The security of modular power algorithm is a very important research topic, which is the core operation of public key cryptography algorithm. Since the first timing attack was public in 1996, the attacker can exploit time differences between specific events to recover a secret key. In 2016, Dugardin took advantage of extra reductions to attack a regular exponentiation algorithm, which did not entirely adapt the fixed window method with Montgomery’s algorithm. The central thesis of this paper is that there exists a positive correlation between extra reductions of pre-computation and post-computation when the calculation has the same multiplier factor. In this article, basing on this dependency we present an attack method, and confirm the feasibility and effectiveness of it by conducting simulation experiments. Experimental results verify that the method can effectively attack modular power algorithm.

Health Regions in Brazil based on hospital admissions: a method to support health regionalization

This study addressed health regionalization on various spatial scales based on patient flow. The article analyzed data through data linkage on the origin and destination of admissions at the municipal level in Brazil in 2016. The analysis is based on graph theory and uses a modularity algorithm that seeks to group municipalities in communities with a large number of interlinks. The algorithm optimizes the number of hospital admissions and discharges, taking patient flow into account. The results are shown, considering different political and administrative spatial structures. Considering patient flow without spatial restrictions, 29 communities were created in the country, compared to 64 communities when the boundaries of the major geographic regions were respected, and 164 when considering only patient flows within the respective states. The results show the importance of historically constituted regions, ignoring formal administrative boundaries, in order to implement access to health services. They also reveal adherence to administrative boundaries in many states of Brazil, demonstrating this spatial scale's importance in the context of access to hospital admissions. The methodology makes relevant contributions to regional health planning.

A Practical Collision-Based Power Analysis on RSA Prime Generation and Its Countermeasure

We analyze the security of RSA prime generation implemented on embedded devices by a practical power analysis attack. Unlike previous differential power analysis-based attack on primality tests of RSA prime generation exploiting the deterministic relationship among multiple prime candidates manipulated by consecutive primality tests, we propose a collision-based power analysis attack on the Miller-Rabin test for a single prime candidate which can recover the secret prime with a single attempt by exploiting collision characteristics of simple power analysis resistant modular exponentiation algorithms. Hence, our attack does not require the incremental prime search assumption and is applicable when countermeasures against previous attacks are deployed since it also does not require the assumption of trial divisions with small primes on prime candidates. For a realistic setting, where five 512-bit modular exponentiations are operated on an ARM Cortex-M4 microcontroller as recommended by FIPS 186-4 standard, we successfully recover the secret exponent to an extent that a feasible exhaustive search is needed for the full recovery of the secret prime. This is a first practical result of recovering a full secret of modular exponentiation which manipulates 512-bit RSA primitives with collision-based power analysis in a single attempt, where the previous attack demonstrates the result for 192-bit ECC primitive implementations. We also present a countermeasure against our attack which requires only one additional modular subtraction for the loop of square-and-multiply-always exponentiation algorithm. An experimental result for the effectiveness of our proposed countermeasure is presented.

Healthcare process modularization using design structure matrix

The healthcare industry is confronted with the challenge of offering customized services while in the meantime to control increasing healthcare costs. Modularization is an important approach to reduce healthcare costs and improve patient-centered services via decreasing process complexity and enhancing flexibility through configuring pre-identified service modules. Recognizing the importance of modularity for healthcare services, this paper introduces Design Structure Matrix (DSM) as a technique for healthcare process modularization. A DSM-based modularization and sequencing algorithm is developed to allocate healthcare activities to service modules using Genetic Algorithm (GA) and arrange sequences of services both within and across service modules to support modular clinical pathway design. The proposed algorithm is implemented with a case study, the results of which have demonstrated the feasibility and applicability of the proposed DSM-based modularization method for healthcare process design.

Pareto Joint Inversion of 2D magnetometric and gravity data- synthetic study

Pareto joint inversion for two or more data sets is an attractive and promising tool which eliminates target functions weighing and scaling, providing a set of acceptable solutions composing a Pareto front. In former author’s study MARIA (Modular Approach Robust Inversion Algorithm) was created as a flexible software based on global optimization engine (PSO) to obtain model parameters in process of Pareto joint inversion of two geophysical data sets. 2D magnetotelluric and gravity data were used for preliminary tests, but the software is ready to handle data from more than two geophysical methods. In this contribution, the authors’ magnetometric forward solver was implemented and integrated with MARIA. The gravity and magnetometry forward solver was verified on synthetic models. The tests were performed for different models of a dyke and showed, that even when the starting model is a homogeneous area without anomaly, it is possible to recover the shape of a small detail of the real model. Results showed that the group analysis of models on the Pareto front gives more information than the single best model. The final stage of interpretation is the raster map of Pareto front solutions analysis.

Separation of Soil Macropore Types in Three‐Dimensional X‐Ray Computed Tomography Images Based on Pore Geometry Characteristics

Core Ideas For preferential flow modeling, different macropore types must be considered. A method was developed for separating biopores and cracks in 3D images from XRCT. The method enabled a more objective determination of structuring element sizes. The voxel‐based approach was found useful for quantification of macropore types. In structured soils, earthworm burrows, root channels, shrinkage cracks, and interaggregate spaces form complex macropore networks. Depending on the type and morphological properties, each macropore surface type is coated with specific organo‐mineral compounds, differently affecting sorption and mass exchange during preferential flow and turnover processes. For a quantitative, macropore type–specific analysis using X‐ray computed tomography (XRCT) with subsequent three‐dimensional (3D) image analysis, a discrimination of biopores from cracks and interaggregate spaces is necessary. We developed a method that allows separating biopores from other larger macropores in 3D images from XRCT of intact soil cores. An image‐processing workflow using the MAVI (Modular Algorithms for Volume Images) software framework ToolIP (Tool for Image Processing) was created to handle XRCT 3D images. Masking steps enabled to retain the surface roughness in the resulting two images of separated biopores and cracks. As a key point, the sizes of the structuring elements used in the spherical opening and dilation were objectively determined. For this purpose, maximum differences in the pore shapes between the 3D images of cylindrical biopores vs. more flat cracks and unregularly interaggregate spaces were focused. At the given resolution of 231‐μm voxel edge length, an optimum size of 2.5 voxels was found for both processing steps. The voxel‐based approach is applicable to XRCT 3D images of different spatial resolution and appears useful for the quantification of physicochemical surface properties of different macropore types for soil volumes, enabling a more precise description of preferential flow and transport.

Proof and Improvement of Shantz’s Modular Division and Lórencz’s Modular Inverse Algorithms

Modular division or inverse is fundamental in finite field arithmetic, and it widely exists in cryptosystems like elliptic curve cryptography. Shantz’s algorithm can be easily used for modular division in addition to the plus and minus methods. Nevertheless, a proof of the algorithm is in lack in the literature so far. This work presents a proof of Shantz’s algorithm by matrix transforms. Also, Lorencz’s modular inverse algorithm is innovative in applying left-shift operations, for which an analysis and an improvement are also given in this paper.

Directed LPA: Propagating labels in directed networks

Ignoring edge directionality and considering the graph as undirected is a common approach to detect communities in directed networks. However, it's not a meaningful way due to the loss of information captured by the edge property. Even if Leicht and Newman extended the original modularity to a directed version to address this issue, the problem of distinguishing the directionality of the edges still exists in maximizing modularity algorithms. To this direction, we extend one of the most famous scalable algorithms, namely label propagation algorithm (LPA), to a directed case, which can recognize the flow direction among nodes. To explore what properties the directed modularity should have, we also use another directed modularity, called LinkRank, and provide an empirical study. The experimental results on both real and synthetic networks demonstrate that the proposed directed algorithms can not only make use of the edge directionality but also keep the same time complexity as LPA.

Memory Tampering Attack on Binary GCD Based Inversion Algorithms

In the field of cryptography engineering, implementation-based attacks are a major concern due to their proven feasibility. Fault injection is one attack vector, nowadays a major research line. In this paper, we present how a memory tampering-based fault attack can be used to severely limit the output space of binary GCD based modular inversion algorithm implementations. We frame the proposed attack in the context of ECDSA showing how this approach allows recovering the private key from only one signature, independent of the key size. We analyze two memory tampering proposals, illustrating how this technique can be adapted to different implementations. Besides its application to ECDSA, it can be extended to other cryptographic schemes and countermeasures where binary GCD based modular inversion algorithms are employed. In addition, we describe how memory tampering-based fault attacks can be used to mount a previously proposed fault attack on scenarios that were initially discarded, showing the importance of including memory tampering attacks in the frameworks for analyzing fault attacks and their countermeasures.

A Modular Multiplier Implemented With Truncated Multiplication

In this brief, we propose a modular multiplication algorithm with four truncated multiplications to reduce the critical path. According to our algorithm, a high-speed 3-stage modular multiplier is constructed. Moreover, synthesized with TSMC 90 nm, our design can perform a 256-bit modular multiplication for every clock period of 3.58 ns with circuit scale of approximately 629K equivalent gates. Further, by utilizing the modular multiplier, an elliptic curves cryptography processor is constructed, and it can perform a scalar multiplication in 19.4 $\mu \text{s}$ , which is one of the fastest to date in published literatures.

Modeling sensory-motor decisions in natural behavior.

Although a standard reinforcement learning model can capture many aspects of reward-seeking behaviors, it may not be practical for modeling human natural behaviors because of the richness of dynamic environments and limitations in cognitive resources. We propose a modular reinforcement learning model that addresses these factors. Based on this model, a modular inverse reinforcement learning algorithm is developed to estimate both the rewards and discount factors from human behavioral data, which allows predictions of human navigation behaviors in virtual reality with high accuracy across different subjects and with different tasks. Complex human navigation trajectories in novel environments can be reproduced by an artificial agent that is based on the modular model. This model provides a strategy for estimating the subjective value of actions and how they influence sensory-motor decisions in natural behavior.

A Bayesian framework for the analog reconstruction of kymographs from fluorescence microscopy data.

Kymographs are widely used to represent and analyse spatio-temporal dynamics of fluorescence markers along curvilinear biological compartments. These objects have a singular geometry, thus kymograph reconstruction is inherently an analog image processing task. However, the existing approaches are essentially digital: the kymograph photometry is sampled directly from the time-lapse images. As a result, such kymographs rely on raw image data that suffer from the degradations entailed by the image formation process and the spatio-temporal resolution of the imaging setup. In this work, we address these limitations and introduce a well-grounded Bayesian framework for the analog reconstruction of kymographs. To handle the movement of the object, we introduce an intrinsic description of kymographs using differential geometry: a kymograph is a photometry defined on a parameter space that is embedded in physical space by a time-varying map that follows the object geometry. We model the kymograph photometry as a Lévy innovation process, a flexible class of non-parametric signal priors. We account for the image formation process using the virtual microscope framework. We formulate a computationally tractable representation of the associated maximum a posteriori problem and solve it using a class of efficient and modular algorithms based on the alternating split Bregman. We assess the performance of our Bayesian framework on synthetic data and apply it to reconstruct the fluorescence dynamics along microtubules in vivo in the budding yeast S. cerevisiae. We demonstrate that our framework allows revealing patterns from single time-lapse data that are invisible on standard digital kymographs.

Pixel image steganography using EOF method and modular multiplication block cipher algorithm

This study aims to hide data or information on pixel image using EOF method and to improve the security of hidden data than combine with cryptography algorithm Modular Multiplication Block Cipher algorithm for the encryption process. The image file used as the test sample consists of 3 types of image types (JPG, BMP, PNG), this file type is the most widely used by users with different width and height as well as various file sizes. The results of this study prove that the data could embed in pixel image using EOF method and the file size of carrier will increase its capacity as byte of embedded text.

Dynamic reshaping of functional brain networks during visual object recognition

Objective. Emerging evidence shows that the modular organization of the human brain allows for better and efficient cognitive performance. Many of these cognitive functions are very fast and occur in a sub-second time scale such as the visual object recognition. Approach. Here, we investigate brain network modularity while controlling stimuli meaningfulness and measuring a participant’s reaction time. We particularly raised two questions: i) does the dynamic brain network modularity change during the recognition of meaningful and meaningless visual images? And (ii) is there a correlation between network modularity and the reaction time of the participants?To tackle these issues, we collected dense-electroencephalography (EEG, 256 channels) data from 20 healthy human subjects performing a cognitive task consisting of naming meaningful (tools, animals…) and meaningless (scrambled) images. Functional brain networks in both categories were estimated at the sub-second time scale using the EEG source connectivity method. By using multislice modularity algorithms, we tracked the reconfiguration of functional networks during the recognition of both meaningful and meaningless images. Main results. Results showed a difference in the module’s characteristics of both conditions in term of integration (interactions between modules) and occurrence (probability on average of any two brain regions to fall in the same module during the task). Integration and occurrence were greater for meaningless than for meaningful images. Our findings revealed also that the occurrence within the right frontal regions and the left occipito-temporal can help to predict the ability of the brain to rapidly recognize and name visual stimuli. Significance. We speculate that these observations are applicable not only to other fast cognitive functions but also to detect fast disconnections that can occur in some brain disorders.

Hole-quality evaluation in drilling fiber-reinforced composites

This paper presents a novel approach for a complex evaluation of the quality of drilled holes in fiber-reinforced composite materials. The proposed methodology is based on non-destructive quantification of visible defects based on the numerical analysis of drilled hole images. Automatic contour definition and profile formation for the uncut fibers and delamination were implemented via modularized algorithms. Four criteria for the evaluation of the defect height and width distribution were developed and combined into an overall quality parameter, Q. The methodology was validated experimentally by drilling carbon-fiber-reinforced polymer (CFRP) samples with a cemented carbide drill bit. The results showed a linear relation between the hole quality Q, the drilled hole number, and tool wear.

Introduction to topological quantum computation with non-Abelian anyons

Topological quantum computers promise a fault tolerant means to perform quantum computation. Topological quantum computers use particles with exotic exchange statistics called non-Abelian anyons, and the simplest anyon model which allows for universal quantum computation by particle exchange or braiding alone is the Fibonacci anyon model. One classically hard problem that can be solved efficiently using quantum computation is finding the value of the Jones polynomial of knots at roots of unity. We aim to provide a pedagogical, self-contained, review of topological quantum computation with Fibonacci anyons, from the braiding statistics and matrices to the layout of such a computer and the compiling of braids to perform specific operations. Then we use a simulation of a topological quantum computer to explicitly demonstrate a quantum computation using Fibonacci anyons, evaluating the Jones polynomial of a selection of simple knots. In addition to simulating a modular circuit-style quantum algorithm, we also show how the magnitude of the Jones polynomial at specific points could be obtained exactly using Fibonacci or Ising anyons. Such an exact algorithm seems ideally suited for a proof of concept demonstration of a topological quantum computer.

A Low Hardware Consumption Elliptic Curve Cryptographic Architecture over GF(p) in Embedded Application

In this paper, a low hardware consumption design of elliptic curve cryptography (ECC) over GF(p) in embedded applications is proposed. The adder-based architecture is explored to reduce the hardware consumption of performing scalar multiplication (SM). The Interleaved Modular Multiplication Algorithm and Binary Modular Inversion Algorithm are improved and implemented with two full-word adder units. The full-word register units for data storage are also optimized. The design is based on two full-word adder units and twelve full-word register units of pipeline structure and was implemented on Xilinx Virtex-4 platform. Design Compiler is used to synthesized the proposed architecture with 0.13 μm CMOS standard cell library. For 160, 192, 224, 256 field order, the proposed architecture consumes 5595, 7080, 8423, 9370 slices, respectively, and saves 17.58∼54.93% slice resources on FPGA platform when compared with other design architectures. The synthesized result uses 35.43 k, 43.37 k, 50.38 k, 57.05 k gate area and saves 52.56∼91.34% in terms of gate count in comparison. The design takes 2.56∼4.07 ms to perform SM operation over different field order under 150 MHz frequency. The proposed architecture is safe from simple power analysis (SPA). Thus, it is a good choice for embedded applications.

Perceived group cohesion versus actual social structure: A study using social network analysis of egocentric Facebook networks

Research on group cohesion often relies on individual perceptions, which may not reflect the actual social structure of groups. This study draws on social network theory to examine the relationship between observable structural group characteristics and individual perceptions of group cohesion. Leveraging Facebook data, we extracted and partitioned the social networks of 109 participants into groups using a modularity algorithm. We then surveyed perceptions of cohesion, and computed group density and size using social network analysis. Out of six linear mixed effects models specified, a random intercept and fixed slope model with group size as a predictor of perceived group cohesion emerged as best fitting. Whereas group density was not linked to perceived cohesion, size had a small negative effect on perceived cohesion, suggesting that people perceive smaller groups as more cohesive. We discuss the potential of social network analysis, visualization tools, and Facebook data for advancing research on groups.