Miracle-dz/UniFAR-FTUs huggingface dataset:
A novel switching boost inverter applied to photovoltaic power generation system

This paper presents a new topology of the input current continuous switch boost inverter (ICCSBI) and a perturbation observation with hysteresis comparator method. Because the traditional inverter needs to join the dead time to avoid short circuit, the dead time will cause distortion of the output current. And the traditional inverter is not suitable for photovoltaic power generation because it is a buck converter. The ICCSBI structure improves the booster circuit, which refers to the Z‐source inverter. It can get higher voltage than traditional inverter. Meanwhile, the ICCSBI not only retains the advantages of the Z‐source inverter but also reduces the number of passive components. In addition, the hysteresis band comparator is added to the perturbation observation method to make the system better robustness at the maximum power point of the photovoltaic system. In the photovoltaic system, to ensure the output current stable, the current control method is added to the ICCSBI. Finial, the experimental results are presented to demonstrate the new topology and control method.
Monocular and Stereo Cues for Landing Zone Evaluation for Micro UAVs

Autonomous and safe landing is important for unmanned aerial vehicles. We present a monocular and stereo image based method for fast and accurate landing zone evaluation for UAVs in various scenarios. Many existing methods rely on Lidar or depth sensor to provide accurate and dense surface reconstruction. We utilize stereo images to evaluate the slope and monocular images to compute homography error. By combining them together, our approach works for both rigid and non-rigid dynamic surfaces. Experiments on many outdoor scenes such as water, grass and roofs, demonstrate the robustness and effectiveness of our approach.
Pivot tracing: dynamic causal monitoring for distributed systems

Monitoring and troubleshooting distributed systems is notoriously difficult; potential problems are complex, varied, and unpredictable. The monitoring and diagnosis tools commonly used today -- logs, counters, and metrics -- have two important limitations: what gets recorded is defined a priori, and the information is recorded in a component- or machine-centric way, making it extremely hard to correlate events that cross these boundaries. This paper presents Pivot Tracing, a monitoring framework for distributed systems that addresses both limitations by combining dynamic instrumentation with a novel relational operator: the happened-before join. Pivot Tracing gives users, at runtime, the ability to define arbitrary metrics at one point of the system, while being able to select, filter, and group by events meaningful at other parts of the system, even when crossing component or machine boundaries. We have implemented a prototype of Pivot Tracing for Java-based systems and evaluate it on a heterogeneous Hadoop cluster comprising HDFS, HBase, MapReduce, and YARN. We show that Pivot Tracing can effectively identify a diverse range of root causes such as software bugs, misconfiguration, and limping hardware. We show that Pivot Tracing is dynamic, extensible, and enables cross-tier analysis between inter-operating applications, with low execution overhead.
Hybrid Context-Aware Multimodal Routing

Selecting the appropriate route in urban multimodal transport networks may require information from several sources, such as user preferences and contextual information. Such information could be used to choose different transport modes for a route, to avoid the use of hired private vehicles (HPV), like taxis, in certain segments of the trip with poor traffic conditions, for example. Therefore, this paper proposes the use of hybrid Hired Private Vehicles and Transit (HPV-transit) to enable a more personalized urban routing. User's impressions over trips' cost and duration are taken into account to select better hybrid routes. A taxi dataset was used to create graphs that map the main mobility flows in New York City. To create these graphs we propose a novel flow-based clustering technique which identifies trending mobility flows based on spatio-temporal datasets containing departures and arrivals. These flows are used to evaluate the performance of the hybrid HPV-transit. We compare the proposed solution with traditional transit or HPV only trips. It is possible to conclude that the adoption of HPV-transit trips can bring benefits in terms of cost and duration for urban mobility.
FPAP: A Folded Architecture for Energy-Quality Scalable Convolutional Neural Networks

Emerging convolutional neural networks (CNNs) tend to be designed with varied per-layer data widths and sparse representations. However, these two features, which bring many redundant computations, have not been exploited simultaneously in existing hardware architectures for CNNs. This paper proposes an energy-quality scalable architecture, namely folded precision-adjustable processor (FPAP), to eliminate all computational redundancies by using folding techniques. On one hand, FPAP decomposes the dominant multiply-accumulate (MAC) operations into multiple adds and folds them into single arithmetic unit. Only effective adds (or part of them) are then calculated serially. Thus, FPAP can adapt to different per-layer data widths and enable precision-adjustable approximate computing. Particularly, FPAP adaptively selects either activation or weight to be decomposed in every single MAC to minimize the total number of adds and clock cycles. On the other hand, a 1-D convolution is undertaken by a multi-tap transposed finite impulse response (FIR) filter, which is folded into one tap to skip MACs with zero weights or activations. Besides, a judicious delay element remapping scheme and a novel genetic algorithm-based kernel reallocation scheme, are developed to reduce the power consumption in a folded FIR filter and mitigate the load imbalance issue caused by irregular sparsity, respectively. With all these optimizations, FPAP is able to reach comparable or even faster processing speed over the corresponding unfolded design in sparse CNNs while consuming smaller area. Experimental results on real CNN models demonstrate that FPAP can scale its energy efficiency from 4.28 to 23.63 TOP/s/W, and area efficiency from 37.79 to 164.15GOP/s/mm2, respectively, under the TSMC 28-nm HPC CMOS technology.
Chemical Composition, Antioxidant and Antimicrobial Activity of Pericarpium Citri Reticulatae Essential Oil

The chemical composition, antioxidant and antimicrobial activity of Pericarpium Citri Reticulatae (PCR) essential oil obtained using an improved Clevenger type apparatus were studied. Among the five different PCRs examined the highest yield of essential oil was found in Chachi 2004 (harvested and stored in 2004) and the lowest in Chachi 2008 (harvested and stored in 2008). Fifty three different volatile compounds were determined, including terpenic hydrocarbons, alcohols, aldehydes, ketones and esters. D-limonene, one of terpenes, was the major constituent in PCR. The antioxidant capacity of PCR essential oil varied considerably with the duration of storage time, and the oil from Chachi 1994 has the strongest ferric-reducing antioxidant power. In addition, the essential oil possessed varying degrees of antimicrobial activity against Gram-positive bacteria (Staphylococcus aureus, Bacillus subtilis, Bacillus cereus), except Streptococcus faecalis, while had no effect on Gram-negative bacteria (Escherichia coli, Enterobacter cloacae).
Application of amplified reality to the cognitive effect of children with attention deficit hyperactivity disorder(ADHD) – An example of Italian Chicco-app interactive building blocks

For children with attention deficit hyperactivity disorder (ADHD), most are mixed symptoms, such as Asperger syndrome (AS), Tourette’s syndrome (TS), Anxiety disorders, and so on, just collectively known as ADHD, but the most obvious symptoms are inattention, learning disabilities, emotional disorders, sensory integration abnormalities. Therefore, this research focuses on building blocks and Augmented Reality (AR) links, using the building blocks of animals as the theme, coupled with the operation of building blocks to enhance patience, touch, construction and other capabilities, through the interaction of AR and dynamic performance can attract attention to ADHD children’s eyes and attention. There are two objectives in this study: Firstly, is to have a positive interaction with AR for children under six years of age who have ADHD. Secondly is to increase the difficulty of interaction, improve the continuous response to designed AR and attract; Not only use the building blocks to interact, but also through the AR increase in children’s patience, initiative, associated with the combination of AR and tablet. From this trend, it can increase the child’s touch operation action. Finally, through interviews, experimental teaching, observation and evaluation, this study explores the effects of APP interactive blocks on the learning of ADHD children. Building Blocks
A Binary Classification Model for PM10 Levels

The particulate matter in the air effects human health in a negative way. Yet, no regression model has estimated the density of PM10 at Istanbul using datasets with imbalanced class distribution. In order to fill this gap, we designed a new regression model that transforms the regression problem into the imbalanced binary classification problem at the initial stage. In this paper, PM10 classification problem is considered as the imbalanced binary classification problem that is coded as harmless class (1) and dangerous class (0). In the sampling part of the solution, the balanced version of the data by Under Sampling methods yielded unsatisfactory results. In the algorithmic part, the performances of RFC (Random Forest Classifier), ETC (Extra Trees Classifier) and GBC (Gradient Boosting Classifier) models, which stand out with their positive effects on unbalanced learning problems, are compared in terms of AUROC. The proposed model, uses all training set samples and predicts through RFC. The experimental results on real world dataset seem quite promising for our further research.
Beyond Domain Adaptation: Unseen Domain Encapsulation via Universal Non-volume Preserving Models

Recognition across domains has recently become an active topic in the research community. However, it has been largely overlooked in the problem of recognition in new unseen domains. Under this condition, the delivered deep network models are unable to be updated, adapted or fine-tuned. Therefore, recent deep learning techniques, such as: domain adaptation, feature transferring, and fine-tuning, cannot be applied. This paper presents a novel Universal Non-volume Preserving approach to the problem of domain generalization in the context of deep learning. The proposed method can be easily incorporated with any other ConvNet framework within an end-to-end deep network design to improve the performance. On digit recognition, we benchmark on four popular digit recognition databases, i.e. MNIST, USPS, SVHN and MNIST-M. The proposed method is also experimented on face recognition on Extended Yale-B, CMU-PIE and CMU-MPIE databases and compared against other the state-of-the-art methods. In the problem of pedestrian detection, we empirically observe that the proposed method learns models that improve performance across a priori unknown data distributions.
An Integrated Thermal and Electrical Model for PV Panel Performance

This work presents an integrated thermal and electrical model for Photovoltaic panel based on Matlab/Simulink tools. The model is designed for extreme and dusty weather conditions. The objective of this work is to reach a realistic online simulation that accommodates all major environmental variables such ambient temperature, relative humidity, wind speed and solar irradiation. The water mass flow rate was used to control the PV panel temperature through surface cooling in order to boost its output power. The output power adjustment was needed for power system operation purposes. The developed model is now ready any future controller development. The obtained results for testing the model under varying online climatic conditions and for various water mass flow rates were very satisfactory. The use of water to cool the panel played two major roles namely PV cleaning and temperature reduction then consequently increasing the panel power output.
${\mathit{MALT^P}}$ : Parallel Prediction of Malicious Tweets

It has been reported that embedded URLs and multimodal content (images, video, and sound recordings) in tweets are increasingly used to seduce users into a “wrong click,” leading to malware infection. In this paper, we predict whether a tweet is malicious or not by examining five classes of features: textual content including sentiment, paths emanating from a URL mentioned in the tweet, attributes associated with URLs, and multimodal content in the tweet. A fifth class of features first constructs a novel “tweet graph” and then defines features by analyzing “metapaths” contained in the tweet graph. Next, we propose a MALicious Tweets in Parallel ( ${\mathit {MALT^{P}}}$ ) collective classification algorithm that merges together tweet graphs, metapaths, and collective classification proposed previously in the literature. We conduct detailed experiments using two data sets—Warningbird (WB) and KBA. We show that our metapath-based approach outperforms past efforts at identifying malicious tweets and further show that metapath-based features in conjunction with Alexa ranks and features from KBA yield very high predictive accuracy—over 0.98 on KBA and over 0.94 on KBA, outperforming past work. More significantly, metapath features alone generate a predictive accuracy of 0.977 and 0.923, respectively, on the KBA and WB data sets, significantly outperforming the other methods in isolation. We conduct a further analysis to identify the most important features; surprisingly, our results show that the presence of multimodal content is not a major factor and that metapath-based features dominate in separating malicious from benign tweets.
Feel-Good Robotics: Requirements on Touch for Embodiment in Assistive Robotics

The feeling of embodiment, i.e., experiencing the body as belonging to oneself and being able to integrate objects into one's bodily self-representation, is a key aspect of human self-consciousness and has been shown to importantly shape human cognition. An extension of such feelings toward robots has been argued as being crucial for assistive technologies aiming at restoring, extending, or simulating sensorimotor functions. Empirical and theoretical work illustrates the importance of sensory feedback for the feeling of embodiment and also immersion; we focus on the the perceptual level of touch and the role of tactile feedback in various assistive robotic devices. We critically review how different facets of tactile perception in humans, i.e., affective, social, and self-touch, might influence embodiment. This is particularly important as current assistive robotic devices – such as prostheses, orthoses, exoskeletons, and devices for teleoperation–often limit touch low-density and spatially constrained haptic feedback, i.e., the mere touch sensation linked to an action. Here, we analyze, discuss, and propose how and to what degree tactile feedback might increase the embodiment of certain robotic devices, e.g., prostheses, and the feeling of immersion in human-robot interaction, e.g., in teleoperation. Based on recent findings from cognitive psychology on interactive processes between touch and embodiment, we discuss technical solutions for specific applications, which might be used to enhance embodiment, and facilitate the study of how embodiment might alter human-robot interactions. We postulate that high-density and large surface sensing and stimulation are required to foster embodiment of such assistive devices.
Human Aquaporin 4 Gene Polymorphisms and Haplotypes Are Associated With Serum S100B Level and Negative Symptoms of Schizophrenia in a Southern Chinese Han Population

Background: Aquaporin 4 (AQP4) polymorphism may influence the required dosage of antipsychotic drugs. However, the roles of AQP4 polymorphisms in the blood—brain barrier (BBB) and different neuroprotective effects need further exploration. This study aims to investigate whether the gene polymorphisms and haplotype of AQP4 are associated with serum S100 calcium-binding protein B (S100B) level and clinical symptoms in patients with schizophrenia (SCZ). Methods: We recruited 190 patients with SCZ. They provided demographic data, completed relevant questionnaires, and submitted samples to test for four AQP4 tag single nucleotide polymorphisms (SNPs) and eight haplotypes. The rating scales of Positive and Negative Syndrome Scale (PANSS), Personal and Social Performance (PSP), the Global Assessment of Functioning (GAF), Clinical Global Impression (CGI) were assessed and serum S100B level were measured repeatedly during antipsychotic treatment at weeks 0 (baseline), 3, 6, and 9. Using generalized estimating equation (GEE) analyses, log-transformed S100B (logS100B) level was tested for associations with haplotype and other dependent variables. Results: Discretization via the median split procedure showed that logS100B level >1.78 or ≤ 1.78 had the best discriminant validity to stratify the patients into two groups. After 9 weeks of treatment, the serum S100B level was decreased. The TAA haplotype of AQP4 SNPs was associated with increased serum S100B level (p = 0.006). The PANSS negative subscale (PANSS-N) (p = 0.001) and Clinical Global Impression–Improvement (CGI-I) (p = 0.003) scores had a positive association with S100B level. Conclusion: Patients with the TAA haplotype of the AQP4 polymorphism are likely to have increased serum S100B level, negative symptoms and poor control of neuroinflammation. A logS100B level >1.78 may be sufficiently specific to predict a higher severity of negative symptoms. Further study including healthy controls and patients with first and recurrent episodes under selective AQP4 modulators will be necessary to explore the profound effects on the treatment of patients with SCZ and may positively influence their overall outcome.
Robust Adaptive Beamforming Based on Non-convex Quadratic Optimization with Semidefinite Relaxation

In practical application scenarios, the performance of robust adaptive beam forming depends on the accurate estimation of steering vector. However, the optimization problem that implements the steering vector estimation is usually non convex. In this paper, the relaxation technique is introduced into adaptive beam forming to solve the non-convex optimization problem and estimate the steering vector accurately. In addition, considering the widespread problem of signal self-cancellation in beam forming, the desired signal component is removed from the sample covariance matrix as far as possible. Computer simulation results show that the proposed SDR-RAB method is effective in steering vector mismatch scenarios, and its performance is better than the existing methods.
On-Line Continuous-Discrete Time Observer for Three-Tank System

This paper deals with the problem of state estimation of continuous dynamical systems with discrete-time output measurements. The observability of this class of systems depends on the system inputs and on the output sampling time. While several studies have addressed this problem in the case of continuous-time output measurements, few are those who have approached the case of discrete-time. Among the estimation strategies proposed in the literature, we used the high gain technique. Having the mathematical model of a hydrographic network formed by three cascaded tanks, we are interested here, in estimating two liquid levels from the discrete measurements of the other level. Simulation and experimental results validate the observation structure adopted.
Joint Space-Frequency Block Codes and Signal Alignment for Heterogeneous Networks

In this paper, we propose a new diversity-oriented space-frequency block codes (SFBC) and signal alignment (SA) enabled physical network coding (PNC) method for the uplink of heterogeneous networks. The proposed joint Dual-SFBC with SA-PNC design substantially reduces interference and enables connecting a larger number of users when compared with methods adopting interference alignment (IA) or PNC. The main motivation behind the dual SFBC and SA-PNC design is that it allows the efficient coexistence of macro and small cells without any inter-system channel information requirements. Numerical results also verify that the proposed method outperforms the existing SA-PNC static method without any additional information exchange requirement between the two systems while achieving the main benefits of IA and SA-PNC coordinated methods recently proposed.
ETSI DCC: Decentralized Congestion Control in C-ITS

ETSI DCC: Decentralized Congestion Control in C-ITS is a mandatory component of the 5.9 GHz ITS-G5 vehicular communication protocol stack that reduces radio channel overload, range degradation, and self interference. In this tutorial article we explain its principle, describe related ongoing standardization activities, evaluate its performance for emerging cooperative driving applications, and identify ways for improvement. We show that failure to use a proper DCC parameterization can impact negatively on the performance of cooperative vehicular applications.
RFOC and DTC Strategies for Reduced Structure B3-Inverter Fed Induction Motor Drives

In variable speed applications based on induction motor (IM) drives, two types of popular control strategies are distinguished: rotor flux oriented control (RFOC) and direct torque control (DTC). This paper investigates an experimental evaluation of two novel control strategies for three-switch three-phase inverter (TSTPI), also called B3- VSI, fed IM drives, namely: (i)DTC, and (ii) RFOC controlled current. The control strategies under consideration are firstly presented and therefore their control performances have been investigated via simulation and experimental results, including steady-state and transient conditions. The comparative evaluation is carried out on the basis of numerous criteria: Torque and flux ripples, steady-state performance, dynamic response, parameter sensitivity, control sampling period, and algorithm complexity.
The Challenges and Potential of Risk Assessment for Active Safety of Unmanned Tram

Assessing the potential dangerous situation in the surrounding environment is a key issue to ensure unmanned tram running safety. However, comparing with driverless vehicle, it is hard to assess the hazard of running safety from the obstacles such as pedestrians and cars around, due to the nonlinear feature of the dynamic model of vehicles and the special running environment. Regarding the problems above, this paper reviews the researches which related to the safety of unmanned tram, and proposes the challenges and potential for active safety of unmanned tram. The technologies and theories suited for tram are discussed. Then a framework of safety assessment for unmanned tram is proposed, which looks forward to improve the works about intelligent and automatic unmanned trams.