Comprehensive Performance Comparison Research Articles

LoRa and Rotating Polarization Wave: Physical Layer Principles and Performance Evaluation

Link reliability and enhanced coverage are the primitive concerns of Low-Power Wide-Area Networks (LPWANs) for suitability to critical Internet of Things (IoT) applications. Reliability is limited by the destructive multipath propagation, data rate and sensitivity, that ultimately limits the coverage range. LoRa by far is the predominant LPWAN operating on unlicensed spectrum. Despite its robust Chirp Spread Spectrum (CSS) modulation, there is a severe degradation in its error performance particularly in hostile propagation environments, and an excessive reduction in coverage. Rotating Polarization Wave (RPW) is a potential LPWAN recently emerged to achieve a highly reliable IoT and Machine-to-Machine (M2M) communication. This is the first paper to provide comprehensive error performance comparison between LoRa and RPW. Okumura-Hata model is used for median path loss calculation. Shadowing and fast fading margins of RPW and LoRa are estimated. Effective gain of RPW is computed from error performance. Results have shown that LoRa offers a sensitivity of 23 dB higher than RPW under AWGN conditions. However, under fading conditions, RPW exhibits a sensitivity of 15 dB higher than LoRa. At a reference distance of 100 m, the maximum received signal strength of RPW is -39 dBm, which is 29 dB above LoRa. The maximum coverage distance attained by RPW is 15 km, which is 1.5 times of LoRa.

Efficient ECC-based Conditional Privacy-preserving Aggregation Signature Scheme in V2V

Vehicular ad-hoc network system provides network communication technology and plays an important role in road safety and traffic control. Conditional privacy-preserving can safeguard privacy as well as trace malicious vehicles, which is critical in the system. However, in the restricted computing and communication resource situation, how to ensure the conditional privacy security remains a challenge. To address this issue, this research work proposes an efficient conditional privacy-preserving scheme. In this scheme, firstly, a certificateless aggregation signature has been performed on board to avoid channel conflicts, and to our best knowledge, this scheme is the first one to propose aggregation on board. Also an elliptic curve point multiplicative operation has been utilized to decrease the cost consumed in computation. Secondly, this paper gives the security proof of the proposed scheme against the existence of unforgeability under adaptively chosen message attack for Type-I and Type-II adversaries in the random oracle model. Lastly, comprehensive performance comparisons covering the computation, communication and storage costs have been done to state that the proposed scheme is more efficient than the existing solutions. Significantly, this scheme saves at least 86% in communication overhead than compared schemes.

A Comparative Analysis of Credit Performance Between Nabil Bank Limited and Nepal Bank Lim

This study conducts a comprehensive comparison of the credit performance of NABIL Bank Limited and Nepal Bank Limited, with a focus on analyzing the impact of specific credit variables on their profitability. The research uses financial instruments to enhance the effectiveness and educational value of the investigation. Through descriptive and analytical research, the study examines how credit-specific factors influence the profitability of both banks. The study utilizes various quantitative factors, including Return on Assets (ROA), Return on Equity (ROE), Loan and Advance to Total Deposit, Loan Loss Provision to Loan and Advance, Non-Performing Loan to Loan and Advance (NPLLA), and Loan Loss Provision to Non-Performing Loan (LLPNPL). It employs correlation and linear multiple regression analysis on secondary data spanning a ten-year period from 2010/11 to 2019/20. The significance of these relationships is tested at a 95% level of significance. The findings reveal that the variables used in the study are more effective in explaining the ROA for NABIL Bank Limited compared to Nepal Bank Limited, as indicated by the higher R2 value for NABIL Bank Limited. Notably, NPLA and LLPNPL have the most positive impact on NABIL Bank Limited's ROA, even though they are statistically insignificant at the 5% level. Conversely, LLPLA and LLPNPL have the most negative impact on the profitability of Nepal Bank Limited, but they also show insignificance at the 5% level of significance.

A Comprehensive Performance Comparison between Segmental and Conventional Switched Reluctance Machines with Boost and Standard Converters

This paper presents the comparisons between two types of switched reluctance machines (SRMs) and SRM converters. An SRM with a segmental rotor is compared with a conventional SRM (CSRM), and an SRM converter containing a passive boost circuit is compared with a conventional asymmetric half-bridge (AHB) converter. The segmental SRM has an asymmetric rotor with a segmented structure. The four rotor segments are made of steel laminations. Two segments are misaligned with the other two by 15 degrees. The torque ripple of the SRM with this structure is decreased, and the static torque is increased compared to a conventional SRM. The boost converter comprises a front-end circuit and a conventional AHB converter. The front-end circuit boosts the voltage level. The boosted voltage accelerates the rising and falling progress of the phase current. In this way, the SRM can obtain a greater speed and a smaller torque ripple. The comparison is conducted in simulation and validated through the experimental results. The experiment results have demonstrated that the segmental SRM obtains a maximum 7% torque ripple reduction at a low-speed range, compared to the CSRM. With the boost converter, both the CSRM and the segmental SRM can achieve a lower torque ripple and a higher maximum speed.

Fretting behavior of piston ring-cylinder liner components of a diesel engine running on TiO2 nanolubricant

This experimental research presents the friction and wear characteristics of piston ring-cylinder liner component of a diesel engine running on commercial engine oil (5W-30) and TiO2 nanoparticle (~20 nm, ≥99.5% trace metals basis) incorporated 5W-30 engine oil (nanolubricant) to observe the performance parameters in terms of mean effective pressures and smoke emissions. Dynamic light scattering was utilized to examine the nanoparticle dispersion in the lubricant. Thermo-gravimetric analysis on nanoparticles was conducted to examine the thermal endurance during abrasion tests. The samples directly cut from the spare piston ring of the test engine underwent severe friction and wear tests via linear friction module. Coefficient of friction was considered as comparison param-eter to understand the tribological behavior of friction pairs submerged in two different lubricants. Scanning electron microscopy analysis was conducted to observe morphology of the nanoparticle and to analyze the surface structure of the samples before and after the abrasion tests. Atomic force microscopy analysis was done to obtain the 3D images of the worn surfaces and to make a comprehensive comparison of tribological performance between engine lubricant and nanolubricant. The results depicted that, TiO2 is effective in reducing coefficient of friction by an average of 10.37% and wear rate by 33.58% as well as improving brake mean effective pressure by an average of 4.95% and reducing friction mean effective pressure by an average of 9.34% when compared to those of the engine oil. In parallel with reduced friction, TiO2 incorporation in engine oil yielded an average reduction of 9.11% in smoke opacity. The experiments suggest promising results in terms of utilization of low friction, fuel efficient and environmental friendly internal combustion engines fulfilling strict emission regulations.

Comparative Analysis of Skeleton-Based Human Pose Estimation

Human pose estimation (HPE) has become a prevalent research topic in computer vision. The technology can be applied in many areas, such as video surveillance, medical assistance, and sport motion analysis. Due to higher demand for HPE, many HPE libraries have been developed in the last 20 years. In the last 5 years, more and more skeleton-based HPE algorithms have been developed and packaged into libraries to provide ease of use for researchers. Hence, the performance of these libraries is important when researchers intend to integrate them into real-world applications for video surveillance, medical assistance, and sport motion analysis. However, a comprehensive performance comparison of these libraries has yet to be conducted. Therefore, this paper aims to investigate the strengths and weaknesses of four popular state-of-the-art skeleton-based HPE libraries for human pose detection, including OpenPose, PoseNet, MoveNet, and MediaPipe Pose. A comparative analysis of these libraries based on images and videos is presented in this paper. The percentage of detected joints (PDJ) was used as the evaluation metric in all comparative experiments to reveal the performance of the HPE libraries. MoveNet showed the best performance for detecting different human poses in static images and videos.

Device capture resilient authentication and key agreement protocol for IoV

With the steady growth of car ownership and the saturation of road traffic, the Internet of vehicle (IoV) is regarded as one of the most effective technologies to improve traffic efficiency and driving experience. Authentication and key agreement protocol (AKA) is a key means to ensure secure interaction between the onboard unit (OBU) and the various information servers. Typically, the private key of AKA protocol is stored in the OBU. However, OBU theft occurs as vehicles are often left unattended. Therefore, it is a challenge to ensure the secure storage of private keys. To address the above problem, a capture-resistant AKA protocol based on oblivious pseudorandom functions (OPRF) and collaborative signature is proposed in this paper. The private key is divided into two parts, one is encrypted using the public key of the auxiliary device and another can only be recovered by running the OPRF protocol between the OBU and the auxiliary device. Since no secret information is stored in the OBU, the adversary cannot obtain the private key even if the OBU is stolen. The comprehensive security analysis and performance comparison of the proposed scheme is provided in this paper. The result demonstrates that the proposed scheme is resistant to various known attacks, especially key leakage caused by device capture. In addition, the proposed scheme can strike a balance between computational and communication overhead.

Adaptive Optics for Orbital Angular Momentum-Based Internet of Underwater Things Applications

Orbital angular momentum (OAM) has the potential to dramatically enhance the amount of information in the Internet of Underwater Things (IoUT) system. Nevertheless, underwater-turbulence-induced scintillation will destroy the orthogonality of OAM modes, hence degrading the performance of the system. In this article, a random-amplitude-mask-based adaptive optics (AOs) technique is proposed for the sake of mitigating the turbulence effects in the OAM-based underwater wireless optical communication (UWOC) system. Combined with phase retrieval algorithms, the magnitudes of linear measurements obtained from the distorted OAM beams modulated with a series of random amplitude masks and focused by a lens are employed for the phase estimation. Furthermore, we present a comprehensive performance comparison against state-of-the-art phaseless wave-front sensing techniques. Moreover, the mixture exponential-generalized gamma (EGG) distribution is applied for characterizing the probability density function (PDF) of reference-channel irradiance of OAM beams coupled into a single-mode fiber (SMF). In the end, the performance metrics, such as the outage probability, the average bit-error-rate (BER), and the ergodic capacity are analyzed with the aid of PDF for both single-input-single-output (SISO) and multiinput-multioutput (MIMO) systems. In a nutshell, this article provides new insights for the applications of AO in the OAM-based UWOC system, which can serve as a candidate for supporting IoUT devices.

A comprehensive performance comparison on the impact of MOF-71, HNT, SiO2, and activated carbon nanomaterials in polyetherimide membranes for treating oil-in-water contaminants

The effective treatment of industrial oil/water emulsion before discharging into the receiving bodies is essential to prevent environmental pollution and its negative impacts on human health. The nanomaterial-incorporated nanocomposite membranes are touted as suitable candidates for removing oil contaminants from water. The selection of nanomaterial in nanocomposite membranes is crucial to fit the intended application. In this study, the performance of four different nanomaterials, i.e., metal-organic frameworks-71 (MOF-71), silicon dioxide (SiO2), activated carbon (AC), and halloysite nanotubes (HNTs), were used as the nanofiller of membranes. The resultant membranes were characterized by FESEM, AFM, water contact angle, and FTIR analysis. The membrane performance was examined in pure water flux (PWF) and oil/water emulsion rejection studies. The enhancement of PWF in SiO2, HNTs, MOF-71, and AC-based PEI/PVP composite membranes compared to neat PEI/PVP membranes was 33.33%, 59.25%, 70.37%, and 77.77%, respectively. The PEI/PVP/MOF-71 and PEI/PVP/AC membranes exhibited the best performance for PWF and oil/water rejection. Engine oil/water, hexane/water, and toluene/water % rejection of PEI/PVP/AC membrane was found to be 91.98%, 90.55%, and 93.74%, respectively, significantly higher than the neat PEI/PVP membrane. Furthermore, the nanomaterials-incorporated membrane showed better antifouling properties than the neat PEI/PVP membrane due to enhanced hydrophilicity. The MOF-71 and AC nanomaterials-based PEI membranes exhibited good oil/water emulsions separation due to their reliable adsorption capacity and improved fouling resistance than HNTs and SiO2 nanomaterials incorporated membranes, which could have a wide range of membrane-based water treatment applications.

Performance analysis of seven Convolutional Neural Networks (CNNs) with transfer learning for Invasive Ductal Carcinoma (IDC) grading in breast histopathological images

Computer-aided Invasive Ductal Carcinoma (IDC) grading classification systems based on deep learning have shown that deep learning may achieve reliable accuracy in IDC grade classification using histopathology images. However, there is a dearth of comprehensive performance comparisons of Convolutional Neural Network (CNN) designs on IDC in the literature. As such, we would like to conduct a comparison analysis of the performance of seven selected CNN models: EfficientNetB0, EfficientNetV2B0, EfficientNetV2B0-21k, ResNetV1-50, ResNetV2-50, MobileNetV1, and MobileNetV2 with transfer learning. To implement each pre-trained CNN architecture, we deployed the corresponded feature vector available from the TensorFlowHub, integrating it with dropout and dense layers to form a complete CNN model. Our findings indicated that the EfficientNetV2B0-21k (0.72B Floating-Point Operations and 7.1 M parameters) outperformed other CNN models in the IDC grading task. Nevertheless, we discovered that practically all selected CNN models perform well in the IDC grading task, with an average balanced accuracy of 0.936 ± 0.0189 on the cross-validation set and 0.9308 ± 0.0211on the test set.

A Review of Electrically Driven Soft Actuators for Soft Robotics.

In recent years, the field of soft robotics has gained much attention by virtue of its aptness to work in certain environments unsuitable for traditional rigid robotics. Along with the uprising field of soft robotics is the increased attention to soft actuators which provide soft machines the ability to move, manipulate, and deform actively. This article provides a focused review of various high-performance and novel electrically driven soft actuators due to their fast response, controllability, softness, and compactness. Furthermore, this review aims to act as a reference guide for building electrically driven soft machines. The focus of this paper lies on the actuation principle of each type of actuator, comprehensive performance comparison across different actuators, and up-to-date applications of each actuator. The range of actuators includes electro-static soft actuators, electro-thermal soft actuators, and electrically driven soft pumps.

Examining the Performance of MIL-STD-188-110D Waveform 0 Against FBMC-SS Over Skywave HF Channels

This paper provides a comprehensive performance comparison between a current robust military waveform; namely, MIL-STD-188-110D, Waveform 0, and a filter bank multicarrier spread-spectrum (FBMC-SS) waveform proposed for communications through ionospheric/skywave HF channels. Waveform 0 is effectively a direct sequence spread spectrum waveform that uses Walsh multi-codes to enhance the information transmission rate. It may thus be referred to as Walsh-DSSS. FBMC-SS, on the other hand, makes use of filter banks to provide excellent performance when the received signal is subject to partial band interference. Successful application of FBMC-SS for communications across skywave HF channels has been previously demonstrated, both theoretically and through experimental work. However, very little has been done to contrast FBMC-SS against Walsh-DSSS. The goal of this paper is to first add new features to FBMC-SS to bring it on par with Walsh-DSSS. These features include: (i), introduction of multi-codes that achieve a comparable (or better) data rate to Walsh-DSSS; and (ii), addition of a scrambling step applied to the multi-codes to make the receiver detection robust against widely spread multipaths. With this established, in the second part of the paper, we examine the performance of the developed FBMC-SS against Walsh-DSSS when both are applied for communications across skywave HF channels. The two waveforms are compared both through a theoretical study and through experimental works across several skywave channels ranging from hundreds to thousands of kilometers.

A comprehensive analysis and performance comparison of CombFET and NSFET for CMOS circuit applications

The performance of comb-like channel field effect transistor (CombFET) and nanosheet FET (NSFET) is addressed at both device and circuit levels at the 3-nm node. The CombFET is realized by combining both FinFET and NSFET structures in the channel area. At the device level, the DC, analog/RF figures of merit (FOMs) are analyzed in detail. From simulations, it is observed that the on current (ION) is enhanced by 42% from NSFET to CombFET at the same off current (IOFF) of 250 pA under the same footprint (FP). CombFET's improvement over NSFET is the result of the channel's wider effective width. Further, an improvement of 30% in cut-off frequency (fT) and a decrement of 20% in intrinsic delay (τ) is noticed from NSFET to CombFET. On top of that dimensional impact on the electrical performance of CombFET analysed in detail. The CMOS circuit behaviour for ring oscillator (RO) is studied using the Verilog-A model for both FETs in the CADENCE tool. An oscillation frequency (fOSC) of 111.9 GHz and 82.7 GHz was noticed for CombFET and NSFET, respectively. Thus, the analyses demonstrate that CombFET offers superior performance over NSFET without increasing the FP.

Comparison of Machine Learning Approaches for Medium-to-Long-Term Financial Distress Predictions in the Construction Industry

A method for predicting the financial status of construction companies after a medium-to-long-term period can help stakeholders in large construction projects make decisions to select an appropriate company for the project. This study compares the performances of various prediction models. It proposes an appropriate model for predicting the financial distress of construction companies considering three, five, and seven years ahead of the prediction point. To establish the prediction model, a financial ratio was selected, which was adopted in existing studies on medium-to-long-term predictions in other industries, as an additional input variable. To compare the performances of the prediction models, single-machine learning and ensemble models’ performances were compared. The comprehensive performance comparison of these models was based on the average value of the prediction performance and the results of the Friedman test. The comparison result determined that the random subspace (RS) model exhibited the best performance in predicting the financial status of construction companies after a medium-to-long-term period. The proposed model can be effectively employed to help large-scale project stakeholders avoid damage caused by the financial distress of construction companies during the project implementation process.

Si-substrate vertical-structure InGaN/GaN micro-LED-based photodetector for beyond 10 Gbps visible light communication

Visible light communication (VLC) has emerged as a promising communication method in 6G. However, the development of receiving devices is much slower than that of transmitting devices, limited by materials, structures, and fabrication. In this paper, we propose and fabricate an InGaN/GaN multiple-quantum-well-based vertical-structure micro-LED-based photodetector (μPD) on a Si substrate. A comprehensive comparison of the photoelectrical performance and communication performance of three sizes of μPDs, 10, 50, and 100 μm, is presented. The peak responsivity of all three μPDs is achieved at 400 nm, while the passband full-widths at half maxima are 87, 72, and 78 nm for 10, 50, and 100 μm μPDs, respectively. The − 20 dB cutoff bandwidth is up to 822 MHz for 50 μm μPD. A data rate of 10.14 Gbps is experimentally demonstrated by bit and power loading discrete multitone modulation and the proposed digital pre-equalizer algorithm over 1 m free space utilizing the self-designed 4 × 4 50 μm μPD array as a receiver and a 450 nm laser diode as a transmitter. This is the first time a more than 10 Gbps VLC system has been achieved utilizing a GaN-based micro-PD, to the best of our knowledge. The investigation fully demonstrates the superiority of Si substrates and vertical structures in InGaN/GaN μPDs and shows its great potential for high-speed VLC links beyond 10 Gbps.

Overview of Voltage Regulator Modules in 48 V Bus-Based Data Center Power Systems

The intermediate dc bus voltage in modern data center backend power supply is evolving from conventional 12 V to 48 V. It still requires the voltage regulator modules (VRM) to feed the terminal loads such as memory and computing units operating with very high current (> 100 A/module) and very low logic voltage (0.8 V-1.8 V). This makes it challenging to optimize the design of load-side VRMs with quadrupled input voltage. This paper comprehensively reviews the state-of-the-art 48 V VRMs and categorizes them according to passive component utilization. The first category is inductive solution which is further divided into coupled-inductor-based converters and transformer-based converters. The second category named capacitive solution is further divided into resonant switched-capacitor-based converters (Resonant SCC) and hybrid switched-capacitor-based converters (Hybrid SCC). Typical topologies are discussed, analyzed and summarized to perform a comprehensive performance comparison, such that the characteristics of different VRMs can be manifested. Some design considerations are also given to facilitate the design of the practical prototypes. Moreover, opportunities and challenges in the future data center power system are presented to provide technical insights.

Privacy-Preserving Bilateral Fine-Grained Access Control for Cloud-Enabled Industrial IoT Healthcare

The expeditious development in cloud-enabled industrial Internet of Things (IIoT) healthcare has significantly reduced the costs to monitor and protect people at home while notably improving the quality of human healthcare. Despite its considerable convenience and benefits, it confronts some security and privacy challenges in the aspects of bilateral fine-grained access control, the authenticity and tamper resistance of shared health data. To tackle these constraints, a secure privacy-preserving bilateral access control scheme with fine granularity (PBAC-FG) is proposed in this article. Our PBAC-FG exploits fine-grained access control and matchmaking encryption technologies to ensure both participants (e.g., patients and healthcare providers) can specify their respective fine-grained access control over the encrypted health data, such that only authorized counterparts can efficiently access the health data. Besides, the correct rigorous security proofs are indicated to verify that our PBAC-FG is indeed secure. We carry out comprehensive performance evaluations and comparisons to demonstrate the efficiency and practicality of the PBAC-FG for IIoT healthcare applications.

Locally Weighted Ensemble-Detection-Based Adaptive Random Forest Classifier for Sensor-Based Online Activity Recognition for Multiple Residents

In recent years, various approaches for multiresident human activity recognition (HAR) in a smart indoor environment have been developed and improved along with the rapid development of sensors and AI technologies. Research in data stream-based online learning (OL) for multiresident HAR is relatively new and a majority of the existing works have been developed based on training batches of data that cannot recognize real-time activities. To address the challenges of OL for multiresident HAR, we propose a novel OL architecture based on a locally weighted ensemble detection-based adaptive random forest (LED-ARF) classifier. We conduct a comprehensive performance comparison of eight famous OL classification techniques and our LED-ARF method. The comparison is evaluated based on the two benchmarking CASAS and ARAS data sets. Our experimental results show that LED-ARF achieves the best performance with the highest robustness for online multiresident HAR.

Comprehensive comparison of stroke risk score performance: a systematic review and meta-analysis among 6 267 728 patients with atrial fibrillation.

Multiple risk scores to predict ischaemic stroke (IS) in patients with atrial fibrillation (AF) have been developed. This study aims to systematically review these scores, their validations and updates, assess their methodological quality, and calculate pooled estimates of the predictive performance. We searched PubMed and Web of Science for studies developing, validating, or updating risk scores for IS in AF patients. Methodological quality was assessed using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). To assess discrimination, pooled c-statistics were calculated using random-effects meta-analysis. We identified 19 scores, which were validated and updated once or more in 70 and 40 studies, respectively, including 329 validations and 76 updates-nearly all on the CHA2DS2-VASc and CHADS2. Pooled c-statistics were calculated among 6 267 728 patients and 359 373 events of IS. For the CHA2DS2-VASc and CHADS2, pooled c-statistics were 0.644 [95% confidence interval (CI) 0.635-0.653] and 0.658 (0.644-0.672), respectively. Better discriminatory abilities were found in the newer risk scores, with the modified-CHADS2 demonstrating the best discrimination [c-statistic 0.715 (0.674-0.754)]. Updates were found for the CHA2DS2-VASc and CHADS2 only, showing improved discrimination. Calibration was reasonable but available for only 17 studies. The PROBAST indicated a risk of methodological bias in all studies. Nineteen risk scores and 76 updates are available to predict IS in patients with AF. The guideline-endorsed CHA2DS2-VASc shows inferior discriminative abilities compared with newer scores. Additional external validations and data on calibration are required before considering the newer scores in clinical practice. ID CRD4202161247 (PROSPERO).

A Hybrid Framework Combining Data-Driven and Catenary-Based Methods for Wide-Area Powerline Sag Estimation

This paper is concerned with the airborne-laser-data-based sag estimation for wide-area transmission lines. A systematic data processing framework is established for multi-source data collected from power lines, which is applicable to various operating conditions. Subsequently, a k-means-based clustering approach is employed to handle the spatial heterogeneity and sparsity of powerline corridor data after comprehensive performance comparisons. Furthermore, a hybrid model of the catenary and XGBoost (HMCX) method is proposed for sag estimation, which improves the accuracy of sag estimation by integrating the adaptability of catenary and the sparsity awareness of XGBoost. Finally, the effectiveness of HMCX is verified by using power data from 116 actual lines.