Miracle-dz/UniFAR-FTUs huggingface dataset:
Femtosecond Laser Microfabricated Optofluidic Mach–Zehnder Interferometer for Refractive Index Sensing

An approach for refractive index (RI) sensing is proposed using a waveguide-based optofluidic Mach–Zehnder interferometer (MZI). In this optofluidic MZI, the sensing arm is an epoxy-based negative photoresist (SU-8) core fabricated with femtosecond laser-induced two-photon polymerization technique, while the reference arm is the glass substrate. By integrating the SU-8 core with a microchannel, a RI sensor is realized with which the RI of sodium chloride (NaCl) solution has been measured. Effects of the chip length, microchannel length, and core size on the sensitivity of RI measurement have been revealed, indicating a shorter chip length, longer microchannel, and smaller core size can achieve a higher sensitivity, which is advantageous for sensing applications.
Beam-Switching Dual-Spherical Lens Antenna With Low Scan Loss at 71–76 GHz

A millimeter-wave communication system needs a directive beam-steering antenna with a low scan loss to compensate the high free space loss. In this letter, an electronically beam-switching integrated lens antenna (ILA) system with low scan loss is designed for lower <italic>E</italic>-band (71–76 GHz). An ILA with hemispherical collimating surface of 26 mm radius and spherical feed surface of 37.5 mm radius is fabricated from Teflon material. A conformal 1 × 16 linear beam-switching feed array based on SP4T switches and Vivaldi antennas is designed. The maximum simulated and measured realized gain including the switch network losses is 21.8 and 19.6 dBi, respectively. The measured scan loss of the ILA excluding the switch network loss is less than 1.3 dB in the lower <italic>E</italic>-band for beams within <inline-formula><tex-math notation="LaTeX">$\pm$ </tex-math></inline-formula>40°.
Containment control of multi-agent systems with nonuniform time-delays under fixed topology

This paper studies the containment control issue of continuous-time multi-agent systems (MASs) with nonuniform time-delays and stationary/dynamic leaders, respectively. Firstly, by the state transformation method, the containment control problem can be converted into the asymptotic stability of the corresponding ordinary differential equations. Then, for single-order MASs with stationary leaders, containment control can be achieved if for each follower, there exists at least a leader which has a directed path to the follower. Moreover, our results show that arbitrary bounded time delays can be tolerated. On the other hand, for second-order MASs with dynamic leaders, by Lyapunov first method, we mainly provide the upper bound $\tau^{*}$ of time delays such that MASs can achieve the containment control in an exponential convergence rate. Finally, experimental results are provided to illustrate the effectiveness of the theoretical results.
Effect of Cultural Priming on Social Behavior and EEG Correlates of Self-Processing

Humans are social beings and the self is inevitably conceptualized in terms of social environment. The degree to which the self is perceived as fundamentally similar or fundamentally different from other people is modulated by cultural stereotypes, such as collectivism and individualism. These stereotypes are not hardwired in our brains and individuals differ in the degree to which they adopt the attitudes that define their culture. Moreover, individuals can acquire multiple sets of cultural knowledge and, depending on the context, either individualistic or collectivistic cultural mindset could be activated. In this study, we used cultural priming techniques to activate either individualistic or collectivistic mindset and investigated the association between source-level EEG connectivity in the default mode network (DMN) and spontaneous self-related thoughts in the subsequent resting state. Afterward, participants performed a social interaction task, in which they were allowed to choose between friendly, avoidant, or aggressive behavior. After collectivism priming, self-related thoughts were associated with increased connectivity of DMN with the right temporoparietal junction (TPJ), which is involved in taking the perspective of others and is more active in representatives of collectivistic cultures, whereas after individualism priming they were associated with increased connectivity with the temporal pole, which is involved in self/other discrimination and is more active in representatives of individualistic cultures. Individual differences in the intensity of post-priming self-related thoughts and the strength of DMN-temporal pole connectivity predicted individual differences in behavior during the social interaction task, with individualistic mindset predisposing to more friendly and trustful social behavior.
Narrow Band Jamming Detection & Filtering in Satellite Transponder

This paper proposes a compressive signal processing (CSP) receiver for detection and filtering of narrowband jamming signal within a wideband satellite transponder. The proposed receiver utilizes Modulated Wideband Converter (MWC) sensing architecture to detect the presence of the jamming signal using sub-Nyquist sampling. An Automatic Frequency Control (AFC) technique is used to estimate and track the center frequency of interfering signal. The interfering signal is removed using an on-board notch filter with programmable center frequency and bandwidth. The MWC technique combined with the adaptive notch filter is used to limit the impact of jamming signal without affecting the ongoing communication within the filtered sub band. The proposed scheme provides for a computationally efficient implementation of the next-generation wideband satellite transponders that are robust to narrowband jamming.
Efficient Global Optimization for Solving Computationally Expensive Bilevel Optimization Problems

A number of real-life optimization problems operate in hierarchical model, where a “leader” entity is trying to optimize its objective(s), while in response a “follower” entity is also optimizing its own objective(s). Such problems are referred to as bilevel optimization problems. Given their nested nature, they have additional challenges compared to the traditional single-level optimization problems. Recent studies in the domain make it evident that the number of function evaluations needed to solve such problems using evolutionary algorithms is excessive. Development of efficient techniques that could reduce this computational effort is therefore of significant interest. In our previous work, we proposed and studied the use of multiple surrogate assisted optimization (SAO) to reduce the required evaluations of the lower level problem. In this work, we extend the idea further through the use of efficient global optimization (EGO) in solving bilevel problems. We refer to the approach as BLEGO (bilevel EGO). Unlike the standard SAO techniques, EGO samples new infill locations based on expected improvement in the objective value. Two different versions of BLEGO are studied - one where EGO is used only at the lower level, and another where EGO is used at both levels. Numerical experiments are conducted on SMD benchmark problem suite and the results are compared with the previously developed surrogate assisted bilevel algorithm (SABLA) to demonstrate its efficacy.
The Application of Gaussian Mixture Models for the Identification of At-Risk Learners in Massive Open Online Courses

With high learner withdrawal rates in the setting of MOOC platforms, the early identification of at-risk student groups has become increasingly important. Although many prior studies consider the dropout issue in form of a sequence classification problem, such works address only a limited set of behavioural dynamics, typically recorded as sequence of weekly interval, neglecting important contextual factors such as assignment deadlines that may be important components of student latent engagement. In this paper we, therefore, aim to investigate the use of Gaussian Mixture Models for the incorporation such important dynamics, providing an analytical assessment of the influence of latent engagement on students and their subsequent risk of leaving the course. Additionally, linear regression and, k-nearest neighbors classifiers were used to provide a performance comparison. The features used in the study were constructed from student behavioural records, capturing activity over time, which were subsequently organized into six-time intervals, corresponding to assignment submission dates. Results obtained from the classification procedure yielded an F1-Measure of 0.835 for the Gaussian Mixture Model, indicating that such an approach holds promise for the identification of at-risk students within the MODe setting.
IPMC Kirigami: A Distributed Actuation Concept

Ahstract- Today's mechatronics relies on conventional transducers, i.e. lumped sensors and actuators with rigid construction. Future consumer products, medical devices and manufacturing processes require sensing and actuation systems with high count and density of individual transducer units. Such systems can be addressed as distributed transducers. Building distributed sensing and actuation systems with conventional transducers is economically unaffordable, and an alternative solution is needed. In this work we propose and study a methodology to build such distributed sensor and actuator systems from soft bending smart material transducers. Individual transducer units can be separated from the planar material substrate by cutting and etching techniques, and transducer counts and densities are only limited by the available smart materials and equipment. In this study we use laser ablation techniques to separate individual transducer units from the ionic polymer-metal composite (IPMC) sheets, and produce translational actuation units on the bending material substrate. IPMCs are manufactured in-house, different bending structure geometries are studied, and four different designs of the em-scale translational platform units are realized and validated experimentally. The results demonstrate that it is possible to etch and cut a multitude of actuation units into planar bending smart material transducers, that bending actuation can be used to realize translation, and that the designs can be further miniaturized. Therefore, bending smart materials can be utilized to build monolithic distributed transducers.
Genetic Programming for Automatic Global and Local Feature Extraction to Image Classification

Feature extraction is an essential process to image classification. Existing feature extraction methods can extract important and discriminative image features but often require domain expert and human intervention. Genetic Programming (GP) can automatically extract features which are more adaptive to different image classification tasks. However, the majority GP-based methods only extract relatively simple features of one type i.e. local or global, which are not effective and efficient for complex image classification. In this paper, a new GP method (GP-GLF) is proposed to achieve automatically and simultaneously global and local feature extraction to image classification. To extract discriminative image features, several effective and well-known feature extraction methods, such as HOG, SIFT and LBP, are employed as GP functions in global and local scenarios. A novel program structure is developed to allow GP-GLF to evolve descriptors that can synthesise feature vectors from the input image and the automatically detected regions using these functions. The performance of the proposed method is evaluated on four different image classification data sets of varying difficulty and compared with seven GP based methods and a set of non-GP methods. Experimental results show that the proposed method achieves significantly better or similar performance than almost all the peer methods. Further analysis on the evolved programs shows the good interpretability of the GP-GLF method.
Research and Implementation of Target Tracking Algorithm Based on Convolution Neural Network

Video target tracking is an important research topic in computer vision, and has been widely used in video surveillance, robot, human-computer interaction and so on. The emergence of large data age and the emergence of in-depth learning methods provide a new opportunity for the study of video target tracking. This paper first analyzes the research problems of video target tracking at present, analyzes the characteristics and trends of video target tracking in the new period, introduces the emerging recursive neural network frame structure, combined with Kalman filterAnd the experimental results show that the accuracy and robustness of the target tracking based on the convolution neural network algorithm are all good.
Double Encoder Conditional GAN for Facial Expression Synthesis

Photorealistic facial expression synthesis from single face image is already a highly challenging research work, in part due to a paucity of labeled and paired facial expression samples. Most existing facial expression synthesis works attempt to learn the transformation between expression domains and thus would require the paired samples as well as the labeled query image. In this paper, we propose the Double Encoder Conditional GAN (DECGAN) for facial expression synthesis. Generative Adversarial Networks (GANs) have demonstrated to successfully approximate complex data distributions. And cGANs, which contain external information, can determine the specific relationship between images. This work inspires us to modify the structure of GAN, and use the target facial expression feature as a condition. In this work, we propose two encoders to encode the original expression and the target expression, respectively, to extract the latent vectors and conditional labels features of the real image. In the meantime, associative learning is used to associate unpaired original emoticons with target emoticons in the database and to share identities.
A Dynamic Programming Based Track-Before-Detect Algorithm for Multi-sensor Systems

In this paper, a dynamic programming (DP) framework for the implementation of track-before-detect (TBD) in multisensor systems is presented. The proposed framework is composed of a pre-processing procedure and a DP-TBD processor. A novel form of equivalent measurements is employed in pre-processing stage which effectively fuses measurements from different sensors, leading to the reduction of computational cost. Numerical examples demonstrate that the proposed algorithm can take advantage of the information from multiple sensors and therefore obtain better performance.
New Method for the Stability Analysis of Neutral Systems with Time-Varying Structured Uncertainties

This study investigates the delay-dependent stability problem for neutral systems with mixed delays. Different from previous works, this paper employs the free-weighting matrix(FWM) approach and integral inequality approach to estimate the derivative of LKF together. Firstly, a new integral inequality based the Wirtinger inequality is proposed. This improved inequality is right used to cope with the terms $\int_{b}^{a}\dot{x}(s)ds$ which are generated by using the free-weighting matrix approach. Then, by integrating the newly integral inequality with free-weighting matrix approach, some less conservative stability criteria are derived. Numerical examples demonstrate the effectiveness of the method and its advantage over existing methods.
Miniaturization of Low Temperature Co-fired Ceramic Packaging for Microwave Filters

Low Temperature Co-fired Ceramic (LTCC) is a multilayer technology and of great importance in 3D packaging. Its typical wiring precision is on micron scale, which corresponds to electronic components/circuits working at lower microwave regime. As the size of electronic devices keeps shrinking, LTCC technology encounters more challenges. Apart from material and fabrication, innovation can also be made from the perspective of structure design. This paper shows examples to demonstrate a further miniaturization of LTCC microwave filters with special constructing and arranging tricks. The size reduction for optimized designs are 58.3% and 73.9%, respectively, while the electrical performance maintain comparable with that of the original ones.
Predictive Power Management for Wind Powered Wireless Sensor Node

A conventional Wireless Sensor Network (WSN) cannot have an infinite lifetime without a battery recharge or replacement. Energy Harvesting (EH), from environmental energy sources, is a promising technology to provide sustainable powering for a WSN. In this paper, we propose and investigate a novel predictive energy management framework that combines the Maximal Power Transferring Tracking (MPTT) algorithm, a predictive energy allocation strategy, and a high efficiency transmission power control mechanism: First, the MPTT optimal working point guarantees minimum power loss of the EH-WSN system; Then, by exactly predicting the upcoming available energy, the power allocation strategy regulates EH-nodes’ duty cycle accurately to minimize the power failure time; Ultimately, the transmission power control module further improves energy efficiency by dynamically selecting the optimum matching transmission power level with minimum energy consumption. A wind energy powered wireless sensor system has been equipped and tested to validate the effectiveness of the proposed scheme. Results indicate that compared with other predictive energy managers, the proposed mechanism incurs relatively low power failure time while maintaining a high-energy conversion rate.
Time-varying formation tracking for UGV swarm systems with switching directed topologies

Time-varying formation tracking problems for unmanned ground vehicle (UGV) swarm systems with switching directed topologies are investigated, where the follower UGVs form a predefined time-varying formation while tracking the leader UGV. A consensus based protocol is constructed using local neighboring information and an algorithm consisting of three steps is also proposed to design the protocol. Sufficient conditions for the UGV swarm systems to achieve the time-varying formation tracking with switching directed topologies under the designed protocol are presented using the piecewise Lyapunov Theory. Moreover, based on a Ultra-Wind Band (UWB) based indoor navigation system, an experimental platform consisting of four omni-directional UGVs is introduced. Finally, both simulation and experimental examples are presented to demonstrate the effectiveness of the theoretical results.
Model Reference Adaptive Control for a Class of Aircraft with Actuator saturation

Model reference adaptive control (MRAC) is known as a proper method to handle uncertainties of systems and achieve precision command tracking. However, actuator saturation of aircraft limits the ability of the adaptive controller in practice. In this paper, a modified automatic control architecture is presented for a class of aircraft in the presence of input saturation. The proposed controller is based on model reference adaptive control augmented with a modern anti-windup (AW) compensator. First, a standard MRAC with integral state feedback is described. Second, a Riccati-based AW compensator is employed to solve the input saturation problem. Finally, the control technique is applied to control the vertical acceleration of the aircraft with significant uncertainty in control effectiveness and matched uncertainties. Simulation results verify the performance of proposed method.
Von Normen, Gratifikationen und Habitualisierung: Ein Erklärungsmodell zu den Determinanten der Nachrichtennutzung

Ob in der gedruckten Zeitung, im Fernsehen, online oder auf dem Smartphone: Aktuelle politische Informationen sind heutzutage nahezu jederzeit und überall zugänglich (Aalberg et al., 2013; Engel & Breunig, 2015). Die stetig steigende Verfügbarkeit ermöglicht es, sich ohne großen Aufwand über das Nachrichtengeschehen auf dem Laufenden zu halten (David, 2009). Doch dieses »Mehr« an Angebot bedeutet nicht gleichzeitig auch zwangsläufig ein »Mehr« an Nutzung: Da Informationsangebote nicht nur untereinander, sondern auch mit einer Vielzahl an Unterhaltungsangeboten konkurrieren, bieten sich Rezipientinnen und Rezipienten zahlreiche Alternativen (Prior, 2007). Individuelle motivationale Faktoren werden damit als Determinanten der Nachrichtennutzung umso relevanter und nehmen eine Schlüsselrolle für deren Selektion ein. So diagnostizieren verschiedene Autoren aktuell eine zunehmende Polarisierung zwischen interessierten Nachrichtennutzern und -nutzerinnen sowie desinteressierten Rezipienten und Rezipientinnen, die sich fast ausschließlich Unterhaltungsformaten zuwenden und Informationsangebote gezielt vermeiden (Ksiazek et al., 2010; Prior, 2007; Strömbäck et al., 2013) – ein Trend, der insbesondere aus der traditionellen normativen Sicht des informierten Bürgers als Ideal einer funktionierenden Demokratie, aber auch im Rahmen aktueller Diskussionen um die Verbreitung von »Fake News« kritisch gesehen wird (Delli Carpini & Keeter, 1996; York & Scholl, 2015). Zur Erklärung dieser Unterschiede im Nachrichtennutzungsverhalten hat die kommunikationswissenschaftliche Forschung in der Vergangenheit zumeist Soziodemografika und/oder den Uses-and-Gratifications-Ansatz (U&G-Ansatz) herangezogen (z. B. Diddi & LaRose, 2006; Lee, 2013; Vincent & Basil, 1997), während andere soziale oder normative Aspekte nur vereinzelt und oft isoliert betrachtet werden (siehe z. B. Curnalia, 2008; Lee & Chyi, 2014). Vor diesem Hintergrund entwickelt und testet die vorliegende Studie ein integratives motivationales Erklärungsmodell
Impact of ascending aortic, hemiarch and arch repair on early and long-term outcomes in patients with Stanford A acute aortic dissection

Background: Stanford A acute aortic dissection (AAD) is a life-threatening emergency associated with major morbidity and mortality. The aim of this study was to compare outcomes of three different surgical approaches in patients with Stanford A AAD. Methods: From January 2006 to March 2015 a total of 240 consecutive patients with diagnosed Stanford A AAD underwent elective, isolated surgical aortic repair in our centre. Patients were divided into three groups according to the extent of surgical repair: isolated replacement of the ascending aorta, hemiarch replacement and total arch replacement. Patients were followed up for up to 9 years. After univariate analysis multinomial logistic regression was performed for subgroup analysis. Baseline characteristics and endpoints as well as long-term survival were analysed. Results: There were no statistically significant differences among the three groups in terms of demographics and preoperative baseline and clinical characteristics. Incidence of in-hospital stroke (p = 0.034), need for reopening due to bleeding (p = 0.031) and in-hospital mortality (p = 0.017) increased significantly with the extent of the surgical approach. There was no statistical difference in terms of long-term survival (p = 0.166) among the three groups. Applying multinomial logistic regression for subgroup analysis significantly higher odds for stroke (p = 0.023), reopening for bleeding (p = 0.010) and in-hospital mortality (p = 0.009) for the arch surgery group in comparison to the ascending aorta surgery group as well as significantly higher odds for stroke (p = 0.029) for the total arch surgery group in comparison to the hemiarch surgery group were identified. Conclusions: With Stanford A AAD the incidence of perioperative complications increased significantly with the extent of the surgical approach. Subgroup analysis and long-term follow up in patients undergoing isolated ascending or hemiarch surgery showed a lower incidence of cerebrovascular events compared with surgery for total arch replacement.