Minimum User Intervention Research Articles

Design, development and performance analysis of cognitive assisting aid with multi sensor fused navigation for visually impaired people

The research and innovations in the field of wearable auxiliary devices for visually impaired and blind people are playing a vital role towards improving their quality of life. However, in spite of the promising research outcomes, the existing wearable aids has several weaknesses such as more weight, limitations in the number of features and cost. The main objective of this manuscript is to provide the detailed design of a novel lightweight wearable aid with higher number of features for visually impaired and blind people. The proposed research aims to design a cognitive assistant that will guide the blind people for walking by detecting the environment around them. The framework include a Multi-Sensor Fused Navigation system comprises of a sensor-based, vision-based, and cognitive (intelligent/smart) application. The visual features for the design include obstacle detection, uneven surface detection, slope and downward steps detection, pothole detection and hallow object detection; location tracking, walking guide, image capturing and video recording. This prototype is named as Blind’s Apron based on its appearance. The invention focusses on parameters like reduction on size (quite handy) and light weight (comfortable to wear), higher number of detection features, and minimum user intervention (high end operations like switching on and off). All user interactions are friendly and affordable to everyone. The results obtained in this research lead to a high end technical intervention with ease of use. Finally, the performance of the proposed cognitive assistant is tested with a user study in real-time. The feedback and corresponding results establish the effective outcome of the proposed invention which is a light weight and feature enhanced device with easily understandable instructions.

Reconfigurable distributed flowshop group scheduling with a nested variable neighborhood descent algorithm

Inspired by a real-world cellular manufacturing system for processing printed circuit boards (PCBs), this study addresses a reconfigurable distributed flowshop scheduling problem (RDFGSP), where the flowline is considered as a cell and a complete process includes two flows through the flowlines. The characteristics of the RDFGSP lie in the reconfigurability of the flowlines and the families with grouped jobs. To solve this problem, at the two flows the family assignment part and sequence part (including family sequence and job sequence) are all required to be addressed. To solve the problem, a mixed integer linear programming (MILP) model is first developed, which can solve the small-scaled instances to optimality. Since the NP-hard property of the problem, a nested variable neighborhood descent (NVND) algorithm is developed. Its core ideas lie in the dynamic solution encoding and decoding strategies and the specially designed nested loops, including the external and internal loops. In the external loop, the solution encoding and decoding only consider the sequence part, and the family assignment part is conceived using the machine property in the decoding process. The internal loop is triggered when a decoding process in the external loop is completed, where the family assignment part and sequence part are all considered. In addition, a collaborative process and a restart strategy are employed to guarantee the global search capability. In the experimental study, the iterated F-Race (I/F-Race) is used to help determine the algorithm parameters with minimum user intervention. Comprehensive computational results demonstrate that the proposed algorithm outperforms the math solver CPLEX and other state-of-the-art metaheuristics.

Novel method to delineate palatal rugae and assess their complexity using fractal analysis

Palatal rugae constitute significant morphological landmarks, with wide clinical applications in forensics, insertion of mini-screws, and superimposition. Their morphology has been studied mainly with indices relevant to their qualitative characteristics. The present paper aims at presenting a new quantitative method to evaluate their complexity, by means of box-counting fractal dimension analysis, and to investigate its inter- and intra-rater reliability. Twenty maxillary plaster models were scanned for the needs of this study. A sequence of steps, including cropping of the mesh, ball pivoting, distance mapping and fractal dimension analysis, performed with Viewbox 4 software, was followed. Box-counting fractal dimensions were calculated as a measure of rugae’s complexity. Inter- and intra-rater reliability were investigated, using Bland–Altman analysis. Fractal dimensions ranged from 1.274 to 1.491 (average: 1.412). Bland–Altman analysis of inter- and intra-examiner reliability demonstrated that the 95% limits of agreement ranged from − 0.012 to 0.011 and from − 0.004 to 0.004, respectively. The method is reliable and can be applied in research and forensics. It offers comprehensive evaluation of the rugae’s complexity and a complete set of information about their outlines and height profiles, with minimum user intervention.

PIMesh: An automatic point cloud and unstructured mesh generation algorithm for meshless methods and finite element analysis-with applications in surgical simulations.

We propose a point cloud and mesh generation algorithm, particle injection mesh generator (PIMesh), that can be used to generate optimized high-quality point clouds and unstructured meshes for domains in any shape with minimum (or even no) user intervention. The domains can be scanned images in OBJ format in 2D and 3D or just a line drawing in 2D. Mesh grading can also be easily controlled. The PIMesh is robust and easy to be implemented and is useful for a variety of applications, ranging from generating point clouds for meshless methods, mesh generation for finite element methods, computer graphics applications and surgical simulators. The core idea of the PIMesh is that a mesh domain is considered as an "airtight container" into which particles are "injected" at one or multiple selected interior points. The motion of the particles is controlled by a pseudo-molecular dynamics (PMD) formulation with a pairwise purely repelling "force" moderated by an absolute velocity dependent drag force. The particles repel each other and occupy the whole domain somewhat like blowing up a balloon. When the container is full of particles and the motion is stopped (the particles can be considered as a point cloud), a Delaunay triangulation algorithm is employed to link the particles together to generate an unstructured mesh. The performance of the PIMesh and the comparison with other unstructured mesh generation approaches are demonstrated through generating node distributions and meshes for several 2D and 3D object domains including a scanned image of bones and others.

Contact modeling from images using cut finite element solvers

This paper presents a robust digital pipeline from CT images to the simulation of contact between multiple bodies. The proposed strategy relies on a recently developed immersed finite element algorithm that is capable of simulating unilateral contact between solids without meshing (Claus and Kerfriden in Int J Numer Methods Eng 113(6):938–966, 2018). It was shown that such an approach reduces the difficulties associated with the digital flow of information from analytically defined geometries to mechanical simulations. We now propose to extend our approach to include geometries, which are not defined mathematically but instead are obtained from images, and encoded in 3D arrays of voxels. This paper introduces two novel elements. Firstly, we reformulate our contact algorithm into an extension of an augmented Lagrangian CutFEM algorithm. Secondly, we develop an efficient algorithm to convert the surface data generated by standard segmentation tools used in medical imaging into level-set functions. These two elements give rise to a robust digital pipeline with minimum user intervention. We demonstrate the capabilities of our algorithm on a hip joint geometry with contact between the femur and the hip bone.

Enhancement Techniques on Medical Thermal Image of Pregnant Women for Fetal Growth Monitoring

ABSTRACT As per the World Health Organization, 94% of all maternal death occurred in low- and middle-income countries. Monitoring foetal heath in these countries is expensive due to the shortage of medical professionals as well as access to facilities. A cost-effective tool for monitoring the foetal growth non-invasively is the need of the hour. One such method is to monitor the skin surface distribution of the abdomen region using an infrared imaging device. The temperature distribution is a physiological response due to the underlying anatomical changes. But, interpretation of this data is qualitative requiring skill and experience. Therefore, there is a need to process the thermal images for quantification for better interpretation with minimum user intervention or expertise. This would help the physician to understand the location of the affected area and its functional disorder to decide on the follow-up treatment. The present research focusses on the enhancement of thermal images using filtering and segmentation techniques. The images were obtained from the abdominal region of pregnant women at various stages of gestation and pregnant with a singleton or twin is processed for enhancement. Histogram method and Otsu thresholding was implemented to augment the image. The correlation was found from the thermal pattern with respect to the foetal gestation period as well as for twin or singleton. Though the sample size is small to confirm this as a stand-alone tool, the results suggest that thermal scanning can be used as a safe and reliable adjunct tool for monitoring foetal growth.

Automatic Glucose Control During Meals and Exercise in Type 1 Diabetes: Proof-of-Concept in Silico Tests Using a Switched LPV Approach

Keeping the blood glucose levels within the safe range during meals and exercise still represents a major hurdle not only for patients with type 1 diabetes (T1D), but also for Artificial Pancreas (AP) systems. One of the reasons a fully (autonomous) closed-loop solution has not been released onto the market yet is the slow action of current insulin analogs. To partially overcome this limitation, the authors have previously designed a switched control strategy equipped with an insulin-on-board (IOB) safety loop that mitigates meal-related glucose excursions without carbohydrate counting. In this letter, a similar strategy based on a Linear Parameter-Varying (LPV) control law has been adapted to safely handle also exercise challenges with minimum user intervention. In silico results using the UVA/Padova simulator evidence that the proposed closed-loop scheme is feasible under moderate-intense exercise bouts by effectively and safely reducing the risk of hypoglycemia.

Probabilistic latent semantic analysis of composite excitation-emission matrix fluorescence spectra of multicomponent system.

In the present work, a simple and fast analytical procedure involving minimum user intervention was developed by combining the excitation-emission matrix fluorescence (EEMF) spectroscopy with Probabilistic latent semantic analysis (pLSA) technique. Akaike Information Criterion (AIC) was used to enable the user to automatically select the optimum model for analysing the mixtures of fluorescent components. The utility of the present work was successfully evaluated by analysing the dilute aqueous mixtures of certain fluorescent molecule such as Catechol, Hydroquinone, Indole, Tryptophan and Tyrosine of biological relevance. The developed AIC assisted pLSA model of five components explained >90% variance of spectral data sets. The identity between the pLSA retrieved spectral profiles was established using similarity index (SI) parameter in automatic manner. The SI values were found to be close to unit values for each of the five analyzed molecules. The regression parameter between the actual and pLSA predicted concentrations were found to be well within acceptable limits. Both root mean square of calibration and predictions for each of the five fluorescent molecules were found to be <1%, whereas, the square of the correlation coefficient (R2) value was found to be >0.98 suggesting the developed pLSA model was quite precise in analysing both calibration and validation set samples. The uniqueness of the developed pLSA model for EEMF spectroscopic data was successfully tested using the sequential quadratic programming (SQP) algorithm. The differences between the upper and lower bands in SQP were found to be ≤0.005. In summary, the proposed approach serve as swift and simple analytical tool for the analysis of fluorescent mixtures without involving pre-separation step.

A new microchannel capillary flow assay (MCFA) platform with lyophilized chemiluminescence reagents for a smartphone-based POCT detecting malaria

There has been a considerable development in microfluidic based immunodiagnostics over the past few years which has greatly favored the growth of novel point-of-care-testing (POCT). However, the realization of an inexpensive, low-power POCT needs cheap and disposable microfluidic devices that can perform autonomously with minimum user intervention. This work, for the first time, reports the development of a new microchannel capillary flow assay (MCFA) platform that can perform chemiluminescence based ELISA with lyophilized chemiluminescent reagents. This new MCFA platform exploits the ultra-high sensitivity of chemiluminescent detection while eliminating the shortcomings associated with liquid reagent handling, control of assay sequence and user intervention. The functionally designed microchannels along with adequate hydrophilicity produce a sequential flow of assay reagents and autonomously performs the ultra-high sensitive chemiluminescence based ELISA for the detection of malaria biomarker such as PfHRP2. The MCFA platform with no external flow control and simple chemiluminescence detection can easily communicate with smartphone via USB-OTG port using a custom-designed optical detector. The use of the smartphone for display, data transfer, storage and analysis, as well as the source of power allows the development of a smartphone based POCT analyzer for disease diagnostics. This paper reports a limit of detection (LOD) of 8 ng/mL by the smartphone analyzer which is sensitive enough to detect active malarial infection. The MCFA platform developed with the smartphone analyzer can be easily customized for different biomarkers, so a hand-held POCT for various infectious diseases can be envisaged with full networking capability at low cost.

Polymer-encapsulated molecular doped epigraphene for quantum resistance metrology

One of the aspirations of quantum metrology is to deliver primary standards directly to end-users thereby significantly shortening the traceability chains and enabling more accurate products. Epitaxial graphene grown on silicon carbide (epigraphene) is known to be a viable candidate for a primary realisation of a quantum Hall resistance standard, surpassing conventional semiconductor two-dimensional electron gases, such as those based on GaAs, in terms of performance at higher temperatures and lower magnetic fields. The bottleneck in the realisation of a turn-key quantum resistance standard requiring minimum user intervention has so far been the need to fine-tune the carrier density in this material to fit the constraints imposed by a simple cryo-magnetic system. Previously demonstrated methods, such as via photo-chemistry or corona discharge, require application prior to every cool-down as well as specialist knowledge and equipment. To this end we perform metrological evaluation of epigraphene with carrier density tuned by a recently reported permanent molecular doping technique. Measurements at two National Metrology Institutes confirm accurate resistance quantisation below 5 nΩ Ω−1. Furthermore, samples show no significant drift in carrier concentration and performance on multiple thermal cycles over three years. This development paves the way for dissemination of primary resistance standards based on epigraphene.

Landslide mapping from multi-sensor data through improved change detection-based Markov random field

Accurate landslide inventory mapping is essential for quantitative hazard and risk assessment. Although multi-temporal change detection techniques have contributed greatly to landslide inventory preparation, it is still challenging to generate quality change detection images (CDIs) for accurate landslide mapping. The recently proposed change detection-based Markov random field (CDMRF) provides an effective approach for rapid mapping of landslides with minimum user interventions. However, when CDI is generated by change vector analysis (CVA) alone, the CDMRF method may suffer from noise especially when the pre- and post-event remote sensing images are acquired under different atmospheric, illumination, and phenological conditions. This paper improved such CDMRF approach by integrating normalized difference vegetation index (NDVI), principal component analysis (PCA), and independent component analysis (ICA) generated CDIs with MRF for landslide inventory mapping from multi-sensor data. To justify the effectiveness and applicability, the improved methods were applied to map rainfall-, typhoon-, and earthquake-triggered landslides from the pre- and post-event satellite images acquired by very high resolution QuickBird, high resolution FORMOSAT-2, and moderate resolution Sentinel-2. Moreover, they were tested on pre-event Landsat-8 and post-event Sentinel-2 datasets, indicating that they are operational for landslide inventory mapping from combined multi-temporal and multi-sensor data. The results demonstrate that the improved δNDVI-, PCA-, and ICA-based approaches perform much better than CVA-based CDMRF in terms of completeness, correctness, Kappa coefficient, and F-measures. To the best of our knowledge, it is the first time that NDVI, PCA, and ICA are integrated with MRF for landslide inventory mapping from multi-sensor data. It is anticipated that this research can be a starting point for developing new change detection techniques that can readily generate quality CDI and for applying advanced machine learning algorithms (e.g., deep learning) to automatic detection of natural hazards from multi-sensor time series data.

A novel method for analyzing long bone diaphyseal cross-sectional geometry. A GNU Octave CSG Toolkit

The diaphyseal cross-sectional geometric properties of the humerus, femur and tibia have been extensively used for studying their adaptation to mechanical loading. To date common practices for such studies involve either computed tomography or the latex cast method in conjunction with image analysis for calculating such properties. With the advent of modern laser scanning and photogrammetry technologies in biological anthropology, the computation of the cross-sectional geometric properties directly from 3D models is a viable and sensible alternative. Nevertheless, such method has not been properly implemented as yet. A dedicated toolkit, named long-bone-diaphyseal-CSG-Toolkit, comprising a set of functions for the GNU Octave programming language, is presented here. Offering a robust analytical implementation and an easy to follow application either for a single bone or in batch-processing mode, the toolkit requires minimum user intervention and also provides functionality for graphical representation of the calculated periosteal contours and their respective cross-sectional geometric properties. Finally, the long-bone-diaphyseal-CSG-Toolkit utilizes advanced optimization algorithms, which eliminate intra- and inter-observer error by reliably orienting the cross-sectional contours to a well-defined orientation and close to the bone’s true anatomical position, which provides a significant advantage over the latex cast method.

InSilc: 3D Reconstruction and plaque characterization tool.

Coronary artery disease (CAD) is the leading cause of mortality in Europe and worldwide. Atherosclerosis is the most common pathologic process that is highly related with CAD, while the implantation of drug-eluting Bioresorbable Vascular Scaffolds (BVS) is the most promising procedure for treating patients with CAD. InSilc is an textbfin silico clinical trial (ISCT) platform for the development and assessment of drugeluting BVS. The InSilc platform provides insight in the performance of drug-eluting BVS in their short term and medium/long term through the Mechanical Modelling Module, the Deployment Module, the Fluid Dynamics Module, the Myocardial Perfusion Module, the Drug-delivery Module and the Degradation Module. In order for the aforementioned modules to be developed, the utilization of the reconstructed patient specific arterial segment and the BVS design are required, which is achieved through the 3D reconstruction and plaque characterization tool.In this study, the overall architecture of the InSilc platform is presented with special emphasis on the 3D reconstruction and plaque characterization tool. The tool will be able to implement different medical image processing workflows. The workflows will require minimum user intervention in order to be used in large scale clinical trials.

An efficient and accurate framework for calculating lattice thermal conductivity of solids: AFLOW\u2014AAPL Automatic Anharmonic Phonon Library

One of the most accurate approaches for calculating lattice thermal conductivity, kappa _ell, is solving the Boltzmann transport equation starting from third-order anharmonic force constants. In addition to the underlying approximations of ab-initio parameterization, two main challenges are associated with this path: high computational costs and lack of automation in the frameworks using this methodology, which affect the discovery rate of novel materials with ad-hoc properties. Here, the Automatic Anharmonic Phonon Library (AAPL) is presented. It efficiently computes interatomic force constants by making effective use of crystal symmetry analysis, it solves the Boltzmann transport equation to obtain kappa _ell, and allows a fully integrated operation with minimum user intervention, a rational addition to the current high-throughput accelerated materials development framework AFLOW. An “experiment vs. theory” study of the approach is shown, comparing accuracy and speed with respect to other available packages, and for materials characterized by strong electron localization and correlation. Combining AAPL with the pseudo-hybrid functional ACBN0 is possible to improve accuracy without increasing computational requirements.

High-resolution Single Particle Analysis from Electron Cryo-microscopy Images Using SPHIRE

SPHIRE (SPARX for High-Resolution Electron Microscopy) is a novel open-source, user-friendly software suite for the semi-automated processing of single particle electron cryo-microscopy (cryo-EM) data. The protocol presented here describes in detail how to obtain a near-atomic resolution structure starting from cryo-EM micrograph movies by guiding users through all steps of the single particle structure determination pipeline. These steps are controlled from the new SPHIRE graphical user interface and require minimum user intervention. Using this protocol, a 3.5 Å structure of TcdA1, a Tc toxin complex from Photorhabdus luminescens, was derived from only 9500 single particles. This streamlined approach will help novice users without extensive processing experience and a priori structural information, to obtain noise-free and unbiased atomic models of their purified macromolecular complexes in their native state.

High-resolution Single Particle Analysis from Electron Cryo-microscopy Images Using SPHIRE.

SPHIRE (SPARX for High-Resolution Electron Microscopy) is a novel open-source, user-friendly software suite for the semi-automated processing of single particle electron cryo-microscopy (cryo-EM) data. The protocol presented here describes in detail how to obtain a near-atomic resolution structure starting from cryo-EM micrograph movies by guiding users through all steps of the single particle structure determination pipeline. These steps are controlled from the new SPHIRE graphical user interface and require minimum user intervention. Using this protocol, a 3.5 Å structure of TcdA1, a Tc toxin complex from Photorhabdus luminescens, was derived from only 9500 single particles. This streamlined approach will help novice users without extensive processing experience and a priori structural information, to obtain noise-free and unbiased atomic models of their purified macromolecular complexes in their native state.

Deformable MRI-CT registration for breast cancer radiation treatment planning using a sequentially applied semi-physical model regularization method

Purpose. To develop an optimization scheme and semi-physical regularization method for accurate and efficient deformable image registration (DIR) between magnetic resonance imaging (MRI) and computed tomography (CT) for breast cancer radiation therapy planning. Methods and Materials. A sequentially-applied semi-physical model regularization method with bending energy limited diffeomorphism (BELD) was developed to regularize transformation in free-form (B-spline) DIR. Bony structures were first segmented to account for their rigidity during deformation. Feature points were identified and filtered to be used as a penalty term. Rigidity, BELD, and feature point distance penalty terms are applied sequentially during the optimization process based on mutual information metric. Sample MRI and CT images of breast cancer patients were used to demonstrate the registration. Agreements of feature point pairs and contour transfer between the two image modalities were measured to illustrate the accuracy and efficiency for the developed DIR method. Results. The newly developed tool can accurately register MRI to CT with minimum user intervention for situations with moderate to large deformations. For the sample cases tested, the average feature point pair distance was reduced from 6.7 to 3.2 mm by simple B-spline registration and to 1.8 mm by the newly-developed registration method. Compared to simultaneous optimization of multiple penalty terms, the computation time was shortened by 30% and the accuracy was improved by 15%. The bone area warping artifact in deformed MRI disappeared in the proposed method. Considering the resolution of MR images is 1.3 mm, the resulting feature point pair distance is satisfactory. Conclusion. The proposed DIR method of sequentially applying physical model regularization terms during the optimization process can reduce the pitfalls of local extremity, weighting factor adjustment and multi-resolution optimization stages, therefore improving the accuracy and efficiency of MRI to CT registration even with moderate to large deformations commonly seen in radiation treatment planning with multi-modality images.

A framework for generic facial expression transfer

This work presents a methodology for generic facial expression transfer, aiming to speed the process of generating facial animation for interactive applications. We propose an adaptive and semiautomatic methodology, which allows to transfer facial expressions from a face mesh to another. The model has three main stages: rigging, expression transfer and animation, where the output meshes can be used as key poses for blendshape-based animation. The input of the model is a face mesh in neutral pose and a set of face data that can be provided from different sources, such as artist crafted meshes and motion capture data. The model generates a set of blendshapes corresponding to the input set, with minimum user intervention. We used a simple rig structure in order to provide a trivial correspondence either with sparse facial feature points based systems or dense geometric data supplied by RGBD based systems. The rig structure can be refined on-the-fly to deal with different input geometric data according to the need. Results show the quality of expressions transfer assessment using face data including artist crafted meshes and performance driven animation.

Mood based Playlist Generation for Hindi Popular Music: A Proposed Model

Large digital databases of Hindi music are available which creates an opportunity of filtering this data with multiple parameters. One of the most important parameter used by the listeners are their moods. This paper focuses on Automatic generation of mood based playlist for Hindi popular music with minimum user intervention. There are two major modules of the proposed system. The first module identifies user’s mood based on the inputs from social media and messaging app like WhatsApp. The second module is responsible for tagging the songs of available database. Tagging is done on the basis of Genre, Artists, Tempo and Lyrics. Using the above mentioned modules, mood based playlist can be generated for user. General Terms Playlist generation, mood analysis, meta-data analysis

ALISA: An automatic lightly supervised speech segmentation and alignment tool

This paper describes the ALISA tool, which implements a lightly supervised method for sentence-level alignment of speech with imperfect transcripts. Its intended use is to enable the creation of new speech corpora from a multitude of resources in a language-independent fashion, thus avoiding the need to record or transcribe speech data. The method is designed so that it requires minimum user intervention and expert knowledge, and it is able to align data in languages which employ alphabetic scripts. It comprises a GMM-based voice activity detector and a highly constrained grapheme-based speech aligner. The method is evaluated objectively against a gold standard segmentation and transcription, as well as subjectively through building and testing speech synthesis systems from the retrieved data. Results show that on average, 70% of the original data is correctly aligned, with a word error rate of less than 0.5%. In one case, subjective listening tests show a statistically significant preference for voices built on the gold transcript, but this is small and in other tests, no statistically significant differences between the systems built from the fully supervised training data and the one which uses the proposed method are found.