Multidimensional Techniques Research Articles

A novel multidimensional uncalibration method applied to six-axis manipulators.

This study proposes a multidimensional uncalibrated technique for tracking and grasping dynamic targets by a robotic arm in the eye-in-hand mode. This method avoids complex and cumbersome calibration processes, enabling machine vision tasks to be adaptively applied in a variety of complex environments, which solved the problem of traditional calibration methods being unstable in complex environments. The specific method used in this study is first, in the eye-in-hand mode, the robotic arm moves along the x, y, and z axes in sequence, and images are taken before and after each movement. Thereafter, the image Jacobian matrix is calculated from the three (or more) sets of images collected. Finally, the robotic arm converts the target coordinates in the real-time captured images by the camera into coordinates in the robotic arm coordinate system through the image Jacobian matrix and performs real-time tracking. This study tests the dynamic quasi-Newton method for estimating the Jacobian matrix and optimizes the initialization coupling problem using the orthogonal moving method. This optimization scheme significantly shortens the iteration process, making the uncalibrated technology more fully applied in the field of dynamic object tracking. In addition, this study proposes a servo control algorithm with predictive compensation to mitigate or even eliminate the systematic error caused by time delay in dynamic target tracking in robot visual servo systems.

Strain-Level Discrimination of Bacteria by Liquid Chromatography and Paper Spray Ion Mobility Mass Spectrometry.

Determining bacterial identity at the strain level is critical for public health to enable proper medical treatments and reduce antibiotic resistance. Herein, we used liquid chromatography, ion mobility, and tandem MS (LC-IM-MS/MS) to distinguish Escherichia coli (E.coli) strains. Numerical multivariate statistics (principal component analysis, followed by linear discriminant analysis) showed the capability of this method to perform strain-level discrimination with prediction rates of 96.1% and 100% utilizing the negative and positive ion information, respectively. The tandem MS and LC separation proved effective in discriminating diagnostic lipid isomers in the negative mode, while IM separation was more effective in resolving lipid conformational biomarkers in the positive ion mode. Because of the clinical importance of early detection for rapid medical intervention, a faster technique, paper spray (PS)-IM-MS/MS, was used to discriminate the E.coli strains. The achieved prediction rates of the analysis of E.coli strains by PS-IM-MS/MS were 62.5% and 73.5% in the negative and positive ion modes, respectively. The strategy of numerical data fusion of negative and positive ion data increased the classification rates of PS-IM-MS/MS to 80.5%. Lipid isomers and conformers were detected, which served as strain-indicating biomarkers. The two complementary multidimensional techniques revealed biochemical differences between the E.coli strains confirming the results obtained from comparative genomic analysis. Moreover, the results suggest that PS-IM-MS/MS is a rapid, highly selective, and sensitive method for discriminating bacterial strains in environmental and food samples.

A high-precision multi-dimensional microspectroscopic technique for morphological and properties analysis of cancer cell

Raman and Brillouin scattering are sensitive approaches to detect chemical composition and mechanical elasticity pathology of cells in cancer development and their medical treatment researches. The application is, however, suffering from the lack of ability to synchronously acquire the scattering signals following three-dimensional (3D) cell morphology with reasonable spatial resolution and signal-to-noise ratio. Herein, we propose a divided-aperture laser differential confocal 3D Geometry-Raman-Brillouin microscopic detection technology, by which reflection, Raman, and Brillouin scattering signals are simultaneously in situ collected in real time with an axial focusing accuracy up to 1 nm, in the height range of 200 μm. The divided aperture improves the anti-noise capability of the system, and the noise influence depth of Raman detection reduces by 35.4%, and the Brillouin extinction ratio increases by 22 dB. A high-precision multichannel microspectroscopic system containing these functions is developed, which is utilized to study gastric cancer tissue. As a result, a 25% reduction of collagen concentration, 42% increase of DNA substances, 17% and 9% decrease in viscosity and elasticity are finely resolved from the 3D mappings. These findings indicate that our system can be a powerful tool to study cancer development new therapies at the sub-cell level.

Multidimensional Separations in Top-Down Proteomics.

Top-down proteomics (TDP) identifies, quantifies, and characterizes proteins at the intact proteoform level in complex biological samples to understand proteoform function and cellular mechanisms. However, analyzing complex biological samples using TDP is still challenging due to high sample complexity and wide dynamic range. High-resolution separation methods are often applied prior to mass spectrometry (MS) analysis to decrease sample complexity and increase proteomics throughput. These separation methods, however, may not be efficient enough to characterize low abundance intact proteins in complex samples. As such, multidimensional separation techniques (combination of two or more separation methods with high orthogonality) have been developed and applied that demonstrate improved separation resolution and more comprehensive identification in TDP. A suite of multidimensional separation methods that couple various types of liquid chromatography (LC), capillary electrophoresis (CE), and/or gel electrophoresis-based separation approaches have been developed and applied in TDP to analyze complex biological samples. Here, we reviewed multidimensional separation strategies employed for TDP, summarized current applications, and discussed the gaps that may be addressed in the future.

Disability-related factors affecting food security status: A case study from southern Ethiopia

This study looked at the state of food security and the factors that affect it among individuals with disabilities in Durame, a town in southern Ethiopia. The study used cross-sectional survey data from 205 disabled individuals who were chosen using a combination of stratified, proportionate to population size, and random sampling techniques. The required primary and secondary data were collected using a variety of instruments, including survey questionnaires, key informant interviews, focus group discussions, and case narrative telling techniques. The disability aspects and severity level among people with disabilities were identified using descriptive statistics in conjunction with multidimensional techniques utilizing principal component analysis. To determine how factors affected the level of food security, ordered logit regression was utilized. The results showed that 10 variables, including education level, sex, food expenditure, empowerment, employment situation, social cohesion, water access, institutional supports, extension contact, and media access, were identified as the significant determinants of food security status poeple with disabilities in the study area out of the 19 variables included in the model. The results led to the presentation of policy recommendations. In order to achieve food security for people with disabilities, coordinated efforts of stakeholders should be increased to improve access to education, employment, empowerment, infrastructure like water and mass media, creating social cohesion, more expansion and recognition of female roles, and institutional supports.

A Physical Layer Key Generation Scheme Based on Deep Learning Compensation and Balanced Vector Quantization

Channel reciprocity is the foundation for physical layer key generation, which is influenced by noise, hardware impairments, and synchronization offsets. Weak channel reciprocity will result in a high key disagreement rate (KDR). The existing solutions for improving channel reciprocity cannot achieve satisfactory performance improvements. Furthermore, the existing quantization algorithms generally use one-dimensional channel features to quantize and generate secret keys, which cannot fully utilize channel information. The multidimensional vector quantization technique also needs to improve in terms of randomness and time complexity. This paper proposes a physical layer key generation scheme based on deep learning and balanced vector quantization. Specifically, we build a channel reciprocity compensation network (CRCNet) to learn the mapping relationship between Alice and Bob’s channel measurements. Alice compensates for channel measurements via a trained CRCNet to reduce channel measurement errors between legitimate users and enhance channel reciprocity. We also propose a balanced vector quantization algorithm based on integer linear programming (ILP-BVQ). ILP-BVQ reduces the time complexity of quantization on the basis of ensuring key randomness and a low KDR. Simulation results showed that the proposed CRCNet performs better in terms of channel reciprocity and KDR, while the proposed ILP-BVQ algorithm improves time consumption and key randomness.

Nanoarchitectonic E-Tongue of Electrospun Zein/Curcumin Carbon Dots for Detecting Staphylococcus aureusin Milk.

We report a nanoarchitectonic electronic tongue made with flexible electrodes coated with curcumin carbon dots and zein electrospun nanofibers, which could detect Staphylococcus aureus(S. aureus) in milk using electrical impedance spectroscopy. Electronic tongues are based on the global selectivity concept in which the electrical responses of distinct sensing units are combined to provide a unique pattern, which in this case allowed the detection of S. aureus through non-specific interactions. The electronic tongue used here comprised 3 sensors with electrodes coated with zein nanofibers, carbon dots, and carbon dots with zein nanofibers. The capacitance data obtained with the three sensors were processed with a multidimensional projection technique referred to as interactive document mapping (IDMAP) and analyzed using the machine learning-based concept of multidimensional calibration space (MCS). The concentration of S. aureus could be determined with the sensing units, especially with the one containing zein as the limit of detection was 0.83 CFU/mL (CFU stands for colony-forming unit). This high sensitivity is attributed to molecular-level interactions between the protein zein and C-H groups in S. aureus according to polarization-modulated infrared reflection-absorption spectroscopy (PM-IRRAS) data. Using machine learning and IDMAP, we demonstrated the selectivity of the electronic tongue in distinguishing milk samples from mastitis-infected cows from milk collected from healthy cows, and from milk spiked with possible interferents. Calibration of the electronic tongue can also be reached with the MCS concept employing decision tree algorithms, with an 80.1% accuracy in the diagnosis of mastitis. The low-cost electronic tongue presented here may be exploited in diagnosing mastitis at early stages, with tests performed in the farms without requiring specialized laboratories or personnel.

Multidimensional Data Analysis of Social and Economic Development in Russian Federation Regions

In this paper, we examine the social and economic development level of the Russian Federation regions in 2021 using data from the Federal State Statistics Service. We employ multidimensional data analysis techniques to reduce the non-orthogonal variables via factor analysis to a small, orthogonal factor space, determine the optimal number of clusters via tree classification, and perform cluster analysis in the factor space. We calculate the position of the regions in the factor space, determine the location, composition, and statistical characteristics of clusters, and compute the volumes and densities of clusters. We identify the densest clusters, including those with close proximity and regions with significant variations from the average level of social and economic development. The study utilizes machine and graphical methods of computational mathematics and has both theoretical and practical implications.

Karst landslides detection and monitoring with multiple SAR data and multi-dimensional SBAS technique in Shuicheng, Guizhou, China

Shuicheng District is a karst mountain area, located in Guizhou Province, China. Its fragile stratum and frequent underground mining activities makes it prone to landslides. Owning to its wide coverage and frequent revisit, the InSAR technology has advantages in potential landslide identification and deformation monitor. However, affected by dense vegetation and atmospheric delay, it is much difficult to get sufficient effective targets to derive the deformation in this area. Besides, deformation derived from single orbit SAR data can result in the missing identification of some potential landslides and the misinterpreting of the real kinematics process of landslides. In this study, the multi-source SAR data, atmospheric error correction by quadratic tree image segmentation method, and phase-stacking method were selected to derive the surface deformation of this area. Besides, DS-InSAR and MSBAS method were combined to derive the deformation of Pingdi landslide. First, the potential landslides in this area were identified, surface deformation result, optical remote sensing images and geomorphological features were jointly considered. Then, the landslide distribution characteristics was analyzed in terms of slope, elevation and stratum. After that, the deformation along the LOS direction was acquired using the DS-InSAR method. The MSBAS method was used to retrieve the two-dimensional deformation of Pingdi landslide. Finally, the comprehensive analysis of triggering factors and failure process were conducted according to the spatial-temporal deformation characteristics and field investigation. The results indicated that landslides in Shuicheng district were mostly located in the junction of T1 and P3 stratum and mining related. Mining activity was the main cause of the Pingdi landslide deformation, the precipitation was the driving factor of the landslide instability. The research provides an insight into the explore the unstable slope distribution characteristic and the failure process of the landslides.

Multidimensional Pattern Recognition in High-Resolution 2D and 3D Spectra of Gas-Phase Molecules

ConspectusWhen molecules transition from the condensed phase to the gas phase, their spectra undergo a dramatic transformation as well; each peak in a condensed-phase spectrum can yield thousands of peaks in the gas phase because the molecules are free to rotate and those rotational motions are quantized. These gas-phase spectra contain a wealth of detailed information about molecular structure and behavior, but peak densities are often so high that congestion obscures the patterns needed to assign peaks and extract molecular constants. This Account describes how coherent multidimensional techniques not only reduce peak densities and congestion in gas-phase spectra but also create multidimensional patterns that are easy to recognize and interpret. First, all peaks with the same vibrational quantum numbers form rotational patterns such as X's, double parabolas, and asterisks. These rotational patterns are composed of basic units and can provide immediate information about the molecule's structure, behavior, and rotational selection rules. Second, groups of these rotational patterns can be arranged into vibrational patterns that form arrays of rectangles or parallelograms. These vibrational patterns can be used to determine wave-mixing processes and measure vibrational constants. Coherent multidimensional spectroscopy therefore automatically separates vibrational and rotational information and then sorts peaks by vibrational and rotational quantum number. Furthermore, if the sample is composed of a mixture, then these patterns can also sort peaks by species, and higher-dimensional techniques can even provide the ability to select a species in the mixture. These techniques have successfully produced highly patterned 2D and 3D spectra for samples that otherwise generate patternless spectra such as isotopologue mixtures and vibronically perturbed molecules such as NO2.High densities of states can lead to congestion and perturbations that make it difficult to accurately assign peaks using the information that is traditionally available from 1D spectra: a peak's intensity and its frequency. Coherent 2D and 3D techniques are well-suited for dealing with and learning from perturbations because the coordinate of each peak in multidimensional space includes multiple frequency values. Accurate assignments are possible when peaks in 2D or 3D spectra that are perturbed along one frequency axis are unperturbed along an orthogonal frequency axis. Furthermore, patterns often repeat in adjacent rows or columns, so regions that are less congested can be used to resolve or identify key peaks or patterns in regions that are severely congested. Perturbations can make the spacings within multidimensional rotational and vibrational patterns slightly irregular, but these automatically generated patterns remain easy to recognize and analyze.This Account describes three high-resolution coherent multidimensional spectroscopy techniques, the types of patterns they can produce, and how information can be extracted from these patterns. This work is being conducted at Spelman College, a historically Black college for women where all of the students are undergraduates. The resulting techniques are not only highly effective for dealing with some of the most congested, perturbed, and challenging spectroscopic systems, but they are relatively easy to use, moderate in price to set up, and quick to run.

CLASSIFICATION OF CITIES IN EUROPE BASED ON SMART CITY INDICATORS USING MULTIDIMENSIONAL SCALING ANALYSIS

A decent life has become a hot issue for discussion as stated in the SDGs points, one of them is Smart City. In this research, the researchers used cities in Europe, a continent with countries that tend to have developed. By using multidimensional scaling analysis techniques, the authors present the mapping from an objective data-based point of view, aiming to analyze the closeness of characteristics between 70 cities spread across Europe, and provide ideas and valuable sources for policymakers. From the results of this analysis using R software, it was found that cities within the same country tend to have similar characteristics, which can take into consideration by the European Union to carry out equitable development for cities that do not have good enough smart city indicators.

Solid-State Synthesis of Aluminophosphate Zeotypes by Calcination of Amorphous Precursors

Because of the growing interest in the applications of zeolitic materials and the various challenges associated with traditional synthesis methods, the development of novel synthesis approaches remains of fundamental importance. Herein, we report a general route for the synthesis of aluminophosphate (AlPO) zeotypes by simple calcination of amorphous precursors at moderate temperatures (250-450 °C) for short reaction times (3-60 min). Accordingly, highly crystalline AlPO zeotypes with various topologies of AST, SOD, LTA, AEL, AFI, and -CLO, ranging from ultra-small to extra-large pores, have been successfully synthesized. Multinuclear multidimensional solid-state NMR techniques combined with complementary operando mass spectrometry (MS), powder X-ray diffraction, high-resolution transmission electron microscopy, and Raman characterizations reveal that covalently bonded fluoride in the intermediates catalyze the bond breaking and remaking processes. The confined organic structure-directing agents with high thermal stability direct the ordered rearrangement. This novel synthesis strategy not only shows excellent synthesis efficiency in terms of a simple synthesis procedure, a fast crystallization rate, and a high product yield, but also sheds new light on the crystallization mechanism of zeolitic materials.

Short term load forecasting based on ARIMA and ANN approaches

Forecasting electricity demand requires accurate and sustainable data acquisition systems which rely on smart grid systems. To predict the demand expected by the grid, many smart meters are required to collect sufficient data. However, the problem is multi-dimensional and simple power aggregation techniques may fail to capture the relational similarities between the various types of users. Therefore, accurate forecasting of energy demand plays a key role in planning, setting up, and implementing networks for the renewable energy systems, and continuously providing energy to consumers. This is also a key element for planning the requirement for storage devices and their storage capacity. Additionally, errors in hour-to-hour forecasting may cause considerable economic and consumer losses. This paper aims to address the knowledge gap in techniques based on machine learning (ML) for predicting load by using two forecasting methods: Auto Regressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN); and compares the performance of both methods using Mean Absolute Percentage Error (MAPE). The study is based on daily real load electricity data for 709 individual households were randomly chosen over an 18-month period in Ireland. The results reveal that the (ANN) offers better results than ARIMA for the non-linear load data.

Trends in EEG signal feature extraction applications.

This paper will focus on electroencephalogram (EEG) signal analysis with an emphasis on common feature extraction techniques mentioned in the research literature, as well as a variety of applications that this can be applied to. In this review, we cover single and multi-dimensional EEG signal processing and feature extraction techniques in the time domain, frequency domain, decomposition domain, time-frequency domain, and spatial domain. We also provide pseudocode for the methods discussed so that they can be replicated by practitioners and researchers in their specific areas of biomedical work. Furthermore, we discuss artificial intelligence applications such as assistive technology, neurological disease classification, brain-computer interface systems, as well as their machine learning integration counterparts, to complete the overall pipeline design for EEG signal analysis. Finally, we discuss future work that can be innovated in the feature extraction domain for EEG signal analysis.

Self-synchronized temporal-spectral characterization system for revealing ultrafast fiber laser dynamics

Due to the electronic bottleneck limited real-time measurement speed of common temporal-spectral detection and the particle-like nature of optical soliton enabled nonrepeatable transient behaviors, capturing the ultrafast laser pulses with unknown times of arrival and synchronously characterizing their temporal-spectral dynamic evolution is still a challenge. Here, using the Raman soliton frequency shift based temporal magnifier and dispersive Fourier transform based spectral analyzer, we demonstrate a self-synchronized, ultrafast temporal-spectral characterization system with a resolution of 160 fs and 0.05 nm, and a recording length above milliseconds. The synchronized nonlinear process makes it possible to image full-filled temporal sub-picosecond pulse trains regardless of their arrival times and without extra pump lasers and photoelectric conversion devices. To demonstrate the significance of this improvement, a buildup dynamic process of a soliton laser with a complex breakup and collisions of multisolitons is visually displayed in the spectral and temporal domains. The soliton dynamic evolution processes observed by our characterization system are in one-to-one correspondence with the numerical simulation results. We believe this work provides a new multidimensional technique to break the electronic bottleneck to gain additional insight into the dynamics of ultrafast lasers and nonlinear science.

Selecting the Optimal Fluorinated Ether Co-Solvent for Lithium Metal Batteries.

To guide the selection of a suitable fluorinated ether (FE) co-solvent for lithium metal batteries, it is crucial to understand the relationship between the organic structures of the FEs and the electrochemical performance of an FE-containing electrolyte. In this work, 1,1,2,2-tetrafluoro-1-(2,2,2-trifluoroethoxy)ethane (FEE), 1,1,2,2-tetrafluoro-3-(1,1,2,2-tetrafluoroethoxy)propane (TTE), and 1,2-bis(1,1,2,2-tetrafluoroethoxy)ethane (OFDEE) were chosen as representative FE co-solvents because of their distinct structural properties. The structure-activity relationship between the FEs and the electrochemical performance of Li||LiNi0.6Mn0.2Co0.2O2 (Li||NMC622) cells was correlated and quantified by Fourier-transform infrared and multi-dimensional nuclear magnetic resonance techniques. Sand's model was also employed to assess the extent of lithium dendrite formation in the cells using various FE electrolytes. The cycling performance of Li||NMC622 cells using different FE co-solvents follows the order FEE > TTE > OFDEE. Since the direct measurement of Sand's time is difficult, we introduced relative Sand's time to probe the diffusion behavior of each electrolyte, and the results showed that the best performance was obtained in the electrolyte with the longest relative Sand's time. Moreover, the lithium metal cell using the electrolyte with FEE co-solvent showed similar capacity retention compared with the baseline electrolyte at room temperature, but it demonstrated significantly improved low-temperature performance. The results indicate that FEE is a promising co-solvent candidate for improving the low-temperature performance of lithium metal batteries because it possesses not only non-solvating behavior but also very low viscosity and non-flammability. The advanced electrolyte LiPF6-FEC-DMC-FEE enables very stable cycling of lithium metal batteries at various temperatures.

Parasitic Component Inclusive Optimum Phase-Frequency Contour Enabled Synchronous Rectification of Asymmetric CLLC Resonant Converter

With an objective to reduce the switching losses in a bidirectional resonant CLLC DC/DC converter for electric vehicle (EV) charging applications, this paper presents an elaborate frequency dependent general harmonic approximation (GHA) based secondary side turnoff current minimization technique by investigating the optimum operating point to achieve synchronous rectification (SR). Formulation of an accurate all-inclusive gain model is presented, specifically focusing on effect of parasitic components on the resultant gain-frequency trend, backed with thorough experimental validation. Further, meticulous modeling of the GHA based state equations is presented to obtain a contour of feasible operational frequencies and corresponding phase shifts to ascertain accurate SR operation with a multi-dimensional optimization technique to ensure reduced switching losses. In addition to that, to precisely characterize the resonant tank equivalent circuit, stressing on its effect on SR phase calculation, a detailed 3D finite element analysis (FEA) based R-L-C modelling of the employed high frequency planar transformer (HFPT) is explained. To validate and benchmark the performance of the proposed gain model while ensuring accurate SR operation, a 1kW all-GaN based CLLC experimental prototype is developed for a resonant frequency of 500kHz, with a power density of 106 W/inch <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> . Experimental waveforms at corner conditions are presented for a wide-gain bidirectional operation, portraying a peak converter efficiency of 98.49%.

DPAN: A Deep Light-Weight Attention-Based Image Super Resolution Network Using Multi-Dimensional Filter Design Technique

DPAN: A Deep Light-Weight Attention-Based Image Super Resolution Network Using Multi-Dimensional Filter Design Technique

Elucidating the nonproductive adsorption mechanism of cellulase with lignin fractions from hydrothermally pretreated poplar using multi-dimensional spectroscopic technologies

A multi-dimensional nondestructive spectroscopic technique is proposed to analyze the influence of pretreatment lignin distribution on subsequent enzymatic hydrolysis and build a “lignin structure-cellulase interaction” relationship.

6G-oriented ultra-wideband fiber-THz-fiber seamless converged communication system: architecture, key techniques and verification

Terahertz (THz) communication is widely regarded as a key component of the future 6G mobile communication system. This paper proposes a novel ultra-wideband fiber-THz-fiber seamless converged real-time architecture that fully exploits the commercially mature digital coherent optical module (DCO) to realize ultra-high capacity THz real-time wireless communication by conducting a comparative analysis for some of the main existing technical routes of THz up- and down-conversion in THz wireless communication systems. (1) The proposed architecture employs the techniques of dual-polarization photonic up-conversion for THz generation and hybrid optoelectronic down-conversion for THz reception, respectively, to enable seamless integration of optical fiber and THz communications. (2) Due to the limited bandwidth of the optoelectronic devices, multi-dimensional modulation techniques are adopted for ultra-wideband terahertz signals to improve the spectral efficiency and transmission capacity. (3) An intelligent nonlinear joint compensation technique based on deep neural network (DNN) is proposed, which can effectively improve the signal-to-noise ratio of the time-varying hybrid fiber-THz-fiber channel. Based on the above-mentioned investigations, we for the first time realize the photonics-aided record-high 100/200 GbE real-time THz wireless transmission at 360 (∼) 430 GHz band, with a capacity of 10 to 20 times that of 5G. The proposed fiber-THz-fiber architecture can realize the smooth conversion between high-speed THz signal and lightwave signal. Furthermore, the architecture can significantly reduce research complexity and development costs and thus greatly accelerate the commercialization of 6G THz technology by highly reusing commercial DCO modules, which is compatible with the physical layer transmission protocols such as IEEE 802.3 and ITU-T G.798. Finally, this paper discusses some potential research and development directions for higher-capacity, longer-distance, and more-integrated fiber-THz-fiber seamless communication.