Miracle-dz/UniFAR-FTUs huggingface dataset:
Research on Deskew Algorithm of Scanned Image

It is difficult to ensure that the scanned document will not be skewed due to the high speed scanning of the paper document by the mechanical feed or the document itself. This will not only affect the visual effects, but also seriously affect the accuracy and speed of subsequent processing. Therefore, this paper presents a Hough transform algorithm based on automatic scanning image correction and cropping, the first to determine whether the scanned image is a text or non-text image, the image preprocessing based on different types of scanned images of the image binarization, Compared with the traditional algorithm has a good anti-interference ability, can accurately detect the scan image skew angle.
Real-time queue length estimation using event-based advance detector data

ABSTRACT Real-time queue length information at signalized intersections is useful for both performance evaluation and signal optimization. Previous studies have successfully examined the use of high-resolution event-based data to estimate real-time queue lengths. Based on the identification of critical breakpoints, real-time queue lengths can be estimated by applying the commonly used shockwave model. Although breakpoints can be accurately identified using lane-by-lane detection, few studies have investigated queue length estimation using single-channel detection, which is a common detection scheme for actuated signal control. In this study, a breakpoint misidentification checking process and two input-output models (upstream-based and local-based) are proposed to address the overestimation and short queue length estimation problems of breakpoint-based models. These procedures are integrated with a typical breakpoint-based model framework and queue-over-detector identification process. The proposed framework was evaluated using field-collected event-based data along Speedway Boulevard in Tucson, Arizona. Significant improvements in maximum queue length estimates were achieved using the proposed method compared to the breakpoint-based model, with mean absolute errors of 35.7 and 105.6 ft. , respectively.
Identification of Novel Gata3 Distal Enhancers Active in Mouse Embryonic Lens

Background: The tissue‐specific transcriptional programs during normal development require tight control by distal cis‐regulatory elements, such as enhancers, with specific DNA sequences recognized by transcription factors, coactivators, and chromatin remodeling enzymes. Gata3 is a sequence‐specific DNA‐binding transcription factor that regulates formation of multiple tissues and organs, including inner ear, lens, mammary gland, T‐cells, urogenital system, and thyroid gland. In the eye, Gata3 has a highly restricted expression domain in the posterior part of the lens vesicle; however, the underlying regulatory mechanisms are unknown. Results: Here we describe the identification of a novel bipartite Gata3 lens‐specific enhancer located ∼18 kb upstream from its transcriptional start site. We also found that a 5‐kb Gata3 promoter possesses low activity in the lens. The bipartite enhancer contains arrays of AP‐1, Ets‐, and Smad1/5‐binding sites as well as binding sites for lens‐associated DNA‐binding factors. Transient transfection studies of the promoter with the bipartite enhancer showed enhanced activation by BMP4 and FGF2. Conclusions: These studies identify a novel distal enhancer of Gata3 with high activity in lens and indicate that BMP and FGF signaling can up‐regulate expression of Gata3 in differentiating lens fiber cells through the identified Gata3 enhancer and promoter elements. Developmental Dynamics 247:1186–1198, 2018. © 2018 The Authors. Developmental Dynamics published by Wiley Periodicals, Inc. on behalf of American Association of Anatomists
Magnetoelectric Dipole Antenna With Low Profile

In this letter, a novel low-profile magnetoelectric (ME) dipole antenna is proposed. By utilizing a pair of vertically oriented folded shorted patches, the antenna height is reduced from 0.25<italic>λ</italic><sub>0 </sub> to 0.116<italic>λ</italic><sub>0</sub> compared with the original ME dipole antenna (<italic>λ </italic><sub>0</sub> is the center-frequency free-space wavelength). A fully functional prototype is constructed and tested. The measurement shows that a fractional impedance bandwidth (<italic>S</italic><sub>11</sub> ≤ –10 dB) of 43.6% from 1.81 to 2.82 GHz is achieved. Within the operating frequency, the antenna gain is stable located at 9 dBi. Besides, stable unidirectional radiation patterns in both E- and H-planes with high front-to-back ratio are obtained. Due to the dc grounded property, the antenna can be applied outdoors.
High-Resolution Forest Mapping from TanDEM-X Interferometric Data Exploiting Nonlocal Filtering

In this paper, we discuss the potential and limitations of high-resolution single-pass interferometric synthetic aperture radar (InSAR) data for forest mapping. In particular, we present forest/non-forest classification mosaics of the State of Pennsylvania, USA, generated using TanDEM-X data at ground resolutions down to 6 m. The investigated data set was acquired between 2011 in bistatic stripmap single polarization (HH) mode. Among the different factors affecting the quality of InSAR data, the so-called volume correlation factor quantifies the coherence loss due to volume scattering, which typically occurs in the presence of vegetation, and is a very sensitive indicator for the discrimination of forested from non-forested areas. For this reason, it has been chosen as input observable for performing the classification. In this framework, both standard boxcar and nonlocal filtering methods have been considered for the estimation of the volume correlation factor. The resulting forest/non-forest mosaics have been validated using an accurate vegetation map of the region derived from Lidar-Optic data as external independent reference. Thanks to their outstanding performance in terms of noise reduction, together with spatial features preservation, nonlocal filters show a level of agreement of about 80.5% and we observed a systematic improvement in terms of accuracy with respect to the boxcar filtering at the same resolution of about 4.5 percent points. This approach is therefore of primary importance to achieve a reliable classification at such fine resolution. Finally, the high-resolution forest/non-forest classification product of the State of Pennsylvania presented in this paper demonstrates once again the outstanding capabilities of the TanDEM-X system for a wide spectrum of commercial services and scientific applications in the field of the biosphere.
Autonomic Function Following Concussion in Youth Athletes: An Exploration of Heart Rate Variability Using 24-hour Recording Methodology

Participation in organized sports makes a significant contribution to youth development, but places youth at a higher risk for sustaining a concussion. To date, return-to-activity decision-making has been anchored in the monitoring of self-reported concussion symptoms and neurocognitive testing. However, multi-modal assessments that corroborate objective physiological measures with traditional subjective symptom reporting are needed and can be valuable. Heart rate variability (HRV) is a non-invasive physiological indicator of the autonomic nervous system, capturing the reciprocal interplay between the sympathetic and parasympathetic nervous systems. There is a dearth of literature exploring the effect of concussion on HRV in youth athletes, and developmental differences preclude the application of adult findings to a pediatric population. Further, the current state of HRV methodology has primarily included short-term (5-15 min) recordings, by using resting state or short-term physical exertion testing to elucidate changes following concussion. The novelty in utilizing a 24 h recording methodology is that it has the potential to capture natural variation in autonomic function, directly related to the activities a youth athlete performs on a regular basis. Within a prospective, longitudinal research setting, this novel approach to quantifying autonomic function can provide important information regarding the recovery trajectory, alongside traditional self-report symptom measures. Our objectives regarding a 24 h recording methodology were to (1) evaluate the physiological effects of a concussion in youth athletes, and (2) describe the trajectory of physiological change, while considering the resolution of self-reported post-concussion symptoms. To achieve these objectives, non-invasive sensor technology was implemented. The raw beat-to-beat time intervals captured can be transformed to derive time domain and frequency domain measures, which reflect an individual's ability to adapt and be flexible to their ever-changing environment. By using non-invasive heart rate technology, autonomic function can be quantified outside of a traditional controlled research setting.
Design of Temperature Intelligent Control System based on Transcranial Micro Current Depression Therapeutic Instrument

The temperature control system designed is mainly based on microcontroller-AT89S52, using sub-PID control algorithm, to achieve accurate temperature measurement and control. This system is mainly composed of temperature sensor, signal conversion and conditioning circuits, A / D module, micro-controller, D / A module, temperature buttons circuit, data display module, thyristor trigger circuit, optical isolators, crystal Gateway tube drive circuit and implementation agencies. This paper describes the hardware design of temperature control system and software realization method in detail. The test results show the temperature control system designed can achieve a more accurate measurement of temperature and runs stably. The technology adopted in the system is practical and worthy of market.
A Contactless RFID System Based on Chipless MIW Tags

In this paper, a contactless RFID system formed by chipless tags based on the magneto-inductive wave (MIW) delay lines is proposed. In order to carry out the reading process, two reader probes have been designed, manufactured, and measured: the first one based on an axial to planar MIW transition and the second one based on a printed monopole. Moreover, a study is presented to verify the probe with better performance in terms of delay and losses when a tag is interrogated. By using the probe based on an axial to planar MIW transition, it is possible to transmit a pulse with a distance between tag and probe higher than 1 cm. Finally, two chipless tags are designed and manufactured to verify the performance of the proposed system. All the designed and manufactured elements are printed on a low-cost substrate (FR4). The manufactured 2 bit tags codify the 01 and 10 patterns. A good decodification of the time-domain response associated with the tags is achieved when using the proposed whole system.
An improved Urdu stemming algorithm for text mining based on multi-step hybrid approach

ABSTRACT Stemming is the basic operation in Natural language processing (NLP) to remove derivational and inflectional affixes without performing a morphological analysis. This practice is essential to extract the root or stem. In NLP domains, the stemmer is used to improve the process of information retrieval (IR), text classifications (TC), text mining (TM) and related applications. In particular, Urdu stemmers utilize only uni-gram words from the input text by ignoring bigrams, trigrams, and n-gram words. To improve the process and efficiency of stemming, bigrams and trigram words must be included. Despite this fact, there are a few developed methods for Urdu stemmers in the past studies. Therefore, in this paper, we proposed an improved Urdu stemmer, using hybrid approach divided into multi-step operation, to deal with unigram, bigram, and trigram features as well. To evaluate the proposed Urdu stemming method, we have used two corpora; word corpus and text corpus. Moreover, two different evaluation metrics have been applied to measure the performance of the proposed algorithm. The proposed algorithm achieved an accuracy of 92.97% and compression rate of 55%. These experimental results indicate that the proposed system can be used to increase the effectiveness and efficiency of the Urdu stemmer for better information retrieval and text mining applications.
An Experimental Study on Hyper-parameter Optimization for Stacked Auto-Encoders

Deep learning algorithms have shown their superiority especially in addressing challenging machine learning tasks. The best performance of deep learning algorithms can be reached only when their hyper-parameters have been successfully optimized. However, the hyper-parameter optimization problem is non-convex and non-differentiable, and traditional optimization algorithms are incapable of addressing them well. Evolutionary algorithms are a class of meta-heuristic search algorithms, preferred for optimizing real-world problems due largely to their no mathematical requirements on the problems to be optimized. Although most researchers from the community of deep learning are aware of the effectiveness of evolutionary algorithms in optimizing the hyper-parameters of deep learning algorithms, they still believe that the grid search method is more effective when the number of hyper-parameters is small. To clarify this, we design a hyper-parameter optimization method by using particle swarm optimization that is a widely used evolutionary algorithm, to perform 192 experimental comparisons for stacked auto-encoders that are a class of deep learning algorithms with a relative small number of hyper-parameters, investigate and compare the classification accuracy and computational complexity with those of the grid search method on eight widely used image classification benchmark datasets. The experimental results show that the proposed algorithm can achieve the comparative classification accuracy but saving 10x-100x computational complexity compared with the grid search method.
Implementing Deep Learning and Inferencing on Fog and Edge Computing Systems

The case for leveraging the computing resources of smart devices at the edge of network was conceptualized almost nine years back. Since then several concepts like Cloudlets, Fog etc. were instrumental in realizing computing at network edge, in physical proximity to the data sources for building more responsive, scalable and available Cloud based services. An essential component in smartphone applications, Internet of Things(IoT), field robotics etc. is the ability to analyze large amount of data with reasonable latency. Deep Learning is fast becoming a de facto choice for performing this data analytics owing to its ability to reduce human interventions in such workflows. Major deterrent of providing Deep Learning based Cloud services are Cloud outages and relatively high latency. In the current article the role of Fog Computing in addressing these issues is discussed, current state of standardization in Fog / Edge Computing is reviewed and the importance of optimum resource provisioning for running Edge-Analytics is highlighted. A detailed design and evaluation of the distribution and parallelization aspects of an Edge based Deep Learning framework using off-the-shelf components is presented along with strategies for optimum resource provisioning in constrained edge devices based on experiments with system resource (CPU, GPU & RAM) consumptions of a Deep Convolutional Neural Network.
Disturbance Rejection Model Predictive Control of Ultra-Supercritical Once-Through Boiler-Turbine Unit

T0633. To overcome this issue, this paper proposes the extended state observer based disturbance rejection model predictive control for the boiler-turbine unit. State space model is firstly established on the basis of subspace identification. To enhance the disturbance rejection property of the controller, the technique of extended state observer is employed to estimate the unknown disturbances. The disturbance estimation based model predictive controller is then devised. The observer gain is designed using a less conservative linear matrix inequality, which will accelerate the convergence speed of estimation error. The effectiveness of the proposed controller is illustrated by simulations.
Research on Traffic Speed Prediction by Temporal Clustering Analysis and Convolutional Neural Network With Deformable Kernels (May, 2018)

Real-time traffic speed prediction is one of the most essential parts of the Intelligent Transportation System. In recent years, with the development of artificial intelligence technology, such as in-depth learning, new prediction methods have emerged in endlessly and achieved good results. However, the spatio-temporal information, traffic environment, and their interaction have been hardly depicted in these methods. Therefore, a novel traffic speed prediction method based on temporal clustering analysis and deformable convolution neural network (TCA-DCNN) is proposed in this paper. Temporal clustering analysis (TCA) based on Differential Evolution and hierarchical clustering can adaptively distinguish traffic environment by discriminating the traffic speed variation pattern. By further introducing the deformable convolutional kernels, the characteristics of spatio-temporal traffic speed variation are precisely located in deformable perception fields. After that, a set of DCNN models are trained by using the data processed by TCA, and the output of one of the basic DCNN model is selected as the final traffic speed prediction result. The simulation results based on the measured data in Hangzhou, China, show that the TCA-DCNN algorithm has better performance than other algorithms in traffic speed prediction.
A Study on Outlet Schemes of Indoor Substation based on AGA-AHP Algorithms

With the limited urban land resources and people's high demand for living environment, the construction of urban substation is being increasingly denser and miniaturized indoor substation is being widely used. The outlet of substation is closely related to land area, so this paper proposes a new outlet scheme named positive and negative triangle outlet, based on common ways of outlet, to reduce the land area of substation. In order to compare the advantages of this outlet scheme with that of other outlet schemes under different indicators, AGA-AHP (Accelerating Genetic Algorithm Analytic Hierarchy Process) are used to perform quantitative evaluation for weight of various outlet schemes in electricity environmental protection, difficulty of installation, operation and maintenance, distribution cost, and land area, as well as its sub-indicators, and determine the optimal outlet scheme that meets the requirements. According to the actual situation of the project, outlet model of indoor substation is built by AGA-AHP algorithms, and judgment matrix is established by related experts. The algorithms can realize the quantification of qualitative indicators and the output of the optimal outlet scheme, so as to provide some reference to the construction of substation.
OFDM-like High Order Backscatter Modulation

Backscatter communication has emerged as a promising technology for green communications and future IoT. This paper studies high order backscatter communication over OFDM carriers. For this purpose, a bank of four band-pass filters has been designed and implemented as well as four 4-QAM backscatter modulators. A prototype of the system is proposed which operate in four different frequencies, 2.39 GHz, 2.46 GHz, 2.57 GHz and 2.67 GHz. Further developments of this system could fulfill the promise of ambient backscatter communications over OFDM carriers in the air.
Supervized Change Detection for SAR Imagery Based on Processing of a Low Size Training Data Set by an Ensemble of Self-Organizing Maps

This paper presents a new method to improve accuracy of supervised change detection in Synthetic Aperture Radar (SAR) imagery. The model is based on the idea to apply a low size labeled dataset to the input of an Ensemble of Self-Organizing Maps (ESOM) for training data generation (TDG). The resulted synthetic data set produced by ESOM substitutes the initial authentically labeled sample set and it is used to train a supervised change detection classifier. The proposed method is evaluated using a TerraSAR-X image of 400×400 pixels acquired in the Fukushima region, Japan, before and after tsunami. As change detection classifiers we have comparatively considered Support Vector Machine (SVM), Nearest Neighbor (NN), the three-Nearest Neighbors (3-NN), and Likelihood Bayes classifier. The experimental results have confirmed the effectiveness of the proposed approach using only 100 authentic labeled pixels.
Analysis and optimization of coupling noise in TSV array

In this paper, in order to further reduce the electromagnetic crosstalk of the TSV array, we proposed a layout of TSV array which the differential signal TSVs are interleaved with the ground TSVs due to the excellent noise suppression of the shielded ground TSV and differential signals compare with the traditional layout of TSV array. Since the spacing between the differential signals used in this paper is half of the single-ended signal, this structure can reduce the coupling noise between the signals by 60% without increasing the silicon substrate area. In addition, we proposed a twisted chain structure based on the differential interleaved layout for the multi-layer substrate stacked. From the results of full-wave electromagnetic field simulation software, it can be seen that the coupling noise can be reduced by up to 25%, which proves the superiority of the proposed structure.
Effect of DRIE on the Structure of Si based Filtration Pore Arrays Fabricated with Double Side Aluminium Coating Layer

This paper reports the effect of DRIE process performed to define silicon filtration passageway underneath the aluminium based uniform pore filtration membrane and the membrane cavity on the back end of the silicon substrate. The aluminium based pore arrays for filtration is fabricated on top of the bare silicon substrate with uniform pore size and pore-to-pore gap at cryogenic temperature DRIE. Initially, the aluminium layer is sputtered on both end surface as a foundation of uniform pore arrays structure fabrication and as a DRIE mask for filtration membrane cavity on the back. Both aluminium based uniform pore filtration membrane with underneath silicon etch depth and its filtration cavity structure on the back are observed after 10 minutes and 1 h 45 minutes of DRIE process respectively. The silicon etch rate selectivity over aluminium is evaluated by verifying it's etch depth after the DRIE process. The etched area of silicon substrate underneath the fabricated aluminium based pore filtration membrane is then performs as filtration passageway when it unites with etched silicon area conducted for filtration cavity structure which started from the back end of the silicon substrate. All results have been verified by optical microscope and scanning electron microscopy. The result of the study should be beneficiary for the development of a reproducible filtration membrane with uniform pore that able to separate waste from required nutrients based on molecule size.
Optical Phased Arrays for Integrated Beam Steering

We present 512-element integrated optical phased arrays with low power operation (<1 mW total), large steering ranges (56°×15°), and high-speed beam steering (10 ns phase shifter time constants). Co-packaging with an integrated III/V laser is shown, enabling compact beam steering capabilities for LiDAR and data-communications.
Direct Modelling of Wiring Junction Within Very Large Scale Simulations

In this paper the Unstructured Transmission Line Modelling (UTLM) method is used to model wave propagation along single wires, T -junctions and crossed-junction wiring structures. The accuracy of the direct meshing of wires is achieved by introducing mesh refinement around the wire. The paper shows the computationally efficient capability of the UTLM method for modelling electrically thin wiring structures and junctions.