Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of digital mirror therapy for stroke-severe patients

Mirror therapy (MT), which is used in the existing stroke rehabilitation environment, has significant limitations for use with severe stroke patients. Since mirrors only reflect symmetrical movement, allowing a patient to observe precise asymmetrical movement is impossible. This study proposes a new MT system by developing a pyramid hologram technology that uses delayed motion to create realistic images. Significant differences, observed via electroencephalogram, were shown in all motor cortex channels immediately after the event in the delayed condition when compared to before the event (C3: p < 0.001; Cz: p < 0.001, C4: p < 0.001). The illusion of asymmetrical movement using the proposed system can be applied to severe stroke patients to increase the positive outcome of rehabilitation.
A Hybrid FNS Generator for Human Trunk Posture Control with Incomplete Knowledge of Neuromusculoskeletal Dynamics

The trunk movements of an individual paralyzed by spinal cord injury (SCI) can be restored by Functional Neuromuscular Stimulation (FNS), a technique that applies low-level current to motor nerves to activate the muscles generating torques, and thus, produce trunk motions. FNS can be modulated to control trunk movements. However, a stabilizing modulation policy (i.e., control law) is difficult to derive due to the complexity of neuromusculoskeletal dynamics, which consist of skeletal dynamics (i.e., multi-joint rigid body dynamics) and neuromuscular dynamics (i.e., a highly nonlinear, non-autonomous, and input redundant dynamics). Therefore, an FNS-based control method that can stabilize the trunk without knowing the accurate skeletal and neuromuscular dynamics is desired. This work proposed an FNS generator, which consists of a robust nonlinear controller (RNC) that provides stabilizing torque command and an artificial neural network (ANN)-based torque-to-activation (T-A) map to ensure that the muscle generates the stabilizing torque to the skeleton. Due to the robustness and learning capability of this control framework, full knowledge of the trunk neuromusculoskeletal dynamics is not required. The proposed control framework has been tested in a simulation environment where an anatomically realistic 3D musculoskeletal model of the human trunk was manipulated to follow a time-varying reference that moves in the anterior-posterior and medial-lateral directions. From the results, it can be seen that the trunk motion converges to a satisfactory trajectory while the ANN is being updated. The results suggest the potential of this control framework for trunk tracking tasks in a clinical application.
An Oceano-Acoustic Simulation Framework for the Design of Lagrangian Systems Drifting in Mesoscale and Sub-Mesoscale Currents

Sub-mesoscale currents are of great interest for oceanographers but unfortunately their observation is a very difficult task. Lagrangian systems can be used to monitor them and require the implementation of an acoustic signal processing chain leading to the localization of each node of that system. In order to help the design of the Lagrangian system prior to sea trials, a simulation framework coupling the results of an oceanographic model with a ray trace software is presented. To illustrate the benefit of this framework, an experimental set-up composed of 5 sources and 20 floats which are drifting for 15 days is analyzed. A dataset of 358,000 frequency responses reflecting sub-mesoscale dynamics is built up. From this dataset, relevant statistics are calculated to define the best transmission parameters of the acoustic sources and to choose the right ranging method. It turns out that using pseudo-random sequences and allocating the spectral resources with Code Division Multiple Access method is a relevant design in our context. Also, it appears that a non-coherent ranging processor gives the best performance.
Onboard Predictive Flocking of Quadcopter Swarm in the Presence of Obstacles and Faulty Robots

Achieving fluent flocking, similar to those observed in birds and fish, on robotic swarms in a desired direction while avoiding obstacles using onboard sensing and computation remains a challenge. In a previous study (Önür et al, Proc. of ANTS'2022), we proposed a predictive flocking model as a computationally efficient method to generate smoother and more robust motion of the swarm. In this study, we extend this model to achieve safe flocking in cluttered environments in the presence of faulty robots that get immobilized during flocking. Systematical evaluation of the model in simulation with different swarm sizes and different faulty robot ratios has shown that safe flocking can be achieved even when 40% of the robots malfunction during flocking. Finally, we validate the model on a swarm of five micro quadcopters using only onboard range and bearing sensors and computation in a distributed manner without any communication11Videos of the experiments are available here: https://www.youtube. com/playlist? list=PLY04vZs6xGr8U5Y8KWMD056ZRjaUVMh63.
How Does Aging Affect Whole-brain Functional Network Connectivity? Evidence from An ICA Method

Many studies have shown that changes in the functional connectivity are diverse along with aging. However, few studies have addressed how aging affects connectivity among large-scale brain networks, and it is challenging to examine gradual aging trajectories from middle adulthood to old age. In this work, based on large-sample fMRI data from 6300 subjects aged between 49 to 73 years, we apply an independent component analysis-based method called NeuroMark to extract brain functional networks and their connectivity (i.e., functional network connectivity (FNC)), and then propose a two-level statistical analysis method to explore robust aging-related changes in functional network connectivity. We found that the enhanced FNCs mainly occur between different functional domains, involving the default mode and cognitive control networks, while the reduced FNCs come from not only between different domains but also within the same domain, primarily relating to the visual network, cognitive control network and cerebellum. Our results emphasize the diversity of brain aging and provide new evidence for non-pathological aging of the whole brain.Clinical Relevance—This provides new evidence for non-pathological aging of functional network connectivity in the whole brain.
A study on dog bite incidence in Union Territory of Dadra & Nagar Haveli, India

The injuries caused by dog bites have grown to such epidemic proportions that they are now considered a major public health concern. The epidemiology of dog bites in the UT of Dadra & Nagar Haveli has been evaluated, for the year 2012. This assessment included victims age, sex, biting site and season of the year. During the study, a total of 938 dog bites, were reported from aforesaid place. Of all the dog bites reported, 39.6% victim belonged to age group upto 0-15 year (children) and 60.4% were more than 15 years old (adults). The majority of the incidents 40.8% occurred during the pre monsoon period (1-20 weeks). There was no case of rabies. There is a need to educate the public about the magnitude of dog-bite problems, enforce leash laws and impound stray dogs as an integral part of prevention programs.
Collision-Aware Data Delivery Framework for Connected Vehicles via Edges

With the rapid advancements in communication technologies, the paradigm of connected vehicles is drastically transforming the automotive industry, enabling efficient data and service delivery to vehicles via edges. Various works have considered data delivery without a Medium Access Layer (MAC), which can result in multiple data frame collisions in the network. The time-slot-based MAC layer strategy uses slot assignment to ensure collision-free data delivery for multiple vehicles across various transmission channels at each edge. However, the increasing requests from various vehicular nodes can increase network congestion, thus servicing fewer vehicles. In the current work, we propose an optimization framework for collision-aware data delivery considering two state-of-art MAC protocols, HCCA and VeMAC. The proposed framework minimizes the global slot utilization cost for edge-to-vehicle data delivery, considering vehicle flow, edge resources, and vehicle overlaps while avoiding possible data transmission collisions. Further, we demonstrate the practicality of the framework in terms of the number of vehicles served, global slot utilization cost, and bandwidth cost. We further analyze the framework with differences in vehicle densities for various problem sizes using a real-world traffic scenario.
An Instrument for High-throughput Testing of Heart Tissue In Vitro

Cardiac trabeculae are small samples of heart muscle tissue that can be dissected and studied in vitro to better understand the underlying physiology of cardiac muscle. However, instruments for such experimentation often (1) involve delicate mounting of the muscle, (2) constrain investigations to one muscle at a time and, thus, (3) cannot retain the muscle in the same experimental configuration for post-experimental assessment including imaging analysis. Here, we present a novel device that allows trabeculae to be secured by a visible-light photo-initiated hydrogel, manipulated via a force sensor, and stimulated while being imaged. We use our robust, accurate image registration techniques to measure cantilever and gel deformation during trabecula contraction and thereby provide a measure of trabecula force production during twitches. A variety of experiments can then be conducted, with the potential for the trabecula to be fixed in place using hydrogel for further post-experiment analysis, as well as longitudinal evaluation. The device has multiple wells making it amenable to high-throughput testing.Clinical Relevance— These methods may allow longitudinal and high-throughput studies of cardiac tissue samples in health and disease.
Spatio-Temporal Patterns of Soil Respiration and the Age of Respired Carbon from High-Elevation Alpine Tundra

Fossil fuel combustion is increasing atmospheric carbon dioxide (CO2), which has augmented the natural greenhouse effect, and is likely to increase flooding, permafrost degradation, hurricanes, and loss of sea-ice and glaciers over at least the next 1,000 years. Since feedbacks between the carbon cycle and the climate system are critical for projecting climate change, it is necessary to constrain the biotic response to climate change in order to accurately forecast the consequences of ongoing CO2 emissions. To advance the understanding of carbon cycling in the mountains, this research characterized the spatio-temporal variability of soil respiration across a broadly representative snow-scoured alpine tundra soil moisture and vegetation gradient, within the footprint of ongoing eddy covariance measurements at Niwot Ridge, Colorado, USA. During the growing season, moisture limitation superseded temperature limitation to soil respiration, and increasing soil moisture invoked a bidirectional soil respiration response from areas of fellfield and dry meadow tundra (directly proportional) compared to moist and wet meadow tundra (inversely proportional). Throughout the winter, soil respiration was restricted to wet meadow locations, and the magnitude of over-winter soil respiration from these areas was comparable to the upper range of carbon respired by seasonally snow-covered soils. These data were corroborated by continuous eddy covariance-based net ecosystem exchange measurements, which showed that the alpine tundra lost an average of 232 g C m to
Quantum Wireless Imaging and Remote Sensing - State-of-the-Art Technologies and Opportunities

Quantum entanglement that does not exist in classic world suggests disruptive quantum i$m$age/sig$n$al processing and wireless sensing techniques beyond the frontier of classic image/signal processing and sensing. By showing the fundamental difference between classic and quantum techniques, this paper comprehensively explains the principles and mechanisms of quantum wireless ghost imaging, quantum radar, and secure quantum remote sensing, with suggestions toward future technological opportunities in communication and signal processing.
Blind Self-Interference Canceller with Adaptive Differential Delay for IBFD in the Presence of Fractional Delay Path

This paper proposes a novel blind self-interference (SI) canceller with adaptive differential delay for in-band full duplex (IBFD) communications, even in the presence of a fractional delay path. As an SI cancellation method, active SI canceller (ASIC) that generates and subtracts a replica of the SI signal from the received signal has been proposed. However, the replica generation requires accurate and periodic channel state information (CSI) acquisition, causing system performance degradation due to increased communication overhead. To avoid this inconvenience, differential active SI canceller (DASIC) without prior channel estimation of SI has been proposed. However, due to the effects of a fractional delay path, DASIC cannot completely cancel SI, resulting in residual SI. The proposed method adaptively controls the differential delay to eliminate the residual SI, according to the occurrence pattern of its own transmission signal. In addition, an appropriate maximum a-posteriori probability (MAP) detector is designed based on the DASIC outputs. Finally, computer simulations confirm the efficacy of the proposed method with and without error correction code (ECC) in terms of bit error rate (BER) performance.
Developing Technical Skillsets in Diverse Audiences

Designing courses that are taken by diverse student audiences is complicated by differing technical backgrounds and desired future roles. One proposed approach to course design bundles course assignments into parallel tracks for students to self-select and complete. An initial set of three tracks of computing laboratory-based assignments were developed and tested in the classroom. Each track option utilized different visualization software and libraries with a range of expected pre-requisite knowledge and technical abilities. Student and instructor reflections on the approach reinforced the suitability of a track-based course design to fulfill course objectives towards the education of diverse audiences.
Bi-Component Silicone 3D Printing with Dynamic Mix Ratio Modification for Soft Robotic Actuators

Pneumatically operated soft actuators are increasingly researched due to their fabrication simplicity, actuation capabilities, and low production cost. Depending on the Soft Pneumatic Actuator (SPA) objective, its design can be modified to reach new bending angles or increase its actuation strength. However, increasing the abilities of Soft Pneumatic Actuators (SPAs) requires increasing the complexity of their air cavities or using multiple materials with different mechanical stiffness. Both solutions complexify the fabrication of SPAs, reducing their primary benefits of manufacturing simplicity and low production cost. This paper presents a novel additive manufacturing fabrication process incorporating multiple mechanical stiffnesses using a single bicomponent soft material. This process aims to integrate multiple bending angles with multi-channel SPAs without increasing their manufacturing complexity. Our process uses a dynamic modification of the bi-component silicone mix ratio to generate the desired mechanical properties of the material. Modifying the mix ratio allows us to control the material's cure time and mechanical properties, such as its final stiffness. We found that using a single 30 shore-A bi-component silicone, we could achieve several stiffness values with different reticulation times and levels of stickiness. Using these shore ranges and our fabrication process, we built several SPAs. We explored how the printing orientation of the SPAs modifies its bending actuation using our fabrication process to illustrate the capabilities of our approach.
An Energy-Efficient Lane-Keeping System Using 3D LiDAR Based on Spiking Neural Network

Lane keeping, as a fundamental functionality of autonomous navigation, remains a challenging task for autonomous robots and vehicles. Recently, spiking neural networks (SNNs) have gained attention and research interest due to their biological plausibility and application potential on neuromorphic processors. SNNs have also been successfully deployed on robots to solve autonomous navigation problems. However, lane keeping with a LiDAR sensor is still an open problem for SNNs. In this work, we propose an end-to-end approach based on an SNN to solve the lane-keeping problem using a 3D LiDAR sensor. For the first time, we explore the capability of the proposed SNN controller to perceive the LiDAR input and exploit the features to perform reward-based feedback learning. To ensure the effectiveness of the controller, the proposed method is deployed and evaluated on two high-fidelity simulators. The experimental results demonstrate the high applicability and performance in different scenarios. Furthermore, experiments show that the SNN is capable of performing lane keeping in a simulated urban environment with only 18 control neurons and 32 synapse connections, producing on average only a 17cm deviation from lane center, which is 4.3 % of the lane width.
Light-Field Visual System for the Remote Robot Operation Interface

Robotic automation is expected to be applicable in various fields. The utilization of robots requires human-robot interaction (HRI) for prolonged direct manipulation or learning. Recently, numerous studies on HRI have been conducted in virtual space using virtual, augmented, and mixed reality (VAM-HRI). In the future, VAM-HRI applications are expected to involve users wearing head-mounted displays (HMDs). However, HMDs present various problems, such as vergence and accommodation conflict (VAC) caused by eye strain, and thus cannot be used for long periods. In this study, a remote robot operation interface is developed to solve this problem. Experiments are conducted from the viewpoint of ophthalmology to determine whether “removal of VAC” and “improvement of depth perception” by Light Field HMDs (LFHMD), and “reduction of eye strain” by TransRay are feasible. The results reveal that the LFHMD is superior to the general HMDs for all items.
Analysing the contributions of lower limb muscles to eccentric cycling using musculoskeletal modeling and simulation

Eccentric (ECC) cycling, compared to traditional concentric cycling, has been shown to improve muscle strength and neuromuscular control at a lower metabolic cost. Despite the popularity of this exercise in the sports and rehabilitation contexts, there is a gap in our knowledge of which muscles are behaving eccentrically during ECC cycling. To this end, we used a musculoskeletal model and computer simulations to calculate joint kinematics and muscle lengths during ECC cycling. Movements were recorded using 3D motion capture technology while cycling eccentrically on a custom-built semi-recumbent ergometer. The software Opensim was used to calculate joint kinematics and muscle lengths from recorded movements. We found that among the primary knee extensors, it was predominantly the Vastii muscles that acted eccentrically in the ECC cycling phase, with other lower limb muscles showing mixed eccentric/concentric activation. Additionally, the muscle force-length and force-velocity factors in the ECC phase suggest that changes to the participant’s pose and pedaling speed may elicit larger active muscle forces. Our work provides an interesting application of musculoskeletal modeling to ECC cycling, and an alternative way to help understand in-vivo muscle mechanics during this activity.
A Machine Learning Approach for Sex and Age Classification of Paediatric EEGs

Electroencephalography (EEG) is an important investigation of childhood seizures and other brain disorders. Expert visual analysis of EEGs can estimate subjects’ age based on the presence of particular maturational features. The sex of a child, however, cannot be determined by visual inspection. In this study, we explored sex and age differences in the EEGs of 351 healthy male and female children aged between 6 and 10 years. We developed machine learning-based methods to classify the sex and age of healthy children from their EEGs. This preliminary study based on small EEG numbers demonstrates the potential for machine learning in helping with age determination in healthy children. This may be useful in distinguishing developmentally normal from developmentally delayed children. The model performed poorly for estimation of biological sex. However, we achieved 66.67% accuracy in age prediction allowing a 1 year error, on the test set.
Multi-class Tissue Segmentation of CT images using an Ensemble Deep Learning method

Microwave ablation (MWA) therapy is a well-known technique for locally destroying lung tumors with the help of computed tomography (CT) images. However, tumor recurrence occurs because of insufficient ablation of the tumor. In order to perform an accurate treatment of lung cancer, there is a demand to determine the tumor area precisely. To address the problem at hand, which involves accurately segmenting organs and tumors in CT images obtained during MWA therapy, physicians could benefit from a semantic segmentation method. However, such a method typically requires a large number of images to achieve optimal results through deep learning techniques. To overcome this challenge, our team developed four different (multiple) U-Net based semantic segmentation models that work in conjunction with one another to produce a more precise segmented image, even when working with a relatively small dataset. By combining the highest weight value of segmentation from multiple methods into a single output, we can achieve a more reliable and accurate segmentation outcome. Our approach proved successful in segmenting four different tissue structures, including lungs, lung tumors, and ablated tissues in CT medical images. The Intersection over Union (IoU) is employed to quantitatively evaluate the proposed method. The method shows the highest average IoU, with 0.99 for the background, 0.98 for the lung, 0.77 for the ablated, and 0.54 for the tumor tissue. The results show that employing multiple DL methods is superior to that of individual base-learner models for all four different tissue structures, even in the presence of the relatively small dataset.Clinical relevance— An essential issue of tumor ablation therapy is to know when the entire tumor tissue has completely been destroyed. However, as it is difficult to distinguish between destroyed and living tumor, this is hardly reliable in clinical practice during MWA therapy, especially when working with a small dataset. Improved AI segmentation methods can help to improve performance to reduce recurrence