Miracle-dz/UniFAR-FTUs huggingface dataset:
96 fs All-Fiber Polarization Maintaining Thulium Doped Amplifier Seeded by Coherent Supercontinuum

Tm-doped fiber amplifiers have the broadest gain bandwidth of all the three main fiber amplifiers, which extends from 1.9 to 2.1 μm. Coherent seeding of Tm:fiber amplifier with solitonic seed, pumped by an Er:fiber system, has resulted in generation of 110fs pulses [1]. Coherent supercontinuum seeded optical parametric chirped pulse amplifier system was reported, delivering CEP-stable, nearly single cycle pulses [2]. Coherent seeding of a Tm+Ho doped fiber amplifier system from an all-normal dispersion (ANDi), photonic crystal fiber (PCF) supercontinuum was recently demonstrated and 80 fs pulses, centered at 1940 nm, 80 MHz, with 120 nm of 3 dB width have been reported at 1 W of average power [3]. Stringent polarization control was of paramount importance in all these cases, contributing to system complexity. Here we realize an all-polarization-maintaining (PM), ultrafast fiber amplifier system, including a PM ANDi PCF for seed signal generation [4].
Novel Design of SSPP based Metamaterial Inspired THz Biosensor

A novel design methodology to realize the planar terahertz (THz) biosensor is presented in this work. The proposed biosensor comprises of the planar spoof surface plasmon polariton (SSPP) based transmission line integrated with the complementary split ring resonator (CSRR) etched on the back side of the line. The SSPP transmission line is realized using a newly proposed dumbbell-shaped metallic strip which actually provides better confinement of the field than the conventional microstrip line, and hence it results into improved sensitivity of the designed sensor. The CSRR structure is incorporated at an appropriate position beneath the SSPP transmission line in order to facilitate its proper electric coupling with the highly confined dumbbell cell of the SSPP transmission line. The proposed biosensor is designed and optimized using the 3D electromagnetic field simulator (CST-MWS) in terahertz frequency region to obtain the desired sensitivity. The dimensions of the proposed designed sensor at THz frequency are scaled up, and the fabricated prototype is experimental tested with biological test specimen in the microwave frequency region. For the microwave testing of biological liquids, the CSRR sensing region possessing higher electric field intensity is integrated with a PDMS channel in order to avoid the contamination of the test specimens using the proposed non-invasive biosensor. As the hypertension and diabetes are the two most common causes for various health problems, therefore in this work, the detection of various volume proportions of saline and glucose concentration inside the solution is carried out to identify the symptom of these two targeted diseases.
Low PRF Low Frequency Radar Sensor for Fall Detection by Using Deep Learning

In this paper, we propose an approach that span a wide range of potential radar data for deep learning to detect fall motion. This approach generates a number of training data from a small set of measurements of the the multiple-input multiple-output (MIMO) stepped-frequency continuous wave ultra-wideband (SFCW-UWB) radar, which can attain 99.38% accuracy in deep learning. Even if the radar is operating in the lower frequency band with low pulse repetition frequency (PRF), which results in the inconspicuous time-frequency (TF) signatures and Doppler ambiguity, greatly increasing the difficulty of fall detection, the proposed method still works well. This work learns and captures the intricate properties of the TF signatures without human intervention and feeds the underlying features to the classifier. Experimental data is used to demonstrate the effectiveness of the proposed fall detection deep learning approach.
Image-Dehazing Method Based on the Fusion Coding of Contours and Colors

The dehazing of images shot in fog is a hot spot in the study of computer vision. Unlike dehazing methods, which use an atmospheric scattering model, the method proposed here is based on fusion coding of contours and colors. It simulates the characteristics of visual perception in blurred scenes and balances the amount of color information in foggy images by actively fusing the contour features, thus preventing issues that often arise in dehazing, such as distortion or halo effects. First, the method constructs a contour feature extractor and extracts the contour features of the image, enhancing their weight in feature coding, and then it constructs a low-level feature-coding region to extract colors while adding the contours to fusion code the contours and colors. Then, the an advanced semantic coding region is constructed with dilated convolution residual blocks to deeply analyze the semantic information from the back propagation. Finally, after the fusion of the outputs of the low-level feature coding and the middle and final outputs of the advanced semantic coding, the method decodes the contours and colors in several layers of a convolutional neural network. This paper discriminates the results of dehazing and the sample labels using the discriminator net, composed of several convolution layers, and then intensifies and generates the network’s dehazing ability while improving the discrimination ability of the discriminator net. Synthetic and natural foggy images are chosen as experimental objects, and the results of the novel methods are compared with those of currently available methods. The results show that, unlike other methods, this method produces good results, is robust, addresses issues like color distortion and halos, and dehazes images with natural saturability and sharpness, providing new potential for study of image dehazing.
Electromagnetic Compatibility in Leakage Current of CMOS Integrated Circuits

This paper presents an analytical model that predicts and characterizes the impact of Electromagnetic Interference (EMI) on the leakage current of CMOS integrated circuits. It is shown that the rate of increase in leakage current follows the modified Bessel function, which can be estimated using only a few primitive device parameters. Unlike other device and circuit parameters, leakage current is very sensitive to EMI, where only a few hundred millivolts of noise can increase the leakage current by a factor of 1000. The developed analytical model is successfully compared against measurement data from CMOS inverters fabricated using various TSMC’s standard CMOS processes, including 180 nm, 130 nm, and 65 nm. Based on the predictive model and experimental data, we show that the impact of EMI on the leakage current of CMOS integrated circuits is independent of technology scaling.
Development of flavonoid database for commonly consumed foods by Koreans

Flavonoids have been hypothesized to reduce the risk of chronic diseases, but the lack of a flavonoid database hampered epidemiological studies addressing this issue in Korea. In this study, we developed a flavonoid database, based on a systematic review. A total of 1549 food items containing flavonoids were selected using the Korean Nutrient Database. Among them, flavonoid contents for only 649 food items were evaluated with analytical values and the remaining 900 items were replaced with adaptations or calculations from similar items. The developed flavonoid database covered 93.2% of fruits and fruit juices, 76.1% of vegetables, 98.4% of legumes and legume products, and 85.0% of all plant foods overall (1,549 items) as reported by the 24-hr dietary recall method regarding the 2008 Korean National Health and Nutrition Examination Survey. We found that this flavonoid database, overall, included 95.6% of all mainly consumed plant foods by Koreans. This flavonoid database is expected to be useful in regards to the correlation study of flavonoid intake and chronic diseases.
Progress on Laser Gauge Interferometer (LIG-A) for high resolution accelerometers

We present the design and preliminary results of an accelerometer based on a classical mass spring mechanical system with an interferometric readout. The heterodyne interferometer is based on a 1064 nm fiber laser source, all fiber components and reaches resolutions of less than 1 pm/√Hz at 1 Hz and about 200 pm/√Hz at 1 mHz.
Collaborative Analysis Framework of Safety and Security for Autonomous Vehicles

Human error has been statistically proven to be the primary cause of road accidents. This undoubtedly is a contributory cause of the rising popularity of autonomous vehicles as they are presumably able to maneuver appropriately/optimally on the roads while diminishing the likelihood of human error and its repercussion. However, autonomous vehicles are not ready for widespread adoption because their safety and security issues are yet to be thoroughly investigated/addressed. Little literature could be found on collaborative analysis of safety and security of autonomous vehicles. This paper proposes a framework for analyzing both safety and security issues, which includes an integrated safety and security method (S&S) with international vehicle safety and security standards ISO 26262 and SAE J3061. The applicability of the proposed framework is demonstrated using an example of typical autonomous vehicle model. Using this framework, one can clearly understand the vehicle functions, structure, the associated failures and attacks, and also see the vulnerabilities that are not yet addressed by countermeasures, which helps to improve the in-vehicle safety and security from researching and engineering perspectives.
A Novel Solutions for Malicious Code Detection and Family Clustering Based on Machine Learning

Malware has become a major threat to cyberspace security, not only because of the increasing complexity of malware itself, but also because of the continuously created and produced malicious code. In this paper, we propose two novel methods to solve the malware identification problem. One is to solve to malware classification. Different from traditional machine learning, our method introduces the ensemble models to solve the malware classification problem. The other is to solve malware family clustering. Different from the classic malware family clustering algorithm, our method introduces the t-SNE algorithm to visualize the feature data and then determines the number of malware families. The two proposed novel methods have been extensively tested on a large number of real-world malware samples. The results show that the first one is far superior to the existed individual models and the second one has a good adaptation ability. Our methods can be used for malicious code classification and family clustering, also with higher accuracy.
Reliability of Self-Applied Smartphone Mechanocardiography for Atrial Fibrillation Detection

Smartphone mechanocardiography (sMCG) is a new technique that allows patients to record a cardiac rhythm strip using a smartphone built-in tri-axial accelerometer and gyroscope. In this study, we investigated how a self-applied sMCG can reliably contribute to the differentiation of atrial fibrillation (AFib) from the sinus rhythm (SR). A study sample of 300 elderly adults including 150 AFib cases with persistent and paroxysmal AFib was recruited. Among the 300 subjects, 182 subjects (82 AFib) completed two recordings, one physician-applied and one self-applied. The remaining patients (n = 118) were nervous, in a quite poor condition, and not interested in or capable of concentrating on using a smartphone in the acute setting of a hospital in order to perform the self-applied recording. Two data processing frameworks were used, knowledge-based learning (KL) which is a rule-based algorithm and machine learning (ML) which is an automated classification technique. For the ML approach, we considered four classifiers, namely random forest (RF), extreme gradient boosting (XGB), support vector machines (SVM), and artificial neural network (NN). For evaluation, a leave-one-subject-out cross-validation was adopted for the ML approach. Compared to physician-interpreted ECG-derived labels, the KL approach predicted AFib with sensitivity values of 0.963 and 0.976, specificity values of 0.980 and 0.929, and F-measure values of 0.972 and 0.952 for the physician- and self-applied measurements, respectively. Similarly, NN which was the best ML classifier according to the F-measure values, demonstrated, on average, sensitivity values of 0.976 and 0.938, specificity values of 0.962 and 0.936, and F-measure values of 0.969 and 0.937, respectively. All other classifiers delivered quite similar results. The sMCG technology for AFib detection, supported by the KL and ML approaches, can accurately differentiate AFib from SR in both physician- and self-applied recording scenarios. This new technology can help to screen patients with episodic or undiagnosed AFib and also be used as a home-based self-applied monitoring technique.
Automatic Container Code Localization and Recognition via an Efficient Code Detector and Sequence Recognition*

The challenge of container code localization and recognition is that the code varies in size, position, and arrangements according to the container style and environmental changes. This paper proposes an automatic container code localization and recognition system via an efficient code detector and a sequence recognizer. The code detector locates text features on the backend of the container and extracts the code through segmentation, which increases the stability of code localization. The proposed recognition algorithm regards the code as three separate sequences and recognizes them through a recurrentconvolutional neural network to improve efficiency and accuracy. To verify the validity, a test dataset is established containing about 700 high-resolution images and over 2000 gray-scale code patches. The proposed code localization and recognition algorithm is verified on the dataset in experiments and achieves overall accuracy of 93.98% at about 0.1s per-frame.
Placing Quantum Dots in 3D Photonic Crystals and Finding Them Back

It is a major outstanding goal in Nanophotonics to precisely place quantum emitters inside a three-dimensional (3D) metamaterial. It is well-known that such control offers exquisite control over cavity QED, spontaneous and stimulated emission, and even non-linear optics [1]. Theory predicts that the emission of an emitter, e.g. a quantum dot, varies spatially on 100s nm scale [2]. Thus, the challenge is to place emitters with a precision better than Δx < 100 nm. We present our newly developed chemical toolbox to fix the positions of quantum dots with a polymer brush layer with thicknesses in the 10s nm range in silicon nanostructures [3].
Infrared Nanoscopy of Alive Biological Cell Surfaces

We report progress towards sealing alive cells in their native, wet environment by single-layer graphene, where graphene stabilizes the cell by tightly conforming with its surface and thus enables topographic AFM maps of the cell’s surface. Furthermore, graphene’s transparency to infrared near-fields enables local chemical recognition, at nominally 20-nm spatial resolution, by scattering scanning mid-infrared microscopy (s-SNOM) and spectroscopy (nano-FTIR). Here we demonstrate first infrared nanoscopy results of the surface of native E. Coli cells that show spatial and spectral contrasts. Our study opens a new door into characterizing subcellular structures and components of cells and of fresh tissue sections. Infrared nanoscopy is certainly free of any need for labelling as in super-resolution fluorescence microscopy, and free of fixation and metallization procedures as in electron microscopy that kill and distort cells.
Non-smooth guidance law with terminal angular constraints for missiles against surface targets

Guidance law with terminal angular constraints is considered for an air-to-surface missile against a surface target. To guarantee the finite-time convergence property, a non-smooth continuous guidance law is designed in the engagement of the missile against a non-maneuvering target. By taking the target’s maneuver as an external disturbance input, the designed guidance law is also applicable to a maneuvering target, which is justified through the performance analysis using the input-to-state stability theory. Simulation is conducted, and the simulation results verify the theory proposed in the paper.
Long wavelength (λ = 10 - 18 μm) mid-IR quantum cascade lasers operating in a continuous wave at room temperature

We report a study of quantum cascade lasers (QCL), made of InAs and AlSb, operating in the long wavelength mid-infrared range. Lasers based on a design with slightly diagonal intersubband transitions demonstrated improved performances as compared to the previously reported devices employing a vertical scheme. The fabricated lasers operated in the CW regime at 40°C up to a wavelength of 18 μm.
Outage Probability of Ultra High Frequency and Millimeter Wave Based HetNets with NOMA

This paper studies the outage performance of heterogeneous network consisting of ultra high frequency base stations (UHBSs) and millimeter wave base stations (MWBSs). Offloading to MWBS is performed based on the average received power from the base stations of the two tiers, i.e., UHBS and MWBS tier. Both the UHBS and MWBS employ non-orthogonal multiple access (NOMA) to support the users. Furthermore, the impact of the channel gain difference (CGD) between the users paired at the MWBS tier is investigated in detail. The numerical results show that offloading to MWBS achieves significant improvement in the outage probability of the offloaded user. In addition, offloading to MWBS tier also improves the outage performance at the UHBS tier. Besides, it is observed that the CGD between the paired users at the MWBS tier plays a vital role in the outage performance of the offloaded user. The analytical results are verified using Monte Carlo simulations.
Generating Picosecond Pulses from Mid-Infrared Fiber Lasers using Frequency-Shifted Feedback

Ultrafast mode-locked fiber lasers in the near infrared have enabled a host of key applications in a variety of fields such as medicine and defense. In recent years there has been significant interest in pushing the emission wavelength of these fiber laser systems to the mid infrared (MIR 3–5 due to the presence of strong molecular absorption features in this spectral range. Various techniques to achieve this have been demonstrated, with a majority of the work focused on implementation of loss modulation via real saturable absorbers. While this has been successful in generating picosecond pulses in the mid-infrared [1], commercial availability of absorbers is currently limited, and alternatives such as nanomaterials (graphene, black phosphorous) raise questions regarding long-term stability. Virtual absorbers based on nonlinear polarization evolution have shown comparatively superior performance in terms of pulse duration [2] but this method is highly sensitive to small environmental perturbations.
Brain-robot Shared Control Based on Motor Imagery and Improved Bayes Filter*

Brain-controlled robots are an innovative means of interacting and can also provide new solutions for disabled and stroke patients to communicate with the outside world. Since the poor real-time performance and poor accuracy of brain-computer interface (BCI) is not precise to control the robot directly, in order to avoid damage to the robot and humans in the process, this paper designs a brain-robot shared control system based on brain-computer interface. The motion direction of the robot controlled via four types of motor imagery (MI) signals. Feature extraction of MI signals is performed using common space pattern (CSP) combined with local characteristic-scale decomposition (LCD). The classification results are obtained with the appropriate features processed by the spectral regression discriminant analysis (SRDA) classifier. The Bayes filter algorithm is used to implement the robot shared control method, the belief of the robot's motion direction is calculated, and then the control ratio of the robot's autonomous motion and the BCI are assigned automatically. Considering that each control instruction given by BCI cost at least 1.5 seconds. To achieve better control effect at the interval between two instructions, the relationship with two steps of Bayes filter is redesigned, even if a new control data is not received, the robot will continuously update the measurement according to the previous control data, assign a new control ratio and execute the corresponding instruction, so that the robot can continuously adjust the movement intention and proportion during the instruction interval of BCI. The control effect was verified by online experiments. Using the improved Bayes filter algorithm, the success rate of the experiment is greatly improved, and the number of instructions used in single trial is reduced by 50%.
DFT study of graphene doping due to metal contacts

The experimental results of Metal-graphene (M-G) contact resistance RC have been investigated in-depth by means of Density Functional Theory (DFT). The simulations allowed us to build a consistent picture explaining the RC dependence on the metal contact materials employed in this work and on the applied back-gate voltage. In this respect, the M-G distance is paramount in determining the RC behavior.
The SIW Cavity Backed Tunable Planar Antenna

A substrate integrated waveguide cavity backed active slot antenna is presented in this paper. The antenna operating frequency band is tuned by varying the capacitance of the three varactor diodes connected across the radiating slot. The overall dimensions of the fabricated antenna prototype are 29.6×34.8×0.508 mm3. The tuning process does not change the antenna radiation pattern.
A Three-dimensional Detector Based on Focal Loss for Pulmonary Nodules Detection

The problem of class imbalance exists in detecting the pulmonary nodules from Computed Tomography (CT) by means of convolutional neural network. A Three-Dimensional Detector Based on Focal Loss (FLTDD) is designed in this paper to ensure that the pulmonary nodules in CT could be identified more exactly. Its framework focuses more on samples that are difficult to be classified. Besides, three dimensional detector contains richer spatial information and gets more distinguishing features. The experiment results obtained from LIDC-IDRI data set show that the average sensitivity score of FLTDD achieves 89.62%. It has a 1.47% improvement compared with the published CASED method.
Survey of college academic stressors: Development of a new measure

This study aimed to develop a survey instrument on college academic stressors. A review of extant literature and a focus-group interview among 20 college students were done. Based on the review of extant literature and focus-group interview, an initial survey instrument was developed. The initial survey instrument was further reviewed by 11 college students. After the review, a trial-run of the survey instrument was conducted among 17 college students. The comments from the trial-run participants became the bases for the development of the main-try-out survey instrument. The main-try-out survey instrument was used in the final test administration with 1,210 college students as participants. Responses from the final test administration participants served as bases in determining the descriptive statistics of items, evaluation of convergent and discriminant validity, and reliability of the survey instrument. Based on information gathered, the final survey instrument was developed and was named “Survey of College Academic Stressors. ”