Theoretical Performance Comparison Research Articles

Intelligent Reflecting Surface-Assisted Multiple Access With User Pairing: NOMA or OMA?

The integration of intelligent reflecting surface (IRS) to multiple access networks is a cost-effective solution for boosting spectrum/energy efficiency and enlarging network coverage/connections. However, due to the new capability of IRS in reconfiguring the wireless propagation channels, it is fundamentally unknown which multiple access scheme is superior in the IRS-assisted wireless network. In this letter, we pursue a theoretical performance comparison between non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA) in the IRS-assisted downlink communication, for which the transmit power minimization problems are formulated under the discrete unit-modulus reflection constraint on each IRS element. We analyze the minimum transmit powers required by different multiple access schemes and compare them numerically, which turn out to not fully comply with the stereotyped superiority of NOMA over OMA in conventional systems without IRS. Moreover, to avoid the exponential complexity of the brute-force search for the optimal discrete IRS phase shifts, we propose a low-complexity solution to achieve near-optimal performance.

Novel Theoretical Performance Comparisons of Open-Loop and Closed-Loop Timing Recovery in Rayleigh Fading Channels With and Without a Receiver Outage Condition

Past work has derived analytical results for the effects of symbol timing recovery error in Rayleigh fading, considering only Gaussian and Tikhonov timing error distributions, without considering maximum likelihood timing estimation and truncated Gaussian distributions; nor were differences between open-loop and closed-loop timing recovery procedures investigated. Further, prior work examined only receivers that do not impose outage conditions on the faded channel signals. Theoretical analyses and solutions accounting for all of these issues are derived herein.

Non-negative constrained inverse eigenvalue problems – Application to damage identification

Abstract Damage identification using eigenvalue shifts is ill-posed because the number of identifiable eigenvalues is typically far less than the number of potential damage locations. This paper shows that if damage is defined by sparse and non-negative vectors, such as the case for local stiffness reductions, then the non-negative solution to the linearized inverse eigenvalue problem can be made unique with respect to a subset of eigenvalues significantly smaller than the number of potential damage locations. Theoretical evidence, numerical simulations, and performance comparisons to sparse vector recovery methods based on l 1 -norm optimization are used to validate the findings. These results are then extrapolated to the ill-posed nonlinear inverse eigenvalue problem in cases where damage is large, and linearization induces non-negligible truncation errors. In order to approximate the solution to the non-negative nonlinear least squares, a constrained finite element model updating approach is presented. The proposed method is verified using three simulated structures of increasing complexity: a one-dimensional shear beam, a planar truss, and a three-dimensional space structure. For multiple structures, this paper demonstrates that the proposed method finds sparse solutions in the presence of measurement noise.

Theoretical Analysis and Performance Comparison of OFDM and GFDM Signals for 5G Cellular Networks: A Review

Theoretical Analysis and Performance Comparison of OFDM and GFDM Signals for 5G Cellular Networks: A Review

Theoretical CSPR Analysis and Performance Comparison for Four Single-Sideband Modulation Schemes With Kramers-Kronig Receiver

Recently, high-speed optical transmission using single-side-band (SSB) modulation with Kramers-Kronig (KK) scheme has gained great attention due to its high spectral efficiency (SE) and capability of electronic chromatic dispersion compensation (CDC), which can be widely employed in optical metro networks as a promising transmission scheme. To our knowledge, the carrier-signal power ratio (CSPR) is the key parameter to SSB-based systems and the optimum CSPRs can vary distinctly in different SSB modulation schemes. In this paper, the SSB-based KK systems with four kinds of SSB signal generation schemes are investigated, i.e. optical carrier-assisted SSB (OCA-SSB), virtual carrier-assisted SSB (VCA-SSB), SSB subcarrier modulation (SSB SCM) with IQ modulator (IQM) and with dual-drive Mach-Zehnder modulator (DDMZM) respectively. Here, the detailed theoretical derivations of these modulation processing are given and the theoretical relationship between device parameters and the CSPR has been further studied for the different SSB schemes. In order to verify those theoretical analyses, a 224-Gb/s direct detection (DD) transmission with SSB 16-ary quadrature amplitude modulation (16-QAM) signal has been demonstrated based on four SSB generation methods. The results show that theoretical derivations are closely consist with numerical analysis, such as $V_{bias}$ in the scheme of SSB SCM with IQM and optical modulation index (OMI) in SSB SCM with DDMZM. Therefore, our research can provide a theoretical guidance of appropriate device parameter configuration so as to achieve the optimum performance for SSB systems.

Novel Multi-Keyword Search on Encrypted Data in the Cloud

Searching on encrypted data has become a very important technique in cloud computing. Such searches enable the data owner to search on the encrypted data stored on the cloud without leaking any information. To obtain a better search experience, researchers have proposed many schemes which mainly focus on conjunctive and disjunctive keyword searches. However, conjunction of all the keywords may result in very few results, whereas a disjunction will return too many results. With the current schemes, customizing the relevancy of the keywords to obtain the desired results is difficult. To solve these problems, we propose a novel scheme that supports the search with the user-specified number of keywords contained in the search result. This number $n$ can be used to customize the keyword relevancy. As a result, the data owner could obtain the desired search results containing any $n$ keywords of a keyword set. The proposed scheme also supports the traditional disjunctive and conjunctive keyword searches when $n$ equals 1 or the size of the keyword set, respectively. The keyword could be positive or negative. We first formally define its security and then prove that the proposed scheme is secure against the adaptively chosen keyword attack in the standard model and can defend against the offline keyword guessing attack to some extent. Furthermore, we present a theoretical performance comparison with other schemes as well as the experimental performance evaluations on our implemented scheme.

High-Efficiency Rectifier With Wide Input Power Range Based on Power Recycling

In this brief, a novel rectifier with power recycling based on a branch-line coupler is proposed to operate within a wide input power range. In the proposed topology, two output ports of the coupler are connected with two subrectifiers dealing with higher power level (main branches), while the isolation port is connected with the subrectifier dealing with lower power level (power recycling branch). By using the branch-line coupler, the power reflected from the two main branches can be efficiently transmitted to the power recycling branch. Consequently, the power can be reused and thus the RF-dc conversion efficiency can be improved. In this way, the rectifier can maintain high efficiency in a wide input power range. Theoretical analysis and performance comparison are carried out, which show that the proposed topology features recycling ability within a wide input power range. For validation, a rectifier working at 2.45 GHz is implemented and compared to other designs. The measured efficiency remains over 50% from 8.5 to 32.5 dBm, indicating that high efficiency can be obtained within wide input power range.

A performance comparison of measurement matrices in compressive sensing

SummaryCompressive sensing involves 3 main processes: signal sparse representation, linear encoding or measurement collection, and nonlinear decoding or sparse recovery. In the measurement process, a measurement matrix is used to sample only the components that best represent the signal. The choice of the measurement matrix has an important impact on the accuracy and the processing time of the sparse recovery process. Hence, the design of accurate measurement matrices is of vital importance in compressive sensing. Over the last decade, a number of measurement matrices have been proposed. Therefore, a detailed review of these measurement matrices and a comparison of their performances are strongly needed. This paper explains the foundation of compressive sensing and highlights the process of measurement by reviewing the existing measurement matrices. It provides a 3‐level classification and compares the performance of 8 measurement matrices belonging to 4 different types using 5 evaluation metrics: the recovery error, processing time, recovery time, covariance, and phase transition diagram. The theoretical performance comparison is validated with experimental results, and the results show that the Circulant, Toeplitz, and Hadamard matrices outperform the other measurement matrices.

Intraclass correlation: Improved modeling approaches and applications for neuroimaging.

Intraclass correlation (ICC) is a reliability metric that gauges similarity when, for example, entities are measured under similar, or even the same, well-controlled conditions, which in MRI applications include runs/sessions, twins, parent/child, scanners, sites, and so on. The popular definitions and interpretations of ICC are usually framed statistically under the conventional ANOVA platform. Here, we provide a comprehensive overview of ICC analysis in its prior usage in neuroimaging, and we show that the standard ANOVA framework is often limited, rigid, and inflexible in modeling capabilities. These intrinsic limitations motivate several improvements. Specifically, we start with the conventional ICC model under the ANOVA platform, and extend it along two dimensions: first, fixing the failure in ICC estimation when negative values occur under degenerative circumstance, and second, incorporating precision information of effect estimates into the ICC model. These endeavors lead to four modeling strategies: linear mixed-effects (LME), regularized mixed-effects (RME), multilevel mixed-effects (MME), and regularized multilevel mixed-effects (RMME). Compared to ANOVA, each of these four models directly provides estimates for fixed effects and their statistical significances, in addition to the ICC estimate. These new modeling approaches can also accommodate missing data and fixed effects for confounding variables. More importantly, we show that the MME and RMME approaches offer more accurate characterization and decomposition among the variance components, leading to more robust ICC computation. Based on these theoretical considerations and model performance comparisons with a real experimental dataset, we offer the following general-purpose recommendations. First, ICC estimation through MME or RMME is preferable when precision information (i.e., weights that more accurately allocate the variances in the data) is available for the effect estimate; when precision information is unavailable, ICC estimation through LME or the RME is the preferred option. Second, even though the absolute agreement version, ICC(2,1), is presently more popular in the field, the consistency version, ICC(3,1), is a practical and informative choice for whole-brain ICC analysis that achieves a well-balanced compromise when all potential fixed effects are accounted for. Third, approaches for clear, meaningful, and useful result reporting in ICC analysis are discussed. All models, ICC formulations, and related statistical testing methods have been implemented in an open source program 3dICC, which is publicly available as part of the AFNI suite. Even though our work here focuses on the whole-brain level, the modeling strategy and recommendations can be equivalently applied to other situations such as voxel, region, and network levels.

High-Order Frequency-Locked Loops: A Critical Analysis

In very recent years, some attempts for designing high-order frequency-locked loops (FLLs) have been made. Nevertheless, the advantages and disadvantages of these structures, particularly in comparison with a standard FLL and high-order phase-locked loops (PLLs), are rather unclear. This lack of knowledge is attributable to the difficulty of small-signal modeling of these high-order FLLs, which has limited their analysis to numerical studies. Focusing on three-phase FLLs, the idea of designing high-order FLLs is first generalized in this letter. Then, a second-order FLL is considered as the case study, and its small-signal modeling, stability analysis, and parameter tuning are presented. Finally, to gain insight about advantages and disadvantages of high-order FLLs, a theoretical and experimental performance comparison between the designed second-order FLL and a standard FLL (first-order FLL) as well as a unification between FLLs and PLLs is presented.

High-Efficiency Broadband Rectifier With Wide Ranges of Input Power and Output Load Based on Branch-Line Coupler

In this work, a novel rectifier based on a branch-line coupler is proposed to operate within wide ranges of input power, operating frequency and output load. In the proposed topology, two output ports of the coupler are connected with two identical sub-rectifiers and the isolation port is directly connected to the ground. The input impedance of the two sub-rectifiers varies with different input power, operating frequency and output load, which leads to impedance mismatching. By using the branch-line coupler with grounded isolation port, the power reflected from the two sub-rectifiers can be partially re-injected back to the sub-rectifiers. Thus, the power can be reused and the RF-dc conversion efficiency can be improved. Theoretical analysis and performance comparison are carried out. The results indicate that the proposed topology is able to realize high efficiency with wide input power, frequency and load dynamic ranges. For validation, a rectifier working at 2.45 GHz is designed. The fabricated rectifier circuit demonstrates a maximum RF-dc conversion efficiency of 80.8%. The measured efficiency remains over 70% with the input power from 10 dBm to 18.6 dBm and the operating frequency from 2.08 to 2.58 GHz.

Design and Comparative Analysis of Thermoacoustic Refrigerators

In this paper, the design of a loudspeaker driven 10[Formula: see text]W cooling power stack-based thermoacoustic refrigerator at 3% drive ratio for a temperature difference of 80[Formula: see text]K using helium as working fluid is discussed. The refrigerator is designed using linear thermoacoustic concepts. Using linear thermoacoustic equations, a theoretical minimum cold heat exchanger temperature is estimated at 75% porosity of stack-heat exchanger system. This paper focuses on the comparative analysis of the present design with the similar 10[Formula: see text]W thermoacoustic refrigerator designs found in the literature. The theoretical performance comparison of TSDH, TDH and CDH resonators designed at 2% and 3% drive ratio is discussed. The resonator designs are validated using DeltaEC software and the results are discussed. DeltaEC simulation results show that the diameter of the vibrating diaphragm of the loudspeaker equal to the diameter of the stack-heat exchangers system has higher electroacoustic efficiency (74.7%) and better performance compared to the smaller diaphragm (42.6%) used in the past research.

Theoretical comparison of performance using transfer functions for reactivity meters based on inverse kinetic method and simple feedback method

ABSTRACTThe performance of two digital reactivity meters, one based on the conventional inverse kinetic method and the other one based on simple feedback theory, are compared analytically using their respective transfer functions. The latter one is proposed by one of the authors. It has been shown that the performance of the two reactivity meters become almost identical when proper system parameters are selected for each reactivity meter. A new correlation between the system parameters of the two reactivity meters is found. With this correlation, filter designers can easily determine the system parameters for the respective reactivity meters to obtain identical performance.

Designed sampling from large databases for controlled trials

ABSTRACTControlled trials are ubiquitously used to investigate the effect of a medical treatment. The trial outcome can be dependent on a set of patient covariates. Traditional approaches have relied primarily on randomized patient sampling and allocation to treatment and control groups. However, when covariate data for a large set of patients are available and the dependence of the outcome on the covariates is of interest, one can potentially design treatment/control groups that provide better estimates of the covariate-dependent effects of the treatment or provide similarly accurate estimates with a smaller trial cohort size. In this article, we develop an approach that uses optimal Design Of Experiments (DOE) concepts to select the patients for the treatment and control groups upfront, based on their covariate values, in a manner that optimizes the information content in the data. For the optimal treatment and control groups selection, we develop simple guidelines and an optimization algorithm that achieves much more accurate estimates of the covariate-dependent effects of the treatment than random sampling. We demonstrate the advantage of our method through both theoretical and numerical performance comparisons. The advantages are more pronounced when the trial cohort size is smaller, relative to the number of records in the database. Moreover, our approach causes no sampling bias in the estimated effects, for the same reason that DOE principles do not bias estimated effects. Although we focus on medical treatment assessment, the approach has applicability in many analytics application domains where one wants to conduct a controlled experimental study to identify the covariate-dependent effects of a factor (e.g., a marketing sales promotion), based on a sample of study subjects selected optimally from a large database of covariates.

Surface Acoustic Wave Devices Based on High Quality Temperature-Compensated Substrates

We report surface acoustic wave (SAW) devices based on two kinds of novel and optimized high quality piezoelectric substrates. SAW devices with excellent temperature stability and electromechanical coupling coefficient using the layered structures LiTaO3 (LT)/Si and LiNbO3 (LN)/SiO2/Si as the substrates are fabricated. These structures exhibit superior temperature performance than the conventional LT, LN substrates and reported related works with the SiO2 interlayer and Si substrate providing temperature compensation and thermal diffusion block, respectively. SAW device fabricated on the optimized LT/Si substrate shows a parabolic frequency–temperature relation with near zero temperature coefficient of frequency (TCF) in a low temperature range. SAW device fabricated on the novel LN/SiO2/Si substrate shows excellent performance with near constant TCF about −18 ppm/°C and electromechanical coupling coefficient ( $K^{2}$ ) near 18.69%. Theoretical analysis and performance comparison with reported works are presented to elucidate the temperature compensation mechanism of the proposed layered structures.

Theoretical Performance Comparison of Stepped Solar Still Using Different Nanofluids

In the present work, theoretical performance comparison of stepped solar still using different nanofluids is performed. Four nano fluids are used in this study. The used nano- particles are aluminium oxide, Copper oxide, Titanmum dioxide, and Silicon oxide. The effect of nano-particles volume fraction on the still daily productivity and efficiency are studied. The estimated cost to produce one distalled liter for conventional and stepped solar stills with and without nano-particles is obtained. It is found that using a nano-fluid may slightly increase the still productivity. Finally a good agreement between the present theoretical work and previous experimental results has been obtained.

Joint Optimization of Sensing Time and Fusion Rules for Cognitive Radios

In Cognitive Radios, sensing-time and fusion rules affect the performance of spectrum sensing when cooperative sensing is applied. Besides, the more unlicensed users are involved in cooperative sensing, the higher spectrum utilization the channel can achieve, while from the unlicensed users’ perspective, the lower average throughput the unlicensed users can obtain. In this paper, we explore the issue on the sensing-time and the fusion rules to optimize the average throughput of the unlicensed users under the constraint that the licensed users are sufficiently protected. At first, we formulate this issue as an optimization problem, and showed the unimodal characteristics of the unlicensed users’ average throughput as a function of the sensing-time and the fusion parameter. Then a numerical optimization algorithm is proposed to obtain the optimal solution. At last, by theoretical analysis and performance comparisons we derived the optimal fusion rule selection scheme under different scenarios. Computer simulations show that significant improvement in the average throughput of unlicensed users is achieved when the sensing-time and the fusion rule are jointly optimized. DOI : http://dx.doi.org/10.11591/telkomnika.v12i4.4793

A maximum entropy reconstruction technique for tomographic particle image velocimetry

This paper studies a novel approach for reducing tomographic PIV computational complexity. The proposed approach is an algebraic reconstruction technique, termed MENT (maximum entropy). This technique computes the three-dimensional light intensity distribution several times faster than SMART, using at least ten times less memory. Additionally, the reconstruction quality remains nearly the same as with SMART. This paper presents the theoretical computation performance comparison for MENT, SMART and MART, followed by validation using synthetic particle images. Both the theoretical assessment and validation of synthetic images demonstrate significant computational time reduction. The data processing accuracy of MENT was compared to that of SMART in a slot jet experiment. A comparison of the average velocity profiles shows a high level of agreement between the results obtained with MENT and those obtained with SMART.

Use of different renewable fuels in a steam reformer integrated into a solid oxide fuel cell: Theoretical analysis and performance comparison

Hydrogen production from renewable energy resources has received significant attention with advances in fuel cell technology. The fuel type and operational reforming conditions directly affect fuel cell electricity generation. This study analyzes the theoretical performance of a solid oxide fuel cell (SOFC) integrated with a steam reforming process using three different renewable fuels: ethanol, glycerol and biogas. The effects of key steam reformer operating parameters on the hydrogen production for SOFCs are investigated. The performances of SOFC systems run on different fuels are compared in terms of electrical and thermal efficiencies. It is found that the biogas-fueled SOFC system requires the most energy, whereas the ethanol-fueled SOFC system achieves the highest electrical and thermal efficiencies.

Including the effect of motion artifacts in noise and performance analysis of dual-energy contrast-enhanced mammography

In contrast-enhanced mammography (CEM), the dual-energy dual-exposure technique, which can leverage existing conventional mammography infrastructure, relies on acquiring the low- and high-energy images using two separate exposures. The finite time between image acquisition leads to motion artifacts in the combined image. Motion artifacts can lead to greater anatomical noise in the combined image due to increased mismatch of the background tissue in the images to be combined, however the impact has not yet been quantified. In this study we investigate a method to include motion artifacts in the dual-energy noise and performance analysis. The motion artifacts are included via an extended cascaded systems model. To validate the model, noise power spectra of a previous dual-energy clinical study are compared to that of the model. The ideal observer detectability is used to quantify the effect of motion artifacts on tumor detectability. It was found that the detectability can be significantly degraded when motion is present (e.g., detectability of 2.5 mm radius tumor decreased by approximately a factor of 2 for translation motion on the order of 1000 μm). The method presented may be used for a more comprehensive theoretical noise and performance analysis and fairer theoretical performance comparison between dual-exposure techniques, where motion artifacts are present, and single-exposure techniques, where low- and high-energy images are acquired simultaneously and motion artifacts are absent.