Miracle-dz/UniFAR-FTUs huggingface dataset:
Wideband Polarization Reconfigurable Differential Circularly Polarized Antenna

This paper presents a polarization reconfigurable differential circularly polarized (PRDCP) antenna with wide impedance and axial ratio (AR) bandwidth for right-hand circular polarization (RHCP) or left-hand circular polarization (LHCP). The antenna is composed of a bandwidth-improved radiator and a reconfigurable differential-to-single-ended sequential feeding network. The radiator consists of three patches, and four feeding lines to achieve the wide AR bandwidth and stable radiation patterns. The feeding network consists of two stepped coupled line couplers (SCLCs), and two switches that provide switchable quadrature excitations. By digitally controlling the inputs of the switches, the LHCP or RHCP mode can be selected. To verify the feasibility of the PRDCP antenna, a prototype is designed, fabricated, and measured. The measured results show that the bandwidths in the LHCP (RHCP) are 75.6% (81.8%) of a 10-dB matching, 66.7% (71.2%) of a 3-dB AR with stable radiation patterns of 7.4 dBi (7.4 dBi) peak gain.
MPEG Double Compression Based Intra-Frame Video Forgery Detection using CNN

Double compression detection is a predominant problem in video forensics. Due to the rapid growth of image/video editing software and multimedia sharing websites, it has become extremely easy to manipulate multimedia data, many times done with malicious intention. One such problem is an intentional modification to videos by carrying out recompression of its (selective) frames. In this paper, we present a forensic solution to detect double compression based forgery in MPEG videos (one of the most commonly used video formats in today's date) as well as to localize the exact region of tampering within the frames. We present a deep learning architecture for the above, which utilizes the video I–frames and the artifacts introduced into those due to double quantization. The proposed method is evaluated using a publicly available standard video dataset to demonstrate the experimental results. Our experimental results prove the efficiency of the proposed technique.
Implementation of SAND Architecture Using SDN

The server and network-assisted DASH (SAND) concept aims to increase the performance of Dynamic Adaptive Streaming over HTTP (DASH) systems. As being one of the future Internet technologies, SDN provides advantages to support Internet applications thanks to its flexibility and design essentials allowing application-specific routing alternatives and providing network information. In this study, we propose an SDN-based architecture and a SAND-aware DASH client that improves viewer experience. The simulation results show that this architecture can help improve the performance of DASH clients as well as provide fair sharing of the network resources among the clients. The results also show that the proposed architecture can adapt to changing network and server conditions with the help of SDN.
Computational Resources of Miniature Robots: Classification and Implications

When it comes to describing robots, many roboticists choose to focus on the size, types of actuators, or other physical capabilities. As most areas of robotics deploy robots with large memory and processing power, the question “how computational resources limit what a robot can do” is often overlooked. However, the capabilities of many miniature robots are limited by significantly less memory and processing power. At present, there is no systematic approach to comparing and quantifying the computational resources as a whole and their implications. This letter proposes computational indices that systematically quantify computational resources—individually and as a whole. Then, by comparing 31 state-of-the-art miniature robots, a computational classification ranging from non-computing to minimally-constrained robots is introduced. Finally, the implications of computational constraints on robotic software are discussed.
Reduced Order Electromagnetic Models based on dual Finite Integration Technique

To solve field-circuits coupled problems, the electromagnetic field equations are discretized by the dual finite integration technique (dFIT), a numerical method which allows the accuracy control of the extracted parameters. Several techniques are used to accelerate the extraction process, such as minimal virtual boundary, minimal mesh and minimal frequency samples set. The behavior of the synthesized SPICE model extracted with proposed algorithm, in the case of passive on-chip devices placed on silicon substrate shows good agreement with respect to the measurements
Image Retrieval based on Color Features and Information Entropy

In this paper, we conduct research on the novel image retrieval algorithm based on color features and information entropy. Method proposed in this paper with the interception of image center position of the rectangular area use the various levels of color utilization rate, as a feature vector, using color information of color image effectively to reduce the whole picture of the color histogram computation. Using local Euclidean distance finding and similar of sub image library, the key figure in a child in the library each image transform, extract the characteristics of secondary retrieval, output the final retrieval results. Experiments show that using this method can reduce the general amount of calculation of histogram, at the same time can reduce the output image has nothing to do with visual image retrieval, and effectively improves the image retrieval rate, shortening the retrieval time.
Atypical Modulations of N170 Component during Emotional Processing and Their Links to Social Behaviors in Ex-combatants

Emotional processing (EP) is crucial for the elaboration and implementation of adaptive social strategies. EP is also necessary for the expression of social cognition and behavior (SCB) patterns. It is well-known that war contexts induce socio-emotional atypical functioning, in particular for those who participate in combats. Thus, ex-combatants represent an ideal non-clinical population to explore EP modulation and to evaluate its relation with SCB. The aim of this study was to explore EP and its relation with SCB dimensions such as empathy, theory of mind and social skills in a sample of 50 subjects, of which 30 were ex-combatants from illegally armed groups in Colombia, and 20 controls without combat experience. We adapted an Emotional Recognition Task for faces and words and synchronized it with electroencephalographic recording. Ex-combatants presented with higher assertion skills and showed more pronounced brain responses to faces than Controls. They did not show the bias toward anger observed in control participants whereby the latter group was more likely to misclassify neutral faces as angry. However, ex-combatants showed an atypical word valence processing. That is, words with different emotions yielded no differences in N170 modulations. SCB variables were successfully predicted by neurocognitive variables. Our results suggest that in ex-combatants the links between EP and SCB functions are reorganized. This may reflect neurocognitive modulations associated to chronic exposure to war experiences.
Application of Improved Variable Learning Rate Back Propagation Neural Network in Energy Dispersion X-Ray Fluorescence Quantitative Analysis

Artificial Neural Network (ANN) can be applied to process data in analysis of Energy Dispersive X-Ray Fluorescence (EDXRF) due to its ability of nonlinear relationship processing. However, considering the conventional Back Propagation (BP) algorithm problems of slow convergence speed and easily getting into local dinky value, an improved Variable Learning Rate Back Propagation (VLBP) neural network is proposed, based on traditional VLBP neural network algorithm, the Lagrange interpolation polynomial is considered to calculate the additional parameter of variable learning rate. In the experiment part, we compare different models in the number of iterations to achieve error precision with the same batch of lead-zinc ore samples. Besides, a 30-time stability test of the models above is implemented. The Zn element concentration of a batch of lead-zinc ore samples is predicted by using the improved algorithm and the predicted values are compared with the chemical analysis values. The results show that the relative error between them is less than 5%. Additionally, 10 groups of samples whose characteristic peak counts exceed the training sample are selected with the purpose of generalization ability test. The relative error is comparatively higher, but still less than 5%, which refers to its certain generalization ability. The results show that the improved VLBP can quickly and accurately predict the concentration of target elements in EDXRF, it has dramatically improvement in convergence speed comparing with BP and VLBP, however, stochastic gradient descent and Adadelta display a more effective way.
Recursive Network with Explicit Neighbor Connection for Image Captioning

Generating natural image descriptions by machine is a non-trivial task. Many researchers have worked in this area and the most common approach is to use encoder-decoder framework based models. Here an image is encoded into feature vector by the encoder and then decoded by the decoder to arrive at the image caption. Generally convolutional neural network (CNN) and recurrent neural network (RNN) are used as the encoder and the decoder, respectively. In this paper, we modify the decoder to have explicit memory connection to the previous sample in the sequence. We use gated recurrent unit (GRU) architecture for decoder and train it on Flickr8K and Flickr30K datasets. For Flickr8K dataset, results show that having explicit memory link in GRU improves the BLEU-4 score by more than 10 percent when compared to that without the explicit memory link. Also, comparing it with the model which uses RNN without the explicit link as the decoder gives an improvement in BLEU-4 score by 20 percent.
Pain Prevalence and Risk Distribution Among Inpatients in a German Teaching Hospital

ObjectivesThis study was designed to provide a cross-sectional analysis of pain prevalence, chronicity, and severity as well as the impact of pain on psychological and social variables in inpatients in various departments of a German teaching hospital. MethodsPatients were asked to complete a questionnaire including sections on sociodemographic and socioeconomic data, pain variables, recent and past health care utilization, and screening questionnaires for depression, anxiety, and health-related quality of life. ResultsOf the 438 patients, 386 (88.1%) had experienced pain in the past 12 months; 367 (83.8%) reported having pain in the previous 3 months. Sixty-four percent of the pain patients stated that pain was the main reason for hospital admission; 48% reported having three or more pain sites. The most common location of pain was the back (26.9%). Pain patients showed significantly higher depression and anxiety scores and markedly reduced physical health when compared to non-pain patients. DiscussionThe results of this study indicate that in most medical disciplines pain is more than merely a symptom of disease. In many instances pain should be considered a serious comorbidity that can influence the outcome of medical and surgical treatment. Recent research has shown that prevention of the pain chronification process is the most promising strategy for avoiding the development of intractable pain. Acceptance, recognition, and assessment of pain as a risk factor at an early stage are essential factors. A first step might involve routine screening for pain on admission to any hospital facility, and subsequently evaluating the impact of pain on biopsychosocial functions.
Multichannel MAC for Wireless Sensor Network applications

Wireless Sensor Network is one of the major areas in wireless communications which extend its application in military, security, internet and scientific purposes. For a network which has dynamically varying channel gain and random topology, selection of operating parameters which improves network performance is always a challenging task. Single channel MAC is generally used in statistical analysis, but providing multi channel MAC will improve the performance of the channel. Multichannel is available in 802.11b/g which operates at 3 different non overlapping channels. In this paper we implemented 802.11g multichannel MAC using NS-2.34 and compared the performance of the network with single channel MAC in terms of throughput, packet delivery ratio and throughput. The multichannel uses back off algorithm and switches between the 3 non overlapping channels whenever the contention window of distributed coordination function reaches the maximum threshold value and thus gives better performance.
Existence and design of functional observers for Takagi-Sugeno systems with time delay

The work deals with the design of functional observers for systems described by Takagi-Sugeno (T-S) fuzzy models with time delay constant time delay and varying time delay. Two design methods are proposed. The design conditions for a fuzzy functional observer are presented in terms of LMI constraints. The proposed approach has been evaluated using the Yalmip/MATLAB toolbox and Simulink/MATLAB software. Finally, two numerical examples are given to show the feasibility of the proposed approaches.
Data Porting Technique Across Scales

The transfer information across scales is of great importance in environmental sciences. The data collection from direct observations is the only fundamental information that one can have about a natural phenomenon. Based on this direct information, the environmental scientists test hypothesis or make scenarios about how the natural systems work. The components of a natural system interact at different levels and in order to explain this, one needs models at different scales. In this paper, we address the problem of building up an interface that allows different scaled models to access a given set of data observations.
Actualizing the Job Scheduling Scheme with Heterogeneous Virtual Map reduce Clusters

Map Reduce is climbing as an essential programming model for extensive scale data parallel applications like web compartmentalization, getting ready, and legitimate duplicate. Hadoop is capacity code data archive use of Map Reduce acquiring a charge out of wide gathering and is at present thus used for transient occupations where low idleness is vital. Hadoop's execution is solidly settling to its trek instrumentation, that really expect that group center points unit of estimation unvaried and endeavors make advance specifically, and uses these assumptions to influence your brain to up on once to with theory re-execute assignments that appear to be stragglers. In watch, the homogeneity assumptions don't continually hold. capability remarkably convincing setting where this happens is also a virtualized data focus, similar to Amazon's Elastic figure Cloud (EC2). we've a tendency to possess a tendency to deduce that Hadoop's instrumentation can cause extraordinary execution defilement in heterogeneous things. we've a tendency to possess a tendency to remain a watch on vogue an exceptionally one among a sort programming rule, Longest Approximate Time to finish (LATE), that is dreadfully vigorous to non consistency. LATE can improve Hadoop response times by a bit of two in groups of two hundred virtual machines on EC2.
Deep Learning Based Robotic Tool Detection and Articulation Estimation With Spatio-Temporal Layers

Surgical-tool joint detection from laparoscopic images is an important but challenging task in computer-assisted minimally invasive surgery. Illumination levels, variations in background and the different number of tools in the field of view, all pose difficulties to algorithm and model training. Yet, such challenges could be potentially tackled by exploiting the temporal information in laparoscopic videos to avoid per frame handling of the problem. In this letter, we propose a novel encoder–decoder architecture for surgical instrument joint detection and localization that uses three-dimensional convolutional layers to exploit spatio-temporal features from laparoscopic videos. When tested on benchmark and custom-built datasets, a median Dice similarity coefficient of 85.1% with an interquartile range of 4.6% highlights performance better than the state of the art based on single-frame processing. Alongside novelty of the network architecture, the idea for inclusion of temporal information appears to be particularly useful when processing images with unseen backgrounds during the training phase, which indicates that spatio-temporal features for joint detection help to generalize the solution.
Electromigration Effects in Corroded BGA

Most reliability or qualification tests focus on a single type of failure mechanism, whereas in the field, the devices are submitted to sequential and superimposed loads. In this paper, the influence on electromigration sensitivity due to corrosion of SAC solder joints is evaluated by finite element simulations. In comparison to new solder joints, the corroded zones induce a change in the weakest part location. Moreover, the maximum electromigration mass flux divergence of corroded bumps is approximately 35% higher than the normal solder bump, which means the electromigration performance is getting worse because of the corrosion. Moreover, the thermomigration effect is also worsened by the corrosion.
Robust Face Tracking Using Siamese-VGG with Pre-training and Fine-tuning

In order to analyze facial expression in human-computer interaction, real time face-tracking has become significant research problem. Traditional face tracking methods have achieved good results in some constrained environments (such as good illumination, no background interference, etc.) However, these methods require to design manual facial features depending on researcher’s experience. In addition, lacking ability for generalization problems is worthy of study. The robustness of face tracking in complex scenes is challenging due to fast moving, multi-scale changes, rotation and occlusion, illumination changes, etc. In view of the above considerations, this paper proposes an improved method based on Siamese-Net to optimize for face tracking tasks. Our work mainly includes four aspects. First, the first two convolutional layers of deeper VGG-16 are used to extract feature. So we call our method Siamese-VGG. Second, we report experiment on face tracking using a pre-trained VGG-Face model which is trained by 2.6M images for face recognition and then fine-tuning to acceleration convergence. Third, in this research the same size crops are input to two branches in the framework and then the inner smaller template feature maps are extracted during training. The proposed method in this paper reduce offset losses by this way. Finally, L2 regularization add to the loss function to improve the generalization ability of the model. The experiment results show better robustness and generalization performance over the original algorithm. In complex scenes, the proposed improved method have achieved the great improvements by almost 11% on average overlap. But, the frame rate of improved method is still 18.5fps on the Nvidia GTX1070Ti GPU.The improved method proposed in this paper is more practical in terms of speed and accuracy.
Adaptive Steganography via Image Complexity Analysis using 3D Color Texture Feature

Human vision system is generally a subjective perception which varies as per individuals. Complexity of an image plays significant role while securing data in to it. In this paper a new steganography approach is presented which uses combined 3D Color Texture Feature (CTF) to identify complex regions of image for data hiding so that visual attack to detect hidden message becomes quite challenging. Frequency domain is used to hide the data in these selected complex regions via Discrete Cosine Transform (DCT). These kinds of areas are originally noisy and extracting extra information is very hard. Each image has different complexity levels and spatial regions, and since data hiding is directly dependent on it, so the steganography system becomes adaptive. The result shows that proposed adaptive method provides secure message hiding while maintaining imperceptibility quality and high embedding capacity. Final cover images maintains PSNR value of above 50. Embedding capacity is around 2 times higher in comparison to similar algorithm which uses Gray Level Co-occurrence Matrices (GLCM) feature to identify complex regions of images for data hiding.
Air-Coupled Array of Pmuts at 100 kHz with PZT Active Layer: Multiphysics Model and Experiments

This work is focused on the multi-physics modelling via the finite element method (FEM) of an air-coupled array of Piezoelectric Micromachined Ultrasonic Transducers (PMUTs) and its preliminary experimental validation in the time domain for the mechanical and acoustic behaviour.Two numerical models are used to simulate the complete performance of the system based on the response of the diaphragm.In the former, the electro-mechanical-acoustic (EMA) problem for the single PMUT is solved, exploiting the axial symmetry of the system, with the following features: the presence of the fabrication induced residual stresses, which determine a non-linear initial deformed configuration and a substantial linearized fundamental mode frequency shift of the piezo-plate; the multiple couplings between different physics, namely piezoelectric coupling in the active layer and acoustic-structural interaction for the waves propagation in the surrounding fluid. When the non-linearities are activated in the dynamic response, by the involved large displacements, the system shows an initial beating behaviour with small steady state amplitude increment as the voltage input increases.In the latter model, the full set of PMUTs belonging to the silicon die in a 4 × 4 array configuration is considered in which the vibrating plates are modelled as equivalent oscillating rigid plane circular pistons, with reduced imposed acceleration amplitude, on a rigid baffle represented by the remaining part of the surface die.The results of the numerical simulations are compared with the experimental ones in terms of the initial static pre-deflection and pressure at 15 cm from the centre of the diaphragm, on the vertical direction along its own acoustic axis, in the case of the TX test with a single actuated transducer for different voltage amplitudes.
Comparing the benefits of cohabitation and marriage for health in mid-life: Is the relationship similar across countries?

Extensive research has found that marriage provides health benefits to individuals. The rise of cohabitation, however, raises questions about whether simply being in an intimate co-residential partnership conveys the same health benefits as marriage. Here we use OLS regression to compare differences between cohabitation and marriage with respect to self-rated health in mid-life, an understudied part of the lifecourse. We pay particular attention to selection mechanisms arising in childhood to investigate how early life conditions shape later life outcomes. We compare results in five countries with different social, economic, and policy contexts. Results show no differences in self-rated health between cohabiting and married people in Norway, Germany, and for Australian women. In the U.K, and U.S., and for Australian men, however, marriage is significantly associated with better health. Much of this association disappears when accounting for childhood disadvantage and union duration in the U.S., Australia, and for British women, but differences persist for British men. Our study indicates that early life conditions can be an important source of selection for explaining marriage benefits, and that policy makers should focus on reducing disadvantage in childhood rather than legislating incentives to marry in adulthood.