Sequential Estimation Technique Research Articles

NFTs versus conventional cryptocurrencies: A comparative analysis of market efficiency around COVID-19 and the Russia-Ukraine conflict

This paper examines the efficiency of the market for non-fungible tokens (NFTs) against the backdrop of the market for fungible tokens (FTs) that includes Bitcoin and Ethereum. We focus on two important shocks: the outbreak of COVID-19 and the Russia-Ukraine conflict. To this end, we employ martingale difference sequence and conditional heteroscedasticity estimation techniques. We find that the efficiency of both markets fluctuates in time and the aforementioned shocks have a profound effect on FTs and NFTs. More specifically, we find that the effect of COVID-19 is heterogeneous for both markets, whereas that of the Russian invasion of Ukraine is homogenous for NFTs but heterogeneous for FTs.

CALCULATING PROBABILITY DENSITIES WITH HOMOTOPY AND APPLICATIONS TO PARTICLE FILTERS

We explore a homotopy sampling procedure and its generalization, loosely based on importance sampling, known as annealed importance sampling. The procedure makes use of a known probability distribution to find, via homotopy, the unknown normalization of a target distribution, as well as samples of the target distribution. We propose a reformulation of the method that leads to a rejection sampling alternative. Estimates of the error as a function of homotopy stages and sample averages are derived for the algorithmic version of the method. These estimates are useful in making computational efficiency decisions on how the calculation should proceed, given a computer architecture. Consideration is given to how the procedure can be adapted to Bayesian stationary and nonstationary estimation problems. The connection between homotopy sampling and thermodynamic integration is made. Emphasis is placed on the non-stationary problems, and in particular, on a sequential estimation technique known as particle filtering. It is shown that a modification of the particle filter framework to include the homotopy process can improve the computational robustness of particle filters.

Surface EMG variability while running on grass, concrete and treadmill

This study aimed to investigate whether inter-trial variability in muscle activity (electromyography, EMG) during running is influenced by the number of acquired steps and running surface. Nine healthy participants ran at preferred speed on treadmill, concrete, and grass. Tibial acceleration and surface EMG from 12 lower limb muscles were recorded. The coefficient of variation (CV) from the average EMG and peak EMG were computed from 5, 10, 25, 50 and 100 steps in each running surface. Data average stability was computed using sequential estimation technique (SET) from 100 steps. The CV for average and peak EMG was lower during treadmill running compared to running on grass (−11 ± 2.88%) or concrete (−9 ± 2.94%) (p < 0.05), without differences across the different number of steps. Moreover, the peak EMG CV from peroneus longus was lower on concrete (p < 0.05), whereas gluteus maximus presented greater variability on grass compared to concrete (p < 0.05). The SET analysis revealed that average stability is reached with up to 10 steps across all running conditions. Therefore, treadmill running induced greater variability compared to overground, without influence of the number of steps on EMG variability. Moreover, average stability for EMG recordings may be reached with up to 10 steps.

Implications of sample size and acquired number of steps to investigate running biomechanics

Low reproducibility and non-optimal sample sizes are current concerns in scientific research, especially within human movement studies. Therefore, this study aimed to examine the implications of different sample sizes and number of steps on data variability and statistical outcomes from kinematic and kinetics running biomechanical variables. Forty-four participants ran overground using their preferred technique (normal) and minimizing the contact sound volume (silent). Running speed, peak vertical, braking forces, and vertical average loading rate were extracted from > 40 steps/runner. Data stability was computed using a sequential estimation technique. Statistical outcomes (p values and effect sizes) from the comparison normal vs silent running were extracted from 100,000 random samples, using various combinations of sample size (from 10 to 40 runners) and number of steps (from 5 to 40 steps). The results showed that only 35% of the study sample could reach average stability using up to 10 steps across all biomechanical variables. The loading rate was consistently significantly lower during silent running compared to normal running, with large effect sizes across all combinations. However, variables presenting small or medium effect sizes (running speed and peak braking force), required > 20 runners to reach significant differences. Therefore, varying sample sizes and number of steps are shown to influence the normal vs silent running statistical outcomes in a variable-dependent manner. Based on our results, we recommend that studies involving analysis of traditional running biomechanical variables use a minimum of 25 participants and 25 steps from each participant to provide appropriate data stability and statistical power.

A Rapid PN Code Acquisition Method for Low Spreading Factor Satellite Communication Systems

A novel rapid code acquisition scheme is proposed for low spreading factor satellite communication systems relying on message passing algorithms (MPA), where the acquisition of pseudonoise (PN) codes is converted to a sequence estimation problem. Due to the presence of data transitions in low spreading factor systems, conventional acquisition methods based on correlation would show poor capture performance. To overcome the impact of data transitions, this letter proposes a joint data chip and PN sequence estimation technique with the aid of factor graph and belief propagation (BP) algorithm. Meanwhile, in view of the short-term invariance of data chips, this letter models data chips as Markov chains to improve prior knowledge. Simulation results demonstrate that the proposed algorithm can save the mean acquisition time (MAT) about three orders of magnitude and improve the acquisition performance by 2 dB at 99% detection probability compared with serial search.

Primitive Polynomials for Iterative Recursive Soft Sequential Acquisition of Concatenated Sequences

An iterative initial sequence acquisition technique is proposed for the pseudo-noise signal derived from a pair of m -sequences used for generating the concatenated sequence, which relies on the soft-in-soft-out detection to improve the acquisition performance. This recursive soft sequential estimation technique has a linearly increasing complexity with the number of chips in the concatenated sequence. Receiving as few as S consecutive chips of a (2 S -1)-chip sequence is sufficient for the local concatenated-sequence generator of the receiver to synchronize. Hence, this initial synchronization technique is eminently suitable for long m -sequences and concatenated sequences. Another key result is the comparison of m -sequences and concatenated sequences regarding the acquisition time. It is also observed that low-order primitive polynomials (PPs) achieve better performances than higher-order polynomials for both the m -sequences and concatenated sequences. When considering PPs having a higher number of taps, the exploitation of concatenated sequences is capable of achieving in excess of 3-dB signal-to-noise ratio gains over m -sequences.

Observational Arc-Length Effect on Orbit Determination for KPLO Using a Sequential Estimation Technique

In this study, orbit determination (OD) simulation for the Korea Pathfinder Lunar Orbiter (KPLO) was accomplished for investigation of the observational arc-length effect using a sequential estimation algorithm. A lunar polar orbit located at 100 km altitude and 90° inclination was mainly considered for the KPLO mission operation phase. For measurement simulation and OD for KPLO, the Analytical Graphics Inc. Systems Tool Kit 11 and Orbit Determination Tool Kit 6 software were utilized. Three deep-space ground stations, including two deep space network (DSN) antennas and the Korea Deep Space Antenna, were configured for the OD simulation. To investigate the arc-length effect on OD, 60-hr, 48-hr, 24-hr, and 12-hr tracking data were prepared. Position uncertainty by error covariance and orbit overlap precision were used for OD performance evaluation. Additionally, orbit prediction (OP) accuracy was also assessed by the position difference between the estimated and true orbits. Finally, we concluded that the 48-hr-based OD strategy is suitable for effective flight dynamics operation of KPLO. This work suggests a useful guideline for the OD strategy of KPLO mission planning and operation during the nominal lunar orbits phase.

Low-Complexity Detection of High-Order QAM Faster-Than-Nyquist Signaling

Faster-than-Nyquist (FTN) signaling is a promising non-orthogonal transmission technique to considerably improve the spectral efficiency. This paper presents the first attempt in the literature to estimate the transmit data symbols of any high-order quadrature amplitude modulation (QAM) FTN signaling in polynomial time complexity. In particular, we propose a generalized approach to model the finite alphabet of any high-orderQAMconstellation as a high degree polynomial constraint. Then, we formulate the high-order QAM FTN signaling sequence estimation problem as a non-convex optimization problem. As an example of a high-order QAM, we consider 16-QAM FTN signaling and then transform the high degree polynomial constraint, with the help of auxiliary variables, to multiple quadratic constraints. Such transformation allows us to propose a generalized approach semidefinite relaxation (SDR)-based sequence estimation (GASDRSE) technique to efficiently provide a sub-optimal solution to the NP-hard non-convex FTN detection problem, with polynomial time complexity. For the particular case of 16-QAM FTN signaling, we additionally propose a sequence estimation technique based on concepts from the set theory. We show that the set theory SDR-based sequence estimation (STSDRSE) technique is of lower complexity when compared with the proposed GASDRSE. Simulation results show the effectiveness of the proposed GASDRSE and STSDRSE techniques in increasing the data rate and spectral efficiency of 16-QAM FTN signaling, without increasing the bit-error-rate, the bandwidth, or the data symbols energy, when compared with the Nyquist signaling.

A Very Low Complexity Successive Symbol-by-Symbol Sequence Estimator for Faster-Than-Nyquist Signaling

In this paper, we investigate the sequence estimation problem of binary and quadrature phase shift keying faster-than-Nyquist (FTN) signaling and propose two novel low-complexity sequence estimation techniques based on concepts of successive interference cancellation. To the best of our knowledge, this is the first approach in the literature to detect FTN signaling on a symbol-by-symbol basis. In particular, based on the structure of the self-interference inherited in FTN signaling, we first find the operating region boundary defined by the root-raised cosine pulse shape, its roll-off factor, and the time acceleration parameter of the FTN signaling where perfect estimation of the transmit data symbols on a symbol-by-symbol basis is guaranteed, assuming noise-free transmission. For noisy transmission, we then propose a novel low-complexity technique that works within the operating region and is capable of estimating the transmit data symbols on a symbol-by-symbol basis. To reduce the error propagation of the proposed successive symbol-by-symbol sequence estimator (SSSSE), we propose a successive symbol-by-symbol with go-back- $K$ sequence estimator (SSSgb $K$ SE) that goes back to re-estimate up to $K$ symbols, and subsequently improves the estimation accuracy of the current data symbol. Simulation results show that the proposed sequence estimation techniques perform well for low intersymbol interference scenarios and can significantly increase the data rate and spectral efficiency. Additionally, results reveal that choosing the value of $K$ as low as 2 or 3 data symbols is sufficient to significantly improve the bit-error-rate performance. Results also show that the performance of the proposed SSSgb $K$ SE, with $K = 1$ or 2, surpasses the performance of the lowest complexity equalizers reported in the literature, with reduced computational complexity.

Battery state estimation with a self-evolving electrochemical ageing model

The parameters for most battery state estimation techniques are usually calibrated during the development stage before vehicle production. However different usages of battery lead to different ageing processes, and end up with different model parameters. As a result, an estimation algorithm based on pre-calibrated parameters may not generate accurate estimates. This paper presents a new advanced methodology to address this challenge. First, we employ a comprehensive electrochemical model for lead acid batteries that were developed recently. The battery model explicitly characterizes the electric and thermal behavior of the battery, as well as the evolution of major battery failure modes. Second, we apply a two-time-scale model-based estimation method, where the micro time-scale algorithm estimates battery SOC and SOH, and the macro time-scale algorithm tracks the changes of the battery being monitored and updates the parameters of the battery model accordingly. The algorithms at both time scales use particle filter, a sequential Monte-Carlo estimation technique. Several datasets based on real vehicle driving profiles are used to validate the proposed algorithms. The experiment results show that the proposed estimation scheme is effective, due to both the careful selection of battery model and the tailored filtering technique.

Basketball lay-up – foot loading characteristics and the number of trials necessary to obtain stable plantar pressure variables

This study aimed (1) to profile the plantar loading characteristics when performing the basketball lay-up in a realistic setting and (2) to determine the number of trials necessary to establish a stable mean for plantar loading variables during the lay-up. Thirteen university male basketball players [age: 23.0 (1.4) years, height: 1.75 (0.05) m, mass: 68.4 (8.6) kg] performed ten successful basketball lay-ups from a stationary position. Plantar loading variables were recorded using the Novel Pedar-X in-shoe system. Loading variables including peak force, peak pressure, and pressure–time integral were extracted from eight foot regions. Performance stability of plantar loading variables during the take-off and landing steps were assessed using the sequential averaging technique and intra-class correlation coefficient (ICC). High plantar loadings were experienced at the heel during the take-off steps, and both the heel and forefoot regions upon landing. The sequential estimation technique revealed a five–eight trial range to achieve a stable mean across all plantar loading variables, whereas ICC analysis was insensitive to inter-trial differences of repeated lay-up performances. Future studies and performance evaluation protocols on plantar loading during basketball lay-ups should include at least eight trials to ensure that the measurements obtained are sufficiently stable.

Adapting the CSM-CROPGRO model for pigeonpea using sequential parameter estimation

Pigeonpea (Cajanus cajan (L.) Millsp.) is an important crop in Asia, Africa, Latin America and Caribbean. Despite pigeonpea's global importance there is a dearth of crop simulation models available for studying pigeonpea growth. The objectives of this study were to adapt the CSM-CROPGRO model for simulating pigeonpea growth and development through parameter modification and to illustrate the use of a sequential parameter estimation technique using a dataset from Gainesville, FL in 1984 and a dataset from India in 2003. The sequential approach to parameter estimation using a hybrid Metropolis–Hastings–Gibbs algorithm worked well at estimating physiologically plausible values for parameters with good correspondence to measured data. Reasonable results were obtained despite the use of approximations for measurement errors for the Gainesville dataset, which contained only treatment means. This study demonstrated that CROPGRO can be used to simulate the growth and development of pigeonpea. However, further testing of CROPGRO with more extensive pigeonpea datasets should be undertaken to confirm the accuracy of the parameter estimates developed from this study. Further theoretical and practical research into the parameter estimation approach is needed.

Selecting the number of trials in experimental biomechanics studies

Experimental biomechanics studies often involve the comparison of mean values from individuals across two or more experimental conditions. The purpose of this study was to evaluate two existing methods for determining the number of trials necessary to estimate these means. The sequential estimation technique (SET) was investigated in terms of the influence of input data distribution on the outcome. Paired samples t-tests were investigated in terms of the interaction between the number of subjects and number of trials necessary to achieve an acceptable level of statistical power. Simulation models were developed to perform SET and paired samples t-tests on representative synthetic input data. The SET results confirmed that the number of trials to achieve a stable estimate of the mean is independent of the input distribution provided the mean and standard deviation are fixed. For the commonly used 20 reference trials and 0.25 standard deviation threshold 9 ± 8 trials were needed to achieve stability. The paired t-test results confirmed that both number of subjects and number of trials can have a marked effect on the statistical power, e.g. a power of 0.80 can be achieved for effect size of 0.80 using 15 subjects and at least 19 trials or 20+ subjects and only 3 trials. The SET method suffers from arbitrary convergence criteria and neglecting intra-subject variance and, thus, should be applied with extreme caution. In contrast, statistical power can provide a more objective and conclusive means for determining the number of trials required for a given experimental situation.

Gaussian Classifier-Based Evolutionary Strategy for Multimodal Optimization

This paper presents a Gaussian classifier-based evolutionary strategy (GCES) to solve multimodal optimization problems. An evolutionary technique for them must answer two crucial questions to guarantee its success: how to distinguish among the different basins of attraction and how to safeguard the already discovered good-quality solutions including both global and local optima. In GCES, multimodal optimization problems are regarded as classification ones, and Gaussian mixture models are employed to save the locations and basins of already and presently identified local or global optima. A sequential estimation technique for the covariance of a Gaussian model is introduced into GCES. To best adjust the global step size, a strategy named top-ranked sample selection is introduced, and a classification method instead of a common but problematic radius-triggered manner is proposed. Experiments are performed on a series of benchmark test functions to compare GCES with the state-of-the-art multimodal optimization approaches. The results show that GCES is not only simple to program and understand, but also provides better and consistent performance.

Automatic detection and measurement of structures in fetal head ultrasound volumes using sequential estimation and Integrated Detection Network (IDN).

Routine ultrasound exam in the second and third trimesters of pregnancy involves manually measuring fetal head and brain structures in 2-D scans. The procedure requires a sonographer to find the standardized visualization planes with a probe and manually place measurement calipers on the structures of interest. The process is tedious, time consuming, and introduces user variability into the measurements. This paper proposes an automatic fetal head and brain (AFHB) system for automatically measuring anatomical structures from 3-D ultrasound volumes. The system searches the 3-D volume in a hierarchy of resolutions and by focusing on regions that are likely to be the measured anatomy. The output is a standardized visualization of the plane with correct orientation and centering as well as the biometric measurement of the anatomy. The system is based on a novel framework for detecting multiple structures in 3-D volumes. Since a joint model is difficult to obtain in most practical situations, the structures are detected in a sequence, one-by-one. The detection relies on Sequential Estimation techniques, frequently applied to visual tracking. The interdependence of structure poses and strong prior information embedded in our domain yields faster and more accurate results than detecting the objects individually. The posterior distribution of the structure pose is approximated at each step by sequential Monte Carlo. The samples are propagated within the sequence across multiple structures and hierarchical levels. The probabilistic model helps solve many challenges present in the ultrasound images of the fetus such as speckle noise, signal drop-out, shadows caused by bones, and appearance variations caused by the differences in the fetus gestational age. This is possible by discriminative learning on an extensive database of scans comprising more than two thousand volumes and more than thirteen thousand annotations. The average difference between ground truth and automatic measurements is below 2 mm with a running time of 6.9 s (GPU) or 14.7 s (CPU). The accuracy of the AFHB system is within inter-user variability and the running time is fast, which meets the requirements for clinical use.

A MAP-Based Image Interpolation Method via Viterbi Decoding of Markov Chains of Interpolation Functions

A new method of image resolution up-conversion (image interpolation) based on maximum a posteriori sequence estimation is proposed. Instead of making a hard decision about the value of each missing pixel, we estimate the missing pixels in groups. At each missing pixel of the high resolution (HR) image, we consider an ensemble of candidate interpolation methods (interpolation functions). The interpolation functions are interpreted as states of a Markov model. In other words, the proposed method undergoes state transitions from one missing pixel position to the next. Accordingly, the interpolation problem is translated to the problem of estimating the optimal sequence of interpolation functions corresponding to the sequence of missing HR pixel positions. We derive a parameter-free probabilistic model for this to-be-estimated sequence of interpolation functions. Then, we solve the estimation problem using a trellis representation and the Viterbi algorithm. Using directional interpolation functions and sequence estimation techniques, we classify the new algorithm as an adaptive directional interpolation using soft-decision estimation techniques. Experimental results show that the proposed algorithm yields images with higher or comparable peak signal-to-noise ratios compared with some benchmark interpolation methods in the literature while being efficient in terms of implementation and complexity considerations.

Real-time estimation of respiratory rate from a photoplethysmogram using an adaptive lattice notch filter

BackgroundMany researchers have attempted to acquire respiratory rate (RR) information from a photoplethysmogram (PPG) because respiration affects the waveform of the PPG. However, most of these methods were difficult to operate in real-time because of their complexity or computational requirements. From these needs, we attempted to develop a method to estimate RR from a PPG with a light computational burden.MethodsTo obtain RR information, we adopt a sequential filtering structure and frequency estimation technique, which extracts a dominant frequency from a given signal. In particular, we used an adaptive lattice notch filter (ALNF) to estimate RR from a PPG along with an additional heart rate that is utilized as an adaptation parameter of our method. Furthermore, we designed a sequential infinite impulse response (IIR) notch filtering system (i.e., harmonic IIR notch filter) to eliminate the cardiac component and its harmonics from the PPG. We compared the proposed method with Burg’s AR modeling method, which is widely used to estimate RR from a PPG, using open-source data and measured data.ResultsBy using a statistical test, it was determined that our adaptive lattice-type respiratory rate estimator (ALRE) was significantly more accurate than Burg’s AR model method (p <0.0001). Furthermore, the ALRE’s tracking performance was better than that of Burg’s method, and the variances of its estimates were smaller than those of Burg’s method.ConclusionsIn short, our method showed a better performance than Burg’s AR modeling method for real-time applications.

Adaptive Selective State-Transition Decoding: A Combined Approach for Trellis Coded Modulation Scheme

We are presenting a new suboptimum decoding strategy, namely, Adaptive Selective StateTransition Decoding (ASSTD) for Trellis Coded Modulation schemes transmission over band-limited ISI channels. The ASSTD is a combined approach provides an improved error performance over Reduced State Sequence Estimation (RSSE) techniques which are in practical use. The ASSTD operates on two different concepts and has the flexibility to work with a controlled complexity. Simulation results are obtained for static band-limited ISI channels. The results show that the ASSTD can be extended to fading channels.

Development of a simple and robust inverse method for determination of thermal diffusivity of solid foods

Thermal properties are essential requirements for mathematical modeling and simulation of various thermal processing operations. Knowledge of accurate values of thermal properties will result in accurate prediction of temperature profiles needed for process selection, design and optimization. The aim of this work was to develop a simple, accurate and robust method for determination of thermal diffusivity of solid foods. An inverse numerical method was developed to estimate thermal diffusivity from transient temperature measurements at any position in a food sample using sequential parameter estimation technique. The developed algorithm was validated using computer generated temperatures in addition to actual experimental temperatures data. The estimated thermal diffusivity from the computer generated data is in excellent agreement with the true value, and the estimated thermal diffusivity from the experimental measurements is in good agreement with literature data. The method presented here was demonstrated to be simple accurate and robust.

History matching with an ensemble Kalman filter and discrete cosine parameterization

History matching of large hydrocarbon reservoirs is challenging because of several reasons including: (1) scarcity of available measurements relative to the number of unknowns, leading to an ill-posed inverse problem, (2) computational effort required for large reservoir problems, and (3) the need to insure that solutions are geologically realistic. All of these problems can be helped by using algorithms that rely on efficient and parsimonious descriptions (or parameterizations) of reservoir properties. This paper demonstrates the use of a novel parameterization approach, the discrete cosine transform, for history matching with a recently introduced sequential estimation technique, i.e., the ensemble Kalman filter. The proposed approach exploits the structure of the estimation and parameterization algorithms to reduce the size of reservoir states (pressures and saturations) as well as parameters (e.g., intrinsic permeability) with a marginal loss in accuracy. The introduced methodology eliminates redundancy in posing the estimation problem and results in additional computational savings. Application and generality of this approach are demonstrated using two waterflooding experiments characterized by different types of geological variability.