Miracle-dz/UniFAR-FTUs huggingface dataset:
CORNET: A Co-Simulation Middleware for Robot Networks

This paper describes CORNET, a co-simulation middleware for applications involving multi-robot systems like a network of Unmanned Aerial Vehicle (UAV) systems. Design of such systems requires knowledge of the flight dynamics of UAVs and the communication links connecting UAVs with each other or with the ground control station. Besides, UAV networks are dynamic and distinctive from other ad-hoc networks and require protocols that can adapt to high-mobility, dynamic topology and changing link quality in power constrained resource platforms. Therefore, it is necessary to co-design the UAV path planning algorithms and the communication protocols. The proposed co-simulation framework integrates existing tools to simulate flight dynamics and network related aspects. Gazebo with robot operating system (ROS) is used as a physical system UAV simulator and NS-3 is used as a network simulator, to jointly capture the cyber-physical system (CPS) aspects of the multi-UAV systems. A particular aspect we address is on synchronizing time and position across the two simulation environments, and we provide APIs to allow easy migration of the algorithms to real platforms.
High Sensitivity Interferometric Optical Fiber Humidity Sensor Based on a Moisture Sensitive Film

A novel interferometric humidity sensor constructed by a pair of fiber optic collimators and a piece of inserted plate with moisture sensitive film on its surface is presented in this paper. The moisture sensitive film was made by spreading a layer of optical adhesive from Norland Company (NOA61) and drying it in air. Owing to the porous structure of the polymer film made by NOA61, its refractive index will change with the change of ambient moisture composed by the water molecules, which will result in the phase change of the light passing through the film. In our experiments, we let only half of the optical beam exiting from the first collimator pass through the moisture sensitive film, and the other half of the beam go through without the film. The two parts of the beam was collected by the second collimator, combining and focusing on to the end of the receiving fiber. Consequently, a Mach-Zehnder interferometer was constructed and periodical change in the transmission spectrum of this optical fiber collimator pairs could be observed clearly. Since the phase difference between the two parts of the transmission beam of the collimators will be affected by the surrounding moisture, one can detect the environmental humidity from the wavelength shift of the peaks or dips in the spectrum. In the experiments, the relative humidity (RH) was changed from 30% to 90% as the temperature kept at 50°C, and with film of 0.08 mm in thickness, the wavelength shift can be as large as 9.01 nm, which corresponding to a humidity sensitivity of 151.39 pm/%RH. A cycling test indicates the sensor has good repeatability and stability. The proposed sensing scheme has advantages of high sensitivity, compactness, low cost, etc., which make it promising in practical applications.
Personal Stress-Level Clustering and Decision-Level Smoothing to Enhance the Performance of Ambulatory Stress Detection With Smartwatches

Researchers strive hard to develop effective ways to detect and cope with enduring high-level daily stress as early as possible to prevent serious health consequences. Although research has traditionally been conducted in laboratory settings, a set of new studies have recently begun to be conducted in ecological environments with unobtrusive wearable devices. Since patterns of stress are ideographic, person-independent models have generally lower accuracies. On the contrary, person-specific models have higher accuracies but they require a long-term data collection period. In this study, we developed a hybrid approach of personal level stress clustering by using baseline stress self-reports to increase the success of person-independent models without requiring a substantial amount of personal data. We further added decision level smoothing to our unobtrusive smartwatch based stress level differentiation system to increase the performance by correcting false labels assigned by the machine learning algorithm. In order to test and evaluate our system, we collected physiological data from 32 participants of a summer school with wrist-worn unobtrusive wearable devices. This event is comprised of baseline, lecture, exam and recovery sessions. In the recovery session, a stress management method was applied to alleviate the stress of the participants. The perceived stress in the form of NASA-TLX questionnaires collected from the users as self-reports and physiological stress levels extracted using wearable sensors are examined separately. By using our system, we were able to differentiate the 3-levels of stress successfully. We further substantially increase our performance by personal stress level clustering and by applying high-level accuracy calculation and decision level smoothing methods. We also demonstrated the success of the stress reduction methods by analyzing physiological signals and self-reports.
Reliability Study of Low Cost TSV-Free Interposer (TFI)

The detail process integration of low cost TSV-Free interposer (TFI) was successfully developed and demonstrated. TFI was protected by the underfill and molding compound after Si substrate fully removal. The TFI chips with under bump metallization (UBM) are subjected to thermal cycling (TC) and highly accelerated stress tests (HAST). There is no yield loss after 1000 TC and HAST for TFI chips. Reliability of temperature cycling on board (TCoB) with/without underfill were tested and experimental data are consistent with the results of TC reliability simulation.
VERNAL KERATOCONJUNCTIVITIS IN SCHOOL CHILDREN IN NORTH BANGALORE: AN EPIDEMIOLOGICAL AND CLINICAL EVALUATION

OBJECTIVE: To describe the demographic profile, clinical presentation and health seeking behaviour in children with vernal keratoconjunctivitis in north Bangalore. MATERIALS AND METHODS: Cross sectional survey of school children conducted between Feb 2014 to May 2014. Children were interviewed using a questionnaire on Vernal keratoconjunctivitis related symptoms. Children received a full eye examination including vision using snellen chart, slit lamp examination and fundus examination where required. RESULTS: A total of 2500 children screened, 450 children had Vernal keratoconjunctivitis related symptoms and signs. Mean age at presentation was 9.1yrs with male predominance of children. Ocular itching was the predominant symptom (88%). Majority of the patients had mixed form of Vernal keratoconjunctivitis (62%). Medical care for eye problems was sought in only 22% of the patients. Vernal keratoconjunctivitis related complications such as corneal scaring (9%), peripheral corneal neovascularization (8%) and limbal stem cell deficiency (2%) were seen. CONCLUSION: This survey of prevalence and clinical pattern of VKC shows higher prevalence rate in this area as compared to other tropical countries but the clinical pattern of VKC seen is essentially similar to that seen in other tropical countries. Long term follow up is required to ascertain the risk factors for increasing prevalence rate and the need for better primary eye care and accessibility of eye care to the population in the community.
Discriminative Feature Learning for Skin Disease Classification Using Deep Convolutional Neural Network

Nowadays, skin disease among humans has been a common disease, especially in America millions of people are suffering from various kinds of skin disease. Usually, these diseases have hidden dangers which lead to human not only lack of self-confidence and psychological depression but also a risk of skin cancer. Diagnosis of these kinds of diseases usually required medical experts with high-level instruments due to a lack of visual resolution in skin disease images. Moreover, manual diagnosis of skin disease is often subjective, time-consuming, and required more human effort. Thus, there is a need to develop a computer-aided system that automatically diagnoses the skin disease problem. Moreover, most of the existing works in skin disease used convolutional neural networks (CNN) with classical loss functions, which limit the model to learn discriminative features from skin images. Thus to address the above mention problem we proposed a new framework by fine-tuning layers of ResNet152 and InceptionResNet-V2 models with a triplet loss function. In the proposed framework, first, we learning the embedding from input images into Euclidean space by using deep CNN ResNet152 and InceptionResNet-V2 model. Second, we compute L-2 distance among corresponding images from euclidean space to learn discriminative features of skin disease images by using triplet loss function. Finally, classify the input images using these L-2 distances. Human face skin disease images used in the proposed framework are acquired from the Hospital in Wuhan China. Experiment results and their analysis shows the effectiveness of the proposed framework which achieve better accuracy than many existing works in skin disease tasks.
An Improved Equivalent Dipole Moment Source Model Based on Regularization Optimization Method for Near Field-Far Field Conversion

The radiation emission from high-speed digital circuit may result in radio-frequency interference (RFI) problems in modern electronic devices. In this paper, an improved equivalent dipole source based on a new transfer model and a new regularization method is proposed. The equivalent source is obtained by solving an inverse equation with near-field data. The tangential component of electric field used in conventional equivalent source extraction is replaced by the normal component, which can reduce the test difficulty and enhance the measurement efficiency. The new regularization method based on Tikhonov and truncated singular value decomposition (TSVD) can enhance the stability and accuracy of the dipole solution. In addition, the new transfer model and regularization method are verified by simulations and measurements.
Are Containers Coupled with NetEm a Reliable Tool for Performance Study of Network Protocols?

NetEm is a good representative of software-based network emulator often used for testing network protocols and applications under specified network conditions. Many scholars have studied it on physical hosts connected over real networks and observed that NetEm could emulate most network parameters accurately. The popularity of containerization motivates us to investigate the accuracy of NetEm on virtual networks composed of containers. In this paper, we investigate the accuracy of NetEm main functionality on the containers run on Docker, a well-recognized solution for containerization, and compare the results with NetEm performance on a real network connecting physical hosts. We found that NetEm provides the same level of accuracy and stability for packet loss on these two platforms. The TCP throughput under emulated loss and latency was found to be very close when measured on containers and hosts under all circumstances except the scenarios where there is no additional delay combined with packet loss greater than 2%. NetEm offers more accurate delay emulation on physical hosts. On containers, we have observed small additional delay and occasional spikes that can be attributed to time-sharing of CPU in the containerized system. While these must be taken into account when performing a protocol performance study, they are not significant and, therefore, we can conclude that containers coupled with NetEm can be considered a reliable tool for the study of network protocol performance.
Chaining a U-Net With a Residual U-Net for Retinal Blood Vessels Segmentation

Retina images are the only non-invasive way of accessing the cardiovascular system, offering us a means of observing patterns such as microaneurysms, hemorrhages and the vasculature structure which can be used to diagnose a variety of diseases. The main goal of this paper is to automate retinal blood vessel segmentation with a good tradeoff between blood vessel classification and training time in the presence of high unbalanced classes. In this work, a novel methodology is proposed using two convolutional neural networks (CNN’s), chained to each other. The second CNN has been designed with residual network blocks, which joined to the information flow from the first, give us metrics like recall and F1-Score, which are, in most cases, superior to state of the art in vessel segmentation task. We tested this work on two public datasets for blood vessel segmentation in retinal images showing that this work outperforms many of other contributions by other authors.
Di-DE: Depth Information-Based Differential Evolution With Adaptive Parameter Control for Numerical Optimization

Differential Evolution (DE) is a simple and effective stochastic algorithm for optimization problems, and it became much more popular in recent year because of its easy-implementation and excellent performance. Nevertheless, the performance of DE algorithm is greatly affected by the trial vector generation strategy including mutation strategy and parameter control, both of which still exist some weaknesses, e.g. the premature convergence to some local optima of a mutation strategy and the misleading interaction among control parameters. Therefore in this paper, a novel Di-DE algorithm is proposed to tackle these weaknesses. A depth information based external archive was advanced in our novel mutation strategy, which can get a better perception of the landscape of objective in an optimization. Moreover, a novel grouping strategy was also employed in Di-DE and parameters were updated separately so as to avoid the misleading among parameters. Moreover, a cooperative strategy for information interchange was also advanced aiming at improving the efficiency of the exploration behavior. By absorbing these advancements, the novel Di-DE algorithm can secure better performance in comparison with other famous optimization algorithms. The algorithm validation was conducted on CEC2013 and CEC2017 test suites, and the results revealed the competitiveness of our Di-DE algorithm in comparison with those famous optimization algorithms including Particle Swarm Optimization (PSO) variants, QUasi-Affine TRansformation Evolution (QUATRE) variants and Differential Evolution (DE) variants.
Automatically Annotate TV Series Subtitles for Dialogue Corpus Construction

In recent years, the scarcity of dialogue corpus is becoming the bottleneck of Chinese dialogue generation systems. Although subtitles provide favorable material to construct dialogue corpus because of their abundance and diversity, lacking speaker information makes it hard to extract dialogues from subtitles directly. To utilize these resources, we proposed an improved method to automatically annotate bilingual TV subtitles with speaker and scene tags using their corresponding scripts. First, tags of speakers and scene boundaries in the scripts are mapped to the subtitles through an information retrieval method. Then, the mapping errors are detected with a convolutional network and corrected by heuristic strategies to improve the annotation quality. We applied this method on 779 bilingual subtitle files of 4 TV series and obtained a Chinese dialogue corpus Tv4Dialog11It is publicly available at https://github.com/zll17/TV4Dialog containing 260674 utterances. Experiment result shows that our method can achieve an accuracy of 94.62% on speaker tag annotation, improving nearly 12% on the previous state-of-the-art result.
Simulation Study on Static and Dynamic Characteristics of Electromagnet for Electro-Hydraulic Proportional Valve Used in Shock Absorber

In order to improve the performance of the electromagnet for electro-hydraulic proportional valve used in shock absorber, the static and dynamic characteristics of the proportional electromagnet are simulated and analyzed, which provides theoretical basis for the design and optimization of structural parameters of the proportional electromagnet. In this paper, the magnetic circuit model and finite element simulation model of the proportional electromagnet used in shock absorber are established, and the Ansoft software is applied to analyze the influence of the key structural parameters of the proportional electromagnet on static output force and dynamic characteristics. The results demonstrate that with the increase of the depth of basin mouth, the effective travel of the proportional electromagnet increases, and the mean value of the electromagnetic force in the working range decreases. The larger the radial clearance between armature and guide sleeve is, the smaller the electromagnetic force in the effective travel is. When the depth of the basin mouth is 3.9 mm, the slope of the magnetic isolation ring is 35°, the chamfer length of the convex platform is 0.2 mm, and the radial clearance is 0.3 mm, the proportional electromagnet has good displacement-force and current-force characteristics. According to the further transient analysis, it is found that when the voltage amplitude is 24 V, the rise time of the electromagnetic force under step excitation signal is about 40 ms.
Clothing Recommendation System based on Visual Information Analytics

Due to the short fashion style life circle, much more different clothing designs show up. It is hard for consumers to find the suitable clothes effectively. To solve this problem, an automatic and reliable recommendation system is in great demand. In this paper, the clothing attributes recognition, gender recognition, and body height are considered to design the recommendation system. Based on the clothing style, gender and body height, the system can recommend the proper clothes with suitable size. On-line texture modeling is proposed to produce the variation of the clothing texture so that the recommendation system can give reasonable and diversified choices for the consumers. Besides, the data of consumers’ wearing style is also useful to make the better marketing strategy. According to the reasons above, the clothing recognition and recommendation system can create a win-win situation between the consumers and the fashion industry.
Extended Factorization Machines for Sequential Recommendation

Users’ historical activities are usually contained in real-life sequential recommendation systems to predict their future behaviors. In this situation, traditional Factorization Machines (FMs) approaches may be not suitable. Recently, a new surge of interest aims to use recurrent neural networks(RNN) to encode users’ dynamic features with temporal characteristics. However, most of these works fail to reproduce computational simplicity of FMs. In this paper, we propose an architecture of extended-FM for sequential recommendation, which presents temporal feature interactions in an explicit way as traditional FM’s formula. Our approach also allows us to accomplish computation of the model in linear time. Furthermore, we merge extended-FM into higher-order interaction framework without significant changes to the deeper models themselves. We conduct comprehensive experiments on two real-world datasets. The results demonstrate that extended-FM outperforms traditional FMs as well as deep learning feature combination models on sequential recommendation tasks.
Demo: A Machine Learning based M-ary Amplitude Modulated Visible Light Communication System

Visible Light Communication (VLC), which operates in the Terahertz band, is theoretically capable of terabits/second data speeds. However, traditional state-of-the-art VLC has largely been limited in the data rates and communication range. A common approach to modulation in VLC is the use of ON-OFF Keying (OOK), where a binary bit 1 is mapped to a high or ON state of the light emitter and bit 0 to a low or OFF state. While there have been approaches to improve the spectral efficiency through other modulation schemes such as color-shift-keying (CSK), frequency-shift-keying (FSK) and orthogonal frequency division multiplexing (OFDM), the fundamental mapping of intensity of light emitters to high and low states is still required. In this work, we explore a novel approach for using multi-level or M-ary amplitude modulation to encode symbols in VLC. We demonstrate a technique that uses machine learning of the VLC channel state to improve demodulation of M-ary signals in a LED-Photodiode VLC system.
Multiuser Correlation and Sum-Rate in Outdoor Measured Massive MIMO Channels

This letter analyzes the impact of interuser distance and angular separation on the channel correlation and achievable sum-rate for a massive multiple-input–multiple-output (MIMO) system in non-line-of-sight (NLOS) conditions. The investigation is based on outdoor measurements on a channel sounding system capturing the dynamic channel of two user arrays. The letter analyzes correlation and sum-rate with varying interuser distance and angular separation of dominant beams toward the users. A large span of correlation and sum-rate values are found across the range of distances and angular separation. The investigation shows a moderate link between interuser distance and correlation, but a strong impact on correlation is found only for low angular separation of the users. The results of this NLOS scenario suggest that a distance-based criteria alone is not sufficient to accurately model shared clusters and their correlation.
An Intelligent Control System of Music Rhythms by RGB-LED Lamp

Based on the STM32 single-chip microcomputer (SCM), the relationship between music rhythm and the output color of red-green-blue-light-emitting-diode (RGB-LED) lamp is studied in this paper. Aiming at the problem that music rhythms and colors of light cannot be fully matched, the idea that the colors of RGB-LED lamp are controlled by music rhythms is put forward. Sound collector, microcontroller, power module, bias circuit, and RGB-LED lamp are adopted in the hardware design. In the software, the music data collected by an analog-to-digital converter (ADC) is converted into digital quantities. The color of RGB tricolor is controlled by changing the ratio of pulse width modulation (PWM). With a linear function, we adjust the lighting effect when the rhythm falls and filter out some smaller values, but the improvement is not satisfactory. Therefore, the nonlinear quadratic function instead of the linear function is used to eliminate the flicker when the music rhythm becomes weak. The experimental results show that the proposed improvement can give rise to a much better control effect for the colors of light by the music rhythms.
A 135–150-GHz Frequency Tripler Using SU-8 Micromachined WR-5 Waveguides

This article presents a 135–150-GHz Schottky diode-based bias-less frequency tripler based on SU-8 micromachined WR-5 waveguides. The waveguides consist of five 432- $\mu \text{m}$ -thick silver-plated SU-8 layers, which house the diode chip and form the output matching network. The input matching circuit is realized in a computer numerical control (CNC) milled waveguide filter, which also provides support and thermal sink to the SU-8 waveguides. Considering the low thermal conductivity of the SU-8 material, auxiliary metallic thermal paths are designed, and the impact of these is discussed through thermal modeling. The thermal simulations show that under 50-mW power dissipation in the diode anodes, the maximum temperature of the SU-8 tripler is predicted to be 346 K at the diode junction, only 7 K higher than in an entirely metal equivalent. The tripler was measured to have a conversion loss of 16–18 dB and the input return loss is better than 18 dB. This work demonstrates that SU-8 micromachined waveguides can be used to package high-frequency semiconductor components, which, like other photolithography-based processes such as silicon deep reactive ion etching (Si-DRIE), has the potential for submicrometer feature resolution.