Use Of Multipliers Research Articles

Cascaded multiplier-free implementation of adaptive anti-jamming filter based on GNSS receiver

Evaluating the computational complexity is critical for assessing the time-domain anti-jamming performance of GNSS receivers. The multiplier is the core component that contributes to the computational complexity in time-domain anti-jamming. However, current algorithms aimed at reducing the complexity of time-domain anti-jamming typically concentrate on shortening the filter length, which fails to address the high computational complexity introduced by the use of multipliers. This paper introduces a cascaded multiplier-free approach for implementing time-domain anti-jamming in navigation receivers. We propose a numerical power decomposition technique based on optimal Canonical Signed Digit coding and coefficient decomposition. By substituting the multiplier with minimal adder and shift operations, the computational complexity of the anti-jamming filter with a high quantization bit-width can be considerably decreased. An optimization strategy is presented, and the low-complexity multiplier-free technique is applied to the time-domain anti-jamming filter. Compared to the traditional Canonical Signed Digit multiplier-free technique, our method can reduce the components required for a 12-bit quantization anti-interference filter by one adder, 20 shift operations, and five coded word lengths, while maintaining a pseudo-range measurement deviation below 0.27 ns.

Sparsity‐optimised farrow structure variable fractional delay filter for wideband array

AbstractIn this paper, a new sparsity‐optimised Farrow structure variable fractional delay (SFS‐VFD) filter is proposed to address the aperture effect in wideband array. Our method is based on coefficient (anti‐)symmetry and optimises the number and orders of its sub‐filters, greatly reducing the non‐zero coefficients. The established cost function is formulated as a parametric minimisation problem with multiple regularisation constraints, and solved by the modified three‐block alternating direction multiplier method (MTB‐ADMM), which is improved by introducing core variable correction items to ensure stable and fast convergence. Experimental results show that the SFS‐VFD filter reduces the complexity of the system by decreasing the use of multipliers and adders while ensuring high delay accuracy. In wideband array, the SFS‐VFD filter effectively corrects the aperture effect and achieves precise beam pointing.

Enhanced FPGA implementation of Echo State Networks for chaotic time series prediction

The Echo State Network (ESN) is one of the most used machine learning methods for predicting chaotic time series. The topology of an ESN comprises three layers, namely: input, hidden, and output layer. In this work, different ESN topologies are analyzed by modifying the structure and number of internal connections in the hidden layer, which neurons are connected randomly. The ESN topologies are analyzed to have 2%, 6%, 20%, 40%,…, and up to 100% of internal connections. The prediction error is associated to the Mean Squared Error (MSE), which results have similar values for all ESN topologies, as long as the Echo state property is guaranteed. It is highlighted that the prediction horizon obtained with 2% of internal connections, is more significant than that obtained with 100%, providing a prediction of 80 and 60 steps-ahead, respectively. The prediction results significantly impact the implementation in a field-programmable gate array (FPGA), so that an enhanced hardware realization is introduced herein by reducing the use of multipliers by more than 90% compared with a fully connected topology, without significantly affecting the measure of the prediction horizon and MSE. Another contribution is the FPGA implementation of the activation function that is an hyperbolic tangent function for which different alternatives are shown to minimize the error of the approximated function without significantly increasing the implementation cost or latency.

Multivariate Characterization, Modeling of Paleo Zone Improves History Matching

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper IPTC 22034, “Rigorous Multivariate Characterization and Modeling of Paleo Zone for Simulation Model History Matching Under Reservoir Uncertainty,” by Nader Y. Bu-Khamseen, SPE, Marko Maucec, SPE, and Anwar R. Awan, SPE, Saudi Aramco, et al. The paper has not been peer reviewed. Copyright 2022 International Petroleum Technology Conference. Reproduced by permission. _ The presence of a paleo zone, which frequently exists below free-water-level (FWL) surfaces, can affect dynamic reconciliation of reservoir simulation models. In the complete paper, the authors present an approach that integrates characterization of paleo zones, parameterization of paleo-zone conductivity, and application of flow profiles as a guide in an accelerated history-matching study of a large-scale dual-porosity/dual-permeability model. The approach to probabilistic parameterization of paleo-zone conductivity has contributed to a model with high quality and rendered a reservoir simulation model with reliable predictive capability in accelerated time. Methods and Models Geological Modeling of Paleo Zone. Structural growth evidences the filling history and displacement occurred by regional tilting, stamping a record of relic (or paleo) oil in the pore system below the current FWL. Applicable saturation-height models are generated based on dedicated core petrophysical rock typing in conjunction with a robust FWL and paleo-oil FWL inversion, a process that allows reproduction of the saturation profile as seen from logs (original logs not affected by production) and the oil-saturation tail as seen below the FWL. The primary methodology behind the approach presented in the paper consists of determining the volume of paleo oil by modeling FWL and paleo-oil FWL surfaces, defining the corresponding paleo zone within the reservoir, and dynamic evaluation of communication between the aquifer, paleo oil, and oil zones; the two surfaces come from an inversion process of the saturation height function. Current developments for the incorporation of the most-important variables affecting dynamic conditions (assisted history matching, uncertainty quantification, multivariate parameterization, and static and dynamic calibration) in the reservoir have allowed incorporation of, and sensitivity studies of, some overlooked parameters, including paleo-oil characterization, modeling, and simulation. Adjusting Petrophysical Properties Inside the Paleo Regions. Petrophysical properties (porosity and permeability) inside the paleo-oil region are modeled based on the bulk volume of water (BVW) from log interpretation. The higher the BVW, the higher the chance of effective communication between the oil leg and the aquifer. These modeled multipliers represent the probability of the sealing character of the paleo-oil region because this accumulated hydrocarbon may not be entirely homogeneous nor uniformly distributed, as suggested by dynamic data and corroborated by core samples. The methodology implemented to represent the presence of the paleo zone in the interpreted and modeled region between the current FWL and the paleo-oil FWL incorporates the use of multipliers in porosity and permeability, allowing the reproduction of the blocking or baffling effect caused by the presence of residual-oil saturation.

Efficient High-Radix GF(p) Montgomery Modular Multiplication Via Deep Use Of Multipliers

High-radix Montgomery Modular Multiplication (MMM) is flexible and consumes fewer cycles, but turns inefficient especially when it comes to low-bit calculation. In this brief, we propose a high-radix MMM algorithm, called Separated Iterative Digit-Digit Modular Multiplication (S-IDDMM), which accelerates the MMM by taking full advantage of multipliers and adders. The relationship among clock cycles, pipeline of multiplications and number of multipliers for the proposed algorithm is derived and utilized to optimize the overall efficiency jointly. Xilinx Virtex-7 FPGA implementations of S-IDDMM in 256 bits and 512 bits are constructed with radix-32 and radix-64, respectively. Compared with IDDMM design reported elsewhere (Amiet et al., 2016), our calculation time is reduced by more than 2 times with a similar area cost, due to deep use of multipliers and higher frequency.

Novel foods as red meat replacers – an insight using Risk Benefit Assessment methods (the NovRBA project)

The project entitled “Novel foods as red meat replacers – an insight using Risk Benefit Assessment methods (NovRBA)” aimed to develop and test harmonised Risk Benefit Assessment (RBA) methods to estimate the overall health impact of replacing red meat with an edible insect species (a novel food). Based on an appraisal of insect products that are more likely to be consumed in Europe, project participants decided to compare the consumption of a beef patty consisting of 100% minced beef meat (reference scenario) with a patty in which beef meat would be fully replaced by an edible insect dough (alternative scenario). The target population was adults. The RBA steps included the problem definition, the identification, prioritization and selection of components together with associated health outcomes. The assessment included the selection of dose-response relationships based on their internal validity (hazard characterisation) and an exposure assessment of nutrient intake and exposures to microbiological hazards and compounds of toxicological concern associated with the reference and alternative scenarios. All health outcomes were quantified according to the disability-adjusted life years (DALYs) composite metric. The project standardised the pivotal step of selecting model components through developing a tiered approach to prioritise components establishing the “long”, the “short” and the “final” list. The final list comprised thirteen nutrients, two heat-resistant spore-forming bacteria and inorganic arsenic. The RBA model was developed using @Risk® add-in software using Monte Carlo simulations considering variability and/or uncertainty. Overall the expected change in DALYs when moving from the reference scenario to the alternative one was estimated to be around 8,753 DALYs (per 100,000 population) saved in Greece, 6,572 DALYs in Denmark and 21,972 DALYs in France. This is mainly due to the overall beneficial nutritional and microbiological impacts. The proposed actions to communicate the project’s findings can be summarised in providing understandable scientific evidence to policy makers; creating opportunities for consumers to engage in-depth with information about insect consumption; making use of multipliers who enjoy a high level of trust and establishing informational exchange with trusted sources.

Testing the effectiveness of debiasing techniques to reduce overprecision in the elicitation of subjective continuous probability distributions

Formal expert elicitation is a widely used method for quantifying uncertain variables in decision and risk analysis. When estimating uncertain variables, experts and laypeople exhibit overprecision, meaning that the ranges of their estimates are too narrow. Overprecision, a form of overconfidence, is pervasive and hard to correct, thus posing a challenge to expert elicitation. Following the increasing interest toward improving judgments in Behavioral Operational Research (OR), and the limited evidence about the effectiveness of debiasing tools, the aim of our research is to test the effectiveness of commonly employed practices for debiasing overprecision. We conducted two experiments, testing a set of debiasing techniques when eliciting points of a cumulative distribution functions for general knowledge questions. The debiasing procedures included hypothetical bets, counterfactual argumentation, and automatic stretching to increase the ranges of subjects’ initial estimates. We find that two debiasing strategies that require further reasoning after initial estimates (hypothetical bets and counterfactuals) were not very effective for reducing overprecision, while the use of multipliers that increase the initial range of distributions, coupled with a re-elicitation of the distribution with the new range, provided more positive results. We provide some recommendations for expert elicitation in OR practice, based on our findings, and suggest avenues for further research into debiasing overprecision.

An Experimental Study of Building Blocks of Lattice-Based NIST Post-Quantum Cryptographic Algorithms

Security of currently deployed public-key cryptography algorithms is foreseen to be vulnerable against quantum computer attacks. Hence, a community effort exists to develop post-quantum cryptography (PQC) algorithms, most notably the NIST PQC standardization competition. In this work, we have investigated how lattice-based candidate algorithms fare when implemented in hardware. To achieve this, we have assessed 12 lattice-based algorithms in order to identify their basic building blocks. We assume the algorithms will be implemented in an application-specific integrated circuit (ASIC) platform and the targeted technology is 65 nm. To estimate the characteristics of each algorithm, we have assessed the following characteristics: memory requirements, use of multipliers, and use of hashing functions. Furthermore, for these building blocks, we have collected area and power figures for all studied algorithms by making use of commercial memory compilers and standard cells. Our results reveal interesting insights about the relative importance of each building block for the overall cryptosystem, which can be used for guiding ASIC designers when selecting an algorithm or when deciding where to focus optimization efforts such that the final design respects requirements and design constraints.

Ethics and biodiversity offsetting.

Biodiversity offsetting is an increasingly applied tool aiming to compensate for environmental damage caused by exploitation projects. Critics, however, raise concerns over the purported effectiveness of offsetting and question the ethical underpinnings and implications of offsetting. These ethical dimensions have largely been overlooked in research, which may lead to offsetting systems that fail to respect the values intended to be safeguarded. To address these dimensions, 5 ethical objections in the scientific literature were identified: offsetting violates nature's intrinsic value; losses of nature cannot be compensated for by human interventions; too little is known to make adequate trades; offsetting impedes virtuous dispositions toward nature; and offsetting has negative justice implications. We examined these objections and arguments against them based on the ethical concepts of intrinsic and instrumental values, anthropocentrism, nonanthropocentrism, and deontological, consequentialist, and virtue‐ethical paradigms. Both nonanthropocentric and anthropocentric concerns were expressed in deontological, consequential, and virtue‐ethical framings. Objections mostly had a deontological or virtue‐ethical basis, whereas counterarguments were based on consequential reasoning, but common ground in practice is often conceivable. Based on our findings, we formulated 10 recommendations for policy makers and 5 questions for practitioners to consider. We propose, for example, that policy makers clarify aims, legislate on no‐go areas, and govern the use of multipliers. We suggest that practitioners consider, for instance, how to improve case‐specific knowledge and promote learning and stakeholder engagement. We hope these recommendations and questions will encourage further discussion of the ethics of biodiversity offsets and ultimately strengthen the respect for biodiversity and human‐welfare values at stake in offsetting projects.

CAxCNN: Towards the Use of Canonic Sign Digit Based Approximation for Hardware-Friendly Convolutional Neural Networks

The design of hardware-friendly architectures with low computational overhead is desirable for low latency realization of CNN on resource-constrained embedded platforms. In this work, we propose CAxCNN, a Canonic Sign Digit (CSD) based approximation methodology for representing the filter weights of pre-trained CNNs.The proposed CSD representation allows the use of multipliers with reduced computational complexity. The technique can be applied on top of state-of-the-art CNN quantization schemes in a complementary manner. Our experimental results on a variety of CNNs, trained on MNIST, CIFAR-10 and ImageNet datasets, demonstrate that our methodology provides CNN designs with multiple levels of classification accuracy, without requiring any retraining, and while having a low area and computational overhead. Furthermore, when applied in conjunction with a state-of-art quantization scheme, CAxCNN allows the use of multipliers, which offer 77% logic area reduction, as compared to their accurate counterpart, while incurring a drop in Top-1 accuracy of just 5.63% for a VGG-16 network trained on ImageNet.

Research and Implementation of Channel Estimation Algorithm Based on Wideband Micro-power System

Channel estimation and equalization are the keys to realize wideband micro-power wireless communication system. In this paper, the channel estimation algorithm is improved according to the frame structure characteristics of wideband micro-power. Compared with the original algorithm, new algorithm is more suitable for this system and its performance is improved by 0.2dB. By establishing the system simulation model in MATLAB environment, the corresponding channel estimation algorithm is used to simulate and compare. The performance of the improved algorithm is verified and the improved algorithm is implemented on the FPGA platform. In the process of hardware implementation, formula transformation is used to reduce the use of multipliers in complex multiplication and reduce resource consumption. The results of ISE Design Suite 14.7 synthesis and Modelsim simulation show that the algorithm has low algorithm complexity, small hardware resources, and the estimation accuracy meets the expectations and meets the application requirements.

Area–Time–Power Efficient FFT Architectures Based on Binary-Signed-Digit CORDIC

Fast Fourier transform (FFT) is a significant technique in the digital signal processing (DSP). The intrinsic weak point of the primary designs of FFT was the use of multipliers. CORDIC is the most prevalent method to replace the multipliers of FFTs. In order to enhance the speed of CORDIC, redundant binary signed-digit (BSD) encodings can be utilized, so-called BSD-CORDIC. The main goal of this paper is to improve the efficiency of BSD-CORDIC-based FFT. The redundant improved composed (RIC) CORDIC is proposed as a novel BSD-CORDIC. The main idea behind the RIC CORDIC lies in prior determination of the required iterations in such a way that the maximum number of iterations is reduced, which results in speedup and decrease in hardware resources. Also, some of the iterations still can be skipped, which leads to further reduction in the power consumption. The synthesis results demonstrate that the FFT based on the RIC BSD-CORDIC enhances speed, power consumption, and area overhead.

Predictive Utility of Cardiovascular Risk Prediction Algorithms in Inflammatory Rheumatic Diseases: A Systematic Review.

We performed a systematic review of the literature to describe current knowledge of cardiovascular (CV) risk prediction algorithms in rheumatic diseases. A systematic search of MEDLINE, EMBASE, and Cochrane Central databases was performed. The search was restricted to original publications in English, had to include clinical CV events as study outcomes, assess the predictive properties of at least 1 CV risk prediction algorithm, and include patients with rheumatoid arthritis (RA), ankylosing spondylitis (AS), systemic lupus erythematosus (SLE), psoriatic arthritis (PsA), or psoriasis. By design, only cohort studies that followed participants for CV events were selected. Eleven of 146 identified manuscripts were included. Studies evaluated the predictive performance of the Framingham Risk Score, QRISK2, Systematic Coronary Risk Evaluation (SCORE), Reynolds Risk Score, American College of Cardiology/American Heart Association Pooled Cohort Equations (PCE), Expanded Cardiovascular Risk Prediction Score for Rheumatoid Arthritis (ERS-RA), and the Italian Progetto CUORE score. Approaches to improve predictive performance of general risk algorithms in patients with RA included the use of multipliers, biomarkers, disease-specific variables, or a combination of these to modify or develop an algorithm. In both SLE and PsA patients, multipliers were applied to general risk algorithms. In studies of RA and SLE patients, efforts to include nontraditional risk factors, disease-related variables, multipliers, and biomarkers largely failed to substantially improve risk estimates. Our study confirmed that general risk algorithms mostly underestimate and at times overestimate CV risk in rheumatic patients. We did not find studies that evaluated models for psoriasis or AS, which further demonstrates a need for research in these populations.

Automation of adjustment stages by high-performance semiconductor converters

In this paper, methods of frequency synthesis of semiconductor converter control are proposed, allowing automation of the commissioning stages of high-performance semiconductor frequency converters. Attention is paid to increasing the reliability of the synthesis of the regulation system using the method of synchronous detection of measured signals. It is shown that the proposed frequency synthesis methods allow us to construct a mathematical model of the object being identified. The proposed generalized methods for the synthesis of current control loops, electromagnetic moment, based on the method of experimental frequency characteristics can be useful for technological objects, which allow the study of frequency characteristics without disturbing the conditions of the process. The proposed method has been quite successfully tested on the example of objects of drilling rigs: electric drive for lifting winch and electric drives for drilling pumps. By increasing the reliability of the synthesis of the current control loops and the electromagnetic torque of semiconductor converters, it was possible to significantly expand the uniform transmission band of the current control loop by about 20%. It is established that an experimental technique for determining the frequency characteristics of CCL has been proposed and tested, which makes it possible to evaluate the capabilities of CCLs of various electric drives and indicates the possibilities for expanding the band of uniform transmission of the frequencies of the CCLs under investigation. On the other hand, the extension of the band of uniform transmission of CCL allows increasing the limiting values of the rotational speed of the motor. This allows you to raise the speed of powerful compressor units without the use of multipliers to 6000 - 9000 rpm.

Discrete-time counterparts of the RL and RC multipliers

The use of multipliers is an important technique for absolute stability analysis. In the continuous-time domain, the RL and RC multipliers preserve the positivity of memoryless and monotone nonlinearities. We classify their discrete-time counterparts and analyse their phase properties. The classification of the discrete-time counterparts of the multipliers is richer than that of their continuous-time counterparts. Some classes of multipliers that preserve the positivity of memoryless and monotone nonlinearities are parametrised in term of their zero-pole description. Similarly we discuss classes that preserve the positivity of memoryless, monotone and symmetric nonlinearities. Multipliers that lie outside such classes are illustrated with numerical counterexamples. We give and prove the phase properties of the discrete-time counterparts of the multipliers. The phase properties are less flexible compared to those of their continuous-time counterparts.

State Investment Policy and Priorities of Macroeconomic Structure of Regional Economy Transformation

The article is dedicated to the urgent problem of the state investment policy formation directed at transforming macrostructure of the regional economy. The aim of such transformation is to form long-term competitiveness of the regional economy, taking into account global changes in domestic and world economy.The object of research is the regional economy of Rostov Region. The article provides the macroeconomic dynamic and comparative analysis of the regional economy; it defines the place of Rostov Region in the economic potential of Russia and Southern macro region.The aspects of goal-setting formation in the investment development of the region are studied. The given article presents detailed analyses of methodological approaches of the prioritization of state investment policy aimed at transforming the macro-structure of the regional economy, including the use of multipliers, production functions, priorities based on the forecast and strategy of Russia's long-term scientific and technological development and the scientific and technological initiative.As a result of the carried out research, the authors systematized approaches to form priorities of the macrostructure transformation of the regional economy in the process of implementation of the state investment policy and identified main tasks for making decisions in this area.

A CORDIC based Configurable Fixed-Point Design on FPGA using Minimal Hardware

Objectives: To design a COordinate Rotation DIgital Computer (CORDIC) based electrical signal processing system for efficient and minimalist electrical power processing. Methods: This stage of assessment describes the design and performance of a true RMS (Root-Mean Square) voltage meter on Xilinx SPARTAN 3E 1600 FPGA. Findings: The effectiveness of the proposed designs is assessed through FPGA implementations and error simulations. Measurement results show that the model can reproduce behaviors similar to the original model traditional use of multipliers, but with less resource consumption (hardware and processing time). Novelty: The use of CORDIC to reduce the computational cost of the algorithm, and its implementation in an embedded system.

Spike-based compact digital neuromorphic architecture for efficient implementation of high order FIR filters

Finite impulse response (FIR) filters have been widely used in many digital signal processing applications because of their interesting properties such as phase linearity, bounded-input-bounded-output (BIBO) stability and easy implementation. However, the implementation of high order filters requires many multipliers. Several authors have proposed new methods such as multiple constant multiplication (MCM), distributed arithmetic (DA) and signed-powers-of-two (SPT) to avoid the use of multipliers by using the minimum number of registers. Nevertheless, the implementation of these strategies generates critical paths that limit their performance. In this work, we propose a compact hardware architecture to compute FIR filters at high processing speeds using new parallel neural multipliers. The scalability of the system enables the construction of many neural multiplier units for higher order FIR filters. To validate the proposal, the hardware architecture was implemented on the Kintex-7 field programmable gate array (FPGA) development kit. The results demonstrate that this new approach can be interfaced with the conventional binary systems to improve advanced signal processing applications such as high quality media contents, wireless communication and biomedical systems.

Noninvasive genetic spatial capture‐recapture for estimating deer population abundance

ABSTRACTInformed management of wildlife populations requires the accurate estimation of abundance, sex ratio, and other population parameters. For deer (Odocoileus spp.), the use of closed‐population, capture‐recapture (CR) methods, in conjunction with noninvasive DNA sampling, has become increasingly practical, but, up to now, these methods have been used in a non‐spatial modeling framework, which has limited their utility for population‐level inferences. In particular, extrapolation of plot‐level CR abundance estimates to the population required the use of multipliers of unknown reliability and potential bias. Spatially explicit capture‐recapture (SCR) models provide an integrated framework for directly estimating density as a function of spatial and habitat variables at landscape scales. We used fecal DNA samples in conjunction with SCR to estimate density, sex ratio, and habitat correlates to density for a mule deer (O. hemionus) population across a large (∼500 km2) area in the central Sierra Nevada Range, California, USA during 2013 and 2014. We surveyed 24 random transects within 4 30‐km2 sites representative of the study area. Based on 411 samples genotyped at a sex marker and 8–10 microsatellite loci, the sex‐ratio for the study area was 62 (95% CI = 41–93) males/100 females in 2013 and 65 (95% CI = 45–94) males/100 females in 2014. Using SCR, we estimated density at 5.0 (95% CI = 2.3–7.8) deer/km2 in 2013 and 5.1 (95% CI = 3.1–7.2) deer/km2 in 2014. In comparison, non‐spatial CR analysis produced density estimates on average 60% higher, likely reflecting bias resulting from use of the commonly employed mean maximum recapture distance (MMRD) to estimate effective sampling area. The SCR models indicated that density was effectively homogeneous throughout the study area, with no strong relationship to habitat correlates. Altogether, these results demonstrate the utility of noninvasive fecal DNA methods in a SCR framework for estimation of abundance and density in deer populations at landscape scales. © 2017 The Wildlife Society

Recurrently Decomposable 2-D Convolvers for FPGA-Based Digital Image Processing

Two-dimensional (2-D) convolution is a widely used operation in image processing and computer vision, characterized by intensive computation and frequent memory accesses. Previous efforts to improve the performance of field-programmable gate array (FPGA) convolvers focused on the design of buffering schemes and on minimizing the use of multipliers. A recently proposed recurrently decomposable (RD) filter design method can reduce the computational complexity of 2-D convolutions by splitting the convolution between an image and a large mask into a sequence of convolutions using several smaller masks. This brief explores how to efficiently implement RD-based 2-D convolvers using FPGA. Three FPGA architectures are proposed based on RD filters, each with a different buffering scheme. The conclusion is that RD-based architectures achieve higher area efficiency than other previously reported state-of-the-art methods, especially for larger convolution masks. An area efficiency metric is also suggested, which allows the most appropriate architecture to be selected.