An Audio-Based SLAM for Indoor Environments: A Robotic Mixed Reality Presentation.

In this paper, we present a novel approach referred to as the audio-based virtual landmark-based HoloSLAM. This innovative method leverages a single sound source and microphone arrays to estimate the voice-printed speaker's direction. The system allows an autonomous robot equipped with a single microphone array to navigate within indoor environments, interact with specific sound sources, and simultaneously determine its own location while mapping the environment. The proposed method does not require multiple audio sources in the environment nor sensor fusion to extract pertinent information and make accurate sound source estimations. Furthermore, the approach incorporates Robotic Mixed Reality using Microsoft HoloLens to superimpose landmarks, effectively mitigating the audio landmark-related issues of conventional audio-based landmark SLAM, particularly in situations where audio landmarks cannot be discerned, are limited in number, or are completely missing. The paper also evaluates an active speaker detection method, demonstrating its ability to achieve high accuracy in scenarios where audio data are the sole input. Real-time experiments validate the effectiveness of this method, emphasizing its precision and comprehensive mapping capabilities. The results of these experiments showcase the accuracy and efficiency of the proposed system, surpassing the constraints associated with traditional audio-based SLAM techniques, ultimately leading to a more detailed and precise mapping of the robot's surroundings.

Detection of tetanus toxoid with iron magnetic nanobioprobe.

Diagnosis of diseases with low facilities, speed, accuracy and sensitivity is an important matter in treatment. Bioprobes based on iron oxide nanoparticles are a good candidate for early detection of deadly and infectious diseases such as tetanus due to their high reactivity, biocompatibility, low production cost and sample separation under a magnetic field. In this study, silane groups were coated on surface of iron oxide nanoparticles using tetraethoxysilane (TEOS) hydrolysis. Also, NH2groups were generated on the surface of silanized nanoparticles using 3-aminopropyl triethoxy silane (APTES). Antibody was immobilized on the surface of silanized nanoparticles using TCT trichlorothriazine as activator. Silanization and stabilized antibody were investigated by using of FT-IR, EDX, VSM, SRB technique. UV/vis spectroscopy, fluorescence, agglutination test and ELISA were used for biosensor performance and specificity. The results of FT-IR spectroscopy showed that Si-O-Si and Si-O-Fe bonds and TCT chlorine and amine groups of tetanus anti-toxoid antibodies were formed on the surface of iron oxide nanoparticles. The presence of Si, N and C elements in EDX analysis confirms the silanization of iron oxide nanoparticles. VSM results showed that the amount of magnetic nanoparticles after conjugation is sufficient for biological applications. Antibody stabilization on nanoparticles increased the adsorption intensity in the uv/vis spectrometer. The fluorescence intensity of nano bioprobe increased in the presence of 10 ng ml-1. Nanobio probes were observed as agglomerates in the presence of tetanus toxoid antigen. The presence of tetanus antigen caused the formation of antigen-nanobioprobe antigen complex. Identification of this complex by HRP-bound antibody confirmed the specificity of nanobioprobe. Tetanus magnetic nanobioprobe with a diagnostic limit of 10 ng ml-1of tetanus antigen in a short time can be a good tool in LOC devices and microfluidic chips.

Urtica dioica Agglutinin: A plant protein candidate for inhibition of SARS-COV-2 receptor-binding domain for control of Covid19 Infection.

Despite using effective drugs and vaccines for Covid 19, due to some limitations of current strategies and the high rate of coronavirus mutation, the development of medicines with effective inhibitory activity against this infection is essential. The SARS-CoV-2 enters the cell by attaching its receptor-binding domain (RBD) of Spike to angiotensin-converting enzyme-2 (ACE2). According to previous studies, the natural peptide Urtica dioica agglutinin (UDA) exhibited an antiviral effect on SARS-CoV, but its mechanism has not precisely been elucidated. Here, we studied the interaction between UDA and RBD of Spike protein of SARS-CoV-2. So, protein-protein docking of RBD-UDA was performed using Cluspro 2.0. To further confirm the stability of the complex, the RBD-UDA docked complex with higher binding affinity was studied using Molecular Dynamic simulation (via Gromacs 2020.2), and MM-PBSA calculated the binding free energy of the system. In addition, ELISA assay was used to examine the binding of UDA with RBD protein. Results were compared to ELISA of RBD-bound samples of convalescent serum IgG (from donors who recovered from Covid 19). Finally, the toxicity of UDA is assessed by using MTT assay. The docking results show UDA binds to the RBD binding site. MD simulation illustrates the UDA-RBD complex is stable during 100 ns of simulation, and the average binding energy was calculated to be -47.505 kJ/mol. ELISA and, MTT results show that UDA binds to RBD like IgG-RBD binding and may be safe in human cells. Data presented here indicate UDA interaction with S-protein inhibits the binding sites of RBD, it can prevent the virus from attaching to ACE2 and entering the host cell.

A Two-Level Speaker Identification System via Fusion of Heterogeneous Classifiers and Complementary Feature Cooperation.

We present a new architecture to address the challenges of speaker identification that arise in interaction of humans with social robots. Though deep learning systems have led to impressive performance in many speech applications, limited speech data at training stage and short utterances with background noise at test stage present challenges and are still open problems as no optimum solution has been reported to date. The proposed design employs a generative model namely the Gaussian mixture model (GMM) and a discriminative model-support vector machine (SVM) classifiers as well as prosodic features and short-term spectral features to concurrently classify a speaker's gender and his/her identity. The proposed architecture works in a semi-sequential manner consisting of two stages: the first classifier exploits the prosodic features to determine the speaker's gender which in turn is used with the short-term spectral features as inputs to the second classifier system in order to identify the speaker. The second classifier system employs two types of short-term spectral features; namely mel-frequency cepstral coefficients (MFCC) and gammatone frequency cepstral coefficients (GFCC) as well as gender information as inputs to two different classifiers (GMM and GMM supervector-based SVM) which in total leads to construction of four classifiers. The outputs from the second stage classifiers; namely GMM-MFCC maximum likelihood classifier (MLC), GMM-GFCC MLC, GMM-MFCC supervector SVM, and GMM-GFCC supervector SVM are fused at score level by the weighted Borda count approach. The weight factors are computed on the fly via Mamdani fuzzy inference system that its inputs are the signal to noise ratio and the length of utterance. Experimental evaluations suggest that the proposed architecture and the fusion framework are promising and can improve the recognition performance of the system in challenging environments where the signal-to-noise ratio is low, and the length of utterance is short; such scenarios often arise in social robot interactions with humans.

Sequential Localizing and Mapping: A Navigation Strategy via Enhanced Subsumption Architecture.

In this paper, we present a navigation strategy exclusively designed for social robots with limited sensors for applications in homes. The overall system integrates a reactive design based on subsumption architecture and a knowledge system with learning capabilities. The component of the system includes several modules, such as doorway detection and room localization via convolutional neural network (CNN), avoiding obstacles via reinforcement learning, passing the doorway via Canny edge's detection, building an abstract map called a Directional Semantic Topological Map (DST-Map) within the knowledge system, and other predefined layers within the subsumption architecture. The individual modules and the overall system are evaluated in a virtual environment using Webots simulator.

A Doorway Detection and Direction (3Ds) System for Social Robots via a Monocular Camera.

In this paper, we propose a novel algorithm to detect a door and its orientation in indoor settings from the view of a social robot equipped with only a monocular camera. The challenge is to achieve this goal with only a 2D image from a monocular camera. The proposed system is designed through the integration of several modules, each of which serves a special purpose. The detection of the door is addressed by training a convolutional neural network (CNN) model on a new dataset for Social Robot Indoor Navigation (SRIN). The direction of the door (from the robot's observation) is achieved by three other modules: Depth module, Pixel-Selection module, and Pixel2Angle module, respectively. We include simulation results and real-time experiments to demonstrate the performance of the algorithm. The outcome of this study could be beneficial in any robotic navigation system for indoor environments.

SLAM in Dynamic Environments: A Deep Learning Approach for Moving Object Tracking Using ML-RANSAC Algorithm.

The important problem of Simultaneous Localization and Mapping (SLAM) in dynamic environments is less studied than the counterpart problem in static settings. In this paper, we present a solution for the feature-based SLAM problem in dynamic environments. We propose an algorithm that integrates SLAM with multi-target tracking (SLAMMTT) using a robust feature-tracking algorithm for dynamic environments. A novel implementation of RANdomSAmple Consensus (RANSAC) method referred to as multilevel-RANSAC (ML-RANSAC) within the Extended Kalman Filter (EKF) framework is applied for multi-target tracking (MTT). We also apply machine learning to detect features from the input data and to distinguish moving from stationary objects. The data stream from LIDAR and vision sensors are fused in real-time to detect objects and depth information. A practical experiment is designed to verify the performance of the algorithm in a dynamic environment. The unique feature of this algorithm is its ability to maintain tracking of features even when the observations are intermittent whereby many reported algorithms fail in such situations. Experimental validation indicates that the algorithm is able to perform consistent estimates in a fast and robust manner suggesting its feasibility for real-time applications.

An Adaptive Augmented Vision-Based Ellipsoidal SLAM for Indoor Environments.

In this paper, the problem of Simultaneous Localization And Mapping (SLAM) is addressed via a novel augmented landmark vision-based ellipsoidal SLAM. The algorithm is implemented on a NAO humanoid robot and is tested in an indoor environment. The main feature of the system is the implementation of SLAM with a monocular vision system. Distinguished landmarks referred to as NAOmarks are employed to localize the robot via its monocular vision system. We henceforth introduce the notion of robotic augmented reality (RAR) and present a monocular Extended Kalman Filter (EKF)/ellipsoidal SLAM in order to improve the performance and alleviate the computational effort, to provide landmark identification, and to simplify the data association problem. The proposed SLAM algorithm is implemented in real-time to further calibrate the ellipsoidal SLAM parameters, noise bounding, and to improve its overall accuracy. The augmented EKF/ellipsoidal SLAM algorithms are compared with the regular EKF/ellipsoidal SLAM methods and the merits of each algorithm is also discussed in the paper. The real-time experimental and simulation studies suggest that the adaptive augmented ellipsoidal SLAM is more accurate than the conventional EKF/ellipsoidal SLAMs.

Gadolinium-deferasirox-D-glucosamine: novel anti-tumor and MR molecular (theranostic) imaging agent.

UNLABELLED: TARGET AND PURPOSE: Cancer and heart disease are hard maladies in human communities. To recognize these kinds of diseases in primary states can help for remission and decreasing the expenses. One of the best techniques for recognizing is imaging of the tissue. METHODS: The main reason of this study is to survey the design of molecules Gd3+ -Defrasirox-DG as a type of glucose labeled with gadolinium to capture more specific cancer tissue and heart by MRI instrument as a technique extremely accurate and sensitive not too costly and lack of radioactive half-life compared with radioactive 18FDG as competing compound. RESULT: In this research, glucose is combined with Deferasirox for making complexes with gadolinium. With replacing the new compound of advanced imaging technology, it transferred from nuclear medicine to Radiology and the results were evaluated in vitro and in vivo that indicated the success in imaging of the heart and cancer in animal tumor model. CONCLUSION: The mechanism of cancer cells death is through activation of TNF-α system. At present, due to the lack of radiation and radioactive half-life and low production cost and high access to MRI compared with PET, this compound can be considered as 18FDG opponent in the near future as the new MRI successor agent.

Intacs Followed by MyoRing Implantation in Severe Keratoconus.

PURPOSE: To describe the outcome of complete intrastromal ring (MyoRing, DIOPTEX GmBH, Linz, Austria) implantation following intrastromal corneal ring segment (Intacs, Addition Technology, Inc., Sunnyvale, CA) implantation for severe keratoconus. METHODS: Case report. RESULTS: A MyoRing was implanted in a keratoconic patient who had undergone a previous Intacs implantation surgery 4 years previously without Intacs explantation and had residual refractive error. There were no intraoperative or postoperative complications. After 1 year; mean keratometric power decreased from 50.3 to 43.6 diopters, uncorrected distance visual acuity improved from 20/400 to 20/50, and corrected distance visual acuity improved from 20/200 to 20/30. CONCLUSIONS: In selected severe keratoconus cases with high myopia and steep keratometry previously treated with Intacs implantation, if significant residual refractive error is observed, secondary implantation of MyoRing over Intacs may improve vision and refraction.

Sensor fusion of monocular cameras and laser rangefinders for line-based Simultaneous Localization and Mapping (SLAM) tasks in autonomous mobile robots.

This paper presents a sensor fusion strategy applied for Simultaneous Localization and Mapping (SLAM) in dynamic environments. The designed approach consists of two features: (i) the first one is a fusion module which synthesizes line segments obtained from laser rangefinder and line features extracted from monocular camera. This policy eliminates any pseudo segments that appear from any momentary pause of dynamic objects in laser data. (ii) The second characteristic is a modified multi-sensor point estimation fusion SLAM (MPEF-SLAM) that incorporates two individual Extended Kalman Filter (EKF) based SLAM algorithms: monocular and laser SLAM. The error of the localization in fused SLAM is reduced compared with those of individual SLAM. Additionally, a new data association technique based on the homography transformation matrix is developed for monocular SLAM. This data association method relaxes the pleonastic computation. The experimental results validate the performance of the proposed sensor fusion and data association method.

Progressive keratectasia after laser in situ keratomileusis.

PURPOSE: We describe ten patients who developed progressive keratectasia following laser in situ keratomileusis (LASIK) and identify possible factors that may lead to ectasia. METHODS: In this retrospective study, we reviewed the files of 3,634 patients (6941 eyes) who had LASIK between March 2000 and April 2003. Ten patients (14 eyes, 0.2%) developed progressive keratectasia. We also evaluated consequent therapeutic measures and final visual status of these patients. RESULTS: Patients were examined at a mean 24.9 +/- 8.1 months after LASIK. Ectasia developed within a mean 14 +/- 0.3 months after surgery. At baseline, mean keratometric power was 44.7 +/- 2.30 D, mean corneal thickness was 516 +/- 18.9 microm, and mean attempted correction was -10.85 +/- 3.20 D. We found a statistically significant correlation between residual stromal thickness, attempted correction, and occurrence of progressive keratectasia. We also found that preexisting abnormal corneal topography was a risk factor for progressive keratectasia. Ultimately, most patients had reasonable visual acuity after penetrating keratoplasty. CONCLUSION: Progressive keratectasia is a vision threatening complication of LASIK that may occur in previously healthy or diseased eyes. The most important risk factors are residual stromal thickness and preexisting abnormal corneal topography. Penetrating keratoplasty may be a reasonable therapeutic measure for severe cases of progressive keratectasia.

Laser in situ keratomileusis and diode thermal keratoplasty for correction of hyperopia from +5.00 to +10.00 diopters.

PURPOSE: To evaluate the effects and safety of laser in situ keratomileusis (LASIK) and diode thermal keratoplasty (DTK) for correction of moderate to high hyperopia (+5.00 to +10.00 D). METHODS: This prospective study included 30 eyes of 15 patients who had LASIK-DTK bioptics. The median age of the patients was 50.5 years. LASIK was performed using a Nidek EC-5000 excimer laser system and DTK by a Prolaser DTK laser, 2 months after LASIK. Follow-up ranged from 9 to 12 months (mean, 10.5 mo). RESULTS: The mean preoperative spherical equivalent refraction was +8.25 +/- 0.25 D and mean postoperative was +1.00 +/- 0.50 D. The preoperative best spectacle-corrected visual acuity (BSCVA) was < or = 20/40 in 10 eyes and > or = 20/25 in 20 eyes. Postoperatively, BSCVA was < or = 20/40 in 8 eyes and > or = 20/25 in 22 eyes. No significant intra- or postoperative complications occurred. CONCLUSION: LASIK-DTK bioptics for correction of moderate to high hyperopia (+5.00 to +10.00 D) was safe and effective. In this method, two different ablative and non-ablative laser systems were used to compensate for regression, which is the most important concern in the correction of hyperopia.