Unknown Shift Research Articles

The Variable Sampling Interval EWMA X¯ Chart with Estimated Process Parameters

Abstract The exponentially weighted moving average (EWMA) X¯ chart with the variable-sampling-interval (VSI) feature is usually scrutinized under the assumption of known process parameters. However, in practice, process parameters are usually unknown, and they need to be estimated from the in-control Phase-I data set. With this in mind, this article proposes the VSI EWMA X¯ chart in which the process parameters are estimated. A Markov Chain approach is adopted to derive the run-length properties of the VSI EWMA X¯ chart with estimated process parameters. The standard deviation of the average time to signal (SDATS) is employed to measure the practitioner-to-practitioner variation in the control chart’s performance. This variation occurs because different Phase-I datasets are used among practitioners to estimate the process parameters. Based on the SDATS criterion, this article provides recommendations regarding the minimum number of required Phase-I samples. For an optimum implementation, this article develops two optimization algorithms for the VSI EWMA X¯ chart with estimated process parameters, i.e., by minimizing the (i) out-of-control expected value of the average time to signal (AATS) and (ii) out-of-control expected value of the AATS (EAATS) for the cases of deterministic and unknown shift sizes, respectively. With the implementation of these new design procedures, the VSI EWMA X¯ chart with estimated process parameters is not only able to achieve a desirable in-control performance, but it is also able to quickly detect changes in the process.

Characterization and compensation of inhomogeneity artifact in spiral hyperpolarized 13 C imaging of the human heart.

This study aimed to investigate the role of regional inhomogeneity in spiral hyperpolarized 13 C image quality and to develop measures to alleviate these effects. Field map correction of hyperpolarized 13 C cardiac imaging using spiral readouts was evaluated in healthy subjects. Spiral readouts with differing duration (26 and 45 ms) but similar resolution were compared with respect to off-resonance performance and image quality. An map-based image correction based on the multifrequency interpolation (MFI) method was implemented and compared to correction using a global frequency shift alone. Estimation of an unknown frequency shift was performed by maximizing a sharpness objective based on the Sobel variance. The apparent full width half at maximum (FWHM) of the myocardial wall on [13 C]bicarbonate was used to estimate blur. Mean myocardial wall FWHM measurements were unchanged with the short readout pre-correction (14.1 ± 2.9 mm) and post-MFI correction (14.1 ± 3.4 mm), but significantly decreased in the long waveform (20.6 ± 6.6 mm uncorrected, 17.7 ± 7.0 corrected, P = .007). Bicarbonate signal-to-noise ratio (SNR) of the images acquired with the long waveform were increased by 1.4 ± 0.3 compared to those acquired with the short waveform (predicted 1.32). Improvement of image quality was observed for all metabolites with correction. -map correction reduced blur and recovered signal from dropouts, particularly along the posterior myocardial wall. The low image SNR of [13 C]bicarbonate can be compensated with longer duration readouts but at the expense of increased artifacts, which can be partially corrected for with the proposed methods.

Optimal monitoring of Poisson data with known and unknown shifts

The number of event occurrences, called counts are prevalent in many fields such as manufacturing industry and public health. Control charts have been widely employed to monitor such count data for quality improvement of products or medical service by assuming the data follows the Poisson distribution. However, the shift information of Poisson mean has not been well considered in current design of the exponentially weighted moving average (EWMA) control chart. This article studies the optimal design of the Poisson EWMA chart with known and unknown shift sizes integrated respectively in order to bridge the research gap. We simplify these two optimization problems to searching for a unique smoothing parameter in minimizing the out-of-control (OC) average run length (ARL) and OC expected ARL (EARL) over random shifts respectively. Due to the intractability of obtaining a closed-form solution, the Fibonacci search algorithm is proposed to find out the optimal smoothing parameter in a short time. The satisfactory performance of proposed optimal design method is demonstrated by numerous simulation results and two real datasets from manufacturing industry and public health.

Adaptive Electrode Calibration Method Based on Muscle Core Activation Regions and Its Application in Myoelectric Pattern Recognition.

To reduce the bad effect of electrode shifts on myoelectric pattern recognition, this paper presents an adaptive electrode calibration method based on core activation regions of muscles. In the proposed method, the high-density surface electromyography (HD-sEMG) matrix collected during hand gesture execution is decomposed into source signal matrix and mixed coefficient matrix by fast independent component analysis algorithm firstly. The mixed coefficient vector whose source signal has the largest two-norm energy is selected as the major pattern, and core activation region of muscles is extracted by traversing the major pattern periodically using a sliding window. The electrode calibration is realized by aligning the core activation regions in unsupervised way. Gestural HD-sEMG data collection experiments with known and unknown electrode shifts are carried out on 9 gestures and 11 participants. A CNN+LSTM-based network is constructed and two network training strategies are adopted for the recognition task. The experimental results demonstrate the effectiveness of the proposed method in mitigating the bad effect of electrode shifts on gesture recognition accuracy and the potentials in reducing user training burden of myoelectric control systems. With the proposed electrode calibration method, the overall gesture recognition accuracies increase about (5.72~7.69)%. In specific, the average recognition accuracy increases (13.32~17.30)% when using only one batch of data in data diversity strategy, and increases (12.01~13.75)% when using only one repetition of each gesture in model update strategy. The proposed electrode calibration algorithm can be extended and applied to improve the robustness of myoelectric control system.

Multi-Reference Alignment in High Dimensions: Sample Complexity and Phase Transition

Multi-reference alignment entails estimating a signal in $\mathbb{R}^L$ from its circularly-shifted and noisy copies. This problem has been studied thoroughly in recent years, focusing on the finite-dimensional setting (fixed $L$). Motivated by single-particle cryo-electron microscopy, we analyze the sample complexity of the problem in the high-dimensional regime $L\to\infty$. Our analysis uncovers a phase transition phenomenon governed by the parameter $\alpha = L/(\sigma^2\log L)$, where $\sigma^2$ is the variance of the noise. When $\alpha>2$, the impact of the unknown circular shifts on the sample complexity is minor. Namely, the number of measurements required to achieve a desired accuracy $\varepsilon$ approaches $\sigma^2/\varepsilon$ for small $\varepsilon$; this is the sample complexity of estimating a signal in additive white Gaussian noise, which does not involve shifts. In sharp contrast, when $\alpha\leq 2$, the problem is significantly harder and the sample complexity grows substantially quicker with $\sigma^2$.

An adaptive nonparametric exponentially weighted moving average control chart with dynamic sampling intervals

AbstractNonparametric statistical control charts have been widely studied over the past decade. The variable sampling intervals (VSIs) methods have been found to exhibit superior performance compared to the traditional fixed sampling policy in this area. In previous VSI studies, the sampling interval function d(·) realized only two distinct values. Limiting the selection of the sampling interval to two fixed values may not be reasonable, especially when the monitoring statistic value is at the edge or near the middle of these two intervals so that the advantages of VSI methods cannot be fully reflected. Therefore, we propose a nonparametric exponentially weighed moving average control chart with a dynamic sampling interval in this article, in which the dynamic sampling interval is an extension of two fixed sampling intervals. The dynamic sampling interval can be continuous valued and is determined by an indicator of the magnitude of the unknown shift. This chart can efficiently detect various magnitudes of shifts and performs robustly for different distributions according to simulation studies. The small computational cost indicates that the method is applicable for monitoring large data streams. Data from an aluminum electrolytic capacitor manufacturing process are used to illustrate the implementation of this chart.

One-sided precedence monitoring schemes for unknown shift sizes using generalized 2-of-(h+1) and w-of-w improved runs-rules

Parametric monitoring schemes are expected to perform better than their nonparametric counterparts when the assumption of a specific form of a distribution such as the normal is met. In practice, such assumption is often violated. Consequently, the performance of parametric schemes deteriorates considerably. To solve this problem, more efficient and flexible monitoring schemes based on nonparametric tests are recommended. In this paper, efficient and robust one-sided monitoring schemes are developed using generalized {1-of-1 or 2-of-(h + 1)} and {1-of-1 or w-of-w} improved runs-rules (IRR) without any distributional assumption in the zero- and steady-states modes. Moreover, the zero- and steady-states run-length properties of the resulting one-sided IRR schemes are formulated and empirically evaluated using the Markov chain technique. Comparisons with other well-known one-sided Shewhart-type nonparametric schemes (e.g., basic precedence scheme and precedence scheme with standard runs-rules) indicate that the proposed schemes have a better performance. Real data are used to demonstrate the design and implementation of the one-sided improved runs-rules precedence schemes. Finally, a discussion on the two-sided IRR precedence schemes is also provided.

Distribution-free mixed GWMA-CUSUM and CUSUM-GWMA Mann–Whitney charts to monitor unknown shifts in the process location

The Mann–Whitney (MW) test is one of the most important nonparametric tests used in the comparison of the location parameters of two populations. Unlike the t-test, the MW test can be used when the assumption of normality fails to hold. In this paper, the MW U statistic is used to construct two efficient distribution-free monitoring schemes, namely the mixed generally weighted moving average-cumulative sum (GWMA-CUSUM) MW U scheme (denoted as U-MGC) as well as its reversed version, i.e. the CUSUM-GWMA MW U scheme (denoted as U-MCG). The performances of the proposed schemes are investigated using the average run-length (ARL) and average extra quadratic loss (AEQL) values through extensive simulations. The newly proposed charts are found to be superior in small shifts detection than their competing (existing and others that are briefly introduced here) distribution-free Shewhart, EWMA, CUSUM, mixed EWMA-CUSUM, mixed CUSUM-EWMA and GWMA MW U charts in many situations. A real-life example is used to demonstrate the design and implementation of the new schemes.

Ageing hallmarks exhibit organ-specific temporal signatures.

Aging is the single greatest cause of disease and death worldwide, and understanding the associated processes could vastly improve quality of life. While the field has identified major categories of aging damage such as altered intercellular communication, loss of proteostasis, and eroded mitochondrial function1, these deleterious processes interact with extraordinary complexity within and between organs. Yet, a comprehensive analysis of aging dynamics organism-wide has been lacking. Here we performed bulk RNA-sequencing of 17 organs and plasma proteomics at 10 ages across the mouse lifespan, and integrated these findings with data from the companion Tabula Muris Senis2,3. We uncover previously unknown linear and non-linear expression shifts during aging, which cluster in strikingly consistent trajectory groups with coherent biological functions, including extracellular matrix regulation, unfolded protein binding, mitochondrial function, and inflammatory and immune response. Remarkably, these gene sets are expressed similarly across tissues, differing merely in age of onset and amplitude. Widespread immune cell activation is especially pronounced and first detectable in white adipose depots during middle age. Single-cell RNA-sequencing confirms the accumulation of adipose T and B cells, including immunoglobulin J-expressing plasma cells, which also accrue concurrently across diverse organs. Finally, we show how expression shifts in distinct tissues are highly correlated with corresponding protein levels in plasma, thus potentially contributing to aging of the systemic circulation. Together, these data demonstrate a similar yet asynchronous inter- and intra-organ progression of aging, thereby providing a foundation to track systemic sources of declining health at old age.

Equivalence of Network Structures in Networks of Random Variables with Known and Unknown Shift Parameter

It is proved that a sign network with an elliptical distribution with known shift parameter is equivalent to a sign network with elliptical distribution with unknown shift parameter estimated by sample mean. This result is proved for the case of independent identically distributed observations and for the case of sample from a matrix elliptically contoured distribution with any dependence between observations.

Enhancement of the Kr81 and Kr85 count rates by optical pumping

We report an increase of up to 60% on the count rates of the rare $^{81}\mathrm{Kr}$ and $^{85}\mathrm{Kr}$ isotopes in the Atom Trap Trace Analysis method by enhancing the production of metastable atoms in the discharge source. Additional atoms in the metastable $ 1s_5 $ level (Paschen notation) are obtained via optically pumping the $1s_4-2p_6$ transition at 819 nm. By solving the master equation for the system, we identify this transition to be the most suitable one and can describe the measured increase in metastable population as a function of the 819-nm laser power. We calculate the previously unknown isotope shifts and hyperfine splittings of the $1s_4-2p_6$ transition in $^{81}\mathrm{Kr}$ and $^{85}\mathrm{Kr}$, and verify the results with count rate measurements. The demonstrated count-rate increase enables a corresponding decrease in the required sample sizes for $^{81}\mathrm{Kr}$ and $^{85}\mathrm{Kr}$ dating, a significant improvement for applications such as dating of ocean water and deep ice cores.

Hidden Convexity in Robust Waveform and Receive Filter Bank Optimization Under Range Unambiguous Clutter

This letter deals with the robust joint design of radar transmit waveform and receive filter bank in a background of range unambiguous signal-dependent clutter. Assuming an unknown Doppler shift for the target, the worst-case Signal-to-Interference-plus-Noise-Ratio (SINR) at the output of the receive filter bank is considered as the figure of merit. The transceiver design is pursued considering a max-min optimization problem with some constraints on the transmit energy, similarity, and signal dynamic range. Hidden convexity is shown and a procedure to derive optimal waveform and filters is given. Simulation results highlight the effectiveness of the devised method.

A new distribution-free generally weighted moving average monitoring scheme for detecting unknown shifts in the process location

A new distribution-free generally weighted moving average monitoring scheme for detecting unknown shifts in the process location

Sequential optimisation of orthogonal waveforms for MIMO radar

The existing methods to design orthogonal waveforms for multiple-input multiple-output radar mainly focus on the optimisation of autocorrelation and cross-correlation properties. Their performance will degrade severely in the presence of Doppler shifts. To overcome this limitation, the authors take an unknown Doppler shifts range into consideration and formulate a new waveform optimisation problem. Since the optimisation problem is highly non-linear, the authors propose an algorithm, called sequential cone programming, to tackle it. The key idea is to use the first-order Taylor expansion to approximate the constraints at each iteration. The authors show that the approximation can be solved via second-order cone programming. In addition, the autocorrelation peak sidelobe level and cross-correlation peak level could be further reduced by setting an appropriate threshold function. Simulation results demonstrate the efficiency of the proposed method compared with state-of-art methods.

Ptychographic modulation engine: a low-cost DIY microscope add-on for coherent super-resolution imaging

Imaging of biological cells and tissues often relies on fluorescent labels, which offer high contrast with molecular specificity. The use of exogenous labeling agents, however, may alter the normal physiology of the bio-specimens. Complementary to the established fluorescence microscopy, label-free quantitative phase imaging provides an objective morphological measurement tool for bio-specimens and is free of variability introduced by contrast agents. Here, we report a simple and low-cost microscope add-on, termed ptychographic modulation engine (PME), for super-resolution quantitative phase imaging. In this microscope add-on module, we attach a diffuser to a 3D-printed holder that can be mechanically moved to different x-y positions. We then use two vibrational motors to introduce random positional shifts to the diffuser. The add-on module can be placed between the objective lens and the specimen in most existing microscope platforms. Thanks to the diffuser modulation process, the otherwise inaccessible high-resolution object information can now be encoded into the captured images. In the ptychographic phase retrieval process, we jointly recover the complex object wavefront, the complex diffuser profile, and the unknown positional shifts of the diffuser. We demonstrate a four-fold resolution gain over the diffraction limit of the employed 2× objective lens. We also test our approach for in vitro cell imaging, where we are able to adjust the focus after the data has been captured. The reported microscope add-on provides a turnkey solution for super-resolution quantitative phase imaging. It may find applications in label-free bio-imaging where both large field-of-view and high resolution are needed.

Sampling and Reconstruction of Bandlimited Signals With Multi-Channel Time Encoding

Sampling is classically performed by recording the amplitude of an input signal at given time instants; however, sampling and reconstructing a signal using multiple devices in parallel becomes a more difficult problem to solve when the devices have an unknown shift in their clocks. Alternatively, one can record the times at which a signal (or its integral) crosses given thresholds. This can model integrate-and-fire neurons, for example, and has been studied by Lazar and T\'oth under the name of ``Time Encoding Machines''. This sampling method is closer to what is found in nature. In this paper, we show that, when using time encoding machines, reconstruction from multiple channels has a more intuitive solution, and does not require the knowledge of the shifts between machines. We show that, if single-channel time encoding can sample and perfectly reconstruct a $\mathbf{2\Omega}$-bandlimited signal, then $\mathbf{M}$-channel time encoding with shifted integrators can sample and perfectly reconstruct a signal with $\mathbf{M}$ times the bandwidth. Furthermore, we present an algorithm to perform this reconstruction and prove that it converges to the correct unique solution, in the noiseless case, without knowledge of the relative shifts between the integrators of the machines. This is quite unlike classical multi-channel sampling, where unknown shifts between sampling devices pose a problem for perfect reconstruction.

Improving semantic change analysis by combining word embeddings and word frequencies

Language is constantly evolving. As part of diachronic linguistics, semantic change analysis examines how the meanings of words evolve over time. Such semantic awareness is important to retrieve content from digital libraries. Recent research on semantic change analysis relying on word embeddings has yielded significant improvements over previous work. However, a recent, but somewhat neglected observation so far is that the rate of semantic shift negatively correlates with word-usage frequency. In this article, we therefore propose SCAF, Semantic Change Analysis with Frequency. It abstracts from the concrete embeddings and includes word frequencies as an orthogonal feature. SCAF allows using different combinations of embedding type, optimization algorithm and alignment method. Additionally, we leverage existing approaches for time series analysis, by using change detection methods to identify semantic shifts. In an evaluation with a realistic setup, SCAF achieves better detection rates than prior approaches, 95% instead of 51%. On the Google Books Ngram data set, our approach detects both known and yet unknown shifts for popular words.

A time travel story: metagenomic analyses decipher the unknown geographical shift and the storage history of possibly smuggled antique marble statues

In this study, three possibly smuggled marble statues of an unknown origin, two human torsi (a female and a male) and a small head, were subjected to molecular analyses. The aim was to reconstruct the history of the storage of each single statue, to infer the possible relationship among them, and to elucidate their geographical shift. A genetic strategy, comprising metagenomic analyses of the 16S ribosomal DNA (rDNA) of prokaryotes, 18S rDNA of eukaryotes, as well as internal transcribed spacer regions of fungi, was performed by using the Ion Torrent sequencing platform. Results suggest a possible common history of storage of the two human torsi; their eukaryotic microbiomes showed similarities comprising many soil-inhabiting organisms, which may indicate storage or burial in land of agricultural soil. For the male torso, it was possible to infer the geographical origin, due to the presence of DNA traces of Taiwania, a tree found only in Asia. The small head displayed differences concerning the eukaryotic community, compared with the other two samples, but showed intriguing similarities with the female torso concerning the bacterial community. Both displayed many halotolerant and halophilic bacteria, which may indicate a longer stay in arid and semi-arid surroundings as well as marine environments. The microbiomes retrieved from each statue showed to be very specific, but some individual members showed to be biological markers for the geographical regions through which the pieces traveled and for the conditions under which they were stored.

Some simultaneous progressive monitoring schemes for the two parameters of a zero-inflated Poisson process under unknown shifts

The zero-inflated Poisson (ZIP) distribution has been extensively studied in the literature during recent years. It is one of the most appropriate models for overdispersed data with an excessive number of zeros. Data of this type frequently arise in manufacturing processes with a low fraction of defective items. A ZIP model has two parameters; one presents the probability of extra zeros and the other stands for the expected Poisson count. In this article, we propose and study three new single control charts for detecting changes in either of the two parameters of a ZIP process. The performance of the schemes is studied via numerical simulation based on Monte-Carlo. We outline that all the existing control charts for monitoring ZIP processes are based on individual observations and assume that the shift sizes in either or both parameters are known. Our proposed schemes do not need any prior information related to shift size and can be used both for individual observations and subgroup samples. The results reveal that they are very effective in the detection of small and moderate shifts in process parameters. Practical implementation of the proposed schemes is also illustrated through an interesting real industrial data set on light emitting diodes (LED).

Detection Schemes for Range-Spread Targets Based on the Semidefinite Problem

Employing the generalized likelihood ratio test and semidefinite programming (SDP), we derive two detectors, called the SDP and maximization in the reduced space (MRS) detectors for range-spread targets with unknown Doppler shift. It is confirmed that, when knowledge of Doppler frequency is unavailable or imperfect, the proposed detectors perform better than the conventional detector. The MRS detector also outperforms the SDP and conventional detectors in some cases, including when the range-spread target is highly fluctuating.