Towards Sensor Enhanced Virtual Reality Teleoperation in a Dynamic Environment

A teleoperation interface is introduced featuring an integrated virtual reality based simulation augmented by sensors and image processing capabilities on-board the remotely operated vehicle. The virtual reality system addresses the typical limitations of video-based teleoperation caused by signal lag and limited field of view, allowing the operator to navigate in a continuous fashion. The vehicle incorporates an on-board computer and a stereo vision system to facilitate obstacle detection. It also enables temporary autonomous operation of the vehicle for local navigation around obstacles and automatic re-establishment of the vehicle’s teleoperated state. Finally, the system provides real time update to the virtual environment based on anomalies encountered by the vehicle. System architecture and preliminary implementation results are discussed, and future work focused on incorporating dynamic moving objects in the environment is described

Hardware module development for RFID drone

Nowadays most of the industry or stores uses RFID (Radio Frequency Identification) technology to monitor the state of the product. But, sometimes there is a difference between registered and the real value. In big stores manual inventory is very hard, expensive and time consuming process. In the present decades many researches are going on to avoid the above problem and one of the solutions is to use drones for RFID inventory. This project is focused on designing and developing hardware for RFID drone to aid RFID inventory and development of a software module capable for reading of tags data. The project presents analysis of the hardware devices such as Passive UHF (Ultra High) RFID Reader, Antenna, CPU (Central Processing Unit), PSU (Power Supply Module) and other interfacing components. Selection of the devices is based on criteria such as size and weight, market options and the components which is going to be used for RFID drone. Finally, software coding to read the RFID tags

Virtual reality based multi-modal teleoperation using mixed autonomy

The thesis presents a multi modal teleoperation interface featuring an integrated virtual reality based simulation aumented by sensors and image processing capabilities onboard the remotely operated vehicle. The virtual reality interface fuses an existing VR model with live video feed and prediction states, thereby creating a multi modal control interface. Virtual reality addresses the typical limitations of video-based teleoperation caused by signal lag and limited field of view thereby allowing the operator to navigate in a continuous fashion. The vehicle incorporates an on-board computer and a stereo vision system to facilitate obstacle detection. A vehicle adaptation system with a priori risk maps and real state tracking system enables temporary autonomous operation of the vehicle for local navigation around obstacles and automatic re-establishment of the vehicle\u27s teleoperated state. As both the vehicle and the operator share absolute autonomy in stages, the operation is referred to as mixed autonomous. Finally, the system provides real time update of the virtual environment based on anomalies encountered by the vehicle. The system effectively balances the autonomy between the human operator and on board vehicle intelligence. The reliability results of individual components along with overall system implementation and the results of the user study helps show that the VR based multi modal teleoperation interface is more adaptable and intuitive when compared to other interfaces

Role of \u3cem\u3eEgr-1\u3c/em\u3e Gene Expression in B Cell Receptor-Induced Apoptosis in an Immature B Cell Lymphoma

Ligation of B cell receptor (BCR) on BKS-2, an immature B cell lymphoma by anti-IgM antibodies (Ab) caused apoptosis. Here we report that signaling through B cell receptor in wild type BKS-2 cells down-regulated the expression of Egr-1, a zinc finger-containing transcription factor. A reduction in the level ofEgr-1 mRNA could be demonstrated as early as 30 min after the ligation of BCR on BKS-2 cells. Immunocytochemical and Western blot analysis revealed that the expression of EGR-1 protein was also inhibited by anti-IgM treatment. Antisense oligonucleotides to Egr-1 caused growth inhibition and apoptosis in BKS-2 cells, suggesting that expression of Egr-1 is important for the survival of these B lymphoma cells. In contrast to wild type BKS-2 cells, the mutant 1.B5 cell line, which is refractory to B cell receptor-mediated growth-inhibitory signals, showed an increased expression of Egr-1 upon treatment with anti-IgM. These results implicate a role for Egr-1 in blocking B cell receptor-mediated apoptosis in immature B cells

Sensor Augmented Virtual Reality Based Teleoperation Using Mixed Autonomy

A multimodal teleoperation interface is introduced, featuring an integrated virtual reality (VR) based simulation augmented by sensors and image processing capabilities onboard the remotely operated vehicle. The proposed virtual reality interface fuses an existing VR model with live video feed and prediction states, thereby creating a multimodal control interface. VR addresses the typical limitations of video based teleoperation caused by signal lag and limited field of view, allowing the operator to navigate in a continuous fashion. The vehicle incorporates an onboard computer and a stereo vision system to facilitate obstacle detection. A vehicle adaptation system with a priori risk maps and a real-state tracking system enable temporary autonomous operation of the vehicle for local navigation around obstacles and automatic re-establishment of the vehicle’s teleoperated state. The system provides real time update of the virtual environment based on anomalies encountered by the vehicle. The VR based multimodal teleoperation interface is expected to be more adaptable and intuitive when compared with other interfaces

Virtual Reality Interface Design for Multi-Modal Teleoperation

A multi modal teleoperation interface is introduced featuring an integrated virtual reality (VR) based simulation augmented by sensors and image processing capabilities on-board the remotely operated vehicle. The proposed virtual reality interface fuses an existing VR model with live video feed and prediction states, thereby creating a multi modal control interface. Virtual reality addresses the typical limitations of video-based teleoperation caused by signal lag and limited field of view. The 3D environment in VR along with visual cues generated from real time sensor data allows the operator to navigate in a continuous fashion. The vehicle incorporates an on-board computer and a stereo vision system to facilitate obstacle detection. A vehicle adaptation system with a priori risk maps and real state tracking system enables temporary autonomous operation of the vehicle for local navigation around obstacles and automatic re-establishment of the vehicle’s teleoperated state. Finally, the system provides real time update of the virtual environment based on anomalies encountered by the vehicle. The VR interface architecture is discussed and implementation results are presented. The VR based multi modal teleoperation interface is expected to be more adaptable and intuitive when compared to other interfaces.This is a conference proceeding from ASME-AFM 2009 World Conference on Innovative Virtual Reality (2009): 169, doi:10.1115/WINVR2009-732. Posted with permission.</p

Mammalian Target of Rapamycin (mTOR) Activity Dependent Phospho-Protein Expression in Childhood Acute Lymphoblastic Leukemia (ALL)

Modern treatment strategies have improved the prognosis of childhood ALL; however, treatment still fails in 25–30% of patients. Further improvement of treatment may depend on the development of targeted therapies. mTOR kinase, a central mediator of several signaling pathways, has recently attracted remarkable attention as a potential target in pediatric ALL. However, limited data exists about the activity of mTOR. In the present study, the amount of mTOR activity dependent phospho-proteins was characterized by ELISA in human leukemia cell lines and in lymphoblasts from childhood ALL patients (n = 49). Expression was measured before and during chemotherapy and at relapses. Leukemia cell lines exhibited increased mTOR activity, indicated by phospho-S6 ribosomal protein (p-S6) and phosphorylated eukaryotic initiation factor 4E binding protein (p-4EBP1). Elevated p-4EBP1 protein levels were detected in ALL samples at diagnosis; efficacy of chemotherapy was followed by the decrease of mTOR activity dependent protein phosphorylation. Optical density (OD) for p-4EBP1 (ELISA) was significantly higher in patients with poor prognosis at diagnosis, and in the samples of relapsed patients. Our results suggest that measuring mTOR activity related phospho-proteins such as p-4EBP1 by ELISA may help to identify patients with poor prognosis before treatment, and to detect early relapses. Determining mTOR activity in leukemic cells may also be a useful tool for selecting patients who may benefit from future mTOR inhibitor treatments

Use of machine learning to identify characteristics associated with severe hypoglycemia in older adults with type 1 diabetes: a post-hoc analysis of a case-control study

Download PDFPDF Download PDF + Supplemental DataPDF + Supplementary Material Epidemiology/Health services research Use of machine learning to identify characteristics associated with severe hypoglycemia in older adults with type 1 diabetes: a post-hoc analysis of a case–control study http://orcid.org/0000-0002-9905-4855Nikki L B Freeman1, Rashmi Muthukkumar2, Ruth S Weinstock3, M Victor Wickerhauser4, http://orcid.org/0000-0003-2701-101XAnna R Kahkoska5,6 Correspondence to Dr Nikki L B Freeman; [email protected] Abstract Introduction Severe hypoglycemia (SH) in older adults (OAs) with type 1 diabetes is associated with profound morbidity and mortality, yet its etiology can be complex and multifactorial. Enhanced tools to identify OAs who are at high risk for SH are needed. This study used machine learning to identify characteristics that distinguish those with and without recent SH, selecting from a range of demographic and clinical, behavioral and lifestyle, and neurocognitive characteristics, along with continuous glucose monitoring (CGM) measures. Research design and methods Data from a case–control study involving OAs recruited from the T1D Exchange Clinical Network were analyzed. The random forest machine learning algorithm was used to elucidate the characteristics associated with case versus control status and their relative importance. Models with successively rich characteristic sets were examined to systematically incorporate each domain of possible risk characteristics. Results Data from 191 OAs with type 1 diabetes (47.1% female, 92.1% non-Hispanic white) were analyzed. Across models, hypoglycemia unawareness was the top characteristic associated with SH history. For the model with the richest input data, the most important characteristics, in descending order, were hypoglycemia unawareness, hypoglycemia fear, coefficient of variation from CGM, % time blood glucose below 70 mg/dL, and trail making test B score. Conclusions Machine learning may augment risk stratification for OAs by identifying key characteristics associated with SH. Prospective studies are needed to identify the predictive performance of these risk characteristics

Nutritional Status, Dietary Intake, and Nutrition-Related Interventions Among Older Adults With Type 1 Diabetes: A Systematic Review and Call for More Evidence Toward Clinical Guidelines.

There is an emerging population of older adults (≥65 years) living with type 1 diabetes. Optimizing health through nutrition during this life stage is challenged by multiple and ongoing changes in diabetes management, comorbidities, and lifestyle factors. There is a need to understand nutritional status, dietary intake, and nutrition-related interventions that may maximize well-being throughout the life span in type 1 diabetes, in addition to nutrition recommendations from clinical guidelines and consensus reports. Three reviewers used Cochrane guidelines to screen original research (January 1993-2023) and guidelines (2012-2023) in two databases (MEDLINE and CENTRAL) to characterize nutrition evidence in this population. We found limited original research explicitly focused on nutrition and diet in adults ≥65 years of age with type 1 diabetes (six experimental studies, five observational studies) and meta-analyses/reviews (one scoping review), since in the majority of analyses individuals ≥65 years of age were combined with those age ≥18 years, with diverse diabetes durations, and also individuals with type 1 and type 2 diabetes were combined. Further, existing clinical guidelines (n = 10) lacked specificity and evidence to guide clinical practice and self-management behaviors in this population. From a scientific perspective, little is known about nutrition and diet among older adults with type 1 diabetes, including baseline nutrition status, dietary intake and eating behaviors, and the impact of nutrition interventions on key clinical and patient-oriented outcomes. This likely reflects the population's recent emergence and unique considerations. Addressing these gaps is foundational to developing evidence-based nutrition practices and guidelines for older adults living with type 1 diabetes

A genetic algorithm-based energy-aware multi-hop clustering scheme for heterogeneous wireless sensor networks

Background: The energy-constrained heterogeneous nodes are the most challenging wireless sensor networks (WSNs) for developing energy-aware clustering schemes. Although various clustering approaches are proven to minimise energy consumption and delay and extend the network lifetime by selecting optimum cluster heads (CHs), it is still a crucial challenge.Methods: This article proposes a genetic algorithm-based energy-aware multi-hop clustering (GA-EMC) scheme for heterogeneous WSNs (HWSNs). In HWSNs, all the nodes have varying initial energy and typically have an energy consumption restriction. A genetic algorithm determines the optimal CHs and their positions in the network. The fitness of chromosomes is calculated in terms of distance, optimal CHs, and the node's residual energy. Multi-hop communication improves energy efficiency in HWSNs. The areas near the sink are deployed with more supernodes far away from the sink to solve the hot spot problem in WSNs near the sink node.Results: Simulation results proclaim that the GA-EMC scheme achieves a more extended network lifetime network stability and minimises delay than existing approaches in heterogeneous nature.peer-reviewe