Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Learning for Pollen Sac Detection and Measurement on Honeybee Monitoring Video

This paper introduces a new model which applies deep learning techniques to pollen sac detection and measurement on honeybee monitoring video. The outcome of this model is a measurement of the number of pollen sacs being brought to the beehive, so that beekeepers will not need to open beehives frequently to check food storage. The pollen sacs are detected on individual bee images which are collected using a bee detection model on the entire video frame. The pollen detection model is built using a deep convolutional neural network. The architecture is Faster RCNN with VGG-16 core network. The network is trained to detect pollen sacs so the individual bee images are identified as either pollen or nonpollen bee images. This pollen sac detection model is then combined with a bee tracking model, so that each flying bee tracked on successive video frames is identified as carrying pollen or not. Finally, the number of pollen-carrying bees can be counted. The experimental results show that the measurement error of this model is 7%. The deep learning model improves the results from the conventional image processing method, which produced 33% measurement error.
An opinion-unaware blind quality assessment algorithm for multiply distorted images

The blind image quality assessment algorithms produced every year are mostly “opinion-aware” (OA). It means that they require large numbers of subjective quality scores for regression model training. Subjective quality scores are not easily available, so people are eager to design an opinion-unaware (OU) algorithm which has free subjective quality scores. Besides, the OU algorithm has greater generalization capability than the OA algorithm. Therefore, we propose an OU algorithm based on a visual codebook for multiply distorted image quality assessment. Extensive experiments conducted on the three databases demonstrate that the proposed method is superior to the existing five OU methods in terms of the coherence with the human subjective rating. The MATLAB code is available at https://tonglewang.github. io.
Origins of Global Illumination

Global illumination refers to a complete shading model that simulates real lighting and reflection as accurately as possible. Whether used for product prototyping or special effects for entertainment, the goal is to match the appearance of the real world. The origins of global illumination come at the intersection of a steady progression of shading models with the ancient simulation technique of ray tracing.
Capped l_2,1-Norm Regularized Dictionary Coding for Scalable Semi-Supervised Learning

During the past decade, graph-based semi-supervised learning has become one of the most important research areas in machine learning and artificial intelligence community. In this paper, we propose a Capped l_2, 1-Norm Regularized Dictionary Learning to construct the graph for semi-supervised learning (SSL). The new sparse coding is robust to the outliers and insensitive to noise. Then, the developed graph construction can better characterize the geometrical structure of data mainfold and be utilized for efficient graph based SSL. the proposed SSL methods can also be easily extended to deal with out-of-sample data. Simulation results show that the proposed method can achieve better performance compared with other state-of-the-art graph based SSL methods.
A State-of-the-Art Review on Conducted Electromagnetic Interference in Non-Isolated DC to DC Converters

One of the most challenging and interesting field in power electronics is the ability to mitigate the Electromagnetic Interference (EMI). A natural source of EMI includes the atmospheric discharge/charge phenomena and extra-terrestrial radiation. Man-made source of EMI are line radiation, auto ignition, radio frequency interference and power lines. Suppression of EMI and enhancing the Electro Magnetic Compatibility (EMC) has become essential in high frequency power electronic converters. This review article is a one stop solution for new researchers and practitioners to understand about the effects of EMI and its suppression techniques in detail.
A Dimmable OFDM Scheme With Dynamic Subcarrier Activation for VLC

Dimmable optical orthogonal frequency division multiplexing (D-OFDM) is challenging to design for visible light communications, due to the high peak-to-average power ratio (PAPR) of OFDM signals and limited dynamic range of LEDs. Current D-OFDM schemes focused on the time domain and spatial domain designs of OFDM signals. This paper proposes a novel dynamic subcarrier activation based OFDM (DSA-OFDM) scheme. In DSA-OFDM, the number of activated subcarriers, as well as the signal's DC bias and the number of activated LEDs, is adjusted to provide both flexible dimming control and reliable communication. A generalized index modulation in both spatial domain and frequency domain is designed to select the activated subcarriers and activated LEDs. To show the influence of frequency domain design analytically, the closed-form expressions of the PAPR and the clipping noise of the DSA-OFDM signals are derived, and the optimized signal form is obtained accordingly. Simulation and numerical results show that the proposed scheme can outperform conventional dimming control schemes at various illumination levels in terms of the bit-error rate performance.
Instrumented shoe-based foot clearance and foot-to-ground angle measurement system for the gait analysis

This paper presents a wireless gait analysis system that incorporates anatomically located infrared (IR) distance sensor on the shoe for the measurement of various gait parameters such as foot-to-ground angle (FGA), foot clearance (FC). The system has been validated against the BTS bioengineering 3D motion capture gold standard method in gait analysis laboratory with FC RMSE error of 6.31% of the full range and FGA RMSE error of 5.53% of the full range. The squared correlation coefficient r2 for FC and FGA is equal to 0.970 and 0.935, respectively. This system has a sensor position adjustment mechanism in two degrees of freedom, which facilitates the adaptability of the system to any foot size. The system is inexpensive, simple to use, and provides accuracy at par to the existing systems. This system finds application in a variety of clinical domains, for example, neurological disease diagnosis affecting ambulation such as Parkinson’s and cerebral palsy, gait rehabilitation, and sports fields. The future scope of this work includes validation of the shoe with different foot sizes and with different walking speed.
Disturbance observers for constant disturbances

The parameterization of linear disturbance observers for constant disturbances has been studied in this paper. By using the states and inputs of the system as variables, the parameterization under constant disturbances was obtained in frequency domain, the validity of the disturbance observer was proofed by simulation. Some types of disturbance observers have been designed and introduced in many papers in detail so far. By comparing with other disturbance observers, it can be found that the form of disturbance observer designed by us is simpler. Also, it is easier to realize and is broader to apply the disturbance observer to the system In addition, the method to obtain the parameterization for constant disturbances can be also applied to get parameterization for other types’ disturbances, so it is a good methodology to achieve the parameter determination of all disturbance observers.
Modules of Interaction for ASD Children Using Rero Robot (Humanoid)

Autism Spectrum Disorder (ASD) is a neurological and developmental disorder that affects one's ability to understand social cues and to communicate. As it begins early in childhood and can last throughout a person's life, it is important to have an early intervention and rehabilitation. Previous research has shown that robotic platforms helped and encouraged positive outcome in ASD children rehabilitation. Early interventions through human-robot interaction (HRI) have the potential when used to improve communication and social skills of the children. In this research, a new framework has been developed featuring a robot and human interaction modules. A commercial robot, Rero has been adopted and used as the interaction medium. This robot has been selected based on its reconfigurable capability and ability to be developed into many forms. It is also mobile, speech enabled, controllable, programmable, and attractive. Five modules of interaction have been developed and approval from special education teachers and therapists has been sought. The modules are created to help the children to be able to imitate actions, follow instructions, name objects, as well as to focus and match colours. Observations from the experiment shows that the children enjoyed the interaction modules with a median of more than 70% of scores and were well engaged with the robots with certain type of modules.
VeriSmart 2.0: Swarm-Based Bug-Finding for Multi-threaded Programs with Lazy-CSeq

Swarm-based verification methods split a verification problem into a large number of independent simpler tasks and so exploit the availability of large numbers of cores to speed up verification. Lazy-CSeq is a BMC-based bug-finding tool for C programs using POSIX threads that is based on sequentialization. Here we present the tool VeriSmart 2.0, which extends Lazy-CSeq with a swarm-based bug-finding method. The key idea of this approach is to constrain the interleaving such that context switches can only happen within selected tiles (more specifically, contiguous code segments within the individual threads). This under-approximates the program's behaviours, with the number and size of tiles as additional parameters, which allows us to vary the complexity of the tasks. Overall, this significantly improves peak memory consumption and (wall-clock) analysis time.
Link between Facial Expressions and Emotional States Induced by Exposure to Multimedia Content

The explosive growth of digital videos has created new challenges for computer science. While many advances on video indexing, retrieval and summarization based on general, subject-independent, objective descriptors have been made in the past years, research on the use of individual subjective preferences and affective states is at the forefront of research and poses great challenges. In this article, we study the relationship between emotional states reported by viewers and their facial physiological changes observed during the display of different video genres. A dataset of twenty videos was created from YouTube video sharing platform. During the exhibition of the videos, the viewer’s facial activities have been recorded and analyzed by means of Action Units (AUs). After that, emotional states self-reported by the viewers were assigned to video shots. Labels were divided into four categories, defined according to a discrete version of Russel’s Circumplex emotion model. Different machine learning models were trained to test the relationship between the measured facial features and the self-reported emotional categories. We obtained k-fold cross validation accuracies that were above chance for the best learned models. As a result of this study, we concluded that AUs can indeed be used as an valuable tool to estimate emotional categories during exposure to audiovisual stimuli, and, therefore, should be used in further studies that take advantage of those categories to devise personalized multimedia retrieval and summarization approaches.
Research on Four-Phase Interleaved Step-Up DC/DC Converter for High-Power Charging System

Intended for the high voltage gain, high efficiency and stable long-term operation of DC/DC converters for high-power charging system, a topology for four-phase interleaved DC/DC converters for high-power charging is proposed. Compared with traditional boost converter topology, the proposed topology Improve system efficiency greatly under the premise of ensuring the boost ratio. Experimental results show that the maximum efficiency of the converter reaches 95.37%. Compared with traditional boost converters, the proposed converter offers obvious advantages in efficiency under the conditions that the output voltage and load are variable.
Sleep Anomaly Events Detection Based on Adaptive Double Threshold CFAR Detector Using UWB Radar

Detection of sleep anomaly events, such as get up, sit up and turn over, is one of the most important tasks in human sleep monitoring. In this paper, we propose a novel sleep anomaly events detection algorithm based on an adaptive double threshold constant-false-alarm-rate (ADT-CFAR) detector using ultra-wideband (UWB) radar. The proposed algorithm firstly removes the static clutter, detects the human and performs the time-frequency transform for the received signals. By extracting the power burst curve (PBC) from the obtained time-frequency maps, we can formulate the sleep anomaly events detection by a binary hypothesis test and then develop an ADT-CFAR detector, which is based on log-normal distribution. A series of experiments are conducted and the results show the effectiveness of the proposed detector.
On ASER Analysis of Energy Efficient Modulation Schemes for a Device-to-Device MIMO Relay Network

In this paper, we investigate the performance of a device-to-device (D2D) multiple-input and multiple-output (MIMO) communications using an amplify-and-forward relaying for various energy-efficient modulation schemes. For analysis, transmit antenna selection strategy is employed at the transmitter to select the best transmit antenna which maximizes the received signal power. In the analysis, we optimized the relay location to obtain the lowest outage probability possible and derived the generalized upper-bound average symbol error rate expressions of energy-efficient futuristic modulation schemes such as square quadrature amplitude modulation (QAM), rectangular QAM, cross QAM, and hexagonal QAM for D2D network. Further, we have derived the ergodic capacity expression to calculate the achievable rates. Comparative analysis of different QAM schemes for various constellation orders is also presented. Furthermore, the impact of the relative position of relay, path loss, and the number of antennas are also highlighted. Finally, the derived analytical results are verified through Monte-Carlo simulations.
Functional Printing of MMIC-Interconnects on LTCC Packages for sub-THz applications

In this paper, the authors presents about an Aerosol Jet printed interconnect technology between a GaAs MMIC and an LTCC packaging. Conductive silver nanoparticle and a dielectric polymer-based glob-top are utilized to fabricate die attach, dielectric ramp, and GCPW (Grounded Coplanar Waveguide) transmission line interconnect structures in order to interface a GaAs MMIC with an LTCC (Low Temperature Co-fired Ceramic) packaging substrate. The GCPW lines were printed using the Optomec Aerosol Jet 300 printer with silver nanoparticle ink from Paru. The printed minimal resolution of the interconnection was $34 \mu \mathrm{m}$ with a gap of $23 \mu \mathrm{m}$. Simulation and measurement results of printed interconnects were evaluated in a frequency range between 1 and 225 GHz. The printed interconnects including the MMICs and GCPWs on LTCC have shown an insertion loss of ca. 2 dB at 140 GHz. The reflection coefficient stayed below −10 dB until 210 GHz.
SLICER: Safe Long-Term Cloud Event Archival

Security Information and Event Management (SIEM) systems have been adopted by organizations to enable holistic monitoring of malicious activities in their IT infrastructures. SIEMs receive events from several devices of the organization's IT infrastructure (e.g., servers, firewalls, IDS), correlate these events, and present reports for security analysts. Given the large number of events collected by SIEMs, it is costly to store such data for long periods. Besides, since organizations store a relatively limited time-frame of events, the forensic analysis capabilities severely become reduced. We present SL I CER an archival system for long-term storage that makes use of multi-cloud storage to guarantee data security, low cost and high scalability, and ensures cost-effectiveness by grouping events in blocks and using indexing techniques to recover them. The system was evaluated using a real dataset, and the results show that it is significantly more cost-efficient than competing alternatives.
Prediction of Solar Power Generation Based on Random Forest Regressor Model

The paper addresses the problem of short-term renewable energy forecasting. The stochastic nature of renewable energy sources affects the power system planning procedures, subsequently reducing the reliability and security of power supply for final customers. The authors propose a machine learning approach for effective day-ahead forecasting using retrospective metering data and open source weather information provided by meteorological services. The paper addresses the problem of feature identification and appropriate error metrics. For the purpose of effective testing the user interface was created and tested on the real solar power plant situated in southern region of Russian Federation. The mean forecasting accuracy was obtained to be about 93%.
Multi-Inputs and Multi-Outputs Mems Resonator for Complex Logic Operations

Microelectromechanical systems MEMS resonatorbased computing devices have recently attracted significant attention due to their simplicity and potential toward energy efficient computing machines. Lately, there have been successful demonstrations of fundamental logic gates. However, the realization of complex multifunctional logic gates that require multi-input and multi-output lines have faced some obstacles, such as the interconnections between multiple resonators and the increase in device complexity due to the large required arrays of resonators. This paper demonstrates a 1:2 demux combinational logic gate from the first and the second vibrational modes of a single MEMS microstructure. The MEMS device consists of three connected inplane microbeams forming a U-shape. The microbeams can be electrostatically actuated individually or collectively.
Wearable Airflow Sensor for Nasal Symmetric Evaluation and Respiration Monitoring

Respiration is an important physiological function in which air is moved into and out of the lungs. This paper designed a flexible patch for continuous detections of respiration, especially for evaluating nasal symmetry. The monitoring system comprises an integrated hot-film and two time-of-flight flow sensors on a flexible polyimide (PI) substrate. The 5 μm-thick polyimide substrate is flexible to construct wearable devices while the hot-wire flow sensor can monitor the respiration conditions and states, such as different breathing patterns of mouth or nose modes, normal or deep forms, and happy or nervous signs. Moreover, the power consumption of the system is low for possible long-term monitoring applications of sleep and medical diagnosis. The two time-of-flight sensors are aligned symmetrically on different sides of the hot-film heating element for differential signal readings, which used asymmetrical thermal distribution to reveal breathing state differences of two nostrils and they don’t require additional energy since they are located near the heating element. The system serves as a noninvasive wearable device to monitor respiratory airflow while avoiding uncomfortable nasal cannula. Various testing results show the feasibility and effectiveness of the proposed system.
Test Migration Between Mobile Apps with Similar Functionality

The use of mobile apps is increasingly widespread, and much effort is put into testing these apps to make sure they behave as intended. To reduce this effort, and thus the overall cost of mobile app testing, we propose APPTESTMIGRATOR, a technique for migrating test cases between apps in the same category (e.g., banking apps). The intuition behind APPTESTMIGRATOR is that many apps share similarities in their functionality, and these similarities often result in conceptually similar user interfaces (through which that functionality is accessed). APPTESTMIGRATOR leverages these commonalities between user interfaces to migrate existing tests written for an app to another similar app. Specifically, given (1) a test case for an app (source app) and (2) a second app (target app), APPTESTMIGRATOR attempts to automatically transform the sequence of events and oracles in the test for the source app to events and oracles for the target app. We implemented APPTESTMIGRATOR for Android mobile apps and evaluated it on a set of randomly selected apps from the Google Play Store in four different categories. Our initial results are promising, support our intuition that test migration is possible, and motivate further research in this direction.
A Novel Data-driven Approach for Tropical Cyclone Tracks Prediction Based on Granger Causality and GRU

The strong tropical cyclones will make a drastic effect on human life and natural environment. In these days, as meteorological data and monitoring data accumulated to a large amount, traditional methods of predicting tropical cyclones tracks face many challenges about the prediction efficiency and accuracy. Recently, deep learning method has proven to be an efficient and accurate way to forecast time series data. Therefore, this paper proposes a novel data-driven deep learning model based on Granger causality and Gated Recurrent unit(GRU) to predict the tropical cyclones tracks by selecting the meteorological factors that affect the tropical cyclone locations. The model comprises several aspects including data preprocessing, the feature selection layer, and the GRU with a customized batch process. The model was trained using a real-world tropical cyclones dataset from the year 1945 to 2017, and the results proved that our proposed model can improve the prediction accuracy in the experimental scenario.
S-band High-efficiency Miniaturized Wideband GaN Power Amplifier

This paper proposes a new high-efficiency miniaturized S-band internally matched high power amplifier (HPA), which developed with 24mm AlGaN/GaN high-electron mobility transistors. Load-pull method is used to design the amplifier, the out network matched to compromise between power added efficiency (PAE) and output power. Experimental results show that the class C GaN HPA can be realized more than 62% PAE and 120W output power over the band of 2.6-3.7 GHz, with drain voltage of 32 V, pulse-width of 3ms and duty-cycle of 30%. The HPA size is 6.6mm×15mm.
Simultaneous Electric Powertrain Hardware and Energy Management Optimization of a Hybrid Electric Vehicle Using Deep Reinforcement Learning and Bayesian Optimization

In order to maximize the energy efficiency of hybrid vehicles, both the electric powertrain hardware and the energy management have to be optimized. Focusing on a certain deterministic driving cycle during design can lead to higher consumption in customer use, as real driving cycles differ to the deterministic cycles. This contribution uses a modern Deep Reinforcement Learning (DRL) energy management, which is able to optimize controls for real stochastic vehicle use. Additionally, a Bayesian Optimization sequentially operating with the DRL selects suitable hardware configurations. The best configuration achieves a fuel consumption reduction of 10 percent for a mild hybrid electric vehicle.