Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of an Electrostatic Oral Cavity Generator Driven by Occlusal Force*

Mouthguard type sensors have been developed to monitor the healthcare-related information, which is expected to be used for pre-illness and preventive medicine. However, a method for supplying the power to these sensors is still an issue to be solved. In this paper, we propose an electrostatic oral cavity generator driven by occlusal force to supply the power for mouthguard type sensors. The proposed generator is a sheet form and consists of the lamination of the electret, dielectric elastomer and copper electrode. In this paper, we estimated the power generation with a piezoelectric generator and the proposed electrostatic generator. Then, the generators were designed and fabricated. The generated power of the prototype electrostatic generator was 18.0 μW. It was about 560 times larger than that of a piezoelectric generator with the area equivalent to four pairs of molars. Although the generated power was still smaller than the required power to drive the mouthguard sensor, we considered they can be comparable with each other by optimizing the design parameters of the generator.
Low Complexity CNN Structure for Automatic Bleeding Zone Detection in Wireless Capsule Endoscopy Imaging

Wireless capsule endoscopy (WCE) is a swallowable device used for screening different parts of the human digestive system. Automatic WCE image analysis methods reduce the duration of the screening procedure and alleviate the burden of manual screening by medical experts. Recent studies widely employ convolutional neural networks (CNNs) for automatic analysis of WCE images; however, these studies do not consider CNN’s structural and computational complexities. In this paper, we address the problem of simplifying the CNN’s structure. A low complexity CNN structure for bleeding zone detection is proposed which takes a single patch as input and then outputs a segmented patch of the same size. The proposed network is inspired by the FCN paradigm with a simplified structure. Since it is based on image patches, the resulting network benefits from moderate-sized intermediate feature maps. Moreover, the problem of redundant computations in patch-based methods is circumvented by non-overlapping patch processing. The proposed method is evaluated using the publicly available KID dataset for WCE image analysis. Experimental results show that the proposed network has better accuracy and AUC than previous structures while requiring less computational operations.
Adaptive Weighted Nuclear Norm Minimization for Removing Speckle Noise from Optical Coherence Tomography Images

Low-rank matrix approximation is widely used in various fields of computer science, and weighted nuclear norm minimization (WNNM) has demonstrated improved results by shrinking the different weights of singular values. In this paper, an adaptive WNNM is proposed, considering the relative significance of image information by modifying the WNNM. As a result, singular values that contain more important information are relatively saved, whereas those that contain less crucial information are drastically reduced. When applying this method to noised image with black and white dot-noised images, the algorithm showed improved performance in both instances. Especially, when applied to images with white dot noise, the denoised results were outstanding. In addition, the proposed algorithm was successfully applied onto the optical coherence tomography images, numerically and visually.
Estimation of Time-Varying Ankle Joint Stiffness Under Dynamic Conditions via System Identification Techniques

An important goal in the design of next-generation exoskeletons and limb prostheses is to replicate human limb dynamics. Joint impedance determines the dynamic relation between joint displacement and torque. Joint stiffness is the position-dependent component of joint impedance and is key in postural control and movement. However, the mechanisms to modulate joint stiffness are not fully understood yet. The goal of this study is to conduct a systematic analysis on how humans modulate ankle stiffness. Time-varying stiffness was estimated for six healthy subjects under isometric, as well as quick and slow dynamic conditions via system identification techniques; specifically, an ensemble-based algorithm using short segments of ankle torque and position recordings. Our results show that stiffness had the lowest magnitude under quick dynamic conditions. Under isometric conditions, with fixed position and varying muscle activity, stiffness exhibited a higher magnitude. Finally, under slow dynamic conditions, stiffness was found to be the highest. Our results highlight, for the first time, the variability in stiffness modulation strategies across conditions, especially across movement velocity.
Offloading Design for Energy and Spectral Efficiencies Tradeoff in Massive MIMO Enabled Heterogeneous Cellular Networks

To fully utilize the system resources and enhance the user experience, two types of offloading mechanisms are designed for massive MIMO (multiple-input-multiple-out) enabled heterogeneous cellular networks (HCNs). Such mechanisms can achieve a tradeoff between energy efficiency (EE) and spectral efficiency (SE) experiences, but also have a difference on whether or not a power coordination measure is mentioned. At last, they are mathematically written as the network-wide utility maximization problems that are closely related to the SEs and EEs of associated users. In them, we introduce a crucial parameter $\alpha $ to adjust the EE-SE preference. It is noteworthy that the finally formulated problems are in some relatively complicated forms. To solve them, some necessary changes should be made at first, and then we can design some feasible algorithms. Specifically, we try to design a distributed algorithm for a mere offloading problem using dual decomposition (DD), and develop a two-layer iterative algorithm for the joint power coordination and offloading problem using DD and two-sided scalable (2.s.s.) function update. Regarding such these algorithms, we show the corresponding computation complexity and convergence analyses. In the simulation, we mainly investigate different network parameters on an EE-SE tradeoff for our advocated mechanisms and another existing mechanism.
Automatic screening of fundus images using a combination of convolutional neural network and hand-crafted features

Diabetic retinopathy (DR) and especially diabetic macular edema (DME) are common causes of vision loss as complications of diabetes. In this work, we consider an ensemble that organizes a convolutional neural network (CNN) and traditional hand-crafted features into a single architecture for retinal image classification. This approach allows the joint training of a CNN and the fine-tuning of the weights of handcrafted features to provide a final prediction. Our solution is dedicated to the automatic classification of fundus images according to the severity level of DR and DME. For an objective evaluation of our approach, we have tested its performance on the official test datasets of the IEEE International Symposium on Biomedical Imaging (ISBI) 2018 Challenge 2: Diabetic Retinopathy Segmentation and Grading Challenge, section B. Disease Grading: Classification of fundus images according to the severity level of diabetic retinopathy and diabetic macular edema. As for our experimental results based on testing on the Indian Diabetic Retinopathy Image Dataset (IDRiD), the classification accuracies have been found to be 90.07% for the 5-class DR challenge, and 96.85% for the 3-class DME one.
Novel metrics to measure the effect of additive inputs on the activity of sensory system neurons

Sensory systems, such as the visual or auditory system, are highly non linear. It is therefore not easy to predict the effect of additive inputs on the spiking activity of related brain structures. Here, we propose two metrics to study the effect of additive covariates on the spiking activity of neurons. These metrics are directly obtained from a generalized linear model. We apply these metrics to the study of the effect of additive input audio noise on the spiking activity of neurons in the auditory system. To do so, we combine clean vocalisations with natural stationary or non-stationary noises and record activity in the auditory system while presenting the noisy vocalisations. We found that non-stationary noise has a greater effect on the neural activity than stationary noise. We observe that the results, obtained using the proposed metrics, is more consistent with current knowledge in auditory neuroscience than the results obtained when using a common metric from the literature, the extraction index.
Automatic Classification for the Type of Multiple Synapse Based on Deep Learning

Recent studies have shown that the synaptic plasticity induced by development and learning can promote the formation of multiple synapse. With the rapid development of electron microscopy (EM) technology, we can closely observe the multiple synapse structure with high resolution. Although the multiple synapse has been widely researched by recent researchers, the classification accuracy for the type of multiple synapse has not been documented. In this paper, we propose an effective automatic classification method for the type of multiple synapse. The main steps are summarized as three parts: synaptic cleft segmentation, vesicle band segmentation, multiple synapse classification. The experiments on four datasets demonstrate that the proposed method can reach an average accuracy about 97%.
Tracking and line integration of diffuse cellular subdomains by mesh advection

Active molecular transport ensures a purposeful spatiotemporal distribution of cellular proteins and is therefore key to a wide range of processes such as morphogenesis, homeostasis or migration. However, redistributions of molecules in bulk are seldom quantified because the regions involved are too diffuse to be segmented consistently. To bridge this gap, we propose a Laplace-corrected Runge-Kutta advection that is based on mesh triangulation. Our framework can follow the movement and deformation of multiple parts of a diffuse region at once and offers a seamless combination with spatiotemporal line integration in Lagrangian coordinates. This allows the flexibility to taylor specific measures to the question at hand, e.g. mechanical work, bringing long-established physics concepts into biology grounds. We exemplify our approach by quantifying how the isotropy of intracellular protein distributions changes during cargo transport.
The association of temperature of Diabetic Foot Ulcers with Chronic Kidney Disorder

The area of the diabetic foot ulcer (DFU) and its reduction over weeks is used for assessment in clinical practices; however, literature reports that this is not reliable parameter. This work has investigated the association of change in the mean temperature of the ulcers with three clinical conditions relevant to wound healing, i.e. peripheral vascular disease (PVD), chronic kidney disease (CKD) and ischemic heart disease (IHD). Thermal and RGB images of 23 DFUs of the first two weeks of ulceration were studied. One-way ANOVA was performed on the change in mean temperature of the ulcers and change in area and it was found that the weekly change in mean temperature was higher for patients with CKD (p-value=0.009). Also, change in area measured from RGB images did not show any association with the clinical conditions. The application of this work is that the temperature obtained from thermal image of the ulcer can be used as a prognostic parameter for its assessment.
Fixing Network Security Vulnerabilities in Local Area Network

The system proposed in this paper can be used to identify, fix and report network security vulnerabilities over the intranet. Nowadays, more importance is given for fixing security vulnerabilities to protect the network from attackers, which can be either extruders or intruders, who can misuse the open access in various ways. This system is developed essentially for Network Administrator Linux/Windows users. This tool is capable of scanning various vulnerabilities in a network such as open ports (both physical and logical) and can secure the physical open ports using a set of both Telnet and SSH commands. We have automated the Telnet-SSH connection using Python, which is used for physical open ports vulnerability in this tool. This can prove to be useful for a person who is not familiar with the required commands. The tool can also be used in office or college network scenarios to enhance security.
Measurement of Interhemispheric Correlation Coefficient in Rodent Model of Middle Cerebral Artery Occlusion Using Near Infrared Spectroscopy*

Near-infrared spectroscopy (NIRS) is a noninvasive brain imaging technique that measures hemodynamics by determining the optical properties of tissue. Clinical potential of NIRS for monitoring cerebral hemodynamics in cerebrovascular diseases, such as stroke, has been studied. However, inconsistencies in measurements among studies, which are believed to be partly due to anatomical variance and diversity in disease presentation, limit the clinical feasibility of NIRS for stroke monitoring. In the present study, bihemispheric frequency-domain NIRS measurements on middle cerebral artery occlusion rats were performed. The discrepancy in interhemispheric synchronicity in hemodynamic oscillation appeared during the early reperfusion stage is related to the size of infarct that developed three days later. These NIRS parameters may have the potential to be early prognostic biomarkers for long-term stroke monitoring in the future translational investigation.
Toward Self-Adaptive Software Defined Fog Networking Architecture for IIoT and Industry 4.0

Industrial Internet of Things (IIoT) interconnects unconventional objects, such as sensors, actuators, robots, and control systems, with the information systems and the business processes to improve the operational efficiency and productivity. In IIoT, diverse, distributed and huge number of devices are collaborating and connecting over the Internet and the Cloud by generating a high and diverse data rate. In addition, industrial networks will be highly heterogeneous since it will connect heterogeneous devices through various communication technologies. Consequently, the industrial processes set new requirements such as reliability, scalability, and low latency that can not be managed by traditional technologies. The advent of Software Defined Networking (SDN) concept, by decoupling control and data planes, enables flexible and dynamic network architecture management by supporting horizontal scalability through distributed SDN controllers. Moreover, Fog computing is recently emerging as the best technology to provide local processing support with acceptable latency for IIoT devices. In this new rich evolving context, we propose an integration of SDN and Fog computing to provide a flexible and scalable solution granting low delays required by IIoT applications. More precisely, we present a novel architecture for IIoT based on SDN-Fog and then we detail the structure of our proposed Fog node enhanced by SDN. We also show some relevant experimental results that assess the performances of the proposed fog node in terms of latency and throughput.
Patient-Specific Debye Parameters for Human Blood

This paper develops a patient-specific model for the Debye parameters of human blood based on hemoglobin content. Blood samples were collected from 176 patients visiting the University Hospital, with both permittivity measurements and standard hematological analysis performed on each blood draw. The complete blood count of each sample provided information on the hemoglobin concentration of each sample; in total there were 73 distinct hemoglobin concentrations reported. An iterative process was used to find patient-specific, based on hemoglobin content, Debye parameters. First, a two-stage genetic algorithm was used to solve for the parameters of a two-pole Debye model based on the mean-blood properties. Then, a modified two-pole Debye model incorporating hemoglobin information was developed, and those parameters were solved for using the same two-stage genetic algorithm. This paper presents the parameters for both the mean-blood model and the patient-specific model. The patient-specific model has a mean-fractional error across all 73 samples of 3.41% compared to 7.64% when using the mean-blood model to represent the entire population. This work demonstrates the range in the dielectric properties of human blood samples and highlights the need for incorporating patient-specific information when using the Debye parameters to model the dielectric properties of human blood.
Model Predictive Control for Parallel Connected Three-Phase Four-Leg Inverters in Islanded AC Microgrids

This paper, a model predictive control (MPC) based primary control strategy is proposed for parallel three-phase four-leg inverters in islanded AC microgrids. To regulate the frequency and voltage in the AC microgrid, a cascaded control structure is used. The used structure consists of a droop control loop, a virtual impedance control loop, and the MPC based voltage control loop. Compared to traditional proportional-integral (PI) based cascaded control, the proposed technique offers faster dynamic response and better steady state operation. The performance of the proposed control system is validated by hardware-in-the-loop (HIL) simulation studies.
Resonant Properties of a T-sbape Aluminum Nitride Piezoelectric Structure for MEMS Device

This paper reports the resonant properties of aluminum nitride (AlN) thin-film MEMS (micro-electromechanical systems) device. A T-shape structure is designed as a key sensitive component. Based on the piezoelectric effect of AIN, the whole structure can be excited to vibrate in-plane by top/bottom electrodes. In this work, a pair of paralleled top electrodes are used to excite the whole structure to vibrate in-plane (x-axis). A serpentine electrode can excite the whole structure to vibrate in-plane along the orthogonal direction (y-axis). The resonant properties were characterized by the dynamic signal analyzer. Resonant frequencies of the two modes are 25.1124 kHz and 24.5401 kHz. Quality factors of the two modes are 2130 and 2210, respectively. The effective efficiency of electromechanical transduction is 0.7848%. The resonant modes can be applied in MEMS gyroscopes or resonant accelerometers.
Automated Pap Smear Cervical Cancer Screening Using Deep Learning

This study aims to apply Mask Regional Con-volutional Neural Network (Mask R-CNN) to cervical cancer screening using pap smear histological slides. Based on our current literature review, this is the first attempt of using Mask R-CNN to detect and analyze the nucleus of the cervical cell, screening for normal and abnormal nuclear features. The data set were liquid-based histological slides obtained from Thammasat University (TU) Hospital. The slides contained both cervical cells and various artifacts such as white blood cells, mimicking the slides obtained in actual clinical settings. The proposed algorithm achieved mean average precision (mAP) of 57.8%, accuracy of 91.7%, sensitivity of 91.7%, and specificity of 91.7% per image. As we needed to evaluate the efficiency of our algorithm in comparison to single cell classification algorithm (Zhang et al., IEEE JBHI, vol. 21, no. 6, pp. 1633, 2017), we modified our method to also classify single cells on TU dataset test using Mask R-CNN segmentation. The results obtained had an accuracy of 89.8%, sensitivity of 72.5%, and specificity of 94.3%.
Impedance-based Sensitivity Analysis of Dual Active Bridge DC-DC Converter

The sensitivity analysis of a circuit reflects the robustness of this circuit to the relevant parameters. This paper investigates the sensitivity of input impedance of DAB converter, which is widely adopted in solid-state transformer (SST) and dc microgrid on more electric aircraft (MEA). In this paper, an improved impedance-based small-signal averaged model is developed to carry out the sensitivity analysis. Bode plots will be used as main analysis tool. The small signal model is compared to a complete switching model to confirm the validity of the proposed approach.
Enhancing Acceptance Test-Driven Development with Model-Based Test Generation

Acceptance test-driven development is widely used in practice. However, writing and maintaining acceptance tests is a costly and time-consuming activity, in particular when a system is tested via the GUI. In model-based testing, the tests are automatically generated from a model of the system. In this paper, we report our experience from applying a combination of acceptance test-driven development and model-based testing in several real-world projects from industry. With the application of model-based testing, we increased test coverage and extend testing to usage scenarios not exercised by the existing acceptance tests. In the industry projects, MBT was used as an enhancement rather than a replacement for ATDD. By creating a layered test automation architecture, we were able to reuse the established automation for model-based testing and to apply both approaches simultaneously. This strategy also helped us to minimize the risks and to reduce the effort involved in introducing MBT in the projects.
Miniaturized Multi Sensor Implant for Monitoring of Hemodynamic Parameters*

t a novel miniaturized multi sensor implant for monitoring hemodynamic parameters in cardiovascular regions. Pressure measurements are performed with a highly accurate capacitive pressure sensor. An additional acceleration and temperature sensor allows compensating the impact of patient’s inclination and temperature variations on the pressure measurement, respectively. A multi-functional transponder application-specific integrated circuit (ASIC) manages sensor signal processing, storage of ID, sensor calibration data, telemetric energy, and data transmission with an extracorporeal reading unit. Each component of the implant is assembled on a low temperature co-fired ceramics (LTCC) circuit board with an integrated antenna coil enabling an inductive near-field coupling at a frequency of 13.56 MHz. For a streamlined shape and reduction of thrombogenicity, the implant is encapsulated by biocompatible polymers.
Defender: A Low Overhead and Efficient Fault-Tolerant Mechanism for Reliable on-Chip Router

The ever-shrinking size of a transistor has made Network on Chip (NoC) susceptible to faults. A single error in the NoC can disrupt the entire communication. In this paper, we introduce Defender, a fault-tolerant router architecture, that is capable of tolerating permanent faults in all the parts of the router. We intend to employ structural modifications in baseline router design to achieve fault tolerance. In Defender we provide the fault tolerance to the input ports and routing computation unit by grouping the neighboring ports together. Default winner strategy is used to provide fault resilience to the virtual channel arbiters and switch allocators. Multiple routes are provided to the crossbar to tolerate the faults. Defender provides improved fault tolerance to all stages of routers as compared to the currently prevailing fault tolerant router architectures. Reliability analysis using silicon protection factor (SPF) and Mean Time to Failure (MTTF) metrics confirms that our proposed design Defender is 10.78 times more reliable than baseline unprotected router and then the current state of the art architectures.