State-of The-art Approaches Research Articles

An Improved Stacked Denoise Autoencoder with Elu Activation Function for Traffic Data Imputation

Traffic data plays a major role in transport related applications. The problem of missing data has greatly impact the performance of Intelligent transportation systems(ITS). In this work impute the missing traffic data with spatio-temporal exploitation for high precision result under various missing rates. Deep learning based stacked denoise autoencoder is proposed with efficient Elu activation function to remove noise and impute the missing value.This imputed value will be used in analyses and prediction of vehicle traffic. Results are discussed that the proposed method outperforms well in state of the art approaches.

Configurable Rotation Matrix of Hyperbolic CORDIC for Any Logarithm and Its Inverse computation

In this paper, we propose a configurable rotation matrix for Hyperbolic COordinate Rotation DIgital Computer (CORDIC) to compute any logarithm and inverse logarithm. The rotation matrix of the conventional Hyperbolic CORDIC computes only natural logarithm and natural inverse logarithm whereas the proposed rotation matrix configures the Hyperbolic CORDIC to compute any logarithm and inverse logarithm. Subsequently, an architecture for configurable Hyperbolic CORDIC has been designed based on the proposed rotation matrix. An extensive vector matching is performed to validate the proposed architecture. Finally, an ASIC implementation for configurable Hyperbolic CORDIC has been performed using TSMC 45-nm CMOS technology @ 1 GHz frequency. The accuracy of the proposed configurable Hyperbolic CORDIC is same as conventional Hyperbolic CORDIC. The proposed approach saves 19.22% on chip area and 17.52% power consumption for decimal logarithm computation and 18.55% on chip area and 16.12% power consumption for inverse logarithm computation when compared with the state of the art approaches.

Mathematical modeling of Electric vehicles - A survey

The paper presents a survey of the existing mathematical models of electric vehicles followed by state of the art approaches on modeling hybridization of the energy sources. The mathematical representation of the electric vehicle (EV) ranging from the simple single Degree of Freedom (DOF) models to complex multi-body dynamic models is discussed in detail. Reduced dynamic models applicable to various control design diligence are discussed which facilitates the selection of the optimal model for design. In addition to vehicle dynamics, the paper consolidates dynamic models of the different components of an electric vehicle including the transmission, brake, battery, wheel and tire dynamics. Comparative analysis of different versions of the models for each component is also presented, focusing on their application in controller design. The paper thus acts as a guide for any EV control design requirement, providing optimal models for particular applications.

Comparison of joint angle, velocity and acceleration estimators for hydraulically actuated manipulators to a novel dynamical approach

Excavators are used for a wide range of applications like earthworks and material handling. Assistance systems are becoming more common to support the operator. For monitoring and control based assistance functions the angular position, velocity and acceleration of the joints from the working implement are required. Commercial systems often use inertial measurement units, consisting of triaxial accelerometers and gyroscopes, to accomplish an estimation of those states. A novel joint angle, velocity and acceleration estimation for hydraulic manipulators is proposed and compared to state of the art methods. A decentralized kinematic filter using no information about the underlying system and a centralized kinematic filter taking the system kinematics into account are implemented as state of the art approaches. Both filters only use inertial measurement units to obtain information about the current state of the system. The novel centralized dynamic filter uses the same information as the centralized kinematic filter and extends it by a dynamic model containing additional information about the angular acceleration due to pressure readings of the hydraulic cylinders. Kalman filtering is used to combine the derived system and measurement models with the sensor information. The methods are evaluated on a material handling excavator for single and coupled movements of the working implement. The novel centralized dynamic filter enables improvements for the angular acceleration estimation compared to the decentralized and centralized kinematic filter. Less noise of the acceleration estimation and a better tracking of the actual acceleration are shown.

Learned Fitting of Spatially Varying BRDFs

AbstractThe use of spatially varying reflectance models (SVBRDF) is the state of the art in physically based rendering and the ultimate goal is to acquire them from real world samples. Recently several promising deep learning approaches have emerged that create such models from a few uncalibrated photos, after being trained on synthetic SVBRDF datasets. While the achieved results are already very impressive, the reconstruction accuracy that is achieved by these approaches is still far from that of specialized devices. On the other hand, fitting SVBRDF parameter maps to the gibabytes of calibrated HDR images per material acquired by state of the art high quality material scanners takes on the order of several hours for realistic spatial resolutions. In this paper, we present a first deep learning approach that is capable of producing SVBRDF parameter maps more than two orders of magnitude faster than state of the art approaches, while still providing results of equal quality and generalizing to new materials unseen during the training. This is made possible by training our network on a large‐scale database of material scans that we have gathered with a commercially available SVBRDF scanner. In particular, we train a convolutional neural network to map calibrated input images to the 13 parameter maps of an anisotropic Ward BRDF, modified to account for Fresnel reflections, and evaluate the results by comparing the measured images against re‐renderings from our SVBRDF predictions. The novel approach is extensively validated on real world data taken from our material database, which we make publicly available under https://cg.cs.uni‐bonn.de/svbrdfs/.

Discrimination of SSVEP responses using a kernel based approach.

Brain Computer Interfaces based on Steady State Visual Evoked Potentials have gained increased attention due to their low training requirements and higher information transfer rates. In this work, a method based on sparse kernel machines is proposed for the discrimination of Steady State Visual Evoked Potentials responses. More specifically, a new kernel based on Partial Least Squares is introduced to describe the similarities between EEG trials, while the estimation of regression weights is performed using the Sparse Bayesian Learning framework. The experimental results obtained on two benchmarking datasets, have shown that the proposed method provides significantly better performance compared to state of the art approaches of the related literature.

Efficient unsupervised variational Bayesian image reconstruction using a sparse gradient prior

In this paper, we present an efficient unsupervised Bayesian approach and a prior distribution adapted to piecewise regular images. This approach is based on a hierarchical prior distribution promoting sparsity on image gradients. It is fully automatic since hyperparameters are estimated jointly with the image of interest. The estimation of all unknowns is performed efficiently thanks to a fast variational Bayesian approximation method. We highlight the good performance of the proposed approach through comparisons with state of the art approaches on an application to a diffraction tomographic problem.

An evolutionary fuzzy rule-based system applied to the prediction of soil organic carbon from soil spectral libraries

The recent efforts for obtaining vast soil spectral libraries covering a significant part of the spatial and compositional variability of soils have underscored the need for accurate and interpretable models. Herein, the application of an evolutionary Fuzzy Rule-based System (FRBS) named DECO3RUM (Differential Evolution based Cooperative and Competing learning of Compact Rule-based Models) for the prediction of soil properties from visible, near-infrared, and shortwave-infrared (VNIR–SWIR) laboratory spectral data obtained from the LUCAS topsoil database is investigated. FRBSs model the input–output relation with fuzzy logic statements, offering an enhanced interpretability degree for the experts over classical rule-based systems and other black box models. The proposed algorithm was statistically compared with other state of the art approaches and was found to outperform other global models, while being statistically similar with local approaches that offer lower interpretation capabilities.

Transmission of natural scene images through a multimode fibre

The optical transport of images through a multimode fibre remains an outstanding challenge with applications ranging from optical communications to neuro-imaging. State of the art approaches either involve measurement and control of the full complex field transmitted through the fibre or, more recently, training of artificial neural networks that however, are typically limited to image classes belong to the same class as the training data set. Here we implement a method that statistically reconstructs the inverse transformation matrix for the fibre. We demonstrate imaging at high frame rates, high resolutions and in full colour of natural scenes, thus demonstrating general-purpose imaging capability. Real-time imaging over long fibre lengths opens alternative routes to exploitation for example for secure communication systems, novel remote imaging devices, quantum state control processing and endoscopy.

Unsupervised Deep Visual-Inertial Odometry with Online Error Correction for RGB-D Imagery.

While numerous deep approaches to the problem of vision-aided localization have been recently proposed, systems operating in the real world will undoubtedly experience novel sensory states previously unseen even under the most prodigious training regimens. We address the localization problem with online error correction (OEC) modules that are trained to correct a vision-aided localization network's mistakes. We demonstrate the generalizability of the OEC modules and describe our unsupervised deep neural network approach to the fusion of RGB-D imagery with inertial measurements for absolute trajectory estimation. Our network, dubbed the Visual-Inertial-Odometry Learner (VIOLearner), learns to perform visual-inertial odometry (VIO) without inertial measurement unit (IMU) intrinsic parameters or the extrinsic calibration between an IMU and camera. The network learns to integrate IMU measurements and generate hypothesis trajectories which are then corrected online according to the Jacobians of scaled image projection errors with respect to spatial grids of pixel coordinates. We evaluate our network against state-of-the-art (SoA) VIO, visual odometry (VO), and visual simultaneous localization and mapping (VSLAM) approaches on the KITTI Odometry dataset as well as a micro aerial vehicle (MAV) dataset that we collected in the AirSim simulation environment. We demonstrate better than SoA translational localization performance against comparable SoA approaches on our evaluation sequences.

Detection of breast cancer mass using MSER detector and features matching

Detection of breast cancer masses in mammogram images is an essential step in any computer-aided system for breast cancer diagnosis. In this paper, we propose a novel technique for breast cancer masses detection in mammograms based on the feature matching of different regions using Maximally Stable Extremal Regions (MSER). Firstly, a pre-processing step is applied to the original mammogram image to produce an enhanced version of this image. Then, MSER regions are extracted from both the original image and its enhanced version using MSER detector. Finally, feature matching process is applied between these regions to detect the mass area. The proposed algorithm has been tested on a collected set of 300 mammogram images containing abnormalities (i.e. benign and malignant masses) from four different databases. The proposed algorithm is able to accurately detect locations of masses with an accuracy of 95%. There aren’t any processing steps for pectoral muscle removal, this results in reducing the processing time. The average time taken by the proposed method to process one mammogram image is 0.14 s. The proposed method is fully automated and there is no need for user intervention or any readjustment. The proposed algorithm is robust against noise and it is not affected by the image quality, breast density category, or mass nature. The results show that the proposed algorithm has higher accuracy than the state of the art approaches.

Space-time variant weighted regularization in compressed sensing cardiac cine MRI

PurposeTo analyze the impact on image quality and motion fidelity of a motion-weighted space-time variant regularization term in compressed sensing cardiac cine MRI. Methodsk-t SPARSE-SENSE with temporal total variation (tTV) is used as the base reconstruction algorithm. Motion in the dynamic image is estimated by means of a robust registration technique for non-rigid motion. The resulting deformation fields are used to leverage the regularization term. The results are compared with standard k-t SPARSE-SENSE with tTV regularization as well as with an improved version of this algorithm that makes use of tTV and temporal Fast Fourier Transform regularization in x-f domain. ResultsThe proposed method with space-time variant regularization provides higher motion fidelity and image quality than the two previously reported methods. Difference images between undersampled reconstruction and fully sampled reference images show less systematic errors with the proposed approach. ConclusionsUsage of a space-time variant regularization offers reconstructions with better image quality than the state of the art approaches used for comparison.

Challenges and Opportunities for Publishing IIoT Data in Manufacturing as a Service Business

Cloud manufacturing (CMfg) can manage mass manufacturing (MM) resources and capabilities, and provide them as services via the Internet. Undoubtedly, multiple manufacturing clouds will have abundant services in terms of function, price, reliability, location, etc. The implementation of such concept in a wider structure than a single MC requires to provide explicit data about production sequence, machine allocation, production parameters, quality of service (QoS), and several other key performance indicators (KPIs). However, the state of the art approach is based on private access to such data, under specific contract requirements, and it becomes a limitation to the evolution of the concept itself. In particular it becomes limiting when the CMfgs involve multiple process owners distributed throughout a complex value creating network, such as production, logistics, quality control, but also, third parties like auditor interested in energy consumption, or machine maintenance provider, also interested in monitoring the effective usage of the machines, etc. This paper will analyze deeply the structural and functional characteristics of such CMfgs and then, it will propose a business intelligence architecture that aims to enable publishing relevant KPIs related to interested process data, with the convenient layer of trustworthiness. Such public access will enable the assessment to different agents at different time frequency and, finally, with different motivation, thus bringing practical information about time delays for the data availability and scalability factors.

Potential-based Technology Planning for Production Companies

Strategic technology planning is a key challenge for manufacturing companies in order to stay innovative and succeed in the dynamic international competition. State of the art approaches are mostly based on technology evaluations that focus on present technology readiness and maturity or qualitative potential assessments. These approaches lack of a quantitative consideration of future development potentials of new or immature technologies. The presented approach includes technology potentials in the strategic technology planning and therefore introduces a concept for the quantitative and comparable evaluation of technology potentials. Based on this evaluation, a technology roadmap can be developed to support strategic technology planning.

Analytic‐based product opinion detection algorithm for twitter microblogging network

SummaryTwitter, with an ever‐increasing user base, has greatly influenced the opinion and purchase habits of the common masses. This has in turn forced the product firms to get involved with sentiment analysis which enables them to mine the actual opinion about their product and make business decisions accordingly. Even though a majority of the existing methods detect sentiment of the tweet with a reasonable accuracy, few ignore emoticons while others consider them as stop words. Emoticons have enabled the users to express their emotion more accurately which eliminates the ambiguity that can arise with usage of words. The trending popularity of emoticons among the users combined with its ease of usage makes it highly lucrative in sentiment analysis. Hence, mining the product opinion without considering the emoticons will severely undermine the accuracy and reliability of the opinion. Moreover, sarcasm detection is still an uncharted territory in opinion mining and is exceedingly difficult to factor it in. Sarcastic tweets when left undetected will affect the accuracy of the opinion. Therefore, the polarity of the individual words and emoticons of the tweets are computed using linguistic analysis. The sarcastic tweets are then classified and eliminated based on their anomalous polarity. By placing a higher emphasis on emoticons, the proposed emoticon‐based linguistic opinion algorithm yields satisfactory results when compared with other traditional and state of the art approaches.

A contextualized approach for segmentation of foliage in different crop species

Computer vision algorithms represent an important tool in agriculture. Particularly, foliage segmentation is a fundamental step to achieve accurate estimations in more complex visual analysis such as plant health monitoring, weed detection, growth estimation, and flower and fruit classification. Unfortunately, this complexity leads to the problem of generalizing between datasets, an issue that has not yet been addressed by the state of the art. Our work aims at filling this gap by investigating the role of vegetative indexes and color spaces in different formulations of machine learning algorithms. To this end, we have considered datasets observing variations with respect to crop species, leaf color, acquisition settings and illumination. Furthermore, we performed a comparison with different state of the art approaches that include both thresholding and machine learning. From our analysis, we have proposed a new formulation that consists of combining the CIE Luv color space and support vector machines in order to benefit from contextualized information obtained through neighboring pixels. Experiments show that our approach achieves the best results.

DESIA

Interlocking assemblies have a long history in the design of puzzles, furniture, architecture, and other complex geometric structures. The key defining property of interlocking assemblies is that all component parts are immobilized by their geometric arrangement, preventing the assembly from falling apart. Computer graphics research has recently contributed design tools that allow creating new interlocking assemblies. However, these tools focus on specific kinds of assemblies and explore only a limited space of interlocking configurations, which restricts their applicability for design. In this paper, we propose a new general framework for designing interlocking assemblies. The core idea is to represent part relationships with a family of base Directional Blocking Graphs and leverage efficient graph analysis tools to compute an interlocking arrangement of parts. This avoids the exponential complexity of brute-force search. Our algorithm iteratively constructs the geometry of assembly components, taking advantage of all existing blocking relations for constructing successive parts. As a result, our approach supports a wider range of assembly forms compared to previous methods and provides significantly more design flexibility. We show that our framework facilitates efficient design of complex interlocking assemblies, including new solutions that cannot be achieved by state of the art approaches.

Robust gait recognition: a comprehensive survey

Gait recognition has emerged as an attractive biometric technology for the identification of people by analysing the way they walk. However, one of the main challenges of the technology is to address the effects of inherent various intra-class variations caused by covariate factors such as clothing, carrying conditions, and view angle that adversely affect the recognition performance. The main aim of this survey is to provide a comprehensive overview of existing robust gait recognition methods. This is intended to provide researchers with state of the art approaches in order to help advance the research topic through an understanding of basic taxonomies, comparisons, and summaries of the state-of-the-art performances on several widely used gait recognition datasets.

CDBR: A semi-automated collaborative execute-before-after dependency-based requirement prioritization approach

The success of requirement prioritization process largely depends upon how well different constraints and influential factors are handled by stakeholders and developers while prioritization. The main goal of this research is to present a semi-automated dependency based collaborative requirement prioritization approach (CDBR), which uses linguistic values, execute-before-after (EBA) relation among requirements and machine learning algorithm to minimize the difference of opinion between stakeholder and developers for effective collaboration and for better approximation of final prioritization results, acceptable to both. The presented approach targets three major constraints rarely addressed in existing work, namely dependencies among requirements, communication among stakeholder and developers and the issue of scalability. Results of performance assessment conducted on several different requirement sets and on a case study by comparing CDBR with other state of the art approaches namely, AHP and IGA. The results are accurate and comparable in terms of effectiveness, efficiency, scalability and disagreement concerns among stakeholder and developers which in turn provides robustness to decision making process of awarding more importance to some requirements over others. CDBR overpowers AHP and IGA in terms of efficiency and processing time respectively.

Post-translational N-terminal Arginylation of Protein Fragments: A Pivotal Portal to Proteolysis.

The majority of tRNA studies has focused on tRNA molecules as pivotal player in the fundamental process of protein synthesis. Mounting studies have unveiled further functions for tRNA beyond protein synthesis, including non-ribosomal amino acid transfer, and regulation of targeted proteolysis. Post-translational N-terminal arginylation of protein fragments, a non-ribosomal amino acid transfer, is one of the crucial ways by which tRNA participates in various protein degradation trajectories and influences global cellular functions. Previous studies demonstrated a role of arginylation by arginyltransferases (ATEs) in protein degradation, autophagy, and cell death in mammalian cells. Notably, recent investigations in plants have revealed some of the crucial aspects regarding the biochemical nature of Nterminal arginylation and some of its physiological roles. Herein, we review some of the key data on Nterminal arginylation of protein fragments with respect to targeted proteolysis in mammalian cells. Future mechanistic studies using state of the art approaches and physiologically-relevant cellular models are warranted to enhance our molecular understanding of this important yet enigmatic protein modification.