Two-phase Scheme Research Articles

A Two-Phase Scheme by Integration of Deep and Corner Feature for Balanced Copy-Move Forgery Localization

A Two-Phase Scheme by Integration of Deep and Corner Feature for Balanced Copy-Move Forgery Localization

A Two-Phase Multi-Bit Incremental ADC With Variable Loop Order

This brief presents a two-phase multi-bit incremental analog-to-digital converter (IADC) with variable loop order. In the 1st phase, the loop filter works as a 1st-order topology. In the 2nd phase, the loop reconfigures to a 3rd-order structure, aiming to get the signal-to-quantization-noise ratio (SQNR) boosted quickly within a few clock cycles. Such a two-phase scheme with variable loop-order combines the features of the KT/C noise suppression and high effectiveness of data weighting averaging (DWA) presented by the 1st-order IADC and fast accumulation obtained from the high-order mode. Thereby, with little additional circuitry effort, the proposed IADC improves DWA effectiveness while mitigating the thermal noise penalty when compared with a pure high-order IADC. The proposed architecture is analytically analyzed and exemplarily simulated. Moreover, a design guideline is provided to optimize the allocation of the clock cycles of two phases, thus balancing various significant parameters. Based on the guideline, a circuit-level simulation of an exemplary 1st-to-3rd order IADC was carried out in a 65-nm CMOS process to confirm the results.

Achieving ultra-uniform temperature distribution with a heterogeneous wettability surface for two-phase flow in microchannels

Microchannel-based two-phase schemes have been proposed as an efficient solution for thermal management. However, the non-uniform temperature distribution in flow boiling limits phase-change performance and impedes commercial applications. In this study, we introduce a novel surface featuring heterogeneous wettability, comprising upstream superhydrophobic, middle plain, and downstream superhydrophilic regions. Hierarchical nanowires are employed to achieve the desired wettability, which can promote bubble departure and enhance capillary pumping. This wettability distribution facilitates upstream bubble nucleation and downstream liquid supply, significantly improving temperature uniformity. Moreover, surface contact angle variations effectively contribute to flow behavior improvement by restricting vapor reversal and fostering churn flow. Our results demonstrate that heat aggregation in the middle region and temperature restriction in the downstream region cause the high-temperature region to migrate upstream. This temperature redistribution stimulates axial heat transfer, substantially suppressing inlet subcooling and restricting temperature gradients. Compared to the plain surface, the heterogeneous wettability surface afforded an 87.8% reduction in temperature difference between Tw1 and Tw5 at approximately 120 °C, suggesting an ultra-uniform temperature distribution. Additionally, the average heat transfer coefficient showed a 35.1% enhancement rate at a wall heat flux of 100 W/cm2, and the performance evaluation criterion showed a 44.2% enhancement rate at an effective heat flux of 190 W/cm2. These results indicate that the proposed surface can efficiently regulate temperature distribution, maintain performance stability, and enhance heat transfer. The heterogeneous wettability surface has considerable potential for practical applications in high-power component heat dissipation.

Hardware-Impaired RIS-Assisted mmWave Hybrid Systems: Beamforming Design and Performance Analysis

Reconfigurable intelligent surface (RIS) has been envisioned as an innovative technology to assist millimeter wave (mmWave) communications. Thanks to both advantages of low hardware cost and low power consumption, the hybrid transceiver structure also becomes an integral component of mmWave systems. However, due to practical limitations of hardware components, the RIS-assisted mmWave communications usually suffer unavoidable hardware impairments (HWIs). In this paper, we aim to minimize the (sum) MSE and maximize the average rate of the hardware-impaired RIS-assisted point-to-point mmWave MIMO system, respectively, by jointly optimizing the hybrid transceiver and RIS reflection coefficients under the realistic discrete phase shift constraints. We firstly consider the single-antenna user case and propose efficient alternating optimization (AO) algorithms to solve the two intractable problems. A binary-oriented exact penalty (BEP) method is developed for the involved discrete optimization, which is able to strike a good trade-off between performance and complexity. Moreover, we analyze the optimality of AO algorithms under the cascaded line-of-sight (LoS) channel condition, and reveal both the MSE floor effect and average rate saturation effect in the high-SNR regime. The above studies are then extended to the general multi-antenna user case, where a low-complexity two-phase scheme with the aim of creating the favorable RIS-cascaded channel in the first phase and enhancing system performance in the second phase is proposed. This two-phase scheme is also demonstrated to attain the optimal performance in the LoS scenario. Numerical results validate our theoretical analysis and illustrate superior performance of the proposed algorithms over various benchmark schemes.

Two-Phase Virtual Machine Placement in Cloud Computing Data Centers Using Extreme Learning Machine Prediction-Based Triggering Method

Background: Two-phase Optimization of Virtual Machine Placement (VMP) Problem considers both the Online Incremental VMP (iVMP) phase in which the new arrival of dynamic requests of Virtual Machines VMs are attended to and the Offline VMP reconfiguration (VMPr) phase that performs placement recalculation. In the two-phase scheme, the first part of the two-phase approach is the iVMP, where virtual machines (VMs) can be built, changed, or destroyed at runtime. While the second phase focuses on raising the standard of solutions produced by the iVMP, several studies have been done in different literature to solve the VMP problem. However, the methods used tend to be over-forecast and have long runs of a linear trend. This affects the prediction and produces a less optimal solution. Objective: The following four objective functions are optimized using the proposed Extreme Learning Machine Prediction-Based Triggering Method for Virtual Machine Placement in Cloud Computing Datacenters in Two-Phases, which combines the advantages of both online (dynamic) and static (offline) VMP formulations: the length of the reconfiguration process, the amount of energy used, the way resources are used, and the financial expenses. This study suggests a brand-new strategy for deciding when to start the VMP reconfiguration phase. Results: The Method provides more accuracy to the predicted requests as well as reduces the total economic penalties for service Level Agreement(SLA) violations. An experimental comparison with the existing approach is conducted utilizing 400 cases. Conclusion: The results demonstrated that, in comparison to the benchmark approach, the proposed work obtained a minimum cost function with a 10.5% improvement

Modeling and simulation of nanofluid in low Reynolds numbers using two-phase Lattice Boltzmann method based on mixture model

In the present work, a novel two-phase scheme was implemented for modeling and simulation of Al2O3/water nanofluid within a rectangular channel with the Lattice Boltzmann method based on the mixture model. In this proposed model, a two-phase approach was adopted to consider the effect of the drift velocity of each phase. The simulations are performed in two cases at laminar flow regime and low Reynolds numbers (Re=0.5 in this case). In the first case, the results of the velocity distribution of the nanofluid through the channel were numerically validated with the finite element method results, and the predicted velocity profile at Reynolds numbers of 1, 10, and 50 showed good agreement with the velocities extracted from FEM. In the second case, the velocity distribution and particle volume fraction of the nanofluid were investigated at Reynolds numbers 0.5, 1, and 10. The results showed that considering the drift velocity in the modeling has remarkable effects only at low Reynolds numbers (Re=0.5 in this case), when the Reynolds number increases, the effect of drift velocity can be ignored. The proposed Lattice Boltzmann model is promisingly strong and suitable for engineering and scientific problems.

An overview of non-Newtonian nanofluid flow in macro- and micro-channels using two-phase schemes

This review paper provides an overview of heat transfer of non-Newtonian nanofluids flowing in macro- and micro-channels using single-phase and two-phase approaches. This paper addresses governing equations for different types of non-Newtonian fluids and provides several advances in the field. The single-phase and two-phase schemes are described and their advantages and drawbacks in the simulation of non-Newtonian nanofluids in macro- and micro-channels are introduced. It is demonstrated that single-phase approaches cannot provide precise predictions due to the lack of accurate correlations because effective parameters change with the properties of nanofluids. The literature review indicates that the distribution of nanoparticles in the base fluid should be recognized properly. Understanding the concentration of nanoparticles and also their migration in channels or ducts results in demonstrating the formation of nanoparticle clustering. It is revealed that almost all investigators utilize the power-law model to simulate non-Newtonian nanofluid flow in macro- and micro-channels. According to the discussion presented in the paper, perspectives and future direction are provided.

A Secure Dynamic Content Delivery Scheme in Named Data Networking

Named data networking (NDN) is a promising alternative data dissemination technology of TCP/IP communication networks, which can bring out much more cost-effective and resilient communication in a highly mobile environment. However, due to the feature of NDN, content poisoning comes out as a potential threat. Hence, state-of-the-art studies introduce network layer approaches based on name-key binding, in which the producer notifies routers of the bindings of names and key values. Key values include publisher public key digest or content digest. Routers check key values to determine whether incoming data packets have been poisoned. Unfortunately, the approaches lead to more vulnerabilities in dynamic content poisoning because attackers can impersonate the producer to alter or fabricate the bindings. Thus, we introduce a consumer-oriented two-phased lightweight security scheme, which consists of an end-to-end authentication and a packet-level name-key query mechanism. Specifically, the name-key bindings are authenticated via an additional verification by the consumer. Furthermore, we also introduce a novel trust model to help routers to determine and disconnect from the malicious nodes. Finally, our extensive experimental results demonstrate that the scheme can work effectively in improving the vulnerability of existing studies on dynamic content poisoning and lowering the system overhead simultaneously.

Structure-Attribute-Based Social Network Deanonymization With Spectral Graph Partitioning

Online social networks have gained tremendous popularity and have dramatically changed the way we communicate in recent years. However, the publishing of social network data raises more and more privacy concerns. To protect user privacy, social networking data are usually anonymized before being released. Nevertheless, existing anonymization techniques do not have sufficient protection effects. A large number of deanonymization attacks have arisen, and they mainly make use of either network topology or node attribute information to successfully reidentify anonymized users. In this article, we model a social network as a structure-attribute network (SAN) integrating the structural characteristics and the attribute information associated with social network users. A novel similarity measurement of social network nodes is proposed by considering the structural similarity and attribute similarity. A two-phase scheme is then designed to perform deanonymization by first dividing a social network (graph) into smaller subgraphs based on spectral graph partitioning and then applying the proposed deanonymization algorithm on each matched subgraph pair. We simulate the deanonymization attack with extensive experiments on three real-world datasets, and the experimental results demonstrate that our approach can improve the accuracy and time complexity of deanonymization compared with the state of the art.

Automatic facial skin feature detection for everyone

Automatic assessment and understanding of facial skin condition have several applications, including the early detection of underlying health problems, lifestyle and dietary treatment, skin-care product recommendation, etc. S Selfies in the wild serve as an excellent data resource to democratize skin quality assessment, but suffer from several data collection challenges. The key to guaranteeing an accurate assessment is accurate detection of different skin features. We present an automatic facial skin feature detection method that works across a variety of skin tones and age groups for selfies in the wild. To be specific, we annotate the locations of acne, pigmentation, and wrinkle for selfie images with different skin tone colors, severity levels, and lighting conditions. The annotation is conducted in a two-phase scheme with the help of a dermatologist to train volunteers for annotation. We employ Unet++ as the network architecture for feature detection. This work shows that the two-phase annotation scheme can robustly detect the accurate locations of acne, pigmentation, and wrinkle for selfie images with different ethnicities, skin tone colors, severity levels, age groups, and lighting conditions.

Remote Arrhythmia Detection for Eldercare in Malaysia.

Cardiovascular disease continues to be one of the most prevalent medical conditions in modern society, especially among elderly citizens. As the leading cause of deaths worldwide, further improvements to the early detection and prevention of these cardiovascular diseases is of the utmost importance for reducing the death toll. In particular, the remote and continuous monitoring of vital signs such as electrocardiograms are critical for improving the detection rates and speed of abnormalities while improving accessibility for elderly individuals. In this paper, we consider the design and deployment characteristics of a remote patient monitoring system for arrhythmia detection in elderly individuals. Thus, we developed a scalable system architecture to support remote streaming of ECG signals at near real-time. Additionally, a two-phase classification scheme is proposed to improve the performance of existing ECG classification algorithms. A prototype of the system was deployed at the Sarawak General Hospital, remotely collecting data from 27 unique patients. Evaluations indicate that the two-phase classification scheme improves algorithm performance when applied to the MIT-BIH Arrhythmia Database and the remotely collected single-lead ECG recordings.

Some Improved Estimators of Population Mean using Two-Phase Sampling Scheme in the Presence of Non-Response

In the present paper, we have proposed some improved estimators of the population mean utilizing the information on two auxiliary variables adopting the idea of two-phase sampling under non-response. In order to propose the estimators, we have assumed that the study variable and first auxiliary variable suffer from non-response while the second (additional) auxiliary variable is free from non-response. We have derived the expressions for biases and mean square errors of the proposed estimators and compared them with that of usual estimator and some well known existing estimators of the population mean. The theoretical results have also been illustrated with some empirical data.

Two-Phase Deep Learning-Based EDoS Detection System

Cloud computing is currently considered the most cost-effective platform for offering business and consumer IT services over the Internet. However, it is prone to new vulnerabilities. A new type of attack called an economic denial of sustainability (EDoS) attack exploits the pay-per-use model to scale up the resource usage over time to the extent that the cloud user has to pay for the unexpected usage charge. To prevent EDoS attacks, a few solutions have been proposed, including hard-threshold and machine learning-based solutions. Among them, long short-term memory (LSTM)-based solutions achieve much higher accuracy and false-alarm rates than hard-threshold and other machine learning-based solutions. However, LSTM requires a long sequence length of the input data, leading to a degraded performance owing to increases in the calculations, the detection time, and consuming a large number of computing resources of the defense system. We, therefore, propose a two-phase deep learning-based EDoS detection scheme that uses an LSTM model to detect each abnormal flow in network traffic; however, the LSTM model requires only a short sequence length of five of the input data. Thus, the proposed scheme can take advantage of the efficiency of the LSTM algorithm in detecting each abnormal flow in network traffic, while reducing the required sequence length of the input data. A comprehensive performance evaluation shows that our proposed scheme outperforms the existing solutions in terms of accuracy and resource consumption.

Calibration estimator of population mean under stratified two-phase sampling using two auxiliary variables

In the present paper, we have tried to discover a new calibration estimator of the population mean under stratified two-phase sampling design utilizing the information on two auxiliary variables. In order to develop the calibration estimator, we have obtained new optimum stratum weights so that the MSE/Variance of the estimator would be minimized. Moreover, we have compared the efficiency of the proposed calibration estimator with that of the calibration estimator considered under the stratified two-phase sampling scheme using single auxiliary variable. An empirical illustration along with a simulation analysis has also been presented to demonstrate the theoretical results.

Deep and Structure-Preserving Autoencoders for Clustering Data With Missing Information

Most real-life data suffer from missing values. Here we deal with the problem of exploratory analysis, via clustering, of data with missing values. For this we need an effective mechanism to deal with missing features so that all available information can be used for clustering. We propose two autoencoder-based methods for handling of missing data for clustering. The autoencoder is trained in a two-phase scheme using only part of the given data set which does not have any incomplete instances in such a manner that the autoencoder is better equipped to deal with incomplete data. To cluster the entire data set which has instances with missing values, we generate the latent space representation of the all instances, with or without, missing information. Before the incomplete instances are submitted to the autoencoder, the missing inputs are filled in by a k-nearest neighbor-based rule. The clustering is then done in the latent space using the fuzzy-c-means (FCM) algorithm. In the second method, to preserve the “structure” of the input data in the latent space we extend our method by adding Sammon's stress as a regularizer to the objective function of the autoencoder. We test the effectiveness of the proposed algorithms on several data sets and compare the results with five state-of-the-art techniques. For comparison, we use two performance indicators: Normalized Mutual Information (NMI) and Adjusted Rand index (ARI).

Quantification of the impact of traffic incidents on speed reduction: A causal inference based approach

This paper designs a systemic framework to quantify speed reduction induced by traffic incidents using a causal inference framework. The results can provide a reference to traffic managers for evaluating incident severities, thus take proper control measures after the incident in order not to underestimate or overestimate the negative impact. A two-phase scheme is proposed, including impacted region determination and speed reduction quantification. We first propose a Frame Region (FR) method, based on the shockwave propagation, to determine the spatiotemporal impacted region (SIR) using speed map. It is worth-noting that we design a statistical experiment to prove the rationality of congestion threshold selection. Secondly, we introduce a causal inference method for identifying the matched freeway segments. The traffic condition of finally matched freeway segments can be served as non-incident traffic condition of the incident occurred location, which contributes to quantifying the incident impact on speed reduction. We further demonstrate the proposed method in a case study by taking advantage of an incident record and related real freeway speed data in China. An interesting observation is that, along with the freeway segments away from the incident location, the congestion duration time of different freeway segments firstly rises and then decreases. The case study also illustrates the impact of incident on speed lasts almost 3 h and the congestion caused by the incident spreads 11 km, while the average causal effect of incident on all the impacted freeway segments is 42.3 km/h.

High speed trains communication systems in 5G cellular networks

In this paper, a new transmitter-receiver structure for high speed train (HST) data transmission and detection in fifth generation (5G) communication systems is proposed. This structure employs a new Doppler shift frequency (DSF) estimation approach, projected in this paper, to calculate the channel taps' DSFs at the base station (BS) receiver. The estimated DSFs are used to calculate the channel taps' angle of departures (AoDs). Considering the estimated AoDs, the transmitter beamformer regulates its coefficients to suppress the non-line of sight channel taps (NLoSCTs) and a DSF compensation block is employed to alleviate the line of sight channel tap's (LoSCT's) DSF. The proposed DSF estimation approach is a two-phase scheme which obtains a coarse estimation of DSFs at the first phase and calculates DSFs more accurately at the second phase. While this two-phase DSF estimation approach is able to estimate DSFs in both multipath rich and Rician channel models, an alternative DSF estimation scheme for Rician channel models is proposed which can estimate the LoSCT's DSF with much lower computational complexity compared to the two-phase DSF estimation method. Simulation results indicate the fidelity of the proposed DSF estimation methods and the proposed transmitter-receiver structure.

Generalized Class of Estimators Using Dual of Auxiliary Variable Under Non-Response

The present study considers the problem of estimating the population mean of study variable by making the dual use of an auxiliary information in the presence of non-response. We have proposed an improved general family of estimators using an auxiliary variable as well as its rank under simple random sampling without replacement (SRSWOR) and two-phase sampling scheme. We have shown the effective performance of proposed class of estimators over usual unbiased, ratio, regression and difference estimators, Singh and Kumar’s (Austral N Z Stat 50(4):395–402, 2008) estimator, Riaz and Shabbir (Pak J Stat, 30(1):83–100, 2014), Yaqub et al.’s (Commun Stat Theory Methods 46(24):12130–12151, 2017) estimator and Irfan et al.’s (Sci Iran, 2020. https://doi.org/10.24200/SCI.2020.53592.3318 ) estimator under SRSWOR and the works: Singh and Kumar’s (Stat Oper Res Trans 33(1):71–84, 2009; Stat Pap 51:559–402, 2010), Kumar’s (Commun Stat Appl Methods 19(5):663–671, 2012), Riaz and Shabbir’s (Pak J Stat, 30(1):83–100, 2014), Yaqub et al.’s (Commun Stat Theory Methods 46(24):12130–12151, 2017) and Irfan et al.’s (Sci Iran, 2020. https://doi.org/10.24200/SCI.2020.53592.3318 ) estimators under two-phase sampling using an empirical study. A simulation study is also carried out in support of our objectives.

Genetic particle filter improved fuzzy-AEEMD for ECG signal de-noising

With the aid of ensemble empirical mode decomposition (EEMD), de-noising of the electrocardiogram (ECG) signal based on the genetic particle filter and fuzzy thresholding is proposed in this paper, which effectively eliminates noise from the ECG signal. This paper proposes a two-phase scheme for removing noise from ECG signal. In the first phase, noisy signal is decomposed into true intrinsic mode functions (IMFs) with the help of EEMD. Adaptive EEMD (AEEMD) is better than EMD because it removes the mode-mixing effect. In the second phase, IMFs which are corrupted by noise are obtained by using spectral flatness of each IMF and fuzzy thresholding. Corrupted IMFs are filtered using genetic particle filter to remove the noise. Finally, the signal is reconstructed with the processed IMFs to get the de-noised ECG. The proposed algorithm is analyzed for different databases and it gives better signal-to-noise ratio and root mean square error than other existing techniques.

Error Quantification and Control for Adaptive Kriging-Based Reliability Updating with Equality Information

• Two-phase reliability updating using adaptive Kriging (two-phase RUAK) is proposed • The maximum error induced by Kriging in posterior failure probability estimate is derived • The error estimate is used as the stopping criterion for the refinement of the Kriging model • This method allows balancing the accuracy and computational demand of reliability updating • Four examples demonstrate the accuracy of the estimated error bounds and updated reliability estimates New information obtained through measurements provide an opportunity to update estimates of a system's reliability. For equality type information, reliability updating is a daunting task. The current state-of-the-art method, reliability updating with equality information using adaptive Kriging (RUAK), integrates an adaptive Kriging process with a transformation of equality information into inequality information. The stopping criterion for training the Kriging model relies on the estimated error for prior failure probability, thus leaving the potential for the true error in posterior failure probability to exceed acceptable thresholds. This study presents an approach to estimate the maximum error in posterior failure probability for a given confidence level . Moreover, a two-phase approach is proposed for active learning and adaptive training of Kriging models in reliability updating problems. The new stopping criterion based on the maximum error of posterior failure probability ensures the accuracy of Kriging and thus the reliability estimates, while the two-phase scheme avoids unnecessary training hence improving the efficiency of reliability updating. Four numerical examples are considered to investigate the performance of the proposed approach. It is demonstrated that this method offers the ability to set and meet target accuracies for reliability updating, which is critical for applications where the consequences of decisions are significant.