Miracle-dz/UniFAR-FTUs huggingface dataset:
Compact SIW Power Divider with CSRRs for WLAN Application

In this paper, the development of compact substrate-integrated-waveguide (SIW) power divider with complimentary-split-ring-resonators (CSRRs) is presented. The proposed SIW power divider is designed to work at S-band frequency for wireless local area network (WLAN) application. Two reversely arranged CSRRs incorporated into the SIW surface are intended to produce filtering effect of power divider useful for suppressing unwanted signals. Some parametric studies upon the physical parameters are carried out through simulation to obtain the compact design of power divider. Based on parametric studies, the proposed SIW power divider with CSRRs is then deployed on a 1.6 mm thick FR4 epoxy dielectric substrate with the size of $40\ \text{mm}\times 30\ \text{mm}$. From the characterization, the proposed SIW power divider shows capability to work at the frequency of 2.4 GHz with the rejection rate more than 20 dB at the stopband area which is suitable for the desired application.
3D Robots Formation cooperative Control Using EFDs

We study in this work the formation cooperative control of 3D holonomic robots group utilizing parametric and implicit properties of the desired 3D planar curve (pattern shape). This latter is considered as a closed contour and generated based on Elliptical Fourier Descriptors (EFDs). The robots control consists of two stages. Firstly, robots move towards the desired closed contour using its Implicit Polynomial function (IP). The IP is deduced from the EFDs and considered as a potential function. The realized formation shape is considered as a virtual Structure modeled by EFDs, where a desired relative position is assigned to each robot with respect to the desired contour frame. The contour center is considered as the virtual leader that has a reference path along to a predefined target. Secondly, a controller is designed to allow robots tracking their reference path and eventually the time-varying desired configuration. The robots coordinate each to other by introducing virtual forces (linear springs) exercised from each robot to its nearest neighbors. The proposed approach is validated in simulation using different robots group sizes showing its feasibility and effectiveness.
A ML-Based Direct Localization Method for Multiple Sources with Moving Arrays

The problem of localizing multiple sources with moving arrays is considered. The conventional two-step methods first extract measurement parameters from the received signals, and then calculate the position via these parameters. Without estimating intermediate parameters, direct position determination (DPD) methods can locate emitter from the signals directly. Due to the great computation load of the straight implement of maximum likelihood (ML) estimators, existing DPD algorithms for multiple sources based on ML criterion remain few. Thus we apply a decoupled iterative idea to the ML-based DPD method. The proposed method decouple one emitter from the other emitters in one step instead of traversal search for all sources, reducing computation complexity substantially. Simulation results demonstrate that our algorithm performs superior and more robust to low signal-to-noise ratios (SNRs) than other location methods.
Disambiguation of Source and Trajectory Non-Stationarities of a Moving Acoustic Source

It is well known that when there is a relative motion between the transmitter (source) and receiver, a Doppler shift is observed in the spectral content of the received signal. In this paper, we investigate a scenario where the source signal itself has an innate spectral non-stationarity in addition to the non-stationarity introduced by the source motion relative to the receiver. Using only a single microphone recording, we show that these two kinds of non-stationarities are distinguishable and propose a method of separating them. Towards this, we propose a novel scheme of simulating the signal from a source traversing an arbitrary trajectory. The proposed simulation mechanism employs band-limited interpolation and nonuniform sampling to incorporate an acoustic source generating an arbitrary band-limited signal and moving along an arbitrary trajectory.
Calibration of Infrared Ear Thermometers (IRETs)

In recent years in our country, calibrations of infrared ear thermometers, which have been used intensively in the health field, can not be done within the traceability chain. The accuracy of measurement in this area depends entirely on the information determined by the device manufacturers or suppliers. However, many years ago, intensive and systematic studies were conducted in America, the European Union and Japan to discipline this area. In this study; calibration bath which has the features such as controlled temperature, mixing, temperature stability and homogeneity and specially manufactured; and together with hot body resources (cavities) in accordance with both EN and ASTM standards, which are central importance to the calibration of infrared ear thermometers (radiation thermometers), "Ear Thermometer Reference System for Infrared (IR) Thermometers" was established in TÜBİTAK UME. Thanks to this system installed, the calibrations of the infrared ear thermometers found in the market will now be able to be done in our institute.
A New Algorithm for Blood Flow Measurement Based on the Doppler Flow Spectrogram

The blood flow is traditionally obtained by multiplying the cross sectional area of the blood vessel and the average blood speed in the cross section, or is given by the integral of the product of the cross section and blood velocity of each element. However, both methods are affected greatly by the measurement precision of the area and velocity. A new algorithm, which is based on the Doppler blood flow spectrogram, is proposed to measure the blood flow in this paper. In the algorithm, the blood flow is calculated according to the double integral of a Doppler blood flow spectrogram. To verify the feasibility of the proposed algorithm, experiments have been performed on the Doppler blood-mimicking system <inline-formula> <tex-math notation="LaTeX">$KS205D-1$ </tex-math></inline-formula> using the SonixTouch ultrasonic system. In addition, linear-regression analysis is carried out to observe the correlation factors between the experimental values and real values of different flow rates. Experimental results show that the calculated values and real values correlate significantly <inline-formula> <tex-math notation="LaTeX">$(r>0.969, P < 0.0000001)$ </tex-math></inline-formula>. Experimental results both on males and females also verified the proposed algorithm <inline-formula> <tex-math notation="LaTeX">$(r>0.915, P < 0.00053)$ </tex-math></inline-formula>. Hence the proposed algorithm is proven effective for relative mean blood flow measurement. Due to the special structure of the human brain, it is difficult to measure the cross sectional area of blood vessel with ultrasound imaging. In this algorithm, there is no need to measure the cross sectional area of the blood vessel. Therefore, the proposed algorithm has the potential to be a new method for clinical ultrasonic blood flow measurement, especially cerebral blood flow measurement.
Octopus: Based on Congestion-aware Scheduling on Geo-distributed Big Data Analytics Cluster

In recent years, big data analytics frameworks spring up rapidly. Meanwhile, it has become routine for large volumes of data to be generated, stored, and processed across geographically distributed datac enters. Network congestion generated by data transfers between networks becomes a major bottleneck to the overall performance of the system in a geo-distributed environment. Many existing methods usually process network congestion after they occurs, which does not solve the problem fundamentally. In this paper, we focus on the problem of predicting and avoiding network congestion in advance in a geo-distributed environment on Apache Spark, in terms of their job completion times. We formulate this problem as a runtime minimization problem, which is challenging to solve in practice due to a scene with different data centers. To address these challenges, we propose a model based on congestion-aware scheduling. In the model, we exploit SDN(Software-Defined Networking) to detect the data size of the data flow in advance from different data centers and then analyze the data characteristics, which predicts the flow that can generate network congestion in advance, so that we can draft two scheme for different flow. In addition, when we detect the network congestion, we choose a path with a greater bandwidth for the congestion flow. The approach can minimize network congestion, promote network utilization and improve system performance in a geo-distributed environment. As a highlight of this paper, we design and implement our proposed solution as a job scheduler based on Apache Spark, a modern data processing framework.
Research on Content-based MOOC Recommender Model

MOOC platform provides favorable conditions for people’s lifelong learning and personalized learning. With the developments of MOOC, the supply of course resources will increase year by year and information overloading will become increasingly highlighted. At present, the major MOOC platforms provide only the classification and search functions of the courses, which are not enough. How to realize the innovative supply of courses resources and help the learners to locate the target course quickly, realizing the individualized learning, is also a question to be considered in the process of building intelligent MOOC platform. This paper, taking the platform of “iCourse”for example, proposed a content-based course recommender model for MOOC. The experiments show that the prediction precision of the proposed recommendation model is much higher than that of random recommendation and the more exact and comprehensive the descriptive data of course is, the higher the recommendation precision of the model proposed is, proving the validity of the content-based MOOC recommender model.
Two Lossy Multi-period Optimal Power Flow Formulations with Renewable Energy and Storage

By considering transmission losses, power system operators can obtain more realistic economic dispatching. This paper proposes a multi-period DC optimal power flow (DC-OPF) with losses formulation for minimizing power system operation costs. The classic DC power flow formulation and the power transfer distribution factors (PTDF) formulation are used to formulate the transmission network, and losses are included by using a piecewise linear approximation approach. The formulations are validated and compared on IEEE 14-bus and 57-bus systems.
Region Selective Information Augmentation for Retinal Images

This paper proposes a scheme for the image super-resolution (SR) of retinal fundus images obtained from the handheld imaging devices. Clinical information in fundus images is present in the blood vessels, the optic disk, the macula and the retinal lesions. The homogeneous regions in the fundus images do not carry much relevant diagnostic information. Therefore, a patch based region selective approach for retinal image SR is proposed for providing a relatively fast and accurate SR results. The contrast sensitivity index (CSI) is applied to coarsely measure the clinical relevance of the input low resolution retinal image patch. If the clinical relevance of the input patch is above a particular threshold, then sparse representation based SR is performed else time efficient bicubic interpolation is performed for the patch. The method attains a quality performance in terms of the peak signal to noise ratio (PSNR) and the structural similarity index measure (SSIM) value.
Cold-Atom-Based Commercial Microwave Clock at the 10−15 Level

Laser cooled atomic clocks allow to both extend spectroscopy duration and better control the systematics. We present a commercial microwave clock that achieve an extreme stability of $3.2\times 10^{-13}$ at 1 s and $1\times 10^{-15}$ in less than 2 days, with a turnkey product dedicated for continuous long-term operation.
Integral-Type Terminal Sliding-Mode Control for Grid-Side Converter in Wind Energy Conversion Systems

This paper investigates robust control strategies for wind energy conversion systems with variable-speed permanent magnet synchronous generators, which are integrated into the grid to provide reliable, secure, and efficient electrical power. A three-phase grid-side converter without a grid transformer is connected to the grid using an LCL filter with low resistive losses. In these working conditions, an instantaneous power proportional integral (PI) controller for the outer voltage-loop in the grid-side converter is used to regulate the DC-link voltage and generate the required currents for the inner current-loop in the grid-side converter. Two integral-type terminal sliding-mode controllers are proposed to control the active and reactive powers exchanged between the converter and the grid. The switching signals in the controllers are softened to attenuate chattering. The time-varying gains in the controllers are constructed to reduce control energy wastage and avoid overestimation of the boundaries of system uncertainties. Simulated and experimental results validate the proposed method.
Light–Matter Interaction in Black Phosphorus Coupled to a Si Photonic Crystal at Near Infrared Band

In this paper, we demonstrate the light interaction mechanism of black phosphorus (BP) in two different structures: the photonic crystal waveguide (PCW) and photonic crystal mirror (PCM). The optical characteristics are simulated by the numerical method finite difference time domain. Specifically, the coupled mode theory is used to investigate the light propagation in PCW structure integrated with BP. By optimizing the geometric parameters, an enhanced light absorption in BP with the maximum of 36.8% at 1550 nm could be obtained. Additionally, with the assistance of BP film, the ultracompact PCM used for a 90° bend connection can achieve a high-reflective efficiency at ∼94% for transverse electric (TE) mode incidence. It is demonstrated that these interesting structures can offer a new way to develop ultracompact photonic integrations based on two-dimensional materials at near infrared field.
Architecture Design of Convolutional Neural Networks for Face Detection on an FPGA Platform

Convolutional neural networks (CNNs) have emerged to provide powerful discriminative capability, especially in the field of image recognition and object detection. However, their massive computation requirements, storage and memory accesses make them hard to be deployed on mobile or embedded systems. In this paper, a few optimizations for a CNN cascade face detection algorithm are proposed to increase throughput while minimizing computation, storage and bandwidth requirement under power constraints. First, the first net of the CNN cascade is converted to a fully convolutional network to reduce 83% of the computation. Second, network retraining is applied to quantize the model parameters from 32-bit floating-point to 2-bit fixed-point, resulting in 93.75% less parameter memory size. Finally, a CNN accelerator is implemented on a Xilinx ZYNQ FPGA board.
Design, Modeling and Simulation for Stand-Alone Electric Vehicle Parking Lots Fed by Photovoltaic Systems

This paper presents a comprehensive design procedure for the supply side of a stand-alone photovoltaic-based electric vehicle parking lot. For this isolated system, a photovoltaic (PV) array installed at the parking roof is in charge for power supply. Also, a battery bank as a storage system is dedicated to ensure the service continuity. A step down DC/DC converter is providing the required voltage to harvest the maximum available power of the PV array. This converter is fed by the main system DC bus. Another bidirectional DC/DC converter is responsible to maintain the suitable voltage profile at the DC bus, in addition to the battery charging. The last stage of the supply is a voltage source converter (VSC) that is fed by the main DC Bus. All three converters are designed and modelled. Further, the appropriate controller for each converter is suggested. Considering the importance of the VSC output filter, an efficient algorithm for filter design is proposed that offers better trade-off between filtering and damping. Also, a controller is suggested for bidirectional buck-boost converter that guarantees its stable operation for sudden change of PV supply and load, for the whole operating region. The effectiveness of the proposed design procedure is validated through time-domain simulations.
A Data Forwarding Algorithm Based on Nodal Activities in Opportunistic Networks

In opportunistic networks such as social networks, the mobile nodes usually encounter others irregularly and frequently, so the data dissemination can be assisted by evaluating the nodal activities in the historical contacts with other nodes. Specially, in this paper the nodal activity is calculated according to the number of encountered nodes during a period. Generally, the higher activity will be obtained when the node has encountered more other nodes. Thus, we propose a Data Dissemination Algorithm based on Nodal Activities (DDANA), where the correlations between nodes and the destination is exploited for the data dissemination as well. Experimental results show that DDANA can improve the delivery ratio and shorten the delivery time.
Full Reference Quality Assessment of Full HD Images Using Combined Saliency Priors in Multi-Scale

Conventional image quality metrics such as SSIM, FSIM etc out performs well in standard definition (SD) and enhanced definition (ED) images. But with the rapid advancement in technology and miniaturization of devices, the present camera devices support high resolution images such as high definition (HD), full HD and ultra HD. Existing 2D IQA methods doesn't perform better over high resolution images, especially over full HD and above resolution images. So we present a saliency based approach whose performance is in par with existing methods but shows state-of-the-method over full HD databases. We also consider gradient magnitudes and inter-gradient maps for predicting structural similarity. The choice of usage of saliency and gradient methods results in best performance over full HD database. Our proposed algorithm also implemented in different scales of image where the scale number depends on the size of the image.
Improving Risk Evaluation in FMEA With Cloud Model and Hierarchical TOPSIS Method

Failure mode and effect analysis (FMEA) is a prospective reliability analysis technique used in a wide range of industries for enhancing the safety and reliability of systems, products, processes, and services. However, the conventional FMEA method has been criticized for inherent drawbacks that limit effectiveness and applications. In this paper, a novel integrated FMEA model based on cloud model theory and hierarchical technique for order of preference by similarity to ideal solution (TOPSIS) method is developed to assess and rank the risk of failure modes. First, individual linguistic assessments of failure modes are converted into normal clouds. Then, FMEA team members’ weights are calculated based on the subjective weighting information. Finally, the risk priority of failure modes is determined by using the cloud hierarchical TOPSIS. The newly proposed FMEA method combines the advantages of the cloud model in coping with fuzziness and randomness of linguistic assessments and the merits of hierarchical TOPSIS in solving complex decision making problems. Two empirical examples to illustrate the feasibility and effectiveness of the proposed FMEA are presented together with a comparison to existing methods.
Research on Embedded Heart Rate Detector Based on Vision

At present, the study of non-contact heart rate measurement based on vision is mostly a theoretical method. A few of the relevant algorithms are only described by the MATLAB script language. The specific research and design are still basically blank. Contact-type heart rate measurement devices, while accurate, are expensive, inconvenient to carry, and have a limited range of applications. Existing non-contact heart rate devices require a dedicated light source for measurement. Therefore, aiming at the above issues, this paper studies the design and implementation of a non-contact embedded device based on vision, proposes a design scheme, and perfects the currently known non-contact heart rate detection theory based on actual test results. Combining the high performance, low cost, and low power consumption of the ARM processor, a new method for determining the heartbeat crest is proposed, and a low-cost vision-based embedded heart rate device is designed and implemented for the first time.
A Polarization-Insensitive Miniaturized Element Frequency Selective Surface using Meander Lines

In this paper, a polarization-insensitive frequency selective surface (FSS) made of miniaturized elements has been presented. The proposed FSS structure is comprised of convoluted meander line geometries printed on both sides of an ultra-thin dielectric substrate. The design exhibits a bandpass response at 1.25 GHz corresponding to the unit cell dimension of $0.042\lambda_{0}\times 0.042\lambda_{0}$ only, where $\lambda_{0}$ represents the free space wavelength at the centre frequency. The novelty of the structure lies in its improved miniaturization performance as compared to the earlier reported spatial filters. Furthermore, the designed FSS is angularly stable upto incident angle of 60°. The proposed structure has also been fabricated and experimentally verified under normal incidence.
An Enhanced Lowrank Algorithm for Image Denoising

There are great breakthroughs in image denoising based on image self-similarity and the introduction of sparse representation and low rank theory. Some state-of-the-art image restoration techniques, including BM3D and SAIST are brought forward and applied to various vision tasks. In this paper, we propose an enhanced SAIST algorithm for image denoising. These improvements are mainly implemented in the following aspects. First, when matching similar blocks, matching results are depended on block distances which affected by noise interference. Thus DCT pre-filtering is introduced before aggregation because it can effectively suppress measurement errors of block distances. Second, the relevance of image patches which affects the singular value thresholding is not considered in sample mean. So a weighted sample mean calculation method is proposed to make the singular value thresholding more adaptive. The experimental results show that this improved algorithm achieves a better performance than the original algorithm in terms of both objective criterion and subjective visual quality.
A Dynamic Packet Scheduling Method for Multipath TCP in Heterogeneous Wireless Networks

Multipath TCP (MPTCP) is considered as a promising protocol that enables multi-homed terminals with multiple interfaces, to transmit data concurrently through all the available paths in heterogeneous wireless networks. However, the dissimilar characteristics among different paths may cause the out-of-order problem at the receiver with limited receiving buffer, which will significantly degrade the overall throughput. Many packet scheduling schemes for MPTCP have been proposed to keep data in-order delivery, but most of them only consider the path latency, ignoring the bandwidth and packet loss rate. In this paper, we propose a new dynamic packet scheduling method for MPTCP. We first use a low-complexity way to estimate the packets sent on other paths concurrently, taking both the bandwidth and packet loss rate into consideration, then modify the scheduling value with the feedback information. Simulation results show that our method can enhance the overall throughput and reduce the unordered packets at the receiver.