Efficient Scheme Research Articles

A robust hybridizable discontinuous Galerkin scheme with harmonic averaging technique for steady state of real-world semiconductor devices

Solving real-world nonlinear semiconductor device problems modeled by the drift-diffusion equations coupled with the Poisson equation (also known as the Poisson-Nernst-Planck equations) necessitates an accurate and efficient numerical scheme which can avoid non-physical oscillations even for problems with extremely sharp doping profiles. In this paper, we propose a flexible and high-order hybridizable discontinuous Galerkin (HDG) scheme with harmonic averaging (HA) technique to tackle these challenges. The proposed HDG-HA scheme combines the robustness of finite volume Scharfetter-Gummel (FVSG) method with the high-order accuracy and hp-flexibility offered by the locally conservative HDG scheme. The coupled Poisson equation and two drift-diffusion equations are simultaneously solved by the Newton method. Indicators based on the gradient of net doping and solution variables are proposed to switch between cells with HA technique and high-order conventional HDG cells, utilizing the flexibility of HDG scheme. Numerical results suggest that the proposed scheme does not exhibit oscillations or convergence issues, even when applied to heavily doped and sharp PN-junctions. Devices with circular junctions and realistic doping profiles are simulated in two dimensions, qualifying this scheme for practical simulation of real-world semiconductor devices.

Quantum next generation reservoir computing: an efficient quantum algorithm for forecasting quantum dynamics

Next Generation Reservoir Computing (NG-RC) is a modern class of model-free machine learning that enables an accurate forecasting of time series data generated by dynamical systems. We demonstrate that NG-RC can accurately predict full many-body quantum dynamics in both integrable and chaotic systems. This is in contrast to the conventional application of reservoir computing that concentrates on the prediction of the dynamics of observables. In addition, we apply a technique which we refer to as skipping ahead to predict far future states accurately without the need to extract information about the intermediate states. However, adopting a classical NG-RC for many-body quantum dynamics prediction is computationally prohibitive due to the large Hilbert space of sample input data. In this work, we propose an end-to-end quantum algorithm for many-body quantum dynamics forecasting with a quantum computational speedup via the block-encoding technique. This proposal presents an efficient model-free quantum scheme to forecast quantum dynamics coherently, bypassing inductive biases incurred in a model-based approach.

Phasor-Based Myoelectric Synergy Features: A Fast Hand-Crafted Feature Extraction Scheme for Boosting Performance in Gait Phase Recognition.

Gait phase recognition systems based on surface electromyographic signals (EMGs) are crucial for developing advanced myoelectric control schemes that enhance the interaction between humans and lower limb assistive devices. However, machine learning models used in this context, such as Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM), typically experience performance degradation when modeling the gait cycle with more than just stance and swing phases. This study introduces a generalized phasor-based feature extraction approach (PHASOR) that captures spatial myoelectric features to improve the performance of LDA and SVM in gait phase recognition. A publicly available dataset of 40 subjects was used to evaluate PHASOR against state-of-the-art feature sets in a five-phase gait recognition problem. Additionally, fully data-driven deep learning architectures, such as Rocket and Mini-Rocket, were included for comparison. The separability index (SI) and mean semi-principal axis (MSA) analyses showed mean SI and MSA metrics of 7.7 and 0.5, respectively, indicating the proposed approach's ability to effectively decode gait phases through EMG activity. The SVM classifier demonstrated the highest accuracy of 82% using a five-fold leave-one-trial-out testing approach, outperforming Rocket and Mini-Rocket. This study confirms that in gait phase recognition based on EMG signals, novel and efficient muscle synergy information feature extraction schemes, such as PHASOR, can compete with deep learning approaches that require greater processing time for feature extraction and classification.

Plug-and-play four-state modulation continuous-variable quantum key distribution

In this paper, a novel and highly efficient secure communication scheme is proposed through the integration of four-state modulation quantum key distribution and plug-and-play technology. This scheme serves the purpose of transmitting information reliably while preserving data confidentiality between two communicating parties. The most important advantage of the proposed continuous-variable QKD system is that the birefringence effect in fiber can be compensated automatically. Notably, simulation experimental evaluations are conducted, unveiling the performance of the plug-and-play four-state QKD which is as good as the four-state protocol when having small Rayleigh scattering noise. Furthermore, we conduct a comprehensive analysis of potential security threats targeted at the proposed protocol, thereby substantiating its robustness against such attacks. Moreover, theoretical justifications are provided to validate the security of the proposed scheme.

Efficient polynomial-time approximation scheme for the genus of dense graphs

The main results of this paper provide an Efficient Polynomial-Time Approximation Scheme (EPTAS) for approximating the genus (and non-orientable genus) of dense graphs. By dense we mean that | E ( G )| ≥ α | V ( G )| 2 for some fixed α > 0. While a constant-factor approximation is trivial for this class of graphs, approximations with factor arbitrarily close to 1 need a sophisticated algorithm and complicated mathematical justification. More precisely, we provide an algorithm that for a given (dense) graph G of order n and given ε > 0, returns an integer g such that G has an embedding in a surface of genus g , and this is ε-close to a minimum genus embedding in the sense that the minimum genus \(\mathsf {g}(G) \) of G satisfies: \(\mathsf {g}(G)\le g\le (1+\varepsilon)\mathsf {g}(G) \) . The running time of the algorithm is O ( f (ε) n 2 ), where f (·) is an explicit function. Next, we extend this algorithm to also output an embedding (rotation system) whose genus is g . This second algorithm is an Efficient Polynomial-time Randomized Approximation Scheme (EPRAS) and runs in time O ( f 1 (ε) n 2 ). Our algorithms are based on the analysis of minimum genus embeddings of quasirandom graphs. We use a general notion of quasirandom graphs [25]. We start with a regular partition obtained via an algorithmic version of the Szemerédi Regularity Lemma (due to Frieze and Kannan [17] and to Fox, Lovász, and Zhao [14, 15]). We then partition the input graph into a bounded number of quasirandom subgraphs, which are preselected in such a way that they admit embeddings using as many triangles and quadrangles as faces as possible. Here we provide an ε-approximation ν ( G ) for the maximum number of edge-disjoint triangles in G . The value ν ( G ) can be computed by solving a linear program whose size is bounded by certain value f 2 (ε) depending only on ε. After solving the linear program, the genus can be approximated (see Corollary). The proof of this result is long and will be of independent interest in topological graph theory.

Emas: an efficient MLWE-based authentication scheme for advanced metering infrastructure in smart grid environment

Emas: an efficient MLWE-based authentication scheme for advanced metering infrastructure in smart grid environment

An unconditional stable modified finite element methods for Maxwell’s equation in Kerr-type nonlinear media

The purpose of this paper is to construct a newly efficient finite element method which named as modified finite element method, for Maxwell’s equation in Kerr-type nonlinear media. Because the efficient unconditional stable alternative direction implicit method can’t extend to the Maxwell’s equations in Kerr-type nonlinear media (the difficulty will be discussed in section 3.1), we design the modified method to construct an efficient unconditional stable scheme for Maxwell’s equations in Kerr-type nonlinear media. Furthermore, combined with the direct method, we construct a more efficient direct modified scheme. Comparisons of the efficiency, stability, convergence rate for our modified method and classical explicit leapfrog method were done by theoretical analysis and numerical experiments. The modified method is unconditional stable and its efficiency is not less than leapfrog method.

Optimized liquid chromatography-tandem mass spectrometry protocol for enhanced detection of 45 pesticides in water and soil samples.

The development of analytical screening techniques for pesticides is crucial for preventing and mitigating environmental contamination. Mass spectrometry-based screening methods differ based on the complexity of the sample matrix and the diversity of the target compounds. One of the major challenges is balancing cost reduction in the extraction process with the optimization of analytical results. This protocol introduces a universal and efficient scheme for the qualitative and quantitative schemes for 45 pesticides within a single analytical run. Water samples were extracted using an SPE column, with the pH adjusted to 7. Soil samples were processed using a modified QuEChERS method. The pretreatment for water samples emphasized selecting appropriate SPE columns and optimizing pH, while for soil samples, the focus was on choosing suitable extraction solvents and extraction salt packages. The enriched samples were then analyzed using liquid chromatography-tandem mass spectrometry (LC-MS/MS). The method was evaluated for accuracy, precision, detection limits, and matrix effects. The method enabled the simultaneous detection of 45 pesticides within a 15-minute analysis period. SPE recoveries ranged from 56.1% to 118.8%. Instrumental detection limits varied between 0.02 and 1pg, while method detection limits extended from 0.05 to 18.47 ng/lin soil and water matrices. The approach was successfully applied to water and soil samples, with the pesticide concentration ranging from 0.1ng/L to 38 μg/L. The protocol substantially enhances the characterization and quantification of 45 pesticides in environmental samples, achieving a remarkable reduction in detection limits by an order of magnitude compared to previous research. This method enables the simultaneous detection of pesticides in both water and soil matrices using a single system, addressing the challenges of using separate systems for different environmental media. Furthermore, this protocol provides a crucial theoretical foundation for managing and safeguarding against pesticide pollution.

Retinal Vessel Segmentation by a Transformer-U-Net Hybrid Model With Dual-Path Decoder.

This paper introduces an effective and efficient framework for retinal vessel segmentation. First, we design a Transformer-CNN hybrid model in which a Transformer module is inserted inside the U-Net to capture long-range interactions. Second, we design a dual-path decoder in the U-Net framework, which contains two decoding paths for multi-task outputs. Specifically, we train the extra decoder to predict vessel skeletons as an auxiliary task which helps the model learn balanced features. The proposed framework, named as TSNet, not only achieves good performances in a fully supervised learning manner but also enables a rough skeleton annotation process. The annotators only need to roughly delineate vessel skeletons instead of giving precise pixel-wise vessel annotations. To learn with rough skeleton annotations plus a few precise vessel annotations, we propose a skeleton semi-supervised learning scheme. We adopt a mean teacher model to produce pseudo vessel annotations and conduct annotation correction for roughly labeled skeletons annotations. This learning scheme can achieve promising performance with fewer annotation efforts. We have evaluated TSNet through extensive experiments on five benchmarking datasets. Experimental results show that TSNet yields state-of-the-art performances on retinal vessel segmentation and provides an efficient training scheme in practice.

An Efficient RLWE-Based Privacy-Preserving Authentication Scheme Based on Edge Computing in Industrial Internet of Things

An Efficient RLWE-Based Privacy-Preserving Authentication Scheme Based on Edge Computing in Industrial Internet of Things

A Distributed Efficient Blockchain Oracle Scheme for Internet of Things

A Distributed Efficient Blockchain Oracle Scheme for Internet of Things

Efficient and Security-Enhanced Certificateless Aggregate Signature-Based Authentication Scheme With Conditional Privacy Preservation for VANETs

Efficient and Security-Enhanced Certificateless Aggregate Signature-Based Authentication Scheme With Conditional Privacy Preservation for VANETs

Efficient numerical scheme with full decoupling, second-order temporal accuracy and unconditional energy stability for a flow-coupled melt-convective dendritic solidification phase-field model

Efficient numerical scheme with full decoupling, second-order temporal accuracy and unconditional energy stability for a flow-coupled melt-convective dendritic solidification phase-field model

An Efficient and Multi-Private Key Secure Aggregation Scheme for Federated Learning

An Efficient and Multi-Private Key Secure Aggregation Scheme for Federated Learning

Efficient option pricing in the rough Heston model using weak simulation schemes

We provide an efficient and accurate simulation scheme for the rough Heston model in the standard (H>0) as well as the hyper-rough regime ( -1/2 $ ]]> H > − 1 / 2 ). The scheme is based on low-dimensional Markovian approximations of the rough Heston process derived in [Bayer and Breneis, arXiv:2309.07023], and provides a weak approximation to the rough Heston process. Numerical experiments show that the new scheme exhibits second-order weak convergence, while the computational cost increases linearly with respect to the number of time steps. In comparison, existing schemes based on discretization of the underlying stochastic Volterra integrals such as Gatheral's HQE scheme show a quadratic dependence of the computational cost. Extensive numerical tests for standard and path-dependent European options and Bermudan options show the method's accuracy and efficiency.

An efficient certificateless blockchain-enabled authentication scheme to secure producer mobility in named data networks

Named Data Networking (NDN) aims to establish an efficient content delivery architecture. In NDN, secure and effective identity authentication schemes ensure secure communication between producers and routers. Currently, there is no feasible solution to perform identity authentication of mobile producers in NDNs. Identity authentication schemes in other networks are either weak in security or performance, such as privacy leakage, difficulty to establish cross-domain trust, and long handover delays, and are not fully adaptable to the security requirements of NDNs. Additionally, the mobility of producers was not fully considered in the initial design of NDNs. This paper first revises the structure of packets and routers to support the identity authentication and mobility of producers. On this basis, this paper proposes a secure and efficient certificateless ECC-based producer identity authentication scheme (CL-BPA), which includes initial authentication and re-authentication, aimed at achieving rapid switch authentication and integrating blockchain to solve single-point failure issues. Using the Canetti and Krawczyk (CK) adversarial model and informal security analysis, the proposed CL-BPA scheme is demonstrated to be resistant to anonymity attacks, identity forgery attacks, and man-in-the-middle attacks. The performance analysis demonstrates that the proposed CL-BPA scheme exhibits excellent capabilities in terms of computation delay, communication cost, smart contract execution time, average response delay, and throughput.

Node Role Selection and Rotation Scheme for Energy Efficiency in Multi-Level IoT-Based Heterogeneous Wireless Sensor Networks (HWSNs).

The emergence of Internet of Things (IoT)-based heterogeneous wireless sensor network (HWSN) technology has become widespread, playing a significant role in the development of diverse human-centric applications. The role of efficient resource utilisation, particularly energy, becomes further critical in IoT-based HWSNs than it was in WSNs. Researchers have proposed numerous approaches to either increase the provisioned resources on network devices or to achieve efficient utilisation of these resources during network operations. The application of a vast proportion of such methods is either limited to homogeneous networks or to a single parameter and limited-level heterogeneity. In this work, we propose a multi-parameter and multi-level heterogeneity model along with a cluster-head rotation method that balances energy and maximizes lifetime. This method achieves up to a 57% increase in throughput to the base station, owing to improved intra-cluster communication in the IoT-based HWSN. Furthermore, for inter-cluster communication, a mathematical framework is proposed that first assesses whether the single-hop or multi-hop inter-cluster communication is more energy efficient, and then computes the region where the next energy-efficient hop should occur. Finally, a relay-role rotation method is proposed among the potential next-hop nodes. Results confirm that the proposed methods achieve 57.44%, 51.75%, and 17.63% increase in throughput of the IoT-based HWSN as compared to RLEACH, CRPFCM, and EERPMS, respectively.

BIAS: A novel secure and efficient biometric-based anonymous authentication scheme

Currently, biometric-based authentication schemes are widely adopted in the field of online payments. Consequently, this has led to an increasing number of people becoming concerned about the privacy-preservation of their biometric data. Gunasinghe et al. presented PrivBioMTAuth, a solution for mobile phone biometric-based authentication designed to protect users’ privacy. However, the solution has drawbacks, such as its impact on the execution efficiency of the authentication protocol or its vulnerability to man-in-the-middle attacks during the authentication phase. Moreover, the user’s biometric image and the password must be revealed to the identity provider, which may raise security concerns. In this work, we present a novel secure and efficient biometric-based anonymous authentication solution with fully succinct verification and significantly lower storage and communication overhead. Different from PrivBioMTAuth, we rely on the NIZK argument given in Groth’s work to reduce the size of the anonymous identity and simplify the verification complexity. In addition, we design a high-performance protocol for conducting large-scale verification of the user’s anonymous identities. We propose an optimized multi-exponentiation argument based on Bayer et al.’s work and utilize it to ensure that a semi-trusted identity provider who seeks to access the users’ sensitive biometric information can faithfully execute the users’ identity registration protocol. The experiment results show that our proposed scheme is efficient and has privacy-preserving capabilities, and it can be applied in the resource-constrained devices.

The Continuous-Time Joint Replenishment Problem: ϵ-Optimal Policies via Pairwise Alignment

The main contribution of this paper resides in developing a new algorithmic approach for addressing the continuous-time joint replenishment problem, termed [Formula: see text]-pairwise alignment. The latter mechanism, through which we synchronize multiple economic order quantity models, allows us to devise a purely combinatorial algorithm for efficiently approximating optimal policies within any degree of accuracy. As a result, our work constitutes the first quantitative improvement over power-of-2 policies, which have been state-of-the-art in this context since the mid-1980s. Moreover, in light of recent intractability results, by proposing an efficient polynomial-time approximation scheme for the joint replenishment problem, we resolve the long-standing open question regarding the computational complexity of this classical setting. This paper was accepted by Chung Piaw Teo, optimization. Funding: This work was supported by the Israel Science Foundation [Grant 1407/20].

Simultaneous frequency and angle-of-arrival measurement of microwave signals utilizing the Talbot effect and pulse interference

We propose and demonstrate a simple and efficient scheme for simultaneous frequency and angle-of-arrival (AOA) measurement of microwave signals using the Talbot effect and pulse interference. This method maps the frequency of the microwave signal to the time intervals of output pulses through the Talbot effect, using a specialized sampling and dispersive structure. Concurrently, the phase difference related to the AOA is encoded onto the relative amplitudes of the output pulses via pulse interference. By analyzing both the time intervals and relative amplitudes of the output pulses, precise frequency and AOA values of the microwave signal are simultaneously derived. Simulation results indicate that the system can perform real-time, gap-free frequency and AOA measurements of multiple signals. Proof-of-concept experiments show this method achieves high accuracy, with AOA measurement errors within ±2.9° over a range of −63° to 63°, and frequency measurement errors within ±60 MHz for frequency bands of 2–3 GHz and 12–13 GHz.