Miracle-dz/UniFAR-FTUs huggingface dataset:
OPDIMA: large-area CCD-based x-ray image sensor for spot imaging and biopsy control in mammography

The detection characteristics of digital x-ray and film-screen mammography systems are different and thus current film-screen techniques are not ideal for digital mammography. Therefore optimum technical parameters required for digital mammography are likely to be different compared with film-screen mammography. The goal of this study is to evaluate the optimum technical parameters for full-field digital mammography by experimental and computer simulation methods. A General Electric Full Field Digital Mammography (FFDM) prototype unit using Cesium Iodide (CsI) on an amorphous Silicon photodiode array was used for the experimental measurements. Using breast equivalent phantoms, images were acquired for a set of x-ray target-filters for a range of peak kilovoltage, varying breast composition and thickness, with and without an anti-scatter grid. The signal-to-noise ratio (SNR) and figure-of-merit (FOM) were determined for simulated calcification and mass targets, independently by the two methods. The results for noise, contrast, SNR and FOM were compared and agree within 5% and 6% respectively. Combined results are presented for the case of 50% glandular - 50% adipose tissue breast composition using the grid and for the calcification target. Based on the FOM approach, preliminary results suggest that a Rhodium target-filter combination will be beneficial for higher breast thickness and for denser breasts.
TECHNICAL ANALYSIS OF AN AGGREGATOR'S OPERATION FOR THE GOTLAND POWER SYSTEM

The large scale deployment of distributed generation from renewable energy sources may undermine the network safe operation and may require network reinforcements. In order to avoid or postpone the infrastructural investments it is possible to exploit other resources. In this paper the operation of a load aggregator is simulated on the Swedish island of Gotland in different wind penetration condition to test two different business models, one oriented to the price following and the other oriented to the wind following. The adopted methodology is described and the results presented and commented.
Residential Water Heater Damage and Fires following the Loma Prieta and Big Bear Lake Earthquakes

To obtain data on the extent of earthquake damage to residential water heaters, and the effectiveness of prevailing water heater restraint methods, we surveyed 299 randomly-selected people who lived in the City of Santa Cruz, California, at the time of the 1989 Loma Prieta Earthquake. We also reviewed the rate of water-heater linked fires in the Big Bear Lake area following the 1992 Big Bear Earthquake. In Santa Cruz, 13% of the respondents' water heaters suffered damage from the earthquake, most commonly water leaks costing under $50 to repair. Two percent of the water heaters suffered gas leaks. No water heater related fires were reported in our sample or in the City of Santa Cruz following the earthquake. Repair costs for solar water heater systems averaged over $600 per damaged system. The sample revealed no quantifiable reduction in earthquake damage associated with reported prior water heater strapping with plumbers tape. In the Big Bear Lake area, 11 out of 13 structural fires were attributed to water heater gas leaks. Water heater-related fires occurred at a rate of roughly 6 per 1,000 structures. Further studies are needed to compare the effectiveness of different water heater restraint methods, and to identify factors that contribute to ignition in water heater related fires.
Validity of the thermal activation model for spin-transfer torque switching in magnetic tunnel junctionsa)

We have performed spin-transfer torque switching experiments with a large number of trials (up to 107 switching events) on nanoscale MgO magnetic tunnel junctions in order to test the validity and the limits of the thermal activation model for spin-torque-assisted switching. Three different methods derived from the model (“read disturb rate,” “switching voltage versus pulse duration,” and “switching voltage distribution” measurements) are used to determine the thermal stability factor and the intrinsic switching voltage. The results obtained from the first two methods agree well with each other as well as with values obtained from quasistatic measurements, if we use only the data for which the voltage is smaller than approximately 0.8 of the intrinsic switching voltage. This agreement also shows that, in our samples, in the low voltage region, the influence from other factors contributing to the switching (such as current-induced heating and field-like torque) is negligible. The third method (switching vo.
Calibration methods of voltage-to-distance function for an electromagnetic articulometer (EMA) system

In electromagnetic articulometer (EMA) systems, the relationship between the voltage of the received signal and the distance between the transmitter and receiver coils is represented by a voltage-to-distance (VD) function. This paper describes an adaptive calibration method for determining unknown parameters in the VD function in which the parameters are determined using automatically selected local calibration data. This method is compared with the conventional fixed method in which the parameters are computed from calibration data covering the entire measurement area. Experimental results show that the measurement error of the adaptive method is 0.106 mm for a 14×14-cm measurement area and is less than half of that in the fixed method. In addition, this error is the same as that when the region of the receiver position is restricted to a 4×4-cm local area in the fixed method.
Wideband reduced size electromagnetic bandgap structure

A novel electromagnetic bandgap element geometry is presented with compact dimensions and a wide bandwidth. The element has a small cell size of 22.8 mm and a measured bandwidth of 24% at 1.6 GHz. A useful second bandgap can be realised with a bandwidth of 6% at 2.5 GHz. Simulations show that using the same element dimensions on a higher dielectric constant substrate provides a bandgap at the lower 900 MHz GSM band with sufficient bandwidth.
A Comparative Study on Performance of SVM and CNN in Tanzania Sign Language Translation Using Image Recognition

ABSTRACT Sign language is an effective form of communication for speech impaired people. However, there is a challenge for people without impairment to communicate with speech impaired people because most are unaware of the language. There are several Machine Learning techniques that have been used in sign language translation. However, no study has been found in Tanzania Sign Language which is the sign language used by speech impaired people in Tanzania. This study seeks to compare the performance of SVM and CNN on translating sign language through the image recognition. The study employs Tanzanian Sign Language images as datasets. Principal Component Analysis was employed for feature extraction. Furthermore, the study used Combined 5x2cv F test to compare the two techniques. The findings indicate that CNN scored 96% in all of the parameters which are accuracy, recall, and precision while SVM scored similar rate in precision but lag behind on recall and accuracy. Additionally, the results show that there is significant difference in performance between the techniques. Therefore, the study recommends the use of CNN since it has high accuracy.
Resistive switching characteristics in printed Cu/CuO/(AgO)/Ag memristors

A resistive switching behaviour of printed, flexible Cu/CuO/(AgO)/Ag memristor devices is demonstrated for the first time. It is suggested that the high resistance state and low resistance state of bipolar resistive switching behaviour are governed by the migration of oxygen between the copper oxide and silver oxide layers. The devices have characteristics of inexpensive low-temperature fabrication, low-power operation and no required electroforming process. Ink-jet printing was used to fabricate the devices on polymer substrates. Since these devices are fabricated on flexible polyimide, they have compatibility with flexible electronic technologies.
APPLICATIONS OF MIXED H2 AND Hinfin; INPUT DESIGN IN IDENTIFICATION

The objective of this contribution is to quantify benefits of optimal input design compared to the use of standard identification input signals, e.g. PRBS signals for some common, and important, application areas of system identification. Two benchmark problems taken from process control and control of flexible mechanical structures are considered. We present results both when the design is based on knowledge of the true system (in general the optimal design depends on the system itself) and for a practical two step procedure when an initial model estimate is used in the design instead of the true system. The results show that there is a substantial reduction in experiment time and input excitation level. A discussion on the sensitivity of the optimal input design to model estimates is provided.
Students’ Motivation in Learning English Through English Learning Videos Making

Video is a common media to use in teaching English in the past few years. English learning video can be an external factor that influence students’ motivation in studying English. English learning video that was designed by involving students as the actors predicted to have some common sense of belonging. Related to the purpose of the research, the result showed the students were motivated even more by learning English in a class using the video they are involved. Focusing on research methodology, descriptive quantitative research was applied to get the purpose of the research. The result in this study was represented in descriptive percentage.
Scalable Anomaly Detection and Isolation in Cyber-Physical Systems Using Bayesian Networks

Anomalies in cyber-physical systems may arise due to malicious cyber attacks or operational faults in the physical devices. Accurately detecting the anomalies and isolating their root-causes is important for identifying appropriate reactive and preventive measures and building resilient cyber-physical systems. Anomaly detection and isolation in cyber-physical systems is challenging, because the impact of a cyber attack on the operation of a physical system may manifest itself only after some time. In this paper, we present a Bayesian network approach for learning the causal relations between cyber and physical variables as well as their temporal correlations from unlabeled data. We describe the data transformations that we performed to deal with the heterogeneous characteristics of the cyber and physical data, so that the integrated dataset can be used to learn the Bayesian network structure and parameters. We then present scalable algorithms to detect different anomalies and isolate their respective root-cause using a Bayesian network. We also present results from evaluating our algorithms on an unlabeled dataset consisting of anomalies due to cyber attacks and physical faults in a commercial building system.
Phasogrammetric optical 3D sensor for the measurement of large objects

In the following, optical 3D-measurement systems based on fringe projection techniques according to the principle of phasogrammetry are introduced. These self-calibrating measuring systems allow the automated measurement of complex objects. In combination with adequate software tools to evaluate the data, a variety of different tasks can be performed in a productive environment.
Seismic performance of self-centering structural walls incorporating energy dissipators

This paper presents elements of seismic design for jointed precast cantilever wall units designed to rock about their foundation. Gravity loading and prestressed unbonded tendons provide the restoring force in these walls. Lateral displacements eventually result in a separation gap forming only at the wall-foundation beam connection. The gap reduces the wall stiffness and results in nonlinear response. Design of these walls is made with the explicit objective of ensuring a self-centering response. That is, the wall returns to its pre-earthquake position upon unloading from a large displacement excursion. The integrity of the walls is maintained because no plastic hinges form and there are no residual postearthquake displacements. Energy dissipators, in the way of longitudinal mild steel reinforcement crossing the joint between the walls and the foundation, are incorporated into these walls to add significant energy dissipation capacity while preserving the self-centering response. This paper also describe.
Experimental and simulated study of metamaterials with varied spacing along magnetic field direction

A type of composite metamaterials (CMMs) with unique configurations is demonstrated and the transmission properties performed in free space exhibit a pass band at about 8.5 GHz.Simulations with lateral displacement of Gaussian beam transmitted through parallelogram‐shaped medium are realized, which can demonstrate the negative refraction index. Further, transmission spectrum of wires alone, split ring resonators alone, and CMMs with varied spacing along magnetic field direction are measured in our experiments. Experimental results and simulated results are in good agreements. With the spacing increased, the pass band drifts to lower frequency which can be applied in tunable metamaterials devices. © 2011 Wiley Periodicals, Inc. Microwave Opt Technol Lett 53:852–855, 2011; View this article online at wileyonlinelibrary.com. DOI 10.1002/mop.25867
Widening Frequency Band of Uniform SPL Distribution Using Adaptive Genetic Algorithm

Uniform sound pressure level (SPL) distribution of linear phased loudspeaker array is limited by frequency. This paper widens the applicable frequency band of uniform SPL distribution in a linear listening area. By using an improved adaptive genetic algorithm (which contains a novel objective function, modified genetic operators and parameter setups) to control the directivity pattern details accurately, uniform distribution of SPL on a linear listening line in a wider frequency is achieved. The simulation and experimental results show that the SPLs on the test listening line are basically uniform from 200Hz to 500Hz, which demonstrates that the improvement of adaptive genetic algorithm is effective.
Perceptions Of Customs Managers And Agents Towards Import Tax Non-Compliance During Goods Clearing In Malawi

Import tax non-compliance is increasing in Malawi. A study was conducted to explore the perceptions of custom managers and agents towards import tax non-compliance with import tax procedures and processes. The design of the study was descriptive and used qualitative data collection and analysis methods. The study was conducted in Malawi’s major border posts of Blantyre and Mwanza in the southern region. A total number of 25 participants were purposively sampled and administered an open-ended data collection tool to collect data using in-depth face-to-face interviews. The qualitative data were analyzed using content analysis. Two themes emerged from the study which were, customs agents' limited knowledge, and corruption. The study results further show that training sessions by the Malawi Revenue Authority created the requisite knowledge to the customs agents which improved import tax compliance. However, corruption needs a long-term solution to change the stakeholders’ mindset. It is recommended that awareness should be created among the stakeholders to change the attitude and perceptions of the Malawian general public towards import tax non-compliance.
Human Factors in Aviation Maintenance and Inspection: Research Responding to Safety Demands of Industry

For over five years the Federal Aviation Administration has conducted extensive research and development to enhance human performance and safety in aircraft maintenance and inspection. Directed by the Office of Aviation Medicine(AAM) the program has engaged in pragmatic research capitalizing on basic scientific principles and a thorough working relationship with all levels of aviation maintenance practitioners. This paper describes a few of the example research products that are currently promoting safety and efficiency in maintenance applications worldwide.
ADaptive-scale damage detection for frame structures using beam-type wavelet finite element: Experimental validation

The superior human vision system provides ingenious insight into an ideal damage detection strategy in which structural modeling scales are not only spatially varying but also dynamically changed according to actual needs. This paper experimentally examines the efficacy of a multi-scale damage detection method based on wavelet finite element model (WFEM). The beam-type wavelet finite element in this study utilizes the second-generation cubic Hermite multi-wavelets as interpolation functions. The dynamic testing results of a one-bay steel portal frame with multiple damages are employed in the experimental validation. Through a multi-stage updating of the WFEM, the multiple damages in the steel portal frame are detected in a progressive manner: the suspected region is first identified using a low-scale structural model, and the more accurate location and severity of the damage can be identified using a multi-scale model with local refinement. As the multi-scale WFEM considerably facilitates the adaptive change of modeling scales, the proposed multi-scale damage detection method can efficiently locate and quantify damage with minimal computation effort and a limited number of updating parameters and sensors, compared with conventional finite element methods.
LED Street Lamp Module Configuration Design

The configuration of LED street lamp is important for its high security and low cost requirements. The modular method is introduced to design LED street lamp with independent light source and heat dissipation structure. In the single module heat pipe technology radiator is used to improve the LED street lamp of the cooling efficiency. For the 60W LED module, the optical lens are designed with the angles of 60 ° and 30 ° respectively and the average temperature is 329 K on each observation sides of the module.
Component Inventory Costs in an Assembly Problem with Uncertain Supplier Lead-Times

We present a generic study of inventory costs in a factory stockroom that supplies component parts to an assembly line. Specifically, we are concerned with the increase in component inventories due to uncertainty in supplier lead-times, and the fact that several different components must be present before assembly can begin. It is assumed that the suppliers of the various components are independent, that the suppliers' operations are in statistical equilibrium, and that the same amount of each type of component is demanded by the assembly line each time a new assembly cycle is scheduled to begin. We use, as a measure of inventory cost, the expected time for which an order of components must be held in the stockroom from the time it is delivered until the time it is consumed by the assembly line. Our work reveals the effects of supplier lead-time variability, the number of different types of components, and their desired service levels, on the inventory cost. In addition, under the assumptions that inventory holding costs and the cost of delaying assembly are linear in time, we study optimal ordering policies and present an interesting characterization that is independent of the supplier lead-time distributions.
Compact circular polarisation design for equilateral-triangular microstrip antenna with spur lines

A novel compact circular polarisation (CP) design for a single feed equilateral-triangular microstrip antenna with inserted spur lines at the patch edges is presented. It is found that, with increasing spur-line length, the resonant frequency of the triangular microstrip patch is significantly lowered. Also, by further adjusting the inserted spur lines to have a length ratio of /spl sim/0.96, the proposed design can achieve CP radiation at a much lowered operating frequency, resulting in compact triangular microstrip antennas with circular polarisation.
Neural Network for Rapid Depth Evaluation of Shallow Cracks in Asphalt Pavements

Abstract: Rapid and nondestructive evaluation of pavement crack depths is a major challenge in pavement maintenance and rehabilitation. This article presents a computer‐based methodology with which one can estimate the actual depths of shallow, surface‐initiated fatigue cracks in asphalt pavements based on rapid measurement of their surface characteristics. It is shown that the complex overall relationship among crack depths, surface geometrical properties of cracks, pavement properties, and traffic characteristics can be learnt effectively by a neural network (NN). The learning task is facilitated by a database that includes relevant traffic and pavement characteristics of Florida's state highway network. In addition, the specific data used for the NN model development also contained laser‐scanned microscopic surface geometrical properties of cracks in 95 pavement sections and pavement core samples scattered within five counties of Florida. Relatively advanced training algorithms were investigated in addition to the Standard Backpropagation algorithm to determine the optimal NN architecture. In terms of optimizing the NN training process, the “early stopping method” was found to be effective. The crack depth evaluation model was validated based on an unused portion of the database and fresh core samples. The results indicate the promise of NN usage in nondestructive estimation of shallow crack depths based on crack‐surface geometry and pavement and traffic characteristics.
Semantic edge-guided object segmentation from high-resolution remotely sensed imagery

ABSTRACT Image segmentation is a basic task of Object-Based Image Analysis (OBIA) in remote sensing. Traditionally, the algorithms for this task are mostly based on region merging procedure. But the segmented objects may correlate poorly with the actual object boundary. Inspired by the recent remarkable improvements on edge detection with deep learning, we propose a semantic edge-guided segmentation with deep learning method to extract meaningful geographic objects from High-resolution Remote Sensing (HRS) images. The method consists of three stages: in the first stage, geographic object boundaries are manually labelled and randomly augmented to generate training data; in the second stage, a fully convolutional neural networks with Encoder-Decoder structure and multiscale supervised nets are trained to detect edges at multiple scales. The detected edges with semantic information do not only include local details but also global edge structure, which are more in accordance with human perception and suitable for conversion to actual geographic boundaries; in the third stage, the detected edges are thinned and extended to construct complete object boundaries according to calculated edge strength. The average precision of our method for the two datasets was 0.902 and 0.866, which is higher than the state-of-the-art deep learning models including RCF, BDCN and DexiNed have obtained. And line IoU improvement of at least 8.46% and F1 score improvement of at least 8.13% in the two datasets. The code of DDLNet is publicly available at https://github.com/Pikachu-zzZ/SEGOS.
Research on Verification Device of DC Electrical Energy Meter for Electric Vehicle Charger

With the increasing demands of electric vehicles, many DC electrical energy meters for vehicle battery charger appear. In this paper, a verification device of DC electrical energy meter is developed that base on real-time pulse period compare method, which used PCI-6281 for data acquisition, used LabVIEW for data processing and human-computer interface. This article describes the working principle of the verification device and core technology. The practical application verify the device can use for calibration of DC electrical energy meter efficiently.