Distance Measurement Approach Research Articles

Single‐molecule Analysis of Protein Glycosylation Patterns for Early Detection of Alzheimer’s Disease Biomarkers

AbstractBackgroundIn recent years, it has become apparent that protein glycosylation plays a variety of roles in the development of Alzheimer’s disease (AD). For instance, characteristic glycosylation patterns of tau and APP have been found in AD patient samples, and O‐GlcNAcylation of alpha‐synuclein (aSyn) has been indicated as a therapeutic anti‐AD target. By extension, the presence of specific glycoforms in patient samples could constitute an important biomarker for early detection purposes (Zhou et al., 2023, Alzheimer’s Dement., https://doi.org/10.1002/alz.13024). However current state‐of‐the‐art methods using mass spectrometry (MS) typically detect proteins starting at millions of copies, thus they lack the sensitivity to perform detection in early disease stages. Moreover, they are prohibitively expensive for routine clinical analysis.MethodWe have developed an ultra‐sensitive protein analysis method that can be used to localize post‐translational modifications in full‐length proteins, including glycans, at single‐molecule resolution (Filius et al., 2021, Nano letters, https://doi.org/10.1021/acs.nanolett.1c00725). Using Förster resonance energy transfer (FRET), a fluorescence‐based distance measurement approach with sub‐nanometer resolution, our method can determine both presence and approximate location of glycans on the protein. Contrary to MS, it does not require complex sample preparation, such as enzymatic digestion or peptide enrichment. We validated this approach by detecting O‐GlcNAcylation on aSyn.ResultBased on measured fluorescence intensities and without using prior information on aSyn’s O‐GlcNAcylation tendencies, we deduced that molecules are doubly O‐GlcNAcylated. Using a calibration construct, we then inferred that the modified residues are in between residues 42 and 78. This is in accordance with MS measurements on bulk samples, which indicate that double glycosylation had taken place at Thr54 and Thr64 respectively. Our results demonstrate for the first time that O‐GlcNAc can be localized on full‐length proteins with single molecule sensitivity.ConclusionUsing our highly sensitive protein analysis approach, we have demonstrated the detection and location of glycans on full‐length aSyn at single‐molecule resolution. We believe that the high resolution of our method will allow for the detection of biomarkers in patient samples in early stages of disease. In future work, we will expand the proof‐of‐concept presented here for such usage by detecting AD‐associated tau and APP glycoforms.

Cross-Layer RF Distance Bounding Scheme for Passive and Semi-passive Ubiquitous Computing Systems

Distance Bounding protocols have surfaced as an appealing security measure in ubiquitous computing systems with access control provisions whereby the physical proximity of a user becomes significantly important for its explicit privileges and assurances. These systems employ cryptographic protocols to ensure security. However, security vulnerabilities have been demonstrated that exploit not only the cryptographic protocol but the physical implementation of the protocol itself. Distance Bounding resolves this problem by tightly integrating cryptographic security with the physical layer through a cross-layer security design. The core idea is to employ physical proximity as a security fingerprint to ensure that the legitimate entity (namely ‘prover’) lies within an agreed spatial range of another entity (‘verifier’). While a large number of Distance Bounding protocols have been proposed in the past, they mainly focus on designing a secure authentication scheme integrated with a suitable distance measurement approach, disregarding multiple concerns in the practical realization of these protocols – which we termed as a cross-layer problem. Only a few practical Distance Bounding realizations have been demonstrated which however focus on some selected cross-layer design parameters. As a result, either these protocols are impractical as a realizable system, or in most cases, an attacker can easily exploit cross-layer vulnerabilities especially if the prover is a resource-constrained entity such as a fully passive or battery-assisted access control token. This requires formulation of a careful prover design while ensuring that secure authentication and precise ranging are tightly integrated in a cross-layer manner for a realizable system. To resolve these issues, we first present a holistic approach to designing a secure Distance Bounding system by identifying essential cross-layer parameters. While analyzing the salient Distance Bounding realizations, we observe them to be designed for a single-bit communication channel. We optimize the communication bit rate by incorporating a high-order modulation technique in our proposed scheme to achieve higher date rates while ensuring the overall integrity of cross-layer design parameters. Besides, we specifically analyze the effects of the prover's impedance mismatch on link efficacy and security. This is a significant factor which depicts the allowable tolerances in the prover's impedance from design to manufacturing phases. Lastly, we present a comparison between prior approaches and our proposed scheme while introducing Qualitative Measure Matrix (QMM-I) and Quantitative Measure Matrix (QMM-II), which are devised from cross-layer design parameters for a secure Distance Bounding realization.

A Distance Measurement Approach for Large Fruit Picking with Single Camera

Target ranging is the premise for manipulators to complete agronomic operations such as picking and field management; however, complex environmental backgrounds and changing crop shapes increase the difficulty of obtaining target distance information based on binocular vision or depth cameras. In this work, a method for ranging large-sized fruit based on monocular vision was proposed to provide a low-cost and low-computation alternative solution for the fruit thinning or picking robot. The regression relationships between the changes in the number of pixels occupied by the target area and the changes in the imaging distance were calculated based on the images of square-shaped checkerboards and circular-shaped checkerboards with 100 cm2, 121 cm2, 144 cm2, 169 cm2, 196 cm2, 225 cm2, 256 cm2, 289 cm2, and 324 cm2 as the area, respectively. The 918 checkerboard images were collected by the camera within the range from 0.25 m to 1.5 m, with 0.025 m as the length of each moving step, and analyzed in MATLAB to establish the ranging models. A total of 2448 images of four oval watermelons, four pyriform pomelos, and four oblate pomelos, as the representatives of large fruit with different shapes, were used to evaluate and optimize the performance of the models. The images of the front were the input, while the imaging distances were the output. The results showed that the absolute error would be less than 0.06 m for both models and would linearly increase with a decrease in the distance. The relative error could be controlled at 5%. The results proved the proposed monocular method could be a solution for the ranging of large fruit targets.

Demonstration of high-accuracy 3D imaging using a Si optical phased array with a tunable radiator.

Precise imaging in three-dimension (3D) is an essential technique for solid-state light detection and ranging (LiDAR). Among various solid-state LiDAR technologies, silicon (Si) optical phased array (OPA)-based LiDAR has the significant advantage of robust 3D imaging due to its high scanning speed, low power consumption, and compactness. Numerous techniques employing a Si OPA have utilized two-dimensional arrays or wavelength tuning for longitudinal scanning but the operation of those systems is restricted by additional requirements. Here, we demonstrate high-accuracy 3D imaging using a Si OPA with a tunable radiator. As we adapted a time-of-flight approach for distance measurement, we have developed an optical pulse modulator that allows a ranging accuracy of less than 2 cm. The implemented Si OPA is composed of an input grating coupler, multimode interferometers, electro-optic p-i-n phase shifters, and thermo-optic n-i-n tunable radiators. With this system, it is possible to attain a wide beam steering range of 45° in a transversal angle with a 0.7° divergence angle, and 10° in a longitudinal angle with a 0.6° divergence angle can be achieved using Si OPA. The character toy model was successfully imaged in three dimensions with a range resolution of 2 cm using the Si OPA. The further improvement of each component of the Si OPA will allow even more accurate 3D imaging over a longer distance.

Photonic approach for precise and efficient distance measurement via utilization of multi-frequency broadband optical carrier-based microwave signal

Distance measurement has received a growing demand with the development of scientific research and modern industry. However, most of the high-precision measure approaches hold stringent requirements on hardware equipment, manufacture accuracy, and operation environment. In this article, a photonic approach for distance measurement that utilizes broadband optical carrier-based microwave signal is proposed. Through switching between multiple modulation frequencies and applying the heterodyne process, a series of synthetic frequencies is constructed. Phase extraction is achieved based on all-phase fast Fourier transform (apFFT) for derivation of the distance. Moreover, we develop a phase unwrapping procedure to resolve phase ambiguity and derive the corresponding criterion for evaluation of reliability. The proposed method can realize tunable measurement range and high resolution within a relatively narrow bandwidth. Experiments verify the feasibility for distance measurement and align well with theoretical analysis. The results show good accuracy and stability with relative errors lower than 0.05%. Discussion demonstrates that apFFT is capable to suppress spectral leakage and mitigate the influence of signal truncation. As the presented approach maintains a simple configuration and competitive performance, it has great potential to become a useful tool for distance measurement in various practical applications.

Experimental study and multi-objective optimization of process parameters during turning of 100Cr6 using C-type advanced coated tools

The drive of this research is to examine the machinability of 100Cr6 bearing steel using advanced C-type cutting tools. Experimental studies investigated the effects of machining variables on the surface quality, chip reduction coefficient and cutting force. Seven advanced coated tools were checked for characterization by micro hardness (VHN), adhesion quality, X-ray diffraction (XRD), scanning electron microscopy (SEM) and energy dispersive X-ray spectroscopy (EDXS). The experimental trials were planned by Taguchi’s L18 orthogonal array using a mixed-level design. Two numerical machining variables feed rate and cutting speed, and one categorical machining variable tool type was taken into consideration while a constant depth of cut was kept for all trails. A combined Taguchi-Satisfaction function distance measure approach was implemented for multi-response optimization. The most promising machining parameter setting for minimization of surface roughness, cutting force, and chip reduction coefficient was identified. The most important process parameter was found to be tool-type. Ceramics tools are found to be best trailed by WC coated tools under most of the conditions. Lower tool wear was observed in the CBN tool as compared to others.

Differential Evolution based Cluster optimization for Multi valued data sets

In data analysis, items were mostly described by a set of characteristics called features,in which each feature contains only single value for each object. Even so, in existence, some features may include more than one value, such as a person with different job descriptions, activities, phone numbers, skills and different mailing addresses. Such features may be called as multi-valued features, and are mostly classified as null features while analyzing the data using machine learning and data mining techniques. In this paper, a proximity function is described to find the proximity between two substances with multi-valued features that are put into effect for Clustering. This distance measure approach allows iterative measurements of the similarities around objects as well as their characteristics. For facilitating the most suitable multi-valued factors, we put forward a model targeting at determining each factor’s relative prominence for diverse data extracting problems. The proposed model is an evolutionary strategy that uses Differential strategy for evolutions, which is using the degree of member ship as fitness function. The proposed clustering algorithm as multi valued attribute data cluster optimization based on the strategy of Differential evolution (MVA-DE). Therefore this becomes feasible using any mechanisms for cluster analysis to group similar data. The effectiveness of our model is evinced by performance analysis carried through experimental study. The outcomes of the experiments carried on proposed model were compared with other strategic clustering approaches like fuzzy c- means based Clustering of Multivalued Attribute Data (FCM-MVA) and K-Means with Tanimoto based multi-valued data clustering. The findings demonstrate that our test not only improves the performance the traditional measure of similarity but also outperforms other clustering algorithms on the multi-valued clustering framework.

An enhanced ground fault selection and distance measurement approach for single and double circuit fixed series compensated transmission line

This work demonstrates a superior local measurement-based ground fault selection and distance computation approach for a single and double circuit series compensated transmission lines. The proposed design was constructed to defeat the weaknesses of the usual protection approaches as influencing of mutual coupling, series capacitor, long outage time, one line operation mode, necessitating of synchronization, the unbalance load switching, the CT saturation, needing to know the status of circuit breakers, and the difficult fault initial condition as weak inception time, fault resistance loop, remote locations, and nearby locations. In this research, the decision data is exported from zero-sequence impedance relaying signals and positive sequence voltage relaying signal based imaginative Cos-Sin concept. To approve the suggested approach, widespread faults were simulated in the ATP program as well as the suggested approach is made in MATLAB program. The outcomes show that the proposed method is correct and overcomes the aforementioned weaknesses and difficulties.

An Efficient Motion Vector Recovery and Reconstruction Method for Spatiotemporal Video Error Concealment

In this article, an efficient spatiotemporal video error concealment (EC) based on motion vector (MV) recovery and a pixel reconstruction (PR) method is proposed. The pixel-based motion vector with partition (PMVP) is modified by using Mahalanobis distance (MD) rather than Euclidean distance (ED) for recovering MVs, as MD uses standard deviation and covariance of available pixels. Further, the MD gives more accuracy for non-square cluster compared to ED. This modified pixel-based motion vector with partition (MPMVP) algorithm is further upgrade by two different strategies. First, by using voting priority of available MVs based on the probabilities of similar directions. Second, by considering separate horizontal and vertical directions of available MVs in voting priority. For pixel reconstruction, modified spiral pixel reconstruction (MSPR) algorithm based on directional edge recovery method using minimum and maximum Mahalanobis distance from available pixels of surrounding MBs is proposed. Mahalanobis distance approach is most optimized similarity measure technique compared to other distance measurement approach to obtained lost motion vectors. These proposed EC techniques are compared with existing EC techniques like, SPR EC using ED, PMVP based EC with ED, and MV Interpolation by Zhou's method for various packet loss rates (PLRs) as 3%, 7%, 16%, 20% and quantization parameters (QPs) as 20, 24, 28, 32, 36. For total average in PLR of 3%, 7%, 16% and 20%, MSPR is having better PSNR compared to PMVP by 2.516, 2.29, 2.06 and 2.02 dB, respectively; and compared to SPR by 0.796, 0.718, 0.643 and 0.631 dB, respectively.

A novel quasi-oppositional modified Jaya algorithm for multi-objective optimal power flow solution

This study introduces a novel meta-heuristic optimization algorithm known as quasi-oppositional modified Jaya (QOMJaya) to solve different multi-objective optimal power flow (MOOPF) problems. An intelligence strategy called quasi-oppositional based learning is incorporated into the proposed algorithm to enhance its convergence property, exploration capability, and solution optimality. Significant modifications to the basic Jaya algorithm are done to create a modified Jaya (MJaya) algorithm that can handle the MOOPF problem. A fuzzy decision-making strategy is proposed and incorporated into the Jaya algorithm as selection criteria for best and worst solutions. A new criterion for comparing updated and current candidate solutions is proposed. The concept of Pareto optimality is used to extract a set of non-dominated solutions. A crowding distance measure approach is utilized to maintain the diversity of Pareto optimality. In addition, a novel external elitist repository is developed to conserve discovered non-dominated solutions and to produce true and well-distributed Pareto optimal fronts. The proposed algorithm is scrutinized and validated using the modified IEEE 30-bus test system. Simulation results reveal the proposed algorithm’s ability to produce real and well-distributed Pareto optimum fronts for all considered multi-objective optimization cases. Furthermore, the obtained results disclose the superiority of the proposed QOMJaya algorithm over both the proposed MJaya algorithm and several previous algorithms in terms of solution optimality and feasibility.

A Novel Satisfaction Function and Distance-Based Approach for Machining Performance Optimization During Electro-Discharge Machining on Super Alloy Inconel 718

The present work aims to determine an appropriate setting of process parameters (viz. gap voltage, peak discharge current, pulse-on time, duty factor and flushing pressure) for achieving optimal machining performance during electro-discharge machining of super alloy Inconel 718 by using copper tool electrode. Experiments have been performed based on \(L_{25}\) orthogonal array design of experiment by varying each of the aforesaid process parameter at five different levels. The machining performances have been evaluated in terms of material removal rate, electrode wear rate, surface roughness (\(R_{\mathrm{a}})\), surface crack density, white layer thickness, and micro-hardness of the EDMed Inconel 718 end product. In this paper, a novel optimization route (combining satisfaction function, distance measure approach in conjugation with Taguchi’s philosophy) has been introduced. Application feasibility of the aforementioned approach has been compared to that of obtained through exploration of principal component analysis and combined quality loss concept integrated with Taguchi method. Additionally, morphology of the EDMed work surface of Inconel 718 has been investigated through scanning electron microscopy.

A Novel Distance Measurement Approach Using Shape Matching in Narrow-Band Ultrasonic System

Nowadays, a large number of novel technologies and systems are invented in order to solve the problems of Indoor Positioning Systems (IPS). One of the most well- known and common positioning technology is ultrasound. The zero rooms’ leakage, low cost, scalability, reliability, and less complexity in hardware and computing are the main advantages of ultrasonic systems. However, existing ultrasonic systems are hampered by many problems such as uncertainty and reduced robustness. This paper contributes an approach with the intention of improving distance measurement for indoor robot positioning by using an ultrasonic sensor. In the proposed method, start point of received signal will be determined more precisely.

Presurgical functional magnetic resonance imaging in patients with brain tumors.

Clinical functional magnetic resonance imaging (fMRI) is still an upcoming diagnostic tool because it is time-consuming to perform the post-scan calculations and interpretations. A standardized and easily used method for the clinical assessment of fMRI scans could decrease the workload and make fMRI more attractive for clinical use. To evaluate a standardized clinical approach for distance measurement between benign brain tumors and eloquent cortex in terms of the ability to predict pre- and postoperative neurological deficits after intraoperative neuronavigation-assisted surgery. A retrospective study of 34 patients. The fMRI data were reanalyzed using a standardized distance measurement procedure combining data from both fMRI and three-dimensional T1 MRI scans. The pre- and postoperative neurological status of each patient was obtained from hospital records. Data analysis was performed using logistic regression analysis to determine whether the distance measured between the tumor margin and fMRI activity could serve as a predictor for neurological deficits. An odds ratio of 0.89 mm(-1) (P = 0.03) was found between the risk of preoperative neurological motor deficits and the tumor-fMRI distance. An odds ratio of 0.82 mm(-1) (P = 0.04) was found between the risk of additional postoperative neurological motor deficits and the tumor-fMRI distance. The tumor was radically removed in 10 cases; five patients experienced additional postoperative motor deficits (tumor-fMRI distance <18 mm) and five did not (tumor-fMRI distance >18 mm) (P = 0.008). This study indicates that the distance measured between the tumor margin and fMRI activation could serve as a valuable predictor of neurological motor deficits.

A Sonic Distance Measurement Approach Using Linear Frequency Modulated Signal

This paper deals with the development of an alternative approach for sonic distance measurement. A linear frequency modulated signal (chirp signal) is regarded as a template signal. The sonic distance measurement system transmits the template signal and receives the echo signal, then calculate the correlation between template and echo signal. The time of flight is determined according to the position of the peak in the correlation result, then the distance is calculated. The major advantage of this approach is that it can detect weak echo signals among noises and increase measurement range significantly.

Online clustering for trajectory data stream of moving objects

Trajectory data streams contain huge amounts of data pertaining to the time and position of moving objects. It is crucial to extract useful information from this peculiar kind of data in many application scenarios, such as vehicle traffic management, large-scale tracking management and video surveillance. This paper proposes a density-based clustering algorithm for trajectory data stream called CTraStream. It contains two stages: trajectory line segment stream clustering and online trajectory cluster updating. CTraStream handles the trajectory data of moving objects as an incremental line segment stream. For line segment stream clustering, we present a distance measurement approach between line segments. Incremental line segments are processed quickly based on previous line clusters in order to achieve clustering line segment stream online, and line-segment-clusters in a time interval are obtained on the fly. For online trajectory cluster updating, TC-Tree, an index structure, which stores all closed trajectory clusters, is designed. According to the linesegment-cluster set, the current closed trajectory clusters are updated online based on TC-Tree by performing proposed update rules. The algorithm has exhibited many advantages, such as high scalability to process incremental trajectory data streams and the ability to discover trajectory clusters in data streams in real time. Our performance evaluation experiments conducted on a number of real and synthetic trajectory datasets illustrate the effectiveness, efficiency, and scalability of the algorithm.

Robust stability and performance analysis for uncertain linear systems—The distance measure approach

SUMMARYThis paper presents readily applicable distance measures, robust stability margins and associated robust stability and robust performance theorems for three commonly used uncertainty structures (additive, input/output multiplicative, output/input inverse multiplicative). Besides providing robust stability results for a larger uncertainty class than previously reported (ℛℒ︁∞ instead of ℛℋ︁∞), this paper also states robust performance theorems for the above uncertainty structures. In contrast to previous methods for robust performance analysis, they only require the computation of two infinity norms for every uncertain plant considered. The theorems in this paper enable practising engineers to choose the most suitable uncertainty structure for a family of uncertain plants, as illustrated through a physically motivated numerical example. Copyright © 2011 John Wiley &amp; Sons, Ltd.

A Distance Measure Approach to Exploring the Rough Set Boundary Region for Attribute Reduction

Feature Selection (FS) or Attribute Reduction techniques are employed for dimensionality reduction and aim to select a subset of the original features of a data set which are rich in the most useful information. The benefits of employing FS techniques include improved data visualization and transparency, a reduction in training and utilization times and potentially, improved prediction performance. Many approaches based on rough set theory up to now, have employed the dependency function, which is based on lower approximations as an evaluation step in the FS process. However, by examining only that information which is considered to be certain and ignoring the boundary region, or region of uncertainty, much useful information is lost. This paper examines a rough set FS technique which uses the information gathered from both the lower approximation dependency value and a distance metric which considers the number of objects in the boundary region and the distance of those objects from the lower approximation. The use of this measure in rough set feature selection can result in smaller subset sizes than those obtained using the dependency function alone. This demonstrates that there is much valuable information to be extracted from the boundary region. Experimental results are presented for both crisp and real-valued data and compared with two other FS techniques in terms of subset size, runtimes, and classification accuracy.

Site-directed solid-state NMR measurement of a ligand-induced conformational change in the serine bacterial chemoreceptor.

The challenging nature of studies of membrane proteins has made it difficult to determine the molecular mechanism of transmembrane signaling. For the bacterial chemoreceptor family, there are crystal structures of the internal and external domains, structural models of the transmembrane domain, and evidence for subtle ligand-induced conformational changes, but the signaling mechanism remains controversial. We have used a novel site-directed solid-state NMR distance measurement approach, using (13)C(19)F REDOR, to measure a ligand-induced change of 1.0 +/- 0.3 A in the distance between helices alpha 1 and alpha 4 of the ligand-binding domain in the intact, membrane-bound serine receptor. This distance change is shown not to be due to motion of the side chain and thus is due to motion of either the alpha 1 or the alpha 4 helix. Additional distance measurements can be used to determine the type of backbone motion and to follow it to the cytoplasm, to test and refine current proposals for the mechanism of transmembrane signaling. This is a promising general method for high-resolution measurements of local structure in intact, membrane-bound proteins.

New approach for distance measurement in locally weighted regression

This paper presents a new approach for distance measurement in locally weighted regression (LWR2) by balancing the information in both chemical and spectral spaces. The new method (LWR2) is compared with the ordinary locally weighted regression method (LWR), another modified LWR method (LWR1), and the linear calibration methods, principal component regression (PCR) and partial least squares (PLS). A simulation was conducted to study how noise in chemical and spectral spaces affects the predictive ability and stability of the LWR2 method with respect to the original LWR