Previous Estimation Research Articles

A Real-Time Low-Power Coding Bit-Rate Control Scheme for High-Efficiency Video Coding in a Multiprocessor System-on-Chip

A real-time high-performance transmission bandwidth-aware (TB-aware) coding bit-rate (CBR) controller design with low power consumption and low hardware complexity is presented in this article for H.265/high-efficiency video coding (HEVC) in a multiprocessor system-on-chip (MPSoC). Previous TB-aware motion estimation designs with CBR-control capability in video coding have focused on algorithm development with precise CBR models, which require a complicated algorithmic derivation according to the system on-demand CBR and are difficult to realize in very large scale integration (VLSI) due to their lack of consideration for hardware implementation and modeling. Consequently, we present a hardware-oriented CBR-control algorithm that uses simple CBR control functions instead of requiring root and exponential operations to realize the real-time low-power design objective for HEVC applications within a mobile MPSoC. Then, an adequate hardware architecture with low hardware complexity is exploited to accomplish a low-power and high-speed VLSI design of a CBR controller for our proposed algorithm. Using diverse video-sized sequences under on-demand system coding-bit-rate constraints, the experimental outcomes demonstrate that the introduced design is capable of low power consumption and high speeds and can utilize low-complexity hardware.

Frequency estimation based on symmetric discrete Fourier transform

Previous frequency estimation methods are based on ordinary discrete Fourier transform (DFT) and not qualified to estimate two closely spaced frequency components. When the distance between two frequency components is less than three bins, the results of previous methods present considerable errors. The authors find that symmetric DFT has better phase characteristics than ordinary DFT, and the phase is more robust than amplitude in various aspects. The authors propose a phase difference method based on the two characteristics. The proposed method provides a good solution to the problem of estimating two closely spaced frequency components. When the frequency distance of two equal-amplitude components is larger than 0.4 bins, the maximum errors on each component frequency are almost within 0.1 bins. A numerical solution can be obtained by using this algorithm to calculate the frequency of a single-tone. The simulation test shows that the frequency of a single-tone is obtained by using four consecutive samples.

Robust adaptive iterative learning control for nonrepetitive systems with iteration-varying parameters and initial state

This paper explores how to construct an adaptive iteration learning control (AILC) mechanism for a class of discrete-time nonrepetitive systems subject to iteration-varying unknown parameters and unidentical initial condition. Firstly, for the linear discrete-time nonrepetitive systems, by minimizing the discrepancy of the real system outputs from the estimated system outputs, a gradient-type adaptation law is designed to estimate the system lower triangular parameter matrix and the system initial state. Especially, the current parametric estimation is updated by virtue of the input-output data and the previous estimation. Secondly, an AILC mechanism is constructed based on the estimated system lower triangular parameter matrix, where the control input algorithm and the adaptation law are scheduled in an interactive mode. Thirdly, the boundedness of the estimation error between the real system matrix and the estimation one is derived by means of vector norm theory. Based on the boundedness of the estimation error, the robust condition of the proposed AILC is given. Finally, the proposed AILC is investigated for a class of nonlinear affine systems and the corresponding results are captured. Simulation results illustrate the validity and effectiveness of the proposed AILC schemes.

Frequency-specific network effective connectivity: ERP analysis of recognition memory process by directed connectivity estimators.

Human memory retrieval is one of the brain's most important, and least understood cognitive mechanisms. Traditionally, research on this aspect of memory has focused on the contributions of particular brain regions to recognition responses, but the interaction between regions may be of even greater importance to a full understanding. In this study, we examined patterns of network connectivity during retrieval in a recognition memory task. We estimated connectivity between brain regions from electroencephalographic signals recorded from twenty healthy subjects. A multivariate autoregressive model (MVAR) was used to determine the Granger causality to estimate the effective connectivity in the time-frequency domain. We used GPDC and dDTF methods because they have almost resolved the previous volume conduction and bivariate problems faced by previous estimation methods. Results show enhanced global connectivity in the theta and gamma bands on target trials relative to lure trials. Connectivity within and between the brain's hemispheres may be related to correct rejection. The left frontal signature appears to have a crucial role in recollection. Theta- and gamma-specific connectivity patterns between temporal, parietal, and frontal cortex may disclose the retrieval mechanism. Old/new comparison resulted in different patterns of network connection. These results and other evidence emphasize the role of frequency-specific causal network interactions in the memory retrieval process. Graphical abstract a Schematic of processing workflow which is consists of pre-processing, sliding-window AMVAR modeling, connectivity estimation, and validation and group network analysis. b Co-registration between Geodesic Sensor Net. and 10-20 system, the arrows mention eight regions of interest (Left, Anterior, Inferior (LAI) and Right, Anterior, Inferior (RAI) and Left, Anterior, Superior (LAS) and Right, Anterior, Superior (RAS) and Left, Posterior, Inferior (LPI) and Right, Posterior, Inferior (RPI) and Left, Posterior, Superior (LPS) and Right, Posterior, Superior (RPS)).

The VANDELS survey: The relation between the UV continuum slope and stellar metallicity in star-forming galaxies at z ∼ 3

The estimate of stellar metallicities (Z*) of high-z galaxies are of paramount importance in order to understand the complexity of dust effects and the reciprocal interrelations among stellar mass, dust attenuation, stellar age, and metallicity. Benefiting from uniquely deep far-UV spectra of > 500 star-forming galaxies at redshifts 2 < z < 5 extracted from the VANDELS survey and stacked in bins of stellar mass (M*) and UV continuum slope (β), we estimate their stellar metallicities Z* from stellar photospheric absorption features at 1501 and 1719 Å, which are calibrated with Starburst99 models and are largely unaffected by stellar age, dust, IMF, nebular continuum, or interstellar absorption. Comparing them to photometric-based spectral slopes in the 1250–1750 Å range, we find that the stellar metallicity increases by ∼0.5 dex from β ∼ −2 to β ∼ −1 (1 ≲ A1600 ≲ 3.2), and a dependence with β holds at fixed UV absolute luminosity MUV and stellar mass up to ∼109.65 M⊙. As a result, metallicity is a fundamental ingredient for properly rescaling dust corrections based on MUV and M*. Using the same absorption features, we analyzed the mass-metallicity relation (MZR), and find it to be consistent with the previous VANDELS estimation based on a global fit of the FUV spectra. Similarly, we do not find a significant evolution between z ∼ 2 and z ∼ 3.5. Finally, the slopes of our MZR and Z* − β relation are in agreement with the predictions of well-studied semi-analytic models (SAM) of galaxy formation, while some tensions remain concerning the absolute metallicity normalization. The relation between the UV slope and stellar metallicity is fundamental to the exploitation of large volume surveys with next-generation telescopes and for the physical characterization of galaxies in the first billion years of our Universe.

The best one-sided approximation of classes $W^r H^{\omega}$ with regard to point location on the interval

The estimation of the best one-sided pointwise approximation to the classes $W^r H^{\omega}$ with the remainder better than one of the previous estimation is obtained.

Estimated Lifetime HIV-Related Medical Costs in the United States.

Lifetime cost estimates are a useful tool in measuring the economic burden of HIV in the United States. Previous estimation methods need to be updated, given improving antiretroviral therapy regimens and updated costs. We used an updated version of the agent-based model progression and transmission of HIV (PATH) 3.0 to reflect current regimens and costs. We simulated a cohort of those infected in 2015 until the last person had died to track the lifetime costs for treatment of HIV, including HIV health care utilization costs (inpatient, outpatient, opportunistic infection prophylaxis, non-HIV medication, and emergency department), opportunistic infection treatment costs, and testing costs. We assumed a median per-person diagnosis delay of 3 years and a 3% base monthly probability of dropout from care for a base-case scenario. Additionally, we modeled a most favorable scenario (median diagnosis delay of 1 year and 1% base dropout rate) and a least favorable scenario (median diagnosis delay of 5 years and 5% base dropout rate). We estimated an average lifetime HIV-related medical cost for a person with HIV of $420,285 (2019 US$) discounted (3%) and $1,079,999 undiscounted for a median 3-year diagnosis delay and 3% base dropout rate. Our discounted cost estimate was $490,045 in our most favorable scenario and $326,411 in our least favorable scenario. Lifetime per-person HIV-related medical costs depend on the time from infection to diagnosis and the likelihood of dropping out of care. Our results, which are similar to previous studies, reflect updated antiretroviral therapy regimens and costs for HIV treatment.

Short-Term Monitoring of Geogenic Soil CO2 Flux in a Non-Volcanic and Seismically Inactive Emission Site, South Korea

Temporal changes of soil CO2 flux (FCO2) and soil CO2 concentration ([CO2]v) were surveyed in a natural CO2 emission site to characterize the factors controlling the short-term temporal variation of geogenic FCO2 in a non-volcanic and seismically inactive area. Due to a lack of long-term monitoring system, FCO2 was discontinuously measured for three periods: Ⅰ, Ⅱ at a high FCO2 point (M17) and Ⅲ about 30 cm away. Whereas [CO2]v was investigated at a point (60 cm depth) for all periods. A 2.1 magnitude earthquake occurred 7.8 km away and 20 km deep approximately 12 h before the period Ⅱ. The negative correlation of FCO2 with air pressure suggested the non-negligible advective transport of soil CO2. However, FCO2 was significantly and positively related with air temperature as well, and [CO2]v showed different temporal changes from FCO2. These results indicate the diffusive transport of soil CO2 dominant in the vadose zone, while the advection near the surface. Meanwhile [CO2]v rapidly decreased while an anomalous FCO2 peak was observed during the period Ⅱ, and the CO2 emission enhanced by the earthquake was discussed as a possible reason for the synchronous decrease in [CO2]v and increase in FCO2. In contrast, [CO2]v increased to 56.8% during the period Ⅲ probably due to low gas diffusion at cold weather. In addition, FCO2 was low during the period Ⅲ and showed different correlations with measurements compared to FCO2 at M17, implying heterogeneous CO2 transport conditions at the centimeter scale. The abnormal FCO2 observed after the earthquake in a seismically inactive area implies that the global natural CO2 emission may be higher than the previous estimation. The study result suggests a permanent FCO2 monitoring station in tectonically stable regions to confirm the impact of geogenic CO2 to climate change and its relation with earthquakes.

Interactions Between Enhanced Polygenic Risk Scores and Lifestyle for Cardiovascular Disease, Diabetes, and Lipid Levels.

Both lifestyle and genetic factors confer risk for cardiovascular diseases, type 2 diabetes, and dyslipidemia. However, the interactions between these 2 groups of risk factors were not comprehensively understood due to previous poor estimation of genetic risk. Here we set out to develop enhanced polygenic risk scores (PRS) and systematically investigate multiplicative and additive interactions between PRS and lifestyle for coronary artery disease, atrial fibrillation, type 2 diabetes, total cholesterol, triglyceride, and LDL-cholesterol. Our study included 276 096 unrelated White British participants from the UK Biobank. We investigated several PRS methods (P+T, LDpred, PRS continuous shrinkage, and AnnoPred) and showed that AnnoPred achieved consistently improved prediction accuracy for all 6 diseases/traits. With enhanced PRS and combined lifestyle status categorized by smoking, body mass index, physical activity, and diet, we investigated both multiplicative and additive interactions between PRS and lifestyle using regression models. We observed that healthy lifestyle reduced disease incidence by similar multiplicative magnitude across different PRS groups. The absolute risk reduction from lifestyle adherence was, however, significantly greater in individuals with higher PRS. Specifically, for type 2 diabetes, the absolute risk reduction from lifestyle adherence was 12.4% (95% CI, 10.0%-14.9%) in the top 1% PRS versus 2.8% (95% CI, 2.3%-3.3%) in the bottom PRS decile, leading to a ratio of >4.4. We also observed a significant interaction effect between PRS and lifestyle on triglyceride level. By leveraging functional annotations, AnnoPred outperforms state-of-the-art methods on quantifying genetic risk through PRS. Our analyses based on enhanced PRS suggest that individuals with high genetic risk may derive similar relative but greater absolute benefit from lifestyle adherence.

3D Hand Pose Estimation via Graph-Based Reasoning

Hand pose estimation from a single depth image has recently received significant attention owing to its importance in many applications requiring human–computer interaction. The rapid progress of convolutional neural networks (CNNs) and technological advances in low-cost depth cameras have greatly improved the performance of the hand pose estimation method. Nevertheless, regressing joint coordinates is still a challenging task due to joint flexibility and self-occlusion. Previous hand pose estimation methods have limitations in relying on a deep and complex network structure without fully utilizing hand joint connections. A hand is an articulated object and consists of six parts that represent the palm and five fingers. The kinematic constraints can be obtained by modeling the dependency between adjacent joints. This paper proposes a novel CNN-based approach incorporating hand joint connections to features through both a global relation inference for the entire hand and local relation inference for each finger. Modeling the relations between the hand joints can alleviate critical problems for occlusion and self-similarity. We also present a hierarchical structure with six branches that independently estimate the position of the palm and five fingers by adding hand connections of each joint using graph reasoning based on graph convolutional networks. Experimental results on public hand pose datasets show that the proposed method outperforms previous state-of-the-art methods. Specifically, our method achieves the best accuracy compared to state-of-the-art methods on public datasets. In addition, the proposed method can be utilized for real-time applications with an execution speed of 103 fps in a single GPU environment.

Feasibility Validation on Healthy Adults of a Novel Active Vibrational Sensing Based Ankle Band for Ankle Flexion Angle Estimation

Goal: In this paper, we introduced a novel ankle band with a vibrational sensor that can achieve low-cost ankle flexion angle estimation, which can be potentially used for automated ankle flexion angle estimation in home-based foot drop rehabilitation scenarios. Methods: Previous ankle flexion angle estimation methods require either professional knowledge or specific equipment and lab environment, which is not feasible for foot drop patients to achieve accurate measurement by themselves in a home-based scenario. To solve the above problems, a prototype was developed based on the assumption that the echo of a vibration signal on the tibialis anterior had different acoustic impedance distribution. By analyzing the frequency spectrum of the echo, the ankle flexion angle can be estimated. Therefore, a surface transducer was utilized to generate frequency-varying active vibration, and a contact microphone was utilized to capture the echo. A portable analog signal processing hub drove the transducer, and was used for echo signal collection from the microphone. Finally, a Random Forest regression model was applied to estimate the ankle flexion angle based on the spectrum amplitude of the echo. Results: Five healthy subjects were recruited in the experiment. The regression estimation error is 4.16 degrees, and the R2 is 0.81. Conclusions: These results demonstrate the feasibility of the proposed ankle band for accurate ankle flexion angle estimation.

Two Heads are Better Than One: A Two-Stage Complex Spectral Mapping Approach for Monaural Speech Enhancement

For challenging acoustic scenarios as low signal-to-noise ratios, current speech enhancement systems usually suffer from performance bottleneck in extracting the target speech from the mixtures within one step. To address this issue, we propose a novel complex spectral mapping approach with a two-stage pipeline for monaural speech enhancement in the time-frequency domain. The proposed algorithm aims to decouple the primal problem into multiple sub-problems, which follows the classic proverb, “two heads are better than one”. More specifically, in the first stage, only magnitude is estimated, which is incorporated with the noisy phase to obtain a coarse complex spectrum estimation. To facilitate the previous estimation, in the second stage, an auxiliary network serves as the post-processing module, where residual noise is further suppressed and the phase information is effectively modified. The global residual connection strategy is adopted in the second stage to accelerate the training convergence speed. To alleviate the parameter burden caused by the multi-stage pipeline, we propose a light-weight temporal convolutional module, which substantially decreases the trainable parameters and obtains even better objective performance over the original version. We conduct extensive experiments on three standard corpora, including WSJ0-SI84, DNS Challenge dataset, and Voice Bank + DEMAND dataset. Objective test results demonstrate that our proposed approach achieves state-of-the-art performance over previous advanced systems under various conditions. Meanwhile, subjective listening test results further validate the superiority of our proposed method in terms of subjective quality.

A Novel Sub-Cycle-Based Method for Estimation of Decaying DC Component and Fundamental Phasor

Accurate estimation of the fundamental phasors of voltage or current is a challenge to power system engineers. It becomes even more challenging when the decaying dc component (DDC) is present in the signal. This article proposes a sub-cycle-based algorithm for reducing the effect of DDC for accurate estimation of the phasor of fault current signal in the presence of odd harmonics and multiple DDCs. In the proposed algorithm, the equality property of the sinusoidal signal samples around its peak is utilized to estimate DDC with only four samples and the window length requirement of half-cycle plus two samples. A novel noise removal methodology is also proposed. The fundamental phasor is calculated by taking the difference of the half-cycle discrete Fourier transform (HCDFT) of fault current and HCDFT of DDC. Simulations are performed to show the effectiveness of the proposed method, and it is compared with few previous estimation algorithms. Tests with the system-generated signals show the efficacy and accuracy of the proposed sub-cycle-based method.

A new criterion for determining the representative elementary volume of translucent porous media and inner contaminant

Abstract. Representative elementary volume (REV) is essential for measuring and quantifying the effective parameters of a complex heterogeneous medium. To overcome the limitations of the existing REV estimation criteria, a new REV estimation criterion (χi) based on dimensionless range and gradient calculation is proposed in this study to estimate the REV of a translucent material based on light transmission techniques. Three sandbox experiments are performed to estimate REVs of porosity, density, tortuosity, and perchloroethylene (PCE) plume using multiple REV estimation criteria. In comparison with χi, previous REV estimation criteria based on the coefficient of variation (CVi), the entropy dimension (DIi) and the relative gradient error (εgi) are tested in REV quantification of translucent silica and the inner PCE plume to achieve their corresponding effects. Results suggest that the new criterion (χi) can effectively identify the REV in the materials, whereas the coefficient of variation and entropy dimension (F=-2.01×10-12+12π×1.50e-(REV-4.35)22⋅1.502) are not effective. The relative gradient error can make the REV plateau obvious, while random fluctuations make the REV plateau difficult to identify accurately. Therefore, the new criterion is appropriate for REV estimation of the translucent materials and inner contaminant. Models are built based on a Gaussian equation to simulate the distribution of REVs for media properties, whose frequency of REV is dense in the middle and sparse on both sides. REV estimation of the PCE plume indicates that a high level of porosity leads to a large value of mean and standard deviation for REVs of PCE saturation (So) and PCE–water interfacial area (AOW). Fitted equations are derived from distribution of REVs for the PCE plume related to dm (distances from mass center to considered point) and dI (distances from injection position to considered point). Moreover, relationships between REVs of the PCE plume and So are fitted using regression analysis. Results suggest a decreasing trend appears for So-REV when So increases, while AOW-REV increases with increasing So.

OpenPose: Realtime Multi-Person 2D Pose Estimation Using Part Affinity Fields.

Realtime multi-person 2D pose estimation is a key component in enabling machines to have an understanding of people in images and videos. In this work, we present a realtime approach to detect the 2D pose of multiple people in an image. The proposed method uses a nonparametric representation, which we refer to as Part Affinity Fields (PAFs), to learn to associate body parts with individuals in the image. This bottom-up system achieves high accuracy and realtime performance, regardless of the number of people in the image. In previous work, PAFs and body part location estimation were refined simultaneously across training stages. We demonstrate that a PAF-only refinement rather than both PAF and body part location refinement results in a substantial increase in both runtime performance and accuracy. We also present the first combined body and foot keypoint detector, based on an internal annotated foot dataset that we have publicly released. We show that the combined detector not only reduces the inference time compared to running them sequentially, but also maintains the accuracy of each component individually. This work has culminated in the release of OpenPose, the first open-source realtime system for multi-person 2D pose detection, including body, foot, hand, and facial keypoints.

High thermoelectric figure of merit in monolayer Tl2O from first principles

The thermoelectric properties of monolayer Tl2O are studied using first-principles calculations with all involved electrical and thermal transport properties calculated in the parameter-free frameworks. It is found that monolayer Tl2O possesses remarkably high thermoelectric figure of merit, zT, due to its ultralow lattice thermal conductivity and fairly good power factor. The room temperature zT can be as high as 1.4 and 1.2 for n- and p-type systems, respectively, whereas the maximum zT values can reach up to 5.3 and 4.2 as the temperature increases to 800 K. In addition, it is clarified that the mobilities of monolayer Tl2O are orders of magnitude smaller than previous estimation from simplified semiempirical models. The room temperature electron and hole mobilities are only about 56 and 11cm2V−1s−1, respectively, due to the heavy effective mass along with strong polar optical phonons coupling scattering. Nonetheless, the intrinsically ultrahigh zT from entire first-principles calculations stimulate that the further experimental verification and exploration for practical application are worthwhile.

Differential Network Analysis via Weighted Fused Conditional Gaussian Graphical Model.

The development and prognosis of complex diseases usually involves changes in regulatory relationships among biomolecules. Understanding how the regulatory relationships change with genetic alterations can help to reveal the underlying biological mechanisms for complex diseases. Although several models have been proposed to estimate the differential network between two different states, they are not suitable to deal with situations where the molecules of interest are affected by other covariates. Nor can they make use of prior information that provides insights about the structures of biomolecular networks. In this study, we introduce a novel weighted fused conditional Gaussian graphical model to jointly estimate two state-specific biomolecular regulatory networks and their difference between two different states. Unlike previous differential network estimation methods, our model can take into account the related covariates and the prior network information when inferring differential networks. The effectiveness of our proposed model is first evaluated based on simulation studies. Experiment results demonstrate that our model outperforms other state-of-the-art differential networks estimation models in all cases. We then apply our model to identify the differential gene network between two subtypes of glioblastoma based on gene expression and miRNA expression data. Our model is able to discover known mechanisms of glioblastoma and provide interesting predictions.

Development of Fail-Safe Algorithm for Exteroceptive Sensors of Autonomous Vehicles

This paper presents a fail-safe algorithm for the exteroceptive sensors of autonomous vehicles. The proposed fault diagnosis mechanism consists of three parts: (1) fault detecting by a duplication-comparison method, (2) fault isolating by possible area prediction and (3) in-vehicle sensor fail-safes. The main ideas are the usage of redundant external sensor pairs, which estimate the same target, whose results are compared to detect the fault by a modified duplication-comparison method and the novel fault isolation method using target predictions. By comparing the estimations of surrounding vehicles and the raw measurement data, the location of faults can be determined whether they are from sensors themselves or a software error. In addition, faults were isolated by defining possible areas where existing sensor coordinates could be measured, which can be predicted by using previous estimation results. The performance of the algorithm has been tested by using offline vehicle data analysis via MATLAB. Various fault injection experiments were conducted and the performance of the suggested algorithm was evaluated based on the time interval between injection and the detection of faults.

End-to-End Monocular Range Estimation for Forward Collision Warning.

Estimating range to the closest object in front is the core component of the forward collision warning (FCW) system. Previous monocular range estimation methods mostly involve two sequential steps of object detection and range estimation. As a result, they are only effective for objects from specific categories relying on expensive object-level annotation for training, but not for unseen categories. In this paper, we present an end-to-end deep learning architecture to solve the above problems. Specifically, we represent the target range as a weighted sum of a set of potential distances. These potential distances are generated by inverse perspective projection based on intrinsic and extrinsic camera parameters, while a deep neural network predicts the corresponding weights of these distances. The whole architecture is optimized towards the range estimation task directly in an end-to-end manner with only the target range as supervision. As object category is not restricted in the training stage, the proposed method can generalize to objects with unseen categories. Furthermore, camera parameters are explicitly considered in the proposed method, making it able to generalize to images taken with different cameras and novel views. Additionally, the proposed method is not a pure black box, but provides partial interpretability by visualizing the produced weights to see which part of the image dominates the final result. We conduct experiments to verify the above properties of the proposed method on synthetic and real-world collected data.

Global Methyl Halide Emissions From Rapeseed (Brassica napus) Using Life Cycle Measurements

AbstractGlobal budgets of methyl halides are not balanced between currently identified sources and sinks. Among biological sources, rapeseed is regarded as the second largest terrestrial source of CH3Br, extrapolated from laboratory‐based incubations and limited field measurements. This study analyzes the CH3Br budget from rapeseed (Brassica napus “Empire”), using field‐based life cycle measurements, yielding a globally scaled emission rate of 2.8 ± 0.7 Gg year−1. Though this verifies that rapeseed is a significant global source, it is just half of the previous estimation, even after accounting for the doubling of global annual rapeseed production since then. The ozone‐depleting potential of rapeseed is further sustained through CH3Cl and CH3I emissions, which were measured for the first time and scaled to 5.3 ± 1.3 and 4.0 ± 0.8 Gg year−1 globally.