Miracle-dz/UniFAR-FTUs huggingface dataset:
Generate Navigations to Guide and Automate Nasotracheal Intubation Process

Nasotracheal Intubation is a complex medical procedure in which a ventilating tube is inserted into the human trachea (windpipe) through the nose. Improper positioning carries potentially fatal consequences and therefore confirmation of correct positioning is mandatory. To overcome the consequences1, this research propose a system to guide and automate Nasotracheal Intubation by providing the physician with necessary directions to move the intubation tube. This study is a proof of concept to prove that generating directions using a trained model has practical potential. In the proposed system, different anatomical landmarks along the path from nose to carina are identified, captured images are preprocessed and use a Convolutional Neural Network based on image classification to predict the class of each anatomical landmark. The proposed system has been trained using the training dataset of 9753 images and the performance of the system evaluated using the testing dataset of 2438 images. The results, obtained using the pre-trained model, show that the system correctly classified 2385 out of 2438 (97.8%). The outcome of this research has been a successful enhancement in correct positioning of the intubation tube with the generation of navigation details of Nasotracheal Intubation process and has established for further improvements.
Design and Optimization of Vertical GaN PiN Diodes With Fluorine-Implanted Termination

Gallium nitride (GaN)-based power devices enable high power density and high switching frequency for power electronics systems. For the emerging vertical GaN devices, the electric field crowding around the edge of the main junction could result in premature breakdown. It is challenging to employ the commonly used junction-based termination techniques as in Si and SiC high-voltage devices for vertical GaN devices, due to the difficulty of selective p-type doping and activation in GaN. In this work, based on the unique feature of the negatively charged F ions in GaN which can favorably modulate the edge electric field, a fluorine-implanted termination (FIT) structure for vertical GaN PiN diodes has been designed and optimized to suppress the electric field crowding at the junction edge for a higher breakdown voltage (BV). The key parameters of the FIT, including the F ion dose, the thickness and width of the FIT and the angle of the bevel structure, have been comprehensively investigated by TCAD simulations to reveal their impacts on BV. Moreover, with a tapered dose distribution in the FIT, the device can achieve a higher BV with an enlarged FIT dose window.
Microwave Planar Sensors for Fully Characterizing Magneto-Dielectric Materials

This paper presents two complementary split-ring resonator (CSRR)-based sensors for full characterization of magneto-dielectric materials. The first proposed sensor is designed by loading the microstrip line with a CSRR, while the second one is by loading the microstrip line with two CSRRs. To distinguish the effects of permittivity and permeability on the shift of resonant frequency, the electric and magnetic fields are designed to be located at two separate zones in the CSRR. Two $50~\Omega $ resistors are mounted at the ends of the microstrip line to increase the quality factor. In addition, the second sensor can perform measurements of complex permittivity and complex permeability of the magneto-dielectric materials in a single run, thereby making the measurement faster, easier, and in a more cost-effective way. The prototypes of two proposed sensors are fabricated and tested for validation of their functionality. A good agreement can be achieved between the measured data and the reference values from the manufacturers’ datasheet, thereby demonstrating the applicability of the proposed sensors for full characterization of the magneto-dielectric materials.
Improving the Performance of Tactical Waveform Software using Free and Open-Source Tools

Tactical waveform software applications tend to suffer from inefficient implementations despite the availability of free and open source software (FOSS) profiling tools. It has been the overwhelming experience of the authors that tactical waveform developers rely too heavily on the optimization power of the compiler rather than on sound, high-performance software engineering practices including performance profiling. The suite of FOSS profiling tools is mature, vast, and growing. Valgrind, gprof, and perf are tools used to identify software deficiencies that diminish the performance and reliability of tactical waveforms and the radios that host them. These deficiencies can be categorized as application-level and microarchitectural deficiencies. Inefficient input/output (I/O), memory leaks, uninitialized variables, race conditions, and improperly prioritized threads are examples of application-level deficiencies. Inefficient instruction and data cache utilization, and severe branch prediction misses are examples of microarchitectural deficiencies. This paper presents a methodology to apply FOSS tools and techniques to improve the performance posture in both the application and microarchitectural domain. Furthermore, results of the approach are presented in a case study involving application of the proposed techniques against a real Department of Defense (DoD) tactical waveform application.
An Extended Convolutional Neural Network with Smaller Structure for Fault Diagnosis of Gearbox

Recent research on fault diagnosis mainly focuses on how to improve diagnostic accuracy. For a given accuracy level, a variety of convolutional neural network architectures have been developed and available to achieve the specific accuracy level. With equivalent fault diagnosis accuracy, smaller convolutional neural network structure offers at least three advantages: (1) it can be deployed on hardware with limited memory, such as FPGAs; (2) the training of smaller convolutional neural networks can be faster under the same processor performance; (3) smaller network structures require less communication across severs during distributed training. So, in this paper, an improved convolutional neural network is constructed, and the following strategies are used to reduce network parameters and further make the convolutional neural networks with smaller structures: (1) equivalent replacement of large-scale convolution layers by multiple small-sized convolution layers; (2) avoid using a fully connected layer, and replace with a global pooling layer. Meanwhile, to ensure the model’s fault diagnosis accuracy and training effectiveness, inspired by the network in network, a 1*1 convolutional layer is inserted into a traditional convolutional layer to improve feature expression, and the batch-normalization layer is used to increase the training effectiveness.
Packaging Effects on MEMS Pressure Sensor Hysteresis

Micro-electro-mechanical systems (MEMS) have allowed high precision pressure sensors to be manufactured at low cost, but they typically exhibit some level of hysteresis behaviour, which is believed to be primarily due to the materials used to package them. In order to evaluate the effects of the different packaging materials and processes, in this study, the performance of piezoresistive absolute pressure MEMS was measured for each of the first two packaging stages of the product assembly process. Sensing elements first packaged in a “floating exposed” configuration and then in an “attached exposed” configuration were compared. These two different packaging configurations exhibited distinct hysteresis behaviour when thermally cycled. After applying a third order polynomial fit to the raw data, stabilized and moving hysteresis loops were observed for the floating exposed and attached exposed configurations respectively. The stabilized hysteresis is believed to originate from the inelastic behaviour of the die metallisation, whereas the non-stabilized behaviour stems from the adhesive used to attach the die to the housing.
Improve Data Utilization with Two-stage Learning in CNN-LSTM-based Voice Activity Detection

Voice activity detection (VAD) is essential for the speech signal processing system. Convolutional long short-term memory deep neural network (CLDNN), which consists of a CNN and an LSTM, has shown excellent improvement in VAD. However, the training data of the CLDNN must be sequence data because of the LSTM. To improve data utilization, we proposed a two-stage training strategy. Specifically, the first stage trains the CNN on shuffled frame-level data to get high-level feature expression, individually. The second stage trains the LSTM to model the speech continuity. We show that our method has obvious advantages in discriminative ability and generalization ability than compared approaches in different scale of training data, especially in small datasets. The proposed method achieves over 2.89% relative improvement than the original CLDNN on noise matched condition and over 1.07% on unmatched condition.
A Gini Index-Based Countermeasure Against Sybil Attack in the Internet of Things

As an essential ingredient of Internet of Things (IoT), IPv6-based Low Power and Lossy Networks (LLNs) largely consisting of various resource-constrained devices are expeditiously proliferating and playing an important role in the realization of ubiquitous computing and communication infrastructure. In order to provide efficient and reliable communication between resource-constrained devices and connect them to the Internet, a novel routing protocol for LLNs, a.k. a. RPL, has been proposed. However, due to wide distribution, openness, and instinctive resource constraints of IoT devices, IoT and its applications become an ideal target for cyber attacks. Thus, investigating potential attacks against IoT-related routing protocol is a top priority to improve the security of the future IoT systems. In this paper, we propose a Gini index-based countermeasure to effectively detect and mitigate sybil attack in RPL- based LLNs, where the malicious node multicasts an excessive number of DODAG Information Solicitation (DIS) messages with different fictitious identities to cause the legitimate nodes to restart the Trickle algorithm frequently and broadcast a large number of DODAG Information Object (DIO) messages to quickly drain the limited energy resource of legitimate nodes. We conduct extensive simulation experiments for performance evaluation and comparison using OMNeT++, and the simulation results show that the proposed countermeasure can accurately detect and effectively mitigate sybil attack, indicating a viable approach against cyber attack in the Internet of Things.
On RF-Chain Limited Spatial Modulation Aided NOMA: Spectral Efficiency Analysis

Recently, the combination of spatial modulation (SM) and non- orthogonal multiple access (NOMA) is proposed to enhance the spectral efficiency (SE) of downlink multi-user (MU) systems. However, traditional SM aided NOMA (SM-NOMA) systems require many radio frequency (RF) chains, which increases the implementation cost and lowers the energy efficiency (EE). In this paper, we propose a novel single-RF SM-NOMA system, in which the spatial domain bits can be flexibly divided for different user equipments (UEs). In addition, we analyze the SE of our proposed single-RF SM-NOMA system, and a closed-form SE lower bound is also derived. The tightness of our proposed SE lower bound is validated via Monte Carlo simulations, and the SE comparisons of different SM-NOMA systems are also shown, which illustrates the effectiveness of the proposed single-RF SM-NOMA system.
Mutual Prediction Model for Predicting Information for Human Motion Generation

As a model for generating human motion, multiple paired forward and inverse models have been proposed. This model has motor control and motor learning functions by multiple forward/inverse model pairs using responsibility signal obtained from the generation of motion by the forward model. However, this system assumes that the target trajectory of the body is given. In order to learn through interaction with the outside world after the birth of a creature, the generation process of the target trajectory should be modeled. In this study, we propose a hypothesis in which the target trajectory is the sense prediction itself, and propose the mutual prediction model in which forward/inverse model pairs governing the sense and motion intention are mutually coupled as a human motion generation model. We also report some experimental results indicating appropriateness of the hypothesis.
PLL-Based Clock and Data Recovery for SSC Embedded Clock Systems

In the paper, we give analysis and comparison for type-2 and type-3 PLLs to develop the clock and data recovery (CDR) for spread-spectrum clocking (SSC) interfaces, respectively. Through theoretical analysis, we observe that the steady-state phase error of type-2 PLL approaches to a constant while the steady-state phase error approaches zero in type-3 PLL. Since the steady-state phase error results in phase offset for CDR to sample data, type-3 PLL can provide an incentive design for SSC applications. In this work, we apply SSC input of 200-kHz modulation frequency and ±10% modulation rate with a triangular-shape frequency modulation for 3 Gb/s data transmission, and the PLL has bandwidth of 4 MHz and phase margin of 60° for simulation. The simulated steady-state phase offsets for type-2 and type-3 PLL CDRs are 1.28UI and 0.004UI, respectively.
Dynamic Range Expansion Using Cumulative Histogram Learning for High Dynamic Range Image Generation

In modern digital photographs, most images have low dynamic range (LDR) formats, which means that the range of light intensities from the darkest to the brightest is much lower than the range that can be perceived by the human eye. Therefore, to visualize images as naturally as possible on devices that display them in high dynamic range (HDR) format, the LDR images need to be converted into HDR images. The aim of this study was to develop an adaptive inverse tone mapping operator (iTMO) that can convert a single LDR image into a realistic HDR image based on artificial neural networks. In contrast to conventional iTMO algorithms, our technique was developed by learning the complicated relationship between various LDR–HDR pair images, which enabled nearly ground-truth HDR images to be generated from various types of LDR images. The novel learning technique is called cumulative histogram-based learning and color difference learning. The superior performance of our technique over conventional methods was assessed through objective evaluations of various types of LDR and HDR images.
A Cooperative Spectrum Sensing Scheme in Malicious Cognitive Radio Networks

Cooperative spectrum sensing has been shown have various advantages including spectrum utilization and robustness in Cognitive Radio (CR) networks. Security concerns are raised for cooperative spectrum sensing due to its vulnerabilities to the potential attacks. In this paper, we first analyze the performance limits of cooperative spectrum sensing under Byzantine attacks where malicious users send tampered data to the fusion center (FC) leading to increased probability of incorrect sensing results. We also propose a novel and easy cooperative spectrum sensing scheme to counter Byzantine attacks in malicious CR networks. In this approach, the FC removes the anomalous value from the data fusion process which most likely from the malicious users. Simulation results show that our proposed scheme is more robust against Byzantine attacks than traditional scheme.
Development of a Food Handling Soft Robot Hand Considering a High-speed Pick-and-place Task

In recent years, industrial robots have been applied to food production because of its huge potential market. Although cooking a large number of foods is done by specially designed cooking machines, dishing up the cooked foods remains a labor-intensive task. To solve this problem, we developed a new food handling robot hand consisting of simple-shaped soft actuators that can handle easily damaged natural food and convey it at high speed. In this paper, the characteristic of the developed soft actuator was shown and the bending mechanism was analyzed by the finite element method. In addition, the prototyped robot hand was evaluated by grasping and conveying experiments with some food samples.
Adhesion Strength and Warpage Behaviors of Advanced Package Substrate by Light-source Irradiation

The semiconductor industry's push toward continued miniaturization and increasing complexity is driving wider adoption of advanced package technology. Advanced package provides potential solution for systems with better performance and more functions at a smaller form factor, lower cost, lower power consumption, and a shorter time to market. Due to these reason, dielectric materials with not only low Dk(Dielectric Constant), Df(Dissipation Factor) but also high resolution to make smaller Via-hole has been becoming used for data server and 5G related areas. To reduce yield loss during the substrate and wafer assembly process, warpage control is becoming even more critical due to increasing number of I/O counts. In this paper, be handled how to improve surface modification on Low-loss material and reduction of substrate warpage with Light-source irradiation.
Progress in systemic therapy of advanced colorectal cancer

Mozliwości leczenia systemowego chorych na zaawansowanego raka jelita grubego byly do początku lat 90. ubieglego wieku ograniczone do fluorouracylu i przynosily niezadowalające wyniki. W ciągu ostatniej dekady nastąpil bardzo szybki postep w leczeniu tego nowotworu. Mediana czasu przezycia chorych na zaawansowanego raka jelita grubego ulegla niemal podwojeniu: z 10–12 miesiecy do 18–20 miesiecy. Taką poprawe osiągnieto dzieki wprowadzeniu kilku nowych lekow o dzialaniu cytotoksycznym i calkowicie nowej kategorii metod leczenia skierowanego na cele molekularne. Chociaz niektore z nowych sposobow terapii są nadal eksperymentalne, to jednak mogą zapowiadac zmiane w postepowaniu u chorych na raka jelita grubego w niedalekiej przyszlości. Wlączenie nowych metod leczenia do praktyki klinicznej powinno sie opierac na rzetelnych danych, ktore stanowilyby wyniki wlaściwie zaplanowanych badan klinicznych. Lekarze powinni wiedziec, jakie dzialania niepoządane mogą wystąpic przy zastosowaniu nowych metod leczenia, a takze miec świadomośc finansowych uwarunkowan tego leczenia.
PRESS/HOLD/RELEASE Ultrasonic Gestures and Low Complexity Recognition Based on TCN

Targeting ultrasound-based gesture recognition, this paper proposes a new universal PRESS/HOLD/RELEASE approach that leverages the diversity of gestures performed on smart devices such as mobile phones and IoT nodes. The new set of gestures are generated by interleaving PRESS/HOLD/RELEASE patterns; abbreviated as P/H/R, with gestures like sweeps between a number of microphones. P/H/R patterns are constructed by a hand as it approaches a top of a microphone to generate a virtual Press. After that, the hand settles for an undefined period of time to generate a virtual Hold and finally departs to generate a virtual Release. The same hand can sweep to a 2nd microphone and perform another P/H/R. Interleaving the P/H/R patterns expands the number of performed gestures. Assuming an on-board speaker transmitting ultrasonic signals, the detection is performed on Doppler shift readings generated by a hand as it approaches and departs a top of a microphone. The Doppler shift readings are presented in a sequence of down-mixed ultrasonic spectrogram frames. We train a Temporal Convolutional Network (TCN) to classify the P/H/R patterns under different environmental noises. Our experimental results show that such P/H/R patterns at a top of a microphone can be achieved with 96.6% accuracy under different noise conditions. A group of P/H/R based gestures has been tested on commercially off-the-shelf (COTS) Samsung Galaxy S7 Edge. Different P/H/R interleaved gestures (such as sweeps, long taps, etc.) are designed using two microphones and a single speaker while using as low as $\sim 5\mathrm{K}$ parameters and as low as $\sim 0.15$ Million operations (MOPs) in compute power per inference. The P/H/R interleaved set of gestures are intuitive and hence are easy to learn by end users. This paves its way to be deployed by smartphones and smart speakers for mass production.
A Miniaturized Broadband Radiating Structure Based on Microstrip and Slot Complementary Coupling

An improved design of the planar broadband radiating element based on complementary microstrip line and slot coupling element is presented in this article, including both theoretical analysis and simulation results. The simulated results indicate that our proposed structure has a passband with relatively constant performance between 5GHz and 16GHz. In the passband, our design has similar impedance and radiating properties compared to the original design, but with a length reduction of 22.5%. The lattice circuit topology is also provided, giving an instructive method for antenna analysis and modification. The proposed radiating structure could be a compact replacement of the previous published structure [1]. The planar and simple structure makes it suitable for forming series-fed linear arrays, such as log-periodic antennas, which can be used in broadband communication applications.
Characterization of Metasurface Lens Antenna for Sub-6 GHz Dual-Polarization Full-Dimension Massive MIMO and Multibeam Systems

A metasurface lens antenna fed by a planar <inline-formula> <tex-math notation="LaTeX">$8\times8$ </tex-math></inline-formula> dual polarized antenna array is proposed and characterized for full-dimensional massive multiple-input multiple-output (MIMO) and multibeam systems at sub-6 GHz bands. The lightweight multilayer metasurface structure consisting of Jerusalem cross elements is used for a planar lens design. The lens antenna with the size of <inline-formula> <tex-math notation="LaTeX">$10\lambda _{0} \times 10\lambda _{0} \times 5\lambda _{0}$ </tex-math></inline-formula> is fed by an <inline-formula> <tex-math notation="LaTeX">$8\times8$ </tex-math></inline-formula> dual-polarized stacked patch array to operate over the range from 5.17 to 6.10 GHz, where <inline-formula> <tex-math notation="LaTeX">$\lambda _{0}$ </tex-math></inline-formula> is the free-space operating wavelength at the center frequency of 5.6 GHz. This article shows the scanning range of ±25° with a maximum gain of 22.4 dBi and the gain variation of 3.3 dB at 5.6 GHz. Beam coverage performance, pattern envelope correlation coefficient, and port isolation properties demonstrate its full-dimension access capability of the antenna. Besides, the multibeam antenna system with high-gain coverage is achieved by beams operating at the same frequencies, while the frequency division multiplexing can be realized for the isolated beams. The proposed metasurface lens antenna, featuring lightweight, compactness, and low cost compared to a 3-D dielectric lens and low-complexity compared to the array scenarios, can be an alternative for fifth-generation (5G) sub-6 GHz frequency bands.