Miracle-dz/UniFAR-FTUs huggingface dataset:
Vehicle Tip-over Prevention Using SDRE Controller

Vehicle rollovers are known to produce the highest number of deaths amongst vehicle accidents. Rollover prevention systems currently available in commercial vehicles attempts to prevent any wheel lift-off the ground to avoid a subsequent rollover. In literature, attempts that were made to recover a vehicle from a tip-over point after a wheel lift-off has occurred are only a few. The present article proposes a State Dependent Riccati Equation (SDRE) based anti-rollover controller to recover a vehicle from tip-over point. SDRE method delivers a simple yet effective solution to the nonlinear optimal control problem. An Inverted Double Pendulum on a Cart Model (IDPC) acts as the base model in the controller. The tip-over point of the IDPC model is an unstable equilibrium point of the model and the gravity torque has a negligibly small effect in this vicinity. In the SDRE method, this unstable equilibrium point acts as a singularity point. A virtual rollover torque is used with the IDPC model instead of the gravity torque to overcome this problem. Essentially the controller makes use of a model that is more prone to rollover than the real model with gravity assistance. The controller is evaluated with IDPC model with gravity torque, as well as a 10 Degrees of Freedom (DOF) full vehicle model with a nonlinear tire model. A study is also conducted on the change in performance of the controller with weight value of roll state used in the performance index. Studies proved the controller to be simple yet effective in reinstating the vehicle model in all the near tip-over case studies conducted. The control input force was found to increase with increase in weight value of the roll state which resulted in a reduced reinstating time duration.
Leveraging Temporal Data for Automatic Labelling of Static Vehicles

The development of advanced 3D object detection algorithms for autonomous driving requires a variety of environments to be captured in labelled datasets. While a number of such datasets exist, new ones will continue to be needed to adapt to new domains, sensors and conditions. In this paper, we propose a method to ease the workload of annotators by automatically proposing high-recall labels for static vehicles. We make use of an object detection network pre-trained on an existing dataset to propose detections within a sequence. By determining the location of each frame in a common reference frame, all detections of a static vehicle will share the same location. By averaging these overlapping detections and extending the prediction to all reasonable frames, we generate identical labels for the same object throughout the sequence. We show how our method sequentially refines predictions in order to improve static object recall by over 20% and precision by 7% over an initial set of network proposals.
Joint Optimization of Survivability and Energy Efficiency in 5G C-RAN With mm-Wave Based RRH

Centralized Radio Access Networks (C-RAN) exploiting millimeter wave (mm-wave) technology in remote radio heads (RRHs) are regarded as a promising approach to satisfy the challenging service requirements of fifth generation (5G) mobile communication. However, ultra-dense deployment of mm-wave RRHs will generate enormous amount of traffic that will require effective design and operation of C-RAN backhaul. In this paper, we focus on developing an optimal mm-wave RRHs placement strategy that exploits resource and traffic assignment in RRHs to achieve reliable and energy efficient backhaul transmissions. Specifically, in this paper, mm-wave is considered both to provide end users access and to interconnect RRHs in same frequency band, hence achieving energy saving thanks to hardware and frequency reuse. In this scenario, leveraging the traffic predictions obtained by a deep neural network, we present a real-time traffic assignment scheme where traffic from affected RRHs can be rerouted to other RRHs to protect against backhaul failures and traffic migrates to as few RRHs as possible to switch off some backhaul links for energy efficiency. Due to the inherent short-range transmission of mm-wave, different RRH deployment locations significantly affect interconnections in RRHs. Therefore, we model the mm-wave RRH placement problem into an optimization framework that jointly maximizes backhaul survivability and energy efficiency, whilst subjects to constraints as network coverage and capacity. To guarantee scalability of the proposed scheme as network scale increases, a heuristic algorithm is also proposed. Numerical evaluations show that, with appropriate RRH placement strategies, significant survivability and energy efficiency improvements can be achieved.
A Novel Encoding Strategy of Enhanced Broadband and Absorption Conformable Metamaterial for MW Applications

Analog metamaterials (MMs) manipulate their effective medium parameters with difficulty, when their geometric architecture is composed of hybrid compositions. However, genetic algorithms, calculation-analog search algorithms that seek for optimal solutions, can be applied to artificial metamaterial-architecture construction. This paper proposes a novel encoding strategy for metamaterial architecture construction that utilizes multi-parameter extremum-seeking optimization. The binary encoding and decoding of the geometric-layer thickness enables the form’s final dimension to be based on the objective fitness function. The genetic algorithm iteratively optimizes the initial geometrical dielectric-layer thicknesses. Then, combining the initial optimized parameter with the composite-metal multi-loops’ spatial distribution, the final metamaterials were analyzed using numerical analysis software. Based on a co-simulation disposition, the proposed metamaterial exhibits 2.5-GHz broadband features with over 80% physical absorption of spatial waves. The proposed metamaterial simultaneously presents low radar cross-sections, wide polarization insensitivity, and dynamical flexibility. Moreover, the proposed metamaterial, when loaded onto a reference antenna, exhibited good real application capability in radar cross-section reduction for physical passive-equipment invisibility. The numerical-simulation and experimental results of the MM’s absorption and flexibility properties showed good agreement, suggesting the advantage of genetic-algorithm optimization co-simulated with numerical-analysis software for metamaterial architecture construction. It shows good potential application for complicated spatial-geometry formations.
Non-Contact Respiratory Rate Estimation in Real-Time With Modified Joint Unscented Kalman Filter

It can be life-saving to monitor the respiratory rate (RR) even for healthy people in real-time. It is reported that the infected people with coronavirus disease 2019 (COVID-19), generally develop mild respiratory symptoms in the early stage. It will be more important to continuously monitor the RR of people in nursing homes and houses with a non-contact method. Conventional, contact-based, methods are not suitable for long-term health monitoring especially in-home care services. The potentials of wireless radio signals for health care applications, such as fall detection, etc., are examined in literature. In this paper, we focus on a device-free real-time RR monitoring system using wireless signals. In our recent study, we proposed a non-contact RR monitoring system with a batch processing (delayed) estimation method. In this paper, for real-time monitoring, we modify the standard joint unscented Kalman filter (JUKF) method for this new and time-critical problem. Due to the nonlinear structure of the RR estimation problem with respect to the measurements, a novel modification is proposed to transform measurement errors into parameter errors by using the hyperbolic tangent function. It is shown in the experiments conducted with the real measurements taken using healthy volunteers that the proposed modified joint unscented Kalman filter (ModJUKF) method achieves the highest accuracy according to the windowing-based methods in the time-varying RR scenario. It is also shown that the ModJUKF not only reduces the computational complexity approximately 8.54% but also improves the accuracy 36.7% with respect to the standard JUKF method.
Rapid translocation and pharmacokinetics of hydroxylated single-walled carbon nanotubes in mice

Determining the in vivo pharmacological profiles of carbon nanotubes (CNTs) is essential for the promising biomedical applications of CNTs, such as drug delivery. Using iodine-131 tracing we studied the fundamental behavior of hydroxylated single-walled CNTs (SWNTols) shortly after they were introduced into the animal body (from 2 min to 1 h) by providing the biodistribution data and pharmacokinetic parameters. The distribution was slightly influenced by injection modes, but in any mode radioactivity was found all over the body within 2 min except brain. Liver, kidneys, stomach and lungs are the target organs with high uptake of SWNTols. The SWNTols content in several tissues, such as heart, lungs, and muscle is positively correlated with its content in the blood, showing clearly that the blood stream brings SWNTols to the whole body. This work presents the initial in vivo behavior of the water-soluble functionalized SWNTs, providing also the basic data to show opportunities and limitations for realization of the CNT-based drug vehicle.
Spike Detection Using Cross-Correlation Based Method

The detection of activities (spikes) in extracellular neural recordings is a crucial step to the classification of neural signals. In a high-channel count microelectrode array the recorded action potentials come from different neurons on different channels, therefore the number of spikes can be huge (more than 10000) after a couple of minutes. The impact on computation resources of feature extraction or classification can be significant when spike detection is working with a high false positive ratio. In this paper a cross-correlation based spike detection algorithm is proposed and compared to other spike detection algorithms. The cross-correlation method uses a predefined average template for the base of the detection. Using this method an average efficiency of 94% with 0.3 false positive rate can be achieved at an estimated signal-to-noise ratio (SNR) of 4 dB. The proposed method is compared to 5 widely used spike detection algorithm namely the Non-Linear Energy Operator (NEO), the Power method, Positive, Negative, and Absolute Amplitude Thresholding (AMPP, AMPN, AMPA) using a neural signal generator with 32 channels, variyng SNR and 12 neurons on the channels. It can be concluded, that the proposed method can achieve a far better false positive ratio, than the others, while maintaining accuracy.
Brain Tumor Segmentation from MRI Data Using Ensemble Learning and Multi-Atlas

Atlases are frequently employed to assist medical image segmentation with prior information. This paper introduces a multi-atlas architecture that is trained to locally characterize the appearance (average intensity and standard deviation) of normal tissues in various observed and computed data channels of brain MRI records. The multiple atlas is then deployed to enhance the accuracy of an ensemble learning based brain tumor segmentation procedure that uses binary decision trees. The proposed method is validated using the low-grade tumor volumes of the BraTS 2016 train data set. The use of atlases improve the segmentation quality, causing a rise of up to 1.5% in average Dices scores.
Dual-Targeting Nanoparticle-Mediated Gene Therapy Strategy for Hepatocellular Carcinoma by Delivering Small Interfering RNA

As a gene therapy strategy, RNA interference (RNAi) offers tremendous tumor therapy potential. However, its therapeutic efficacy is restricted by its inferior ability for targeted delivery and cellular uptake of small interfering RNA (siRNA). This study sought to develop a dual-ligand nanoparticle (NP) system loaded with siRNA to promote targeted delivery and therapeutic efficacy. We synthesized a dual receptor-targeted chitosan nanosystem (GCGA), whose target function was controlled by the ligands of galactose of lactobionic acid (LA) and glycyrrhetinic acid (GA). By loading siPAK1, an siRNA targeting P21-activated kinase 1 (PAK1), a molecular-targeted therapeutic dual-ligand NP (GCGA–siPAK1) was established. We investigated the synergistic effect of these two targeting units in hepatocellular carcinoma (HCC). In particular, GCGA–siPAK1 enhanced the NP targeting ability and promoted siPAK1 cell uptake. Subsequently, dramatic decreases in cell proliferation, invasion, and migration, with an apparent increase in cell apoptosis, were observed in treated cells. Furthermore, this dual-ligand NP gene delivery system demonstrated significant anti-tumor effects in tumor-bearing mice. Finally, we illuminated the molecular mechanism, whereby GCGA–siPAK1 promotes endogenous cell apoptosis through the PAK1/MEK/ERK pathway. Thus, the dual-target property effectively promotes the HCC therapeutic effect and provides a promising gene therapy strategy for clinical applications.
Chemical signals from eggs facilitate cryptic female choice in humans

Mate choice can continue after mating via chemical communication between the female reproductive system and sperm. While there is a growing appreciation that females can bias sperm use and paternity by exerting cryptic female choice for preferred males, we know surprisingly little about the mechanisms underlying these post-mating choices. In particular, whether chemical signals released from eggs (chemoattractants) allow females to exert cryptic female choice to favour sperm from specific males remains an open question, particularly in species (including humans) where adults exercise pre-mating mate choice. Here, we adapt a classic dichotomous mate choice assay to the microscopic scale to assess gamete-mediated mate choice in humans. We examined how sperm respond to follicular fluid, a source of human sperm chemoattractants, from either their partner or a non-partner female when experiencing a simultaneous or non-simultaneous choice between follicular fluids. We report robust evidence under these two distinct experimental conditions that follicular fluid from different females consistently and differentially attracts sperm from specific males. This chemoattractant-moderated choice of sperm offers eggs an avenue to exercise independent mate preference. Indeed, gamete-mediated mate choice did not reinforce pre-mating human mate choice decisions. Our results demonstrate that chemoattractants facilitate gamete-mediated mate choice in humans, which offers females the opportunity to exert cryptic female choice for sperm from specific males.
Can We Trust Privacy Policy: Privacy Policy Classification Using Machine Learning

Mobile applications frequently request privacy information from users to supposedly use to improve online service and applications. The collected data, such as personally identifiable information, raises users’ concerns since some applications actually have malicious intentions to leak personal data. Privacy policies are an important resource as they are the sole source of information users can easily gain access in order to determine how applications plan to collect and use their data prior to downloading and using the application. However, users tend to ignore or gloss over privacy policies as they are often written in the complicated hard-to-understand language. Thus, users often miss crucial privacy-related information after reading such documents. In this paper, we experimentally determine how much we can trust an application’s privacy policy by looking at the language used in more than 9,000 privacy policies and compare them to what the applications actually do. We attempt to classify whether or not applications transmit privacy-related information using machine learning with three classifiers, support vector machines (SVMs), k- nearest neighbors (KNN), logistic regression (LR). The best results show the average recall and precision of 0.81 and 0.31, respectively. High recall indicates that we are able to correctly identify most of the applications that transmit personally identifiable information. But, low precision indicates that we often over-identify applications as ones that transmit personally identifiable information when in reality they do not.
Assessment of Satellite Contacts Using Predictive Algorithms for Autonomous Satellite Networks

Upcoming Low Earth Orbit Satellite Networks will provide low-latency and high downlink capacity necessary for future broadband communications and Earth Observation missions. This architecture was proposed at the beginning of the 90’s, although it has just recently re-gained popularity thanks to the so-called Mega-Constellations. This network is composed of satellites that have Inter-Satellite Links (ISL) to communicate between them. Due to the satellite motion, an ISL is a temporal contact between two satellites characterized by a lifetime in which the communication remains feasible. The determination of a route between distant satellites is a challenging problem in this context. However, the satellite follows a well-known deterministic orbit trajectory, being feasible the prediction of its position by propagating a trajectory model over time. The Contact Graph Routing protocol uses this feature to determine the evolution of the routes by pre-computing on-ground a planning of the satellite contacts. This centralized ground-dependent solution cannot be directly applied in the Internet of Satellites paradigm, which proposes the autonomous deployment of heterogeneous satellite networks without pre-assuming any specific satellite system architecture. Following this concept, the present work proposes a distributed algorithm by which a satellite predicts neighbor contacts, and generates a global contact plan without trajectory propagation. To achieve this solution, an ISL has been modeled as a “close approach” between two satellites, which is characterized by their relative motion. The present work details the predictive algorithm, and evaluates its performance in two scenarios with a hybrid satellite constellation and a mega-constellation.
Detection of Multiple Function Dependency Violations for Distributed Big Data

It is usually necessary to move data from one site to another when detecting function dependency violations under distributed data environment, which leads to low efficiency in big data processing. In this paper, a novel detection method of multiple function dependency violations was proposed based on the concept of equivalence class, and a response time cost model for the method was provided. Because it is a NP-hard for function dependency violation detection to allocate tasks under distributed environment, we converted response time minimum of violation detection into an integer programming problem, and provided near-optimal solution. Aiming at the different cluster scale and the number of function dependencies, different task assignment policies were provided, and load balancing problem was also considered adequately. The experimental results on real and artificial data set show that, compared to the centralized detection methods on Hadoop 2.0, the proposed method in the paper has an obvious efficiency promotion and good extensibility in big data processing.
Peer-to-peer loan acceptance and default prediction with artificial intelligence

Logistic regression (LR) and support vector machine algorithms, together with linear and nonlinear deep neural networks (DNNs), are applied to lending data in order to replicate lender acceptance of loans and predict the likelihood of default of issued loans. A two-phase model is proposed; the first phase predicts loan rejection, while the second one predicts default risk for approved loans. LR was found to be the best performer for the first phase, with test set recall macro score of 77.4%. DNNs were applied to the second phase only, where they achieved best performance, with test set recall score of 72%, for defaults. This shows that artificial intelligence can improve current credit risk models reducing the default risk of issued loans by as much as 70%. The models were also applied to loans taken for small businesses alone. The first phase of the model performs significantly better when trained on the whole dataset. Instead, the second phase performs significantly better when trained on the small business subset. This suggests a potential discrepancy between how these loans are screened and how they should be analysed in terms of default prediction.
Frequency Domain Multi-Carrier Modulation Based on Prolate Spheroidal Wave Functions

A novel frequency domain multi-carrier modulation (MCM-FD) scheme is proposed based on prolate spheroidal wave functions (PSWFs) for reducing the high complexity in the conventional time domain PSWFs multi-carrier modulation (MCM-PSWFs-TD) schemes. By constructing the relationship between discrete representation and exponential function representation of MCM-PSWFs-TD signals, it can be observed that the orthogonality and parity symmetry of the waveforms of PSWFs in the frequency domain are the same as that in the time domain. Thus, the PSWFs signal can be divided into two groups based on their parity symmetry, while these two groups of PSWFs signals can be processed simultaneously. Based on this concept, signal waveforms with only half spectrum range are invoked in the process of information loading and signal detection for reducing the number of sampling points participated in the signal operation. Compared to the MCM-PSWFs-TD scheme, the proposed MCM-PSWFs-FD scheme is capable of significantly reducing the computational complexity without severely degrading the system performance, such as spectral efficiency (SE), bit error rate performance, signal energy concentration and peak-to-average power ratio (PAPR). Furthermore, the cyclic-prefix orthogonal frequency division multiplexing (CP-OFDM), OFDM with weighted overlap and add (WOLA-OFDM), filter OFDM (F-OFDM), universal filtered multi-carrier (UFMC), as well as the filter bank multi-carrier with offset quadrature amplitude modulation (FBMC-OQAM) are also demonstrated as benchmarks. Simulation results are provided for illustrating that the proposed MCM-PSWFs-FD scheme is capable of striking a favorable tradeoff between the computational complexity and the system performance (i.e. SE, out-of-band energy leakage, adjacent frequency band interference, and PAPR), while the signal waveform design of the MCM-PSWFs-FD scheme is also more concise and flexible than the benchmarks.
Study on Optimization Design of Vehicle Stability Fuzzy Control System

In order to ensure vehicle driving stability during critical and dangerous conditions, a new vehicle stability control strategy is proposed, which is to generate compensating yaw moment by using the combined action of active rear-wheel steering and differential braking. Subsequently, the corresponding fuzzy controller is designed to achieve this thought. Furthermore, the genetic algorithm is used to optimize this fuzzy controller so as to improve the performance and overcome the subjectivity in the division of membership functions and the definition of controller parameters. The numerical simulation tests are performed in critical condition by means of the vehicle dynamic model and the virtual prototype model. Simulation results show that the designed fuzzy controller can efficiently prevent vehicle from losing stability in danger.