Statistical Performance Analysis Research Articles

Tight Logarithmic Approximations and Bounds for Generic Capacity Integrals and Their Applications to Statistical Analysis of Wireless Systems

We present tight yet tractable approximations and bounds for the ergodic capacity of any communication system in the form of a weighted sum of logarithmic functions, with the focus on the Nakagami and lognormal distributions that represent key building blocks for more complicated systems. A minimax optimization technique is developed to derive their coefficients resulting in uniform absolute or relative error. These approximations and bounds constitute a powerful tool for the statistical performance analysis as they enable the evaluation of the ergodic capacity of various communication systems that experience small-scale fading together with the lognormal shadowing effect and allow for simplifying the complicated integrals encountered when evaluating the ergodic capacity in different communication scenarios. Simple and tight closed-form solutions for the ergodic capacity of many classic and timely application examples are derived using the logarithmic approximations. The high accuracy of the proposed approximations is verified by numerical comparisons with existing approximations and with those obtained directly from numerical integration methods.

Compact Modeling of Nanocluster Functionality as a Higher-Order Neuron

Disordered nanoclusters with multielectrode input–output functionality had recently been experimentally realized with energy-efficient and emergent computational capacity, and thus an interconnected network of several such nanoclusters had been proposed to realize artificial neural networks. To aid that end, here we show that nanocluster functionality can be fit to the simplest dendritic neuron model (DNM), where the only form of nonlinearity is due to multiplicative interactions. This work brings into the spotlight higher-order neural networks (known for their efficient encoding of geometric invariances) to serve as an explainable baseline model of nano-networks against which experimentalists can compare more sophisticated models (deep neural networks or physics-based models such as the lin-min network introduced here) and provides ground for designing novel approximate hardware and a statistical mechanics analysis of the learning performance of interconnected nanoclusters versus perceptrons (where neurons output a nonlinear function of the weighted sum of their inputs). A network with just ten higher-order neurons is shown to achieve a classification accuracy of more than 96% on the MNIST benchmark for handwritten digit recognition (which required 100 times more neurons in three-layer perceptrons).

Experimental and statistical analysis of the hydrodynamic performance of planing boats: A Comparative study

Hydrofoil-supported catamarans (Hysucats) have emerged in the last two decades as competitors against monohulls in terms of efficiency in high-speed planing regimes. Nonetheless, according to the literature, they have not been compared experimentally. In this work, a monohull, a catamaran and a Hysucat hull were compared experimentally in order to evaluate the geometry with the best hydrodynamic behavior. The hulls were towed along a body of water with a test bench and variables such as drag, trim angle, wetted area, wetted keel length, and sinkage were measured both with sensors, installed in the boat and in the test bench, and multimedia data. These geometries had the same length, width, draft, submerged volume, and weight distribution. The equations derived by Savitsky were used as a precursor for the experimental design and for a deeper understanding of the planing phenomena. It was found that the Hysucat and the catamaran boats present up to 50% higher drag in planing speeds than the monohull, basically due to their much higher wetted area and lower trim angle. However, some potential alternatives for improving the hydrodynamic efficiency of the catamaran and the Hysucat are proposed.

Robust OCC System Optimized for Low-Frame-Rate Receivers

Light emitting diodes (LED) are becoming the dominant lighting elements due to their efficiency. Optical camera communications (OCC), the branch of visible light communications (VLC) that uses video cameras as receivers, is a suitable candidate in facilitating the development of new communication solutions for the broader public because video cameras are available on almost any smartphone nowadays. Unfortunately, most OCC systems that have been proposed until now require either expensive and specialized high-frame-rate cameras as receivers, which are unavailable on smartphones, or they rely on the rolling shutter effect, being sensitive to camera movement and pointing direction, they produce light flicker when low-frame-rate cameras are used, or they must discern between more than two light intensity values, affecting the robustness of the decoding process. This paper presents in detail the design of an OCC system that overcomes these limitations, being designed for receivers capturing 120 frames per second and being easily adaptable for any other frame rate. The system does not rely on the rolling shutter effect, thus making it insensitive to camera movement during frame acquisition and less demanding about camera resolution. It can work with reflected light, requiring neither a direct line of sight to the light source nor high resolution image sensors. The proposed communication is invariant to the moment when the transmitter and the receiver are started as the communication is self-synchronized, without any other exchange of information between the transmitter and the receiver, without producing light flicker, and requires only two levels of brightness to be detected (light on and light off). The proposed system overcomes the challenge of not producing light flicker even when it is adapted to work with very low-frame-rate receivers. This paper presents the statistical analysis of the communication performance and discusses its implementation in an indoor localization system.

Statistical analysis of the corrosion inhibition performance of three inorganic compounds on mild steel acid media

Sodium benzoate, zinc benzoate and zinc bromide were studied for their corrosion inhibition effect on mild steel in 0.5 M H2SO4 and HCl solution. Data obtained showed the performance of the inorganic compounds significantly varied with respect to exposure time. Zinc bromide (ZBM) exhibited the most effective inhibition performance on mild steel in H2SO4 solution with optimal inhibition value of 90.96% at 50% concentration, corresponding to corrosion rate of 1.330 mm/y. Sodium benzoate (SB) displayed the least effective inhibition performance with optimal value of 50.5% at 70% concentration corresponding to corrosion rate of 7.284 mm/y. Zinc benzoate (ZB) performed most effectively in HCl solution with inhibition value of 70.17% at 50% inhibitor concentration corresponding to corrosion rate of 1.251 mm/y while zinc bromide contrary to its performance in H2SO4 solution displayed weak inhibition performance in HCl solution with peak value 55.40% at 30% concentration corresponding to corrosion rate of 1.870 mm/y. Statistical data showed in H2SO4 solution, inhibitor concentration significantly influenced the inhibition performance of ZB and ZBM compounds with values of 98.37% and 94.57%. The effect of exposure time was negligible but statistically relevant. The statistical relevance value obtained for SB inhibitor concentration and exposure time are 65.96% and 25.20%. In HCl solution, the statistical relevance of ZBM and ZB exposure time at 58.4% and 41.51% is greater than the corresponding value for concentration at 32.46% and 38.14%. However, SB concentration overwhelmingly influenced the performance of SB compound at statistical relevance value of 95.75%.

Reasoning Graph: A Situation-aware framework for cooperating unprotected turns under mixed connected and autonomous traffic environments

Cooperating unprotected turns in mixed connected and autonomous traffic environments is beneficial to improving the systemic utility of intersections. However, interactions among connected and automated vehicles (CAVs) and manually driving vehicles (MVs) are varied and interrelated. These factors make the cooperation a multimodal problem and bring many difficulties to interactions analyzing and the optimal plan solving. This paper proposes a situation-aware Reasoning Graph (RG) framework that consists of the Reasoning Process and the Solution Process. Complex interactions are first described by a graph, where the mapping from situations to the graph is constructed. Then the Reasoning Process divides future motion space into numerous situations and reasons potential situations using the graph search-based algorithm. Some rules for maneuver compatibility and social interaction customs are integrated into the graph search to ensure the maneuver compatibility and social compliance of these reasoned situations. The Solution Process develops a heuristic algorithm to quickly search for speed profiles that follow the reasoned situations. After comparative tests of the reservation-based and the optimization-based methods, the results show that the RG-based method helped decrease the average systemic cost by approximately 10%. However, it did not cause losses in individual performances. The statistical analysis of individual performance shows that the RG even increased the individual performances regarding safety, efficiency, and energy-saving.

EVOLUTION AND ANALYSIS OF SECURED HASH ALGORITHM (SHA) FAMILY

With the rapid advancement of technologies and proliferation of intelligent devices, connecting to the internet challenges have grown manifold, such as ensuring communication security and keeping user credentials secret. Data integrity and user privacy have become crucial concerns in any ecosystem of advanced and interconnected communications. Cryptographic hash functions have been extensively employed to ensure data integrity in insecure environments. Hash functions are also combined with digital signatures to offer identity verification mechanisms and non-repudiation services. The federal organization National Institute of Standards and Technology (NIST) established the SHA to provide security and optimal performance over some time. The most well-known hashing standards are SHA-1, SHA-2, and SHA-3. This paper discusses the background of hashing, followed by elaborating on the evolution of the SHA family. The main goal is to present a comparative analysis of these hashing standards and focus on their security strength, performance and limitations against common attacks. The complete assessment was carried out using statistical analysis, performance analysis and extensive fault analysis over a defined test environment. The study outcome showcases the issues of SHA-1 besides exploring the security benefits of all the dominant variants of SHA-2 and SHA-3. The study also concludes that SHA-3 is the best option to mitigate novice intruders while allowing better performance cost-effectively.

Are Polymer‐Based Electrolytes Ready for High‐Voltage Lithium Battery Applications? An Overview of Degradation Mechanisms and Battery Performance

AbstractHigh‐voltage lithium polymer cells are considered an attractive technology that could out‐perform commercial lithium‐ion batteries in terms of safety, processability, and energy density. Although significant progress has been achieved in the development of polymer electrolytes for high‐voltage applications (> 4 V), the cell performance containing these materials still encounters certain challenges. One of the major limitations is posed by poor cyclability, which is affected by the low oxidative stability of standard polyether‐based polymer electrolytes. In addition, the high reactivity and structural instability of certain common high‐voltage cathode chemistries further aggravate the challenges. In this review, the oxidative stability of polymer electrolytes is comprehensively discussed, along with the key sources of cell degradation, and provides an overview of the fundamental strategies adopted for enhancing their cyclability. In this regard, a statistical analysis of the cell performance is provided by analyzing 186 publications reported in the last 17 years, to demonstrate the gap between the state‐of‐the‐art and the requirements for high‐energy density cells. Furthermore, the essential characterization techniques employed in prior research investigating the degradation of these systems are discussed to highlight their prospects and limitations. Based on the derived conclusions, new targets and guidelines are proposed for further research.

Statistical analysis and ANN modelling of tribological performance of aluminium hybrid composites fabricated by PM method

This study represents the wear behaviour of the aluminium hybrid composites developed through powder metallurgy. The pure Al-matrix powder was blended with Si3N4/ZrO2 ceramic powders in a high-centrifugal ball mill and then compacted at three different compaction pressure. The sintering of the composites was done in an environment-controlled tube furnace at 500 °C for 1 h. The effect of wt% of the reinforcement, compaction pressure and applied load was investigated on the wear results performed on the pin-on-disc setup. The Analysis of variance (ANOVA) and artificial neural network (ANN) was used for the wear loss results prediction. The ANOVA results revealed that load is a highly influential parameter for wear loss followed by reinforcement and compaction pressure. Furthermore, an ANN model using feed-forward backpropagation analysis was built which found that the predicted and experimental results are close.

Development and Evaluation of an Interactive Instructional Package for Teaching Engineering Graphics Skills

Exposure to modern pedagogical approaches and methods with appropriate instructional media can enhance the development of high-level critical thinking and technical skills. This study examined the development of an interactive instructional software package for teaching engineering graphics and evaluated its relative effectiveness on second-year undergraduate engineering students' academic achievement, skill transfer, and retention. In this study, the quasi-experimental, pre-test, post-test, control group design was employed. 45 research participants were sampled, employing two-stage stratified sampling technique, which comprises the simple random sampling to assign students into three groups from the study population and systematic sampling with k = 3 to select 15 students from each group to a control group and experimental groups A and B. The control group was exposed to conventional classroom instruction (CI), group A to computer-aided instruction (CAI); and group B to conventional and computer-aided instructions (CCAIs). A computer-aided learning package on engineering graphics was developed using the Camtasia software package, which served as the treatment instrument. The pre-test and post-test data used for analysis stemmed from a validated Engineering Graphics Achievement Test instrument. Analysis of covariance and Sidak post hoc test statistical analysis of the groups' performance provided the results on the comparative effects of the treatment conditions. Findings indicated significant differences between the academic achievement, skill transfer, and retention of students, exposed to CCAIs, and CI or CAI strategies. When used together, a significant improvement in students' academic achievement, transfer, and retention of engineering graphics skills occurred than either the CI or CAI strategy used alone.

An improved deep convolutional neural network by using hybrid optimization algorithms to detect and classify brain tumor using augmented MRI images

Automated brain tumor detection is becoming a highly considerable medical diagnosis research. In recent medical diagnoses, detection and classification are highly considered to employ machine learning and deep learning techniques. Nevertheless, the accuracy and performance of current models need to be improved for suitable treatments. In this paper, an improvement in deep convolutional learning is ensured by adopting enhanced optimization algorithms, Thus, Deep Convolutional Neural Network (DCNN) based on improved Harris Hawks Optimization (HHO), called G-HHO has been considered. This hybridization features Grey Wolf Optimization (GWO) and HHO to give better results, limiting the convergence rate and enhancing performance. Moreover, Otsu thresholding is adopted to segment the tumor portion that emphasizes brain tumor detection. Experimental studies are conducted to validate the performance of the suggested method on a total number of 2073 augmented MRI images. The technique’s performance was ensured by comparing it with the nine existing algorithms on huge augmented MRI images in terms of accuracy, precision, recall, f-measure, execution time, and memory usage. The performance comparison shows that the DCNN-G-HHO is much more successful than existing methods, especially on a scoring accuracy of 97%. Additionally, the statistical performance analysis indicates that the suggested approach is faster and utilizes less memory at identifying and categorizing brain tumor cancers on the MR images. The implementation of this validation is conducted on the Python platform. The relevant codes for the proposed approach are available at: https://github.com/bryarahassan/DCNN-G-HHO .

Effects of fixed versus variable task prioritization during short-term dual task practice on motor and cognitive task performance in young adults.

ObjectiveIt has been shown that variable compared to fixed task prioritization during dual task practice more effectively improves motor (i.e., postural control) and cognitive (i.e., memory) performance in older adults. However, it is unclear whether this finding is also valid in young adults. Thus, the present study examined the effect of fixed (allocate equal priority on both tasks) versus variable (vary priority between both tasks) priority during short-term motor-cognitive dual task practice on single and dual task performance in healthy young adults (age range: 20–30 years).ResultsDuring two days of practice, significant improvements of motor (i.e., balance task: reduced root mean square error; p < 001, ηp2 = .72) and cognitive (i.e., arithmetic task: increased serial three subtractions; p < .001, ηp2 = .78) task performance were observed and that was irrespective of group (“fixed priority” and “variable priority”). Further, the statistical analysis of post-practice single and dual task performance revealed no significant differences between groups, irrespective of task (i.e., motor or cognitive). This indicates that in young as opposed to old adults, single and dual task performance improvements are independent of task prioritization (i.e., fixed or variable priority) during short-term motor-cognitive dual task practice.

Design and analysis of a multiple collaborative beamforming scheme in the realm of Wireless Sensor Networks featuring 3-dimension node configuration

Collaborative Beamforming (CB) is an essential tool towards achieving long-range transmission in Wireless Sensor Networks (WSNs). In some instances, there may be multiple intended data destinations (sinks) in a WSN. This calls for multi-CB. In comparison to sink-by-sink CB, multi-CB implies improved data rates and decreased co-channel interference; and consequently increased network capacity. In current literature, there is no research in multi-CB particularly in 3-dimension WSNs. In this paper, a novel multi-CB mechanism is brought to the fore. This is from the point of view of a random arrangement of sensor nodes in a 3-dimension manner. It is assumed that all sinks' directions are known at the CB cluster head. Node transmit amplitude and phase are optimized using a Particle Swarm Optimization (PSO) algorithm variant to concurrently achieve balanced multiple narrow beams and minimal radiation in undesired directions. The performance of the proposed scheme is checked against that of a pure multiple beam steering approach (without beam power balancing and minimization of radiation in undesired directions). Moreover, an analysis of beam power, width and steering accuracy is done upon varying the number of collaborating nodes and the collaborating cluster radius. Increasing the count of collaborating nodes yields improved beam precision/accuracy, lower radiation in undesired directions and appreciable stability in beam power performance Increasing the collaborating nodes' cluster radius yields narrow beams, improved beam precision and appreciably lower radiation in undesired directions. The contributions of this work to current literature include: (i) formulation and analysis of a multiple beamforming scheme in the realm of 3-dimension WSNs; (ii) design of a multi-CB scheme taking into account minimization of radiation in undesired directions; (iii) a statistical multi-CB performance analysis upon varying collaborating nodes' cluster radius and collaborating node count.

Indices relation and statistical probability analysis of physical and mechanical performance of asphalt mixtures

Rutting deformation is one of the most prominent diseases in asphalt pavement’s early damage. In this paper, it analyzes the relation and their distribution law between Marshall stability and the physical and mechanical indices of asphalt mixtures based upon a correlation analysis and statistical principles, which is to find the reasonable range of rutting performance design parameters of asphalt pavement. The results showed that the asphalt mixture’s stability increased with the increase in bulk volume relative density and asphalt saturation, but decreased with the increase in the percent of air-voids and the voids in the mineral aggregate. The optimal fitting function of bulk volume relative density and asphalt saturation for AC-13 asphalt mixture follows a Weibull distribution, that of Marshall stability and air-voids follows a Gamma distribution, and that of percent voids in the mineral aggregate and flow value follows a Lognormal distribution. For a GAM-16 asphalt mixture, the optimal fitting function of bulk volume relative density and Marshall stability follows the Weibull distribution, the optimal fitting function of air-voids follows a Gamma distribution, that of flow value and percent voids in the mineral aggregate follows a Lognormal distribution, and asphalt saturation follows a Normal distribution. It is recommended that AC-13 asphalt mixtures’bulk volume relative density ranges from 2.27～2.38 g·cm3, the air-voids ranges from 3.4～6.0%, and GAM-16 asphalt mixtures’bulk volume relative density ranges from 2.29～2.37 g·cm3, the voids in mineral aggregate ranges from 17～18.17%, the asphalt saturation ranges from 75～82.13%, which can provide a reference for in-depth understanding of the deformation characteristics and construction of highways’ pavement in similar areas.

Statistical Analysis of Catalytic Performance in Ethylene/Methyl Acrylate Copolymerization Using Palladium/Phosphine-Sulfonate Catalysts

Statistical Analysis of Catalytic Performance in Ethylene/Methyl Acrylate Copolymerization Using Palladium/Phosphine-Sulfonate Catalysts

Toward automated segmentation for acute ischemic stroke using non-contrast computed tomography.

Non-contrast computed tomography (NCCT) is a first-line imaging technique for determining treatment options for acute ischemic stroke (AIS). However, its poor contrast and signal-to-noise ratio limit the diagnosis accuracy for radiologists, and automated AIS lesion segmentation using NCCT also remains a challenge. In this paper, we propose R2U-RNet, a novel model for AIS lesion segmentation using NCCT. We used an in-house retrospective NCCT dataset with 261 AIS patients with manual lesion segmentation using follow-up diffusion-weighted images. R2U-RNet is based on an R2U-Net backbone with a novel residual refinement unit. Each input image contains two image channels from separate preprocessing procedures. The proposed model incorporates multiscale focal loss to mitigate the class imbalance problem and to leverage the importance of different levels of details. A proposed noisy-label training scheme is utilized to account for uncertainties in the manual annotations. The proposed model outperformed several iconic segmentation models in AIS lesion segmentation using NCCT, and our ablation study demonstrated the efficacy of the proposed model. Statistical analysis of segmentation performance revealed significant effects of regional stroke occurrence and side of the stroke, suggesting the importance of region-specific information for automated segmentation, and the potential influence of the hemispheric difference in clinical data. This study demonstrated the potentials of R2U-RNet model for automated NCCT AIS lesion segmentation. The proposed model can serve as a tool for accelerating AIS diagnoses and improving the treatment quality of AIS patients.

Evaluation of the diagnostic performance and the utility of Helicobacter pylori stool antigen lateral immunochromatography assay

BackgroundHelicobacter pylori causes the most common human gastric infection. H. pylori Stool Antigen Lateral Flow Immunochromatography assay (HpSA-LFIA) is considered one of the most cost-effective and rapid non-invasive assays (active tests). Evaluating HpSA-LFIA is of crucial for ensuring accuracy and utility assurance. This study aimed to evaluate the polyclonal antibody-based HpSA-LFIA in comparison to a monoclonal antibody-based ELISA kit. MethodologyStool samples were collected from 200 gastric patients for HpSA-LFIA and semiquantitative HpSA-ELISA tests. A statistical analysis of the diagnostic performance was performed using MedCalc software. Chi-square tests were performed to determine the effects of gender and age. Results and conclusionThe results showed that HpSA-LFIA achieved remarkable sensitivity (93.75%) and NPV (98.00%). However, it had poor specificity, PPV, and accuracy of 59.76%, 31.25%, and 65.31%, respectively. LR+ and LR-were 2.33% & 0.1%, respectively. Gender didn't affect the diagnostic performance of HpSA-LFIA. Age groups had irrelevant sensitivity; however, specificity was significantly higher in patients aged >45 years. We can conclude that HpSA-LFIA was not accurate enough to be the sole test for diagnosis and suggest developing other confirmatory tests in case of positive conditions.

Three-Dimensional Simulation of Decaying Turbulent Swirling Flow Using Different Turbulence Models

This paper presents a numerical study of the turbulent swirling flow in a horizontal tangential inlet tube. The commercial CFD code ANSYS FLUENT 15 was used for solving the set of governing equations using different turbulence models. Eight turbulence models are tested which are, standard k–ε, realizable k–ε, RNG k–ε, SST k–ω, Non-Linear k–ε, v2-f, RSM (Quadratic Pressure-Strain Model), and RSM (Stress-Omega Model). All these turbulence models are available directly in the ANSYS FLUENT except the non-linear k–ε which was implemented in the solver using User Defined Functions (UDF). The numerical predictions are compared with experimental measurements from literature for tangential, axial velocity profiles and Reynolds stresses profiles within the tested tube. The results indicated that the axial velocity is predicted fairly well by the standard k–ε model while the tangential velocity is well predicted by RSM. On the other hand, v2-f model predicts the Reynolds stresses better than the other tested models. The statistical analysis of turbulence model performance showed that, the RSM (Quadratic Pressure-Strain) model gives the best agreement with all data of experiments followed by non-linear k–ε and standard k–ε turbulence models.

Statistical Analysis of the Dynamic Performance of Reinforcement Steel at Elevated Temperatures: The Case of Johnson-Cook Model

Description of mechanical properties of reinforcement steel by means of mathematical models known as constitutive laws is considered. The attention is focussed on the Johnson-Cook (JC) model developed to express the stress-strain relation by considering the coupled effect of strain and strain rate hardening as well as thermal softening of steel. The JC model is analysed due to its prevailing role in the practice of constitutive relation of properties of reinforcement steels. The key element of this study is a new look at the JC model from the statistical viewpoint. The JC model is subjected to examination by confronting its deterministic nature with statistical variability of experimental data that can be acquired from stress-strain records. It is stated that to now this variability has been largely ignored. The current practice of fitting the JC model to individual and non-repetitive stress-strain records is analysed. It is suggested how to address the problem of the model fitting in the case where stress-strain data is obtained by repetitive measurements. A procedure for processing small-size statistical samples extracted from this data is proposed. The essential idea of this procedure is to fit components of the JC model to limits of one-sided confidence intervals calculated by means of the statistical technique known as bootstrap resampling.

ДИНАМІКА ТА ЗАКОНОМІРНОСТІ ЕКОНОМІЧНОГО РИВКА В КРАЇНАХ АЗІЙСЬКОГО РЕГІОНУ

The article is devoted to the study consists in analyzing the dynamics and patterns of economic growth in Germany and the countries of the Asian region. The statistical analysis of economic performance, historical analysis, generalization, comparison, analysis and synthesis, economic and mathematical modeling methods are used in the study. The dynamics and patterns of growth of China and the “Asian Tigers” of the first generation are analyzed in the article. A comparison of the features of the economic "leap" in Asian countries (China, Singapore, Hong Kong, the Republic of Korea and Taiwan) was done. For each country studied, the existing preconditions are listed and the incentives for intensifying the economic "leap" are named. It is emphasized that Taiwan, Singapore and the Republic of Korea are united by another feature: their rapid economic development took place under the strict leadership of the authorities, i.e. with the use of an authoritarian regime. The rapid economic development of the countries studied is due to the flexibility of state policy on economic restructuring, the development of foreign trade based on export-oriented economies, as well as the creation of a favorable climate for foreign investment. In order to identify the key factors that contributed to the economic growth of these countries, a correlation-regression analysis of the dependence of gross domestic product as the main indicator of economic growth on a number of macroeconomic indicators. Four countries are selected for analysis: China, Singapore, Hong Kong and South Korea in the period of 1960–2020-s. In general it is established, the rapid economic development of Asian countries is associated with on the combination of state regulation and market mechanism, particularly with the flexibility of government policy on restructuring the economy on a high-tech basis, export orientation, creating a favorable climate for foreign investment and financial sector development, as well as the formation of sufficient savings proper rights, institutional environment are transformed into investment, contributing to further economic growth.