Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards CMOS Bulk Sensing for In-Situ Evaluation of ALD Coatings for Millimeter Sized Implants

To meet the dimensional requirements for bioelectronic medicine, new packaging solutions are needed that could enable small, light-weight and flexible implants. For protecting the implantable electronics against biofluids, recently various atomic layer deposited (ALD) coatings have been proposed with high barrier properties. Before implantation, however, the protective coating should be evaluated for any defects which could otherwise lead to leakage and device failure. In these cases, the conventional helium leak test method can no longer be used due to the millimeter size of the implant. Therefore, an in-situ sensing platform is needed that could evaluate the coating and justify the implantation of the final device. In this work, we explore the possibility of using the CMOS bulk for such a platform. Towards this aim, as a proof of concept, test chips were made in a standard 6-metal 0.18 µm CMOS process and for the connection to the bulk, a p+ diffusion was used. A group of samples was then coated with an ALD multilayer. For coating evaluation, off-chip DC current leakage and impedance measurements were carried out in saline between the CMOS bulk and a platinum reference electrode. Results were compared between non-coated and coated chips that clearly demonstrated the potential of using the bulk as a sensing platform for coating evaluations. This novel approach could pave the way towards an all integrated in-situ hermeticity test, currently missing in mm-size implants.
Design and Preliminary Implementation of an Air Microfluidics Enabled Soft Robotic Knee Brace Towards the Management of Osteoarthritis*

A dynamic and low-profile unloader tibiofemoral knee brace is designed and prototyped by synergizing concepts from the fields of microfluidics and soft robotics. Microfluidics provides strategies for miniaturization and multiplexing while soft robotics afford the tools to create soft fluidic actuators and allow compliant and inherently safe robotic assistance as part of clothing. The unloader knee brace provides dynamic response during the gait cycle, where a three-point leverage torque is provided only during the stance phase to contribute to joint stability when required and enhance comfort and compliance.Clinical Relevance— This novel soft robotic brace has the potential to reduce device abandonment due to aesthetics, user non-compliance and discomfort due to a constant three-point leverage torque during the gait cycle. Also, this air microfluidics enabled soft robotic knee brace could be expanded upon to improve the efficacy of braces in general and augment the effects of physical therapy, rehabilitation and treatment of musculoskeletal conditions
Lung CT Image Registration through Landmark-constrained Learning with Convolutional Neural Network

Accurate registration of lung computed tomography (CT) image is a significant task in thorax image analysis. Recently deep learning-based medical image registration methods develop fast and achieve promising performance on accuracy and speed. However, most of them learned the deformation field through intensity similarity but ignored the importance of aligning anatomical landmarks (e.g., the branch points of airway and vessels). Accurate alignment of anatomical landmarks is essential for obtaining anatomically correct registration. In this work, we propose landmark constrained learning with a convolutional neural network (CNN) for lung CT registration. Experimental results of 40 lung 3D CT images show that our method achieves 0.93 in terms of Dice index and 3.54 mm of landmark Euclidean distance on lung CT registration task, which outperforms state-of-the-art methods in registration accuracy.
Manipulating The Body Representation: Assessment Of A Novel Platform

The Rubber Hand Illusion can be used to induce the illusion that a fake hand is part of one’s own body. Thus, it can be used to alter the body representation. It was also reported that the Rubber Hand Illusion induces a proprioceptive drift of one’s real hand toward the fake hand. The Rubber Hand Illusion can be induced when the fake hand is placed farther in the sagittal plane (distally) compared to the real hand. In this case, the induced update of the body representation is an elongation of the arm. Virtual Reality and haptic technologies can be used to manipulate the perceived scenario in a virtual version of the Rubber Hand Illusion, the Virtual Hand Illusion. We developed a novel platform consisting in a virtual reality application integrating an optical motion capture device and haptic stimulators to study the manipulation of the body representation. We developed two experimental protocols to induce embodiment of an elongated arm: one validated in previous studies, that employs congruent visuo-motor-tactile stimulation, and one reproducing the typical Virtual Hand Illusion where only congruent visuo-tactile stimulation was employed. We tested both protocols with healthy participants.
Haptic Coupling in Dyads Improves Motor Learning in a Simple Force Field

In dyadic motor learning, pairs of people learn the same motion while their limbs are loosely coupled together using haptic devices. Such coupled learning has been shown to outperform solo learning (including robot-guided learning) for simple one-degree-of-freedom tasks. However, results from more complex tasks are limited and sometimes conflicting. We thus evaluated coupled learning in a two-degree-of-freedom tracking task where participants also had to compensate for a simple force field. Participant pairs were split into two groups: an experiment group that experienced a compliant haptic coupling between participants’ hands and a control group that did not. The study protocol consisted of 70 repetitions of 18.9-second tracking trials: 10 initial solo trials with no coupling, 50 "learning" trials (where participants in the experiment group were coupled), and 10 final solo trials with no coupling. The experiment group (coupled) improved their solo tracking performance both in the presence (p = 0.008) and absence (p < 0.001) of the force field; however, the control group (no coupling) only improved their solo performance in the absence of the force field (p < 0.001) but not in the presence of the field (p = 0.81). This suggests that dyadic motor learning can outperform solo learning for two-dimensional tracking motions in the presence of a simple force field, though the mechanism by which learning is improved is not yet clear.Clinical Relevance—As motor learning is critical for applications such as motor rehabilitation, dyadic training could be used to achieve a better overall outcome and a faster learning speed in these applications compared to solo training.
Crack Detection with Multi-task Enhanced Faster R-CNN Model

To solve the low accuracy problem when dealing with multiple or small objects detection, a Multi-task Enhanced crack detection based on Faster R-CNN (ME-Faster R-CNN) was proposed in the dam safety monitoring. ME-Faster R-CNN adopts the Multi-source Adaptive Balance TrAdaBoost based on K-means (K-MABtrA) to enhance the small-scale samples. ME-Faster R-CNN uses RestNet-50 network to extract feature and apply multi-task enhanced RPN module to generate the candidate regions through adopting the appropriate size and dimension of anchor box. The features map and candidate regions are processed to detect the dam cracks. Experimental results demonstrate that the proposed ME-Faster R-CNN with K-MABtrA method can obtain 82.52% average IoU and 80.02% average precision mAP, respectively. Compared with Faster R-CNN with the same parameters, the average IoU and mAP can increase 1.06% and 1.56%, respectively.
Blind Estimation of Central Blood Pressure Using Least-Squares with Mean Matching and Box Constraints

Central aortic blood pressure (CABP) is a very-well recognized source of information to asses the cardiovascular system conditions. However, the clinical measurement protocol of this pulse wave is very intrusive and burdensome as it requires expert staff and complicated invasive settings. On the other hand, the measurement of peripheral blood pressure is much more straightforward and easy-to-get non-invasively. Several mathematical tools have been employed in the past few decades to reconstruct CABP waveforms from distorted peripheral pressure signals. More specifically, the cross-relation approach together with the widely used least-squares method, are shown to be effective as a way to estimate CABP waves. In this paper, we propose an improved cross-relation method that leverages the values of the diastolic and systolic pressures as box constraints. In addition, a mean-matching criterion is introduced to relax the need for the input and output mean values to be strictly equal. Using the proposed method, the root mean squared error is reduced by approximately 20% while the computational complexity is not significantly increased.
Guided Image Filtering Reconstruction Based on Total Variation and Prior Image for Limited-Angle CT

For limited-angle computed tomography (CT) image reconstruction, the classical total variation (TV) based algorithms suffer from the limited-angle artifacts, because TV only used the gradient information of the image. The priori image constrained compressed sensing (PICCS) based reconstruction algorithms can reduce the limited-angle artifacts by using a priori image consistent with the target image. However, it is difficult to ensure the consistency of priori image and target image in practice. In order to reconstruct high quality image when the prior image is inconsistent with the target image, we proposed a guided image filter reconstruction based on TV and prior image (TVPI-G). In each iteration phase, our algorithm first performs a TV step (include simultaneous algebra reconstruction technique (SART) and TV) to get initiatory reconstruction image; Then, the results of TV iteration are combined with prior images to form an intermediate result; Finally, we use the guided image filter to modify the intermediate results with the TV result as the guide image. Numerical reconstruction results on simulation phantom with different intensities Poisson noise illustrates that our proposed TVPI-G algorithm is better than other comparison algorithms in both qualitative and quantitative aspects, including TV, PICCS, and SART guided image filtering.
Regularized Hard Thresholding Pursuit (RHTP) for Sparse Signal Recovery

Hard thresholding pursuit (HTP) is a recently proposed iterative sparse recovery algorithm which is a result of combination of a support selection step from iterated hard thresholding (IHT) and an estimation step from the orthogonal matching pursuit (OMP). HTP has been seen to enjoy improved recovery guarantee along with enhanced speed of convergence. Much of the success of HTP can be attributed to its improved support selection capability due to the support selection step from IHT. In this paper, we propose a generalized HTP algorithm, called regularized HTP (RHTP), where the support selection step of HTP is modified by replacing the cost function involved in the support selection with a regularized cost function. With decomposable regularizer, satisfying certain regularity conditions, the RHTP algorithm is shown to produce a sequence dynamically equivalent to a sequence evolving according to a HTP-like evolution, where the identification stage has a gradient premultiplied with a time-varying diagonal matrix. RHTP is also proven, both theoretically, and numerically, to enjoy faster convergence visa-vis HTP with both noiseless and noisy measurement vectors.
Power Quality Enhancement in Hybrid AC/DC Microgrid System

Due to the increasing deployment of different types of electricity generation and the grid has caused great concern with existing AC systems. Most of the micro-network with DGs and renewable energy sources worked in parallel with the main AC networks. Since most electrical networks are currently of the AC type, the AC micro-networks are till now dominanting one and the purely DC micro-networks should not emerge exclusively in electrical networks. Given the benefits about DC micro-networks, the conventional AC distribution network can be implemented using a DC micro-network. This can be justified by creation of an AC / DC hybrid micro-network. In this paper, a new generalised solutions are presented for the improvement energy quality and for compensating the reactive power exciting in an AC microgrid. The methodology is to combine the both AC and DC micro-networks through the power electronic based bidirectional AC / DC converters and to establish an hybrid AC / DC micro-network the power can flow evenly between the two microgrids. In this paper, an overview of power quality analysis and improvement of hybrid AC/DC microgrid system is presented. First hybrid AC/DC structure, power quality problems and its compensation in hybrid AC/DC system, power electronic converter interfacing for power quality control and then smart interfacing units for harmonic compensation are analyzed. A thorough review of interfacing converters control for power quality compensations in hybrid system for unbalance and harmonic compensations are also presented.
Minimizing Hybrid Dice Loss for Highly Imbalanced 3D Neuroimage Segmentation

Recent advances in medical image segmentation have largely been driven by the success of deep learning algorithms. However, one main challenge for the training of one- stage segmentation networks is the serious imbalance between the number of examples that are easy and hard to classify or in positive and negative classes. In this paper, we first investigate and compare the strategies that were proposed parallelly to handle one or two of these imbalance problems. And we propose a hybrid loss that addresses these two imbalance problems together by combining the merits of Exponential logarithmic Dice and weighted Cross entropy Loss (EDCL). Without any whistles and bells, the proposed EDC loss with 3D Unet achieves mean dice of 57.38%, which surpasses the other state-of-the- art methods with 5-fold cross-validation on a public dataset for 3D brain lesion segmentation, Anatomical Tracings of Lesions After Stroke (ATLAS) v1.2.
Onset detection of Tabla Strokes using LP Analysis

Onset detection is an important first step in music analysis. We propose a pre-processing scheme for improved onset detection of complex strokes of Indian percussion instruments with resonance characteristics. In this work, we explore the onset detection of Tabla (Indian percussion instrument) strokes. The resonance characteristics of tabla strokes poses challenges to onset detection. In such cases, the energy-based and spectral flux-based onset detectors are often inaccurate on the raw signal. We propose an onset detection algorithm addressing these challenges using Linear Prediction (LP) analysis and Hilbert envelope (HE) in tandem. Tabla signal is modeled using LP, and its residual highlights the onset time instances very well. Unipolar nature of HE on top of LP residual further enhances the onset instances. Onset detection is performed using energy based, spectral flux based and the state of the art Machine Learning based onset detectors on the Hilbert envelope of LP residual (HELP). Experiments were performed on tabla solo played at various tempi and the results show that the HELP based approach provides 12% relative improvement in F-measures compared to the performance on raw tabla signal.
6-Axis Force/Torque Sensor With a Novel Autonomous Weight Compensating Capability for Robotic Applications

Force/Torque(F/T) sensing technology enables a dexterous robot control such as direct teaching, master-slave system, and pick-and-place task. In general, 6-axis F/T sensor is attached to the end-effector of the robot manipulator to assist in utilizing advanced robot systems. However, in actual applications, various tools such as robotic grippers, robotic hand, grinders are attached to the sensor and it causes F/T offsets with respect to the gravity. In this letter, Autonomous Weight Compensating(AWC) technique for 6-axis F/T sensor is presented. The proposed AWC technique can reduce the F/T offsets by estimating the F/T offsets through installed Inertial Measurement Unit(IMU) sensor. In this study, the 6-axis F/T are measured based on capacitance sensing scheme and to estimate the orientation of the sensor, a 9-axis IMU sensor is installed inside of the sensor. Then, the F/T offsets are calibrated via Artificial Neural Network(ANN) model. Finally, the performance of the proposed method is demonstrated through comparing the F/T data with both trained data and untrained data.
Unobtrusive Inter-beat Interval Estimation from Multichannel Ballistocardiogram Signal Using Kalman Filter

Unobtrusively detecting inter-beat interval (IBI) from ballistocardiogram (BCG) is useful for monitoring cardiac activity at home, especially for calculating heart rate variability (HRV), the critical indicator to evaluate heart health. Compared to single-sensor system in most studies, this research used a bed-embedded 9 by 2 array sensors system to improve measurement coverage and precision of IBI estimation. Based on this system, we proposed a mode-switch based algorithm to solve the problem on array sensor signal selection and multichannel data fusion using linear regression model and Kalman filter. In addition, a peak detection algorithm was designed to estimate IBI from each channel signal. The algorithm was validated by approximately 48 hours BCG recordings captured from 24 subjects with different sleeping positions. A mean absolute error of 31ms at 83% average coverage was obtained by the proposed method, which has proven to be a promising candidate for IBI estimation from BCG signal on multichannel array sensors system.
Adaptive Brain-Computer Interface with Attention Alterations in Patients with Amyotrophic Lateral Sclerosis

The users’ mental state such as attention variations can have an effect on the brain-computer interface (BCI) performance. In this project, we implemented an adaptive online BCI system with alterations in the users’ attention. Twelve electroencephalography (EEG) signals were obtained from six patients with Amyotrophic Lateral Sclerosis (ALS). Participants were asked to execute 40 trials of ankle dorsiflexion concurrently with an auditory oddball task. EEG channels, classifiers and features with superior offline performance in the training phase of the classification of attention level were selected to use in the online mode for prediction the attention status. A feedback was provided to the users to reduce the amount of attention diversion created by the oddball task. The findings revealed that the users’ attention can control an online BCI system and real-time neurofeedback can be applied to focus the attention of the user back onto the main task.
Interphase Cell Cycle Staging using Deep Learning *

The progression of cells through the cell cycle is a tightly regulated process and is known to be key in maintaining normal tissue architecture and function. Disruption of these orchestrated phases will result in alterations that can lead to many diseases including cancer. Regrettably, reliable automatic tools to evaluate the cell cycle stage of individual cells are still lacking, in particular at interphase. Therefore, the development of new tools for a proper classification are urgently needed and will be of critical importance for cancer prognosis and predictive therapeutic purposes. Thus, in this work, we aimed to investigate three deep learning approaches for interphase cell cycle staging in microscopy images: 1) joint detection and cell cycle classification of nuclei patches; 2) detection of cell nuclei patches followed by classification of the cycle stage; 3) detection and segmentation of cell nuclei followed by classification of cell cycle staging. Our methods were applied to a dataset of microscopy images of nuclei stained with DAPI. The best results (0.908 F1-Score) were obtained with approach 3 in which the segmentation step allows for an intensity normalization that takes into account the intensities of all nuclei in a given image. These results show that for a correct cell cycle staging it is important to consider the relative intensities of the nuclei. Herein, we have developed a new deep learning method for interphase cell cycle staging at single cell level with potential implications in cancer prognosis and therapeutic strategies.
Comparing Spatially Distributed and Single Electrode Stimulation on Individuals with Spinal Cord Injury

It is still a challenge to delay the onset of fatigue on muscle contraction induced by Functional Electrical Stimulation (FES). We explored the use of two stimulation methods with the same total area, single electrode stimulation (SES), and spatially distributed electrical stimulation (SDSS) during isometric knee extension with spinal cord injured (SCI) volunteers. We applied stimulation on the left and right quadriceps of two SCI participants with both methods and recorded isometric force and evoked electromyography (eEMG). We calculated the force-time integral (FTI) and eEMG-time integral (eTI) for each stimulation series and used a linear regression as a measure of decay ratio. Moreover, we also estimated the contribution from each channel from eEMG.
Time-Multiplexed Illumination for simultaneous Structural and Functional Voltage Sensitive Dye Recordings with a single Photo Sensor

The in-vivo optical imaging of the cortical surface provides the ability to record different types of biophysiological signals, e.g., structural information, intrinsic signals, like blood oxygenation coupled reflection changes as well as extrinsic properties of voltage sensitive probes, like fluorescent voltage-sensitive dyes. The recorded data sets have very high temporal and spatial resolutions on a meso– to macroscopic scale, which surpass conventional multi-electrode recordings. Both, intrinsic and functional data sets, each provide unique information about temporal and spatial dynamics of cortical functioning, yet have individual drawbacks. To optimize the informational value it would thus be opportune to combine different types of optical imaging in a near simultaneous recording.Due to the low signal-to-noise ratio of voltage-sensitive dyes it is necessary to reduce stray light pollution below the level of the camera’s dark noise. It is thus impossible to record full-spectrum optical data sets. We address this problem by a time-multiplexed illumination, bespoke to the utilized voltage sensitive dye, to record an alternating series of intrinsic and extrinsic frames by a high-frequency CMOS sensor. These near simultaneous data series can be used to compare the mutual influence of intrinsic and extrinsic dynamics (with regards to extracorporeal functional imaging) as well as for motion compensation and thus for minimizing frame averaging, which in turn results in increased spatial precision of functional data and in a reduction of necessary experimental data sets (3R principle).
Preparation of Size-controlled Giant Vesicles Under Physiological Conditions

Giant vesicles (GVs) are model cell membranes that function as tools for the study of cell membrane properties. Recently, researchers have been calling for GVs of specific sizes for use in studies with precise needs. In this paper, we report a method of forming GVs of specific sizes by using an agarose-swelling approach. The resulting GVs had a narrow size distribution and were successfully formed under physiological conditions.