Miracle-dz/UniFAR-FTUs huggingface dataset:
Model Predictive Longitudinal Motion Control for the Unmanned Ground Vehicle With a Trajectory Tracking Model

Longitudinal motion controllers based on over- simplified models result in steady-state errors, oscillations, and overshoots of the velocity, all of which impair the unmanned ground vehicle (UGV) multiple objectives (trajectory tracking capability, energy economy, and ride comfort). While it is challenging for complicated methods to accomplish real-time control in the vehicle-mounted electronic control unit (ECU), which meets harsh working conditions but has limited computing power. This paper proposes a multi-objective model predictive longitudinal motion control approach for an ECU based on a trajectory tracking model. For the trajectory tracking model, we establish an internal powertrain/brake dynamics model and divide the external resistance into three components. They are identified using a variety of practical and straightforward methods. The control strategy is composed of a high-level multi-objective model predictive controller (MPC) that obtains the optimal internal acceleration and a low-level controller that calculates control inputs based on the powertrain/brake model. To implement real-time control on the ECU, the MPC optimization problem and its solver that combines offline and online calculation are elaborately designed. Finally, the method is validated through simulations in three typical driving scenarios and vehicle tests on a hybrid-electric sport utility vehicle (SUV) and a 6-speed dual- clutch transmission (DCT) fuel sedan. Simulations and experiments demonstrate that the proposed approach is superior to the other four commonly used methodologies in terms of achieving multiple objectives.
Weather Research and Forecasting model validation with NREL specifications over the New York/New Jersey Bight for offshore wind development

The New York and New Jersey Bight is the new epicenter for offshore wind development. This region has many different meteorological and ocean-based processes that affect this area for potential offshore wind development. Using a locally focused Weather Research and Forecasting Model (WRF) application can help with this analysis of the wind energy area. WRF outputs several variables that can be used in this evaluation. This study will focus on wind speed, wind direction, sea surface temperature, and a wind shear exponent calculated by wind speeds at available heights. The information extracted from these WRF models is vital to wind developers as it allows them to more thoroughly understand the available wind resource given limited observational data, and help them better understand the surrounding environment. To make sure that these products are producing the best output possible, models are validated against both each other and available observations. In this study, we will be validating national models including the North American Regional Model (NAM), the Global Forecast System (GFS), and the High-Resolution Rapid Refresh model (HRRR) against the Rutgers WRF model setup (RU WRF). It is important to have other models as a comparison point so we can see how a more scaled regional model can depict the wind area in comparison to the larger scaled models. We use publicly available observation data from the New York State Energy Research and Development Authority (NYSERDA) and Atlantic Shores Offshore Wind Development floating LIDAR. The observational points are either inside the wind energy areas or close to them. With the validated wind data, we can begin to explore and learn from how the model captures oceanographic processes and phenomena such as wind shear and stability.
Video-Based Ski Jump Style Scoring from Pose Trajectory

Ski jumping is one of the oldest winter sports and takes also part in the Winter Olympics from the very start in 1924. One of the components of the final score, which is used for ranking the competitors, is the style score, given by five judges. The goal of this work was to develop a prototype for automatic style scoring from videos. As the main source of information, the proposed approach uses the detected locations of the ski jumper body parts and his skis to capture a full-body movement through the entire ski jump. We extended a method for human pose estimation from images to detect also the tips and the tails of the skies and adapted it to the domain of ski jumping. We proposed a method to utilize the detected trajectories along with the scores given by real judges to build a model for predicting the style scores. The experimental results obtained on the data that we had available show that the proposed computer-vision-based system for automatic style scoring achieves an error comparable to the error of real judges.
Screen Printing Reliable Wearable Microstrip Antennas on Rough Textile Substrates

Textile-based antennas are typically realized using conductive fabrics adhered to thick non-woven fabrics such as felt, which are not suited for additive manufacturing due to their roughness. This paper presents screen printing on thin and breathable polyurethane films as a method for realizing e-textile antennas without altering the feel of the fabric, demonstrated through a microstrip antenna on standard felt fabric. The fabricated proximity-coupled patch maintains at least 100 MHz (4%) bandwidth from 2.46 to 2.56 GHz regardless of bending and human proximity, 6.6 dBi measured gain, and 69% radiation efficiency. The robustness of the proposed antenna is demonstrated, for the first time, through the measured radiation properties, showing that the antenna maintains its peak gain and radiation patterns after more than 10,000 bending cycles.
Optimal Design of 90°-Bend in NRD Guide Using DBS Algorithm and 2D-FVFEM

An optimal design approach based on the mosaic-like structure concept for NRD guide devices is proposed in this paper. In order to achieve efficient design with high transmission efficiency, and minimum computational effort, we employ recently proposed two-dimensional full-vectorial finite element method (2D-FVFEM) for numerical simulations. Non-linear direct binary search algorithm is implemented as an optimization problem to obtain the optimal NRD guide device at operating frequency 60GHz. In order to prove the significance of the proposed optimization approach we designed an NRD 90°-bend guide and it achieves more than 99% transmission efficiency. The analysis of numerical results by 2D-FVFEM are also verified by 3D-FVFEM.
Accelerator-Aware Kubernetes Scheduler for DNN Tasks on Edge Computing Environment

The compute capability of edge devices is expanding owing to the wide adoption of edge computing for various application scenarios and specialized hardware explicitly developed for an edge environ-ment. A container orchestration platform, Kubernetes is widely used to maintain edge computing resources efficiently, but it suf-fers from a limited scheduling capacity. We present a design and implementation of an accelerator information extraction module to improve the scheduling capability of a standard Kubernetes imple-mentation by providing rich hardware information. Furthermore, we present a plausible advancement of the Kubernetes scheduler by considering detailed workload characteristics and attached spe-cialized accelerator hardware information.
Jamming Detection and Classification in OFDM-Based UAVs via Feature- and Spectrogram-Tailored Machine Learning

In this paper, a machine learning (ML) approach is proposed to detect and classify jamming attacks against orthogonal frequency division multiplexing (OFDM) receivers with applications to unmanned aerial vehicles (UAVs). Using software-defined radio (SDR), four types of jamming attacks; namely, barrage, protocol-aware, single-tone, and successive-pulse are launched and investigated. Each type is qualitatively evaluated considering jamming range, launch complexity, and attack severity. Then, a systematic testing procedure is established by placing an SDR in the vicinity of a UAV (i.e., drone) to extract radiometric features before and after a jamming attack is launched. Numeric features that include signal-to-noise ratio (SNR), energy threshold, and key OFDM parameters are used to develop a feature-based classification model via conventional ML algorithms. Furthermore, spectrogram images collected following the same testing procedure are exploited to build a spectrogram-based classification model via state-of-the-art deep learning algorithms (i.e., convolutional neural networks). The performance of both types of algorithms is analyzed quantitatively with metrics including detection and false alarm rates. Results show that the spectrogram-based model classifies jamming with an accuracy of 99.79% and a false-alarm of 0.03%, in comparison to 92.20% and 1.35%, respectively, with the feature-based counterpart.
Development of a Chair to Support Human Standing Motion -Seat movement mechanism using zip chain actuator-

Many elderly people suffer from declined motor ability, and it is important to develop an assistive device to support their daily life. Some assistive device have been developed previously to help human sit-to-stand motion, but they generally help body extension slowly and thus the user of the device could not utilize their own muscles. Here, we develop a chair type assist device which lift up the seat to push the hip joint for supporting hip rising and body extension. Our developed device employed a zip chain actuator which realizes fast and strong support to follow the user’s movement and provide assistance. Evaluation experiment was performed to assess the effect of developed device on muscle activation of lower limbs during sit-to-stand motion. Results show that the muscle activation of knee extensor decreased significantly compared to that without device support. However, the activation level of flexor is found to be increased to deaccelerate the body extension.
Pattern Recognition of Partial Discharge in Oil-paper Insulation Based on Analytic Hierarchy Process

The pattern recognition of partial discharge(PD) can facilitate estimations of PD mode and grade,and design of protection scheme.To recognize partial discharges,five kinds of typical defects in oil-paper insulation are set up.Through K-W test,11 features with the strongest ability of classification are chosen from the statistical operators of Phase Resolved Partial Discharges(PRPD). In the case of being trained by small sample,the analytic hierarchy process(AHP) is employed to recognize these typical partial discharges(PD) based on the chosen features.Compared with the artificial neural network(ANN) in the same condition,AHP achieves higher recognition accuracy of over 85%. This study may lay a foundation for rapid recognition of typical partial discharges in the case of small training sample.
Orientation-Aware Feature Fusion Network for Ship Detection in SAR Images

Recently, deep learning methods have been successfully applied to the ship detection in synthetic aperture radar (SAR) images. It is still a great challenge to detect SAR ships, due to the extremely poor image quality and complex background. To solve the problems, a novel method named orientation-aware feature fusion network (OFF-Net) for ship detection in SAR images is proposed in this letter. OFF-Net consists of global context path aggregation (GCPA) module and local rotated contrast enhance (LRCE) module, which fuses the global and local information in feature extraction. First, GCPA module is explored to integrate the global context block with path aggregation network (PAN) to learn the global background information. Second, by designing a rotation scheme based on feature map cyclic shift with four directions, LRCE module is developed to enhance the targets and suppress the background clutters in SAR images. Finally, a decoupled orientation-aware head is proposed to handle the arbitrarily rotated ships more robustly and alleviate the conflict between classification and regression tasks during detection. In addition, a high-resolution SAR-ship detection dataset (OBB-HRSDD) with rotatable bounding boxes is provided. The detection results on the SAR ship detection dataset (SSDD+) and OBB-HRSDD illustrate that our method outperforms all the compared methods. The code and OBB-HRSDD are released at https://github.com/SJX152/papercode
Multi-Domain Learning Scheme for Full-Wave Nonlinear Inverse Scattering Problems

In this paper, a multi-domain learning scheme (MDLS) that combines frequency- and real-space features of contrast is proposed to solve full-wave nonlinear inverse scattering problems (ISPs). A noticeable characteristic of the proposed MDLS is that interlacer layers are added in traditional deep learning scheme, which serves as information-interchange layer to exchange the information in contrast space and its Fourier space. The main advantage of MDLS is to achieve a more robust and accurate nonlinear reconstruction by combining information from contrast and its Fourier domain. It is shown that the reconstructed results are robust to noise contamination and the reconstructed image quality is quantitatively better compared with the traditional architecture built in a single domain.
Characteristics of partial discharges in an experimental setup reproducing HVDC cable insulation operating conditions

The occurrence of Partial Discharge (PD) inside a cavity subjected to HVDC voltage can be considered as one of the most likely sources of insulation ageing and failure. In practical applications, the recognition of PD under HVDC is more difficult than under AC stress because of the lower repetition rates and the impossibility to obtain a PD pattern synchronized with the waveform of the applied voltage. In this paper, the relationship between conduction phenomena in dielectrics and the occurrence of PD is investigated by means of a novel experimental setup. A couple of dielectric cells connected in series is subjected to various HVDC stress levels. The applied voltage is divided between the two cells by subjecting them to a temperature difference. The PD activity inside an air void defect existing within one of the two dielectric cell is measured by means of a PRY-CAM®. This setup reproduces, to the extreme, the effect of the distribution of the electric field in an HVDC cable on PD activity when it is exposed to a thermal gradient across the insulation. In addition, an equivalent circuit model has been developed to evaluate the PD Inception Voltage (PDIV) across the cavity. The comparison between the experimental and model results demonstrates the capability of the macroscopic conduction model to reproduce the inception of PD phenomena in solid dielectrics under HVDC stress and temperature gradient.
RoboWhaler: A Robotic Vessel for Marine Autonomy and Dataset Collection

In this paper, we present a manned/unmanned vehicle that is used for autonomous surface vessel algorithmic development. This mobile marine autonomy laboratory is built upon a modified Boston Whaler and can be operated in both manual and fully autonomous modes. The primary driver in development was the creation of a marine autonomy sensor suite capable of high resolution sensing in all weather conditions. The vessel now serves as a mobile marine autonomy laboratory and is used to collect on-water datasets as well as testing behaviors and algorithms for autonomous operations. The vessel is equipped with a SBG Ellipse2-D RTK INS, a Velodyne VLP-16 lidar for high resolution 360° sensor coverage, a Simrad 4G broadband radar for long-range 360° sensor coverage, forward facing FLIR ADK infrared cameras, and forward facing FLIR Blackfly 1.3 MP cameras. All data is available in ROSBag format and can be manipulated and visualized using the open source Robot Operating System (ROS). We also make available tools to extract timestamped data presented in standard human readable formats. Datasets have been collected with the current marine autonomy sensor suite from fall 2019 to summer of 2021. These datasets have been curated for consumption and are available on the MIT Sea Grant website https://seagrant.mit. edu/auvlabdatasets-marine-perception-1.
Peer Support as a Method of Risk Reduction in Injecting Drug-User Communities: Experiences in Dutch Projects and the “European Peer Support Project”

This article discusses the central importance in Western Europe of active drug users/injectors working with their peers to combat the spread of HIV and reduce other health problems. The structure and workings of several different peer organizations are described, and an important distinction is made between “self-help” versus drug-user “unions” or “interest” groups. The roots of the first drug-user unions are traced to the junkiebonden that surfaced in the Netherlands in the early 1970s. Now, the European Peer Support Project, which is described, coordinates projects in six different western European countries, supporting several different initiatives, and producing training programs and manuals for users to assist them in their organizing efforts. The underlying premise of drug-user self-organizations is that repressive drug policy, as defined by criminal laws, is not only a fundamentally inappropriate approach to the “drug problem;” repressive drug policy is partly responsible for the creation of the “drug problem” in the first place.
Generative Adversarial Network-Based Design of Dielectric Resonator Antenna for mmWave 5G Applications

Characterized by low conduction loss and high radiation efficiency, dielectric resonator antennas (DRA) have been considered as a promising antenna type for the applications of 5G technologies at 60 GHz band. However, the design of the DRA mainly depends on the intuitive reasoning and trial-and-error process, which is time-consuming and resource-demanding. To address this challenge, we developed a generative adversarial network (GAN)-based approach to assist the design of DRA structures. This GAN model incorporates a simulator and generator for the forward prediction and inverse design, respectively. A paired dataset of geometric shapes and S11 parameter was fed into the GAN model to train the neural network. During the training process, the distribution of this dataset was captured by the end-to-end GNA model. The final model is capable of generating new DRA structures according to the desired S11 parameter.
Sleep EEG Analysis Based on a Scale Mixture Model and its Application to Sleep Spindle Detection

This paper presents analysis of sleep electroen-cephalogram (EEG) based on a scale mixture model. In the scale mixture model, the EEG signal is assumed to be a random variable that follows a infinite mixture of Gaussian distributions with the same mean and different covariance matrices, thereby allowing the representation of the stochastic fluctuation of the EEG amplitude. First, a sleep EEG analysis method was proposed by combining the scale mixture model with band-pass filters and a sliding window, thereby allowing the time-series estimation of the model parameters in a specific frequency band. Then, in experiments, we analyzed the EEG signals during rapid eye movement (REM) sleep and sleep stage II using the proposed analysis method. The results showed that the proposed method captures the characteristic changes in the amplitude distribution of the EEG depending on the sleep stage. Furthermore, we focused on sleep spindles in sleep stage II, which are distinctive waves in sleep EEG, and verified their detectability by machine learning using the features defined by the proposed method as input.
Adjuvant Chemotherapy in pT2N0M0 Gastric Cancer: Findings From a Retrospective Study

Background: There is no global consensus on adjuvant chemotherapy (ACT) for pT2N0M0 gastric cancer. We conducted a retrospective study to reveal the role of ACT in such patients. Methods: Patients with pT2N0M0 gastric cancer who underwent radical resection with D2 lymphadenectomy for primary gastric cancer between January 2012 and May 2016 were included. Kaplan–Meier and Cox regression were used to evaluate overall survival (OS), disease-specific survival (DSS) and predictors of prognosis. Stratified analysis based on high-risk factors was conducted. Results: Of enrolled 307 patients, 111 patients underwent surgery alone and 196 patients received ACT. Surgery alone (HR = 2.913, 95% CI: 1.494-5.682, p = 0.002) and total gastrectomy (HR = 2.445, 95% CI: 1.279-4.675, p = 0.007) were independently associated with decreased OS. With the median follow-up of 73.1 months, the 5-year OS rate was 87.9% and 5-year DSS rate was 91.8%. Patients receiving ACT showed a better 5-year OS rate (92.9 vs. 79.3%, p < 0.001) and DSS rate (96.8 vs. 83.0%, p < 0.001) than patients underwent surgery alone. Patients receiving monotherapy (n = 130) had a relatively poor prognosis compared to patients receiving dual-drug (n = 66) without a significant difference (92.3 vs. 93.9%, p = 0.637). In patients without high-risk factors based on the Chinese Society of Clinical Oncology (CSCO) Guidelines, ACT also provided survival benefit (96.0 vs 82.9%, p = 0.038). Conclusions: ACT was accompanied with higher 5-year OS and DSS rates of patients with pT2N0M0 gastric cancer. Patients with pT2N0M0 gastric cancer, regardless of high-risk factors based on the CSCO guidelines, might be considered candidates for ACT. In regard to the therapy regimen, monotherapy might be the optimal choice, considering the adverse events.
Promising Antiproliferative Compound From the Green Microalga Dunaliella tertiolecta Against Human Cancer Cells

Dunaliella tertiolecta is a green flagellated microalga with a high tolerance to salinity and high production of pigments such as zeaxanthin, a xanthophyll carotenoid present in higher plants known for its antioxidant potential. In the current study, the antiproliferative activity of raw extracts and fractions of D. tertiolecta (clone CCMP 1320) was evaluated against four different human cancer cell lines: melanoma, hepatocellular liver carcinoma, and two lung adenocarcinoma cell lines. In addition, a normal cell line (lung fibroblast) was used as toxicity control. The activity was evaluated by treatment with the extracts/fractions following the MTT colorimetric assay procedures. HPLC-UV-HRMS based dereplication helped to identify the bioactive metabolites. A glycoglycerolipid was identified in the active fraction, being involved in the bioactivity of this microalga. This compound, glycerol 1-(9Z,12Z,15Z-octadecatrienoate)-2-(4Z,7Z,10Z,13Z-hexadecatetraenoate)-3-O-β-D-galactopyranoside (1), was purified and its antiproliferative activity was confirmed. This work gives new insights on the antiproliferative activity of the green alga D. tertiolecta and its potential industrial applications.