Miracle-dz/UniFAR-FTUs huggingface dataset:
Aircraft Detection Based on Eigen Decomposition and Scattering Similarity for PolSAR Image

Aiming at the problem of aircraft detection in complex scene, a novel aircraft detection algorithm based on eigen-decomposition and scattering similarity for PolSAR image is proposed. Firstly, Image denoising is performed by using typical scattering difference index (TSDI) filter. Then, region of interest (ROI) containing airport runway and apron is extracted with the characteristics of scattering power, where suspected aircraft targets may exist. Finally, detection statistics are constructed with eigenvalue and scattering similarity parameters to determine true aircraft from suspected targets. Experimental results with real PolSAR data acquired by USA NASA UAVSAR system show that the proposed method can detect aircrafts in complex scene with low resolution effectively.
A New System Diagnostic Method for Powertrain of Automated Electric Vehicles

To enhance the reliability of the powertrain system of automated vehicles, a diagnostic concept to detect various sensor faults is presented in this paper. By analyzing the signals of existing sensors in the vehicle and using a physical model of a powertrain system, the analytic redundancy relations between signals are derived. Based on the structural system analysis theory, eleven residuals are designed in this paper to detect and isolate various sensor faults in the powertrain system, e.g. voltage, current and position sensors. The proposed method has been implemented and simulated in Matlab/Simulink. As a result, the analyzed sensor faults can be detected successfully. This new method shows good robustness against measurement tolerance and model uncertainty.
An Efficient Estimator for TDOA-Based Source Localization With Minimum Number of Sensors

In this letter, the problem of source localization using time difference of arrival is investigated. Then, a closed-form two-stage solution is proposed based on estimation of the range nuisance parameter in the first stage and refinement of initial solution in the next stage. The proposed solution is shown analytically and verified by simulations to be an efficient estimate, which can attain the CRLB performance under mild Gaussian noise assumption. This method is able to locate the source with the minimum number of sensors required for $N$ -dimensional localization. Numerical simulations demonstrate significant performance improvement of the proposed method compared with the state-of-the-art algorithms and corroborate the theoretical developments.
An Intelligent Method for Very-Short Range Multi-Step Wind Power Forecasting

This paper proposes the development of very-short range multi-step wind power forecasting model based on functional network (FN), a modern intelligent paradigm. Although FNs are a well-developed form of neural networks, but the use of these models in renewable power forecasting is a new and emerging concept. The inherent architecture of FN offers problem-driven network topologies and optimal neural functions with various mathematical structures as opposed to classical neural networks. These advantages of functional networks produce a high-performance wind power forecasting model which is further validated in comparison with a benchmark model as well as a conventional neural network model for very-short range multi-step wind power forecasting. The results obtained through a real-world case study indicate notable improvement in forecast accuracy in terms of standard performance indices. Hence the proposed FN forecast model can become a useful tool for wind power system operators in multiple aspects of power system planning and dispatch.
An Unsupervised Approach for Aspect Category Detection Using Soft Cosine Similarity Measure

Aspect category detection is one of the important and challenging subtasks of aspect-based sentiment analysis. Given a set of pre-defined categories, this task aims to detect categories which are indicated implicitly or explicitly in a given review sentence. Supervised machine learning approaches perform well to accomplish this subtask. Note that, the performance of these methods depends on the availability of labeled train data, which is often difficult and costly to obtain. Besides, most of these supervised methods require feature engineering to perform well. In this paper, we propose an unsupervised method to address aspect category detection task without the need for any feature engineering. Our method utilizes clusters of unlabeled reviews and soft cosine similarity measure to accomplish aspect category detection task. Experimental results on SemEval-2014 restaurant dataset shows that proposed unsupervised approach outperforms several baselines by a substantial margin.
Attack Intent Analysis Method Based on Attack Path Graph

At present, with the increase of automated attack tools and the development of the underground industrial chain brought by network attack, even well-managed network is vulnerable to complex multi-step network attack, which combines multiple network vulnerabilities and uses the causal relationship between them to achieve the attack target. The detection of such attack intention is very difficult. Therefore, in order to solve the problem that the real attack intention of the attackers in complex network is difficult to be recognized, this paper proposes to assume the possible targets in the network according to the important asset information in the network. By constructing the hierarchical attack path graph, the probability of each hypothetical attack intention target is calculated, and the real attack intention and the most likely attack path of the attacker are deduced. The hierarchical attack path graph we use can effectively overcome the cognitive difficulties caused by network complexity and large scale, and can quantitatively and qualitatively analyze the network status. It is of great importance to make the protection and strategy of network security.
Minor Fault Detection for Permanent Magnet Synchronous Motor Based on Fractional Order Model and Relative Rate of Change

Permanent magnet synchronous motor is a kind of typical nonlinear complex system. With its excellent performance such as high torque density, high efficiency and high reliability, it becomes the mainstream motor in the fields of active aircraft, electric vehicles and industrial servo drives. However, the existing fault diagnosis based on integer order model does not consider the fractional-order characteristics contained in the electromagnetic coupling and friction in the motor system, then it is difficult to effectively diagnose minor faults of the current with the residual error signal. In this paper, based on the traditional method, the state space representation based on fractional order model and the fault detection method of Kalman filter algorithm are introduced, and the secondary detection is adopted to calculate the relative change rate of typical fault feature quantity, and the experiment is verified.
An Indian, Comic-based, Online-EEG Paradigm for Theory of Mind: An Exploratory, Pilot Study on Schizophrenia Patients

Background: False-belief (FB) tasks are used to assess the theory of mind (ToM) functioning, which has been found to be impaired in schizophrenia. FB task stimuli used so far in neuroimaging studies in schizophrenia have been sentence-based ones. We aimed to validate an Indian, colour-comic based FB task by using an online-electroencephalogram (EEG) paradigm discriminating schizophrenia patients and healthy controls. Materials and Methods: Fifteen schizophrenia patients and 15 healthy controls performed online FB task during a 256-channel-EEG recording. 'Content' and 'known-groups' validity were examined using offline behavioural measures. Evoked gamma spectral-power in four regions of interest (ROIs) was compared between groups. Social functioning was also assessed. Results: Strength of classifying the groups was significant for both the number of correct responses and the reaction-times on the FB tasks. Social functioning was found to be poorer in patients. On the comparative analysis of evoked gamma spectral-power in the ROIs, very small effect size and observed power were noted. Conclusion: 'Content' and 'known-groups' validity of the culturally undermined comic-based FB task are good. Our findings reiterate that ToM functioning is impaired in schizophrenia. Our results were inconclusive in inferring whether evoked gamma spectral-power could be used as a neural validator for poor ToM functioning.
Sentiment Analysis Based on MapReduce: A survey

Sentiment analysis is the process of analyzing people's sentiments, opinions, evaluations and emotions by studying their written text. It attracts the interest of many researchers, since it is useful for many applications, ranging from decision making to product evaluation to mention a few. Sentiment analysis can be conducted using machine-learning techniques, lexicon-based techniques or hybrid techniques that combines both. As people are more reliant on social networks such as Twitter, this has become a valuable source for sentiment analysis. However, the existence of big data frameworks require adaptation of these techniques to run within such frameworks. This paper reviews sentiment analysis techniques, focusing on the MapReduce-based analysis techniques. We found that the Naïve Bayes algorithm was the most used machine learning technique for extracting sentiments from big datasets because of its high accuracy rates. However, the dictionary-based techniques achieved better results in terms of execution time.
Dynamic Fine-Grain Power Gating Design in WiNoC

The distribution of traffic in the WiNoC system is variable in time and space. Based on this feature, researchers have proposed a kind of scheme which is dynamic MAC mechanism to adaptively allocate bandwidth and improve data communication efficiency. However, this kind of scheme has a large power consumption overhead. Therefore, this paper designs a dynamic and finegrained method based on different wireless usage rates. The fine-grained distributed power gate control scheme dynamically switches the power gated working state according to different mechanisms of the MAC mechanism, and effectively reduces the data communication power consumption in the network with a small area overhead. Experiments show that: although this scheme increases the area overhead by 6.42%, it reduces the power consumption overhead by 26.4%
Massive Open Online Courses (MOOCs) and Their Effect on Learners' Motivation

Massive Open Online Course (MOOC) have become a new form innovation whose development has shown great challenges to the traditional classroom mode of teaching, they have also proven to be an alternative way of delivering interactive teaching as well as learning. The aim of this paper is to determine the key factors affecting student motivation in course completion as well as the approaches used by teachers to ensure that the students complete the course. To help in building a better understanding, an in-depth interview was conducted with a course facilitator at the International Islamic University. To get the students perspective on learning through MOOCS, a survey was distributed among students from several universities across Malaysia. The purpose of this survey was to probe students motivation, learning perceptions, and experiences towards MOOCS, focusing on MOOCS affordances and challenges that may lead to high dropout rates. This research paper has helped identify the learning motivations, learning styles, and a number of factors that appear to influence student motivation.
Internal Flow Model and Behavioral Design for an Anthropomorphic Agent's Ownership-Desire Model

In this research, we aimed to encourage a common recognition of object ownership on the part of agents and humans to elicit more human empathy for the agent. To do so, we propose an ownership-desire model in which the agent's behaviors are generated based on its internal states of attention, interest and desire for ownership. We conducted an experiment to verify the effectiveness of the designed behaviors corresponding to our proposed model, and the results showed that all behaviors corresponding to the internal states were perceived as we designed. We conjectured that the behavioral durations of the agent's attention and interest would affect the guesses of the agent's internal states.
Eye-to-eye Calibration for Cameras with Disjoint Fields of View

The calibration of the relative pose between cameras with disjoint fields of view mounted on a common rigid object is a prerequisite for many applications. For the extrinsic calibration of such cameras, we develop an improved optimization method applicable to different calibration setups, in which the reprojection error of 3D-2D point correspondences is used as the objective constrained by rigid 3D-3D closed-loop pose transformations AX = YB. We introduce a quality measure for all entries of A and B based on the projection size of known planar calibration patterns on the image. The quality measure serves as an additional weighting in the optimization objective that improves calibration accuracy and increases robustness against noise. We compare our method to state-of-the-art methods both in simulation and real-world experiment, and we demonstrate the improvement in terms of accuracy and robustness. In addition, we show that the spatial distribution of transforms A, B, and their measurement quality also have an influence on the estimation accuracy.
Comparison of Region of Interest Segmentation Methods for Video-Based Heart Rate Measurements

Conventional contact photoplethysmography (PPG) sensors are not suitable in situations of skin damage or when unconstrained movement is required. As a consequence, remote photoplethysmography (rPPG) has recently emerged because it provides remote physiological measurements without expensive hardware and improves comfort for long term monitoring. RPPG estimation methods use the spatially averaged RGB values of pixels in a Region Of Interest (ROI) to generate a temporal RGB signal. The selection of ROI is a critical first step to obtain reliable pulse signals and must contain as many skin pixels as possible with a low percentage of non-skin pixels. In this paper, we experimentally compare seven ROI segmentation methods in the perspective of heart rate (HR) measurements with dedicated metrics. The algorithms are compared using our in-house database UBFC-RPPG, comprising of 53 videos specifically geared towards rPPG analysis.
Expression of OsTPX Gene Improves Cellular Redox Homeostasis and Photosynthesis Efficiency in Synechococcus elongatus PCC 7942

Cyanobacterial 2-Cys peroxiredoxin (thioredoxin peroxidase, TPX) comprises a family of thiol antioxidant enzymes critically involved in cell survival under oxidative stress. In our previous study, a putative TPX was identified using a proteomics analysis of rice (Oryza sativa L. japonica, OsTPX) seedlings exposed to oxidative stress. This OsTPX gene is structurally similar to the Synechococcus elongatus TPX gene in the highly conserved redox-active disulfide bridge (Cys114, Cys236) and other highly conserved regions. In the present study, the OsTPX gene was cloned into rice plants and S. elongatus PCC 7942 strain to study hydrogen peroxide (H2O2) stress responses. The OsTPX gene expression was confirmed using semi-quantitative RT-PCR and western blot analysis. The OsTPX gene expression increased growth under oxidative stress by decreasing reactive oxygen species and malondialdehyde level. Additionally, the OsTPX gene expression in S. elongatus PCC 7942 (OT) strain exhibited a reduced loss of chlorophyll and enhanced photosynthesis efficiency under H2O2 stress, thereby increasing biomass yields twofold compared with that of the control wild type (WT) strain. Furthermore, redox balance, ion homeostasis, molecular chaperone, and photosynthetic systems showed upregulation of some genes in the OT strain than in the WT strain by RNA-Seq analysis. Thus, OsTPX gene expression enhances oxidative stress tolerance by increasing cell defense regulatory networks through the cellular redox homeostasis in the rice plants and S. elongatus PCC 7942.
A Storage Centric Approach to Authentication on Internet of Things

Nowadays, the Internet of Things (IoT) is among the most important topics of research. It is essentially an interconnection of a great number of small devices over the Internet. These small devices are generally equipped with low power radios and suffer from numerous constraints, notably limited memory, limited power, and low computational capacity. Developing security mechanisms for such Internet enabled low power networks pose a real challenge. Authentication and access control are significant and critical functionalities in the context of IoT to permit secure communication between devices. For the IoT, it is not possible to carry out directly the classic security countermeasures since most of the existing mechanisms are designed for devices with higher computational capacity as compared to IoT devices. Security in IoT is often bootstrapped with the aid of cryptographic key distribution amongst devices. In this case, one of the greatest research challenges is to assure secure key storage via sensors taking their limited resources into account. This paper introduces an enhancement of the existing secure bootstrapping mechanism of the standard IETF 6TiSCH protocol in which a distributed data storage is used for the management of the authentication keys. With this distributed approach, the objective is to minimize the communication overhead of the IETF 6TiSCH authentication mechanism, enable an efficient authentication process and enhance energy efficiency of the network through holding the authentication parameters at the edge of the IoT network.
CIPA: Concurrent IC and PCB Authentication Using On-chip Ring Oscillator Array

The counterfeit parts, e.g. integrated circuits (ICs) and printed circuit boards (PCBs), have shown significant increase over the recent years. However, existing counterfeit detection techniques deal with IC or PCB separately. Hence cannot verify the authenticity of the electronic system as a whole. In this paper, we propose a novel methodology, named CIPA, which concurrently verifies the authenticity of both IC and PCB through extracting the signature pairs generated by ring oscillator (RO) array without/with PCB cavity resonance. Remote authentication is allowable by transmitting CIPA signatures between the verifier and system vendor. CIPA structure has shown insignificant area overhead (0.945% on average), when implemented on a number of benchmarks. Both CIPA and the benchmarks have been implemented on authentic and counterfeit FPGA systems, results giving 100% confidence in detecting counterfeit ones.
Improvement of LED Non-Invasive Blood Glucose Meter

The improvement of a conventional LED non-invasive blood glucose meter (NBGM) and analyze the ability of receiving near-infrared (NIR) light in different modules are study in this paper. To compare the optical efficiency of NBGM and NIR, a simple human skin optical model with three-layer structures by optical simulation is build-up basing on Henyey-Greenstein scattering model. The light scattering behaviors of the three-layer skin structure contain of epidermis layer, dermis layer, and subcutaneous fat layer. At last, we design a bilateral symmetrical semi-ellipsoidal mirror (BSSEM), which can effectively increase the optical efficiency of NBGM by 1.4 times.
Adaptive Deployment for UAV-Aided Communication Networks

Unmanned aerial vehicle (UAV) as an aerial base station is a promising technology to rapidly provide wireless connectivity to ground users. Given UAV’s agility and mobility, a key question is how to adapt UAV deployment to the best cater to instantaneous wireless traffic in a territory. In this paper, we propose an adaptive deployment scheme for a UAV-aided communication network, where the UAV adapts its displacement direction and distance to serve randomly moving users’ instantaneous traffic in the target cell. In our adaptive scheme, the UAV does not need to learn users’ exact locations in real time, but chooses its displacement direction based on a simple majority rule by flying to the spatial sector with the greatest number of users in the cell. To balance the service qualities of the users in different sectors, we further optimize the UAV’s displacement distance in the chosen sector to maximize the average throughput and the successful transmission probability, respectively. We prove that the optimal displacement distance for average throughput maximization decreases with the user density: the UAV moves to the center of the chosen sector when the user density is small and the UAV displacement becomes mild when the user density is large. In contrast, the optimal displacement distance for success probability maximization does not necessarily decrease with the user density and further depends on the target signal-to-noise ratio (SNR) threshold. The extensive simulations show that the proposed adaptive deployment scheme outperforms the traditional non-adaptive scheme, especially when the user density is not large.
Cost Savings Oriented Microgrid Control Strategy Considering Battery Degradation

In this paper a cost savings oriented centralised control strategy for grid connected hybrid microgrid consisting of renewable energy sources and energy storage systems is proposed. In this work, cost savings are achieved by a power management algorithm (PMA) which takes battery degradation cost and grid pricing into account. In-addition the proposed PMA always operates battery within SOC limits and also ensures microgrid reliability. The cost savings are computed as the difference between battery degradation cost and electricity cost. However, the battery degradation cost is calculated using depth of discharge (DOD) which is computed on a real time basis. In this work, hybrid energy storage system (HESS) is introduced to absorb the sudden transients and oscillations in battery current thereby increasing battery performance and life span. Apart from cost savings analysis, power quality aspects such as harmonic mitigation, reactive power support and unity power factor are achieved. The proposed control strategy is simulated using a MATLAB based simulink environment during both excess and deficit modes of microgrid by taking PV variations into account.