Miracle-dz/UniFAR-FTUs huggingface dataset:
Four-Way Orthomode Waveguide Power Dividers: Subtractive and Additive Manufacturing

This paper presents two waveguide power dividers in a planar 4-way equiphasic dual-polarization power division configuration operating in the transmit Ku-band (10.7-12.75 GHz). Both concepts are primarily targeted as excitation networks for multiple accesses radiating elements or antenna arrays. The presented solutions are characterized by two different variants that accommodate for different manufacturing processes, namely subtractive manufacturing (SM) and additive manufacturing (AM). Their principal operations are described and finally their RF performance is presented and compared experimentally inter alia. The principal objective is to demonstrate the mechanical complexity of this type of microwave components and how 3D-printing enables its efficient realization via co-design.
Wideband Reduction of the Metal-Frame Blockage to mm-Wave Antennas

In this paper, a method to reduce the metal-frame obstruction to the radiation of endfire mm-wave arrays with wideband operation is presented. A 7 mm-wide frame is chosen to emulate the current commercial bezel thickness. Slots of three different sizes are etched on the frame to ensure the wideband endfire behavior. The proposed solution allows the mm-wave array to radiate through the metal frame from 27.27 GHz to 37.43 GHz, which corresponds to a fractional bandwidth of 31.4 %. The presented design is compared with the free space mm-array and with the array with a normal unmodified frame. The results show that the proposed solution provides similar performance to the free space counterpart, while a 15 dBi gain improvement of the antenna array comparing to a normal frame is obtained. A good beam-steering performance can also be supported with the proposed slotted-frame structure of the mm-wave array.
Generating Constrained Multi-target Scene Images Using Conditional SinGAN

As a variant of generative adversial network(GAN), SinGAN has been attracting a lot of research interest. Aiming at the disadvantage that SinGAN can easily generate multi-target images that do not conform to human logic, and the randomness of generating images is exsiting, simutaneously, the controllability of the generated images are poor, we have improved SinGAN’s architecture and loss function, and made it a conditional GAN instead of the original nonconditional GAN, so as to make the images more reasonable in spatial layout and semantic information. Conditional SinGAN realizes the function of manipulating the number and layout of the target in the image with the subjective will of the users. The experimental results show that conditional SinGAN has achieved ideal effect.
Modeling Business Intelligence Process: Toward Smart Data-Driven Strategies

This paper aims to provide an updated conceptualization of the Business Intelligence Process in general and how the operational data are transformed into valuable insights to enhance business processes design and achieve strategic competitive advantage. The current research will review previous studies on effectively adopting and implementing data-driven strategies for intelligent decision-making using emerging technologies such as Applied Artificial Intelligence, Machine Learning, and Big Data Analytics. This work’s contribution will be an updated conceptual framework of the modern Business Intelligence Process constructed using the concept mapping tool. Such a conceptual framework will provide a fresh look into future research projects in this area and help BI practitioners and organizations plan, develop, and implement big data strategies to gain a competitive advantage in decision-making and innovation over their competitors. Additionally, this paper explores the process of Intelligent Business decisions, the various tools, techniques used for making an Intelligent Business decision.
A Compact Transformation Optics-based Luneburg Lens Antenna using Curved Reactive Impedance Surface

This paper is to investigate the pattern distortion that happens in the extremely low incidence angle for a flat TM-polarized half Luneburg lens antenna (HLLA) by using a purely reactive impedance surface (RIS). For the scenario of a radar mounted on an unmanned aerial vehicle, a curved RIS is further examined to verify that the proposed conformal HLLA would be able to work for a non-flat RIS-coated object. The simulation results of the proposed antenna show that the right-hand 1st sidelobe at the lowest incidence of y = 15 mm is suppressed to −14.9 dB at 10 GHz and the single-side scanning range is broadened to −20 ~ −57 degree, while the antenna gain of ~13.5 dBi is as smooth as the original one. Therefore, a miniaturized Luneburg lens antenna with the wide-scanning capability is successfully achieved.
On the Link Between Leaky-Wave Mechanism and Parasitic Resonances Observed in a Reflecting Periodic Surface Made of Phoenix Cells

This communication investigates a parasitic resonance with strong losses that is exhibited by a periodic structure composed of 4th order Phoenix cells on top of a metal-backed dielectric slab. The physical origin of the phenomenon is examined through simulations. This study shows that this parasitic resonance is linked to a trapped wave propagating in the structure. It corresponds to a leaky-wave mechanism occurring under certain conditions. In order to avoid such undesired resonances, a design rule is derived which defines a new constraint on the cell periodicity.
Analysing Business Process Anomalies Using Discrete-time Markov chains

Within a business context, anomalies can be viewed as indicators for inefficiencies or fraud, which impact upon product quality and customer satisfaction. The development of approaches to monitor, detect and predict anomalous business processes remains an important research topic. In this paper, we propose a method, combining Discrete-time Markov chains (DTMCs) and hitting probabilities (HP), for detecting anomalies occurring in the execution of business processes. Our method extends standard DTMCs to be able to estimate the probability of occurring for a process instance even though it is partially recorded (i.e., the initial executions are missing). The proposed method, denoted as HPDTMC, does not rely on prior knowledge about anomalies and the business process and can be trained on datasets already consisting of anomalies. A Šidák correction is applied to balance the probability of instances of varying length since naturally, process instances with more executions have lower sequence probability and more likely to be detected as anomalies by using DTMCs. We demonstrate the effectiveness of the method by evaluating it on two artificial datasets and one real-life dataset against seven classic anomaly detection methods. In the experiments, our approach reached an F1 score of 0.904 on average. Moreover, the proposed method outperforms competitors under noisy conditions. The main contribution of this paper is the proposed noise-robust method which is able to detect fully or partially recorded process instances of varying lengths.
A Survey on Infant Emotion Recognition through Video Clips

First few years of life is a crucial time for growth and change, both physically and mentally. Thus understanding the baby at this time for laying a good foundation is a must. But recognizing an infant's emotions and needs is complex. The only way of communication is their cries, murmurs or facial expressions. So to put a light on it, this paper presents the various ways in which we can recognize an infant’s emotion through video clips. What is modality, types of modality and its importance will be seen. Different unimodal and multimodal systems will be studied. Image processing, speech processing and classification algorithms required for the systems will also be covered.
Behavioural Biometrics in Feature Profiles-Engineering Healthcare and Rehabilitation Systems

With biometrics replacing the age-old password verification process and identification of an individual, it is time to explore the applications of biometrics in health monitoring of patients, specifically senior citizens, and for rehabilitation. The aim of this study is to use behavioural biometrics to save data of patients and make their feature profiles. A check-up can then include the comparison of newly taken data with the feature profiles to facilitate an early diagnosis of brain deteriorating diseases. These biometrics can also be applied to patients in rehabilitation to provide therapy which can be personalised by comparing their data to that of a healthy person. The data would include patients’ gait, handwriting (signature), speech and keystroke dynamics. This novel study is also proposing a framework named BBFP (Behavioural Biometrics in Feature Profiles) for implementing the techniques in hospitals.
Intrusion Detection System Based on Fast Hierarchical Deep Convolutional Neural Network

Currently, with the increasing number of devices connected to the Internet, search for network vulnerabilities to attackers has increased, and protection systems have become indispensable. There are prevalent security attacks, such as the Distributed Denial of Service (DDoS), which have been causing significant damage to companies. However, through security attacks, it is possible to extract characteristics that identify the type of attack. Thus, it is essential to have fast and effective security identification models. In this work, a novel Intrusion Detection System (IDS) based on the Tree-CNN hierarchical algorithm with the Soft-Root-Sign (SRS) activation function is proposed. The model reduces the training time of the generated model for detecting DDoS, Infiltration, Brute Force, and Web attacks. For performance assessment, the model is implemented in a medium-sized company, analyzing the level of complexity of the proposed solution. Experimental results demonstrate that the proposed hierarchical model achieves a significant reduction in execution time, around 36%, and an average detection accuracy of 0.98 considering all the analyzed attacks. Therefore, the results of performance evaluation show that the proposed classifier based on Tree-CNN is of low complexity and requires less processing time and computational resources, outperforming other current IDS based on machine learning algorithms.
Boosting Magnetic Resonance Image Denoising With Generative Adversarial Networks

Denoising plays an important role in the Magnetic Resonance Imaging (MRI) applications for medical diagnosis. MRI images usually contain undesired noises which would negatively affect the exactitude of pathological diagnosis. Recently, many models for MRI denoising have been developed from deep learning networks. In this paper, we propose a novel MRI image denoising method using the conditional Generative Adversarial Networks (GANs). Specifically, a Convolutional Neural Network (CNN) is utilized as the discriminator in the process to distinguish whether the image pair obtained from the conditional GAN is a real pair which consists of a noisy image and a noise-free image or a fake pair which, on the other hand, contains a noisy image and a denoised image. In our design, the convolutional encoder-decoder networks-based generator is used to remove the noise in the noisy MRI images as much as possible. The whole architecture is trained by adversarial learning. Experiments using both synthetic and real clinical MRI datasets are conducted. When tested on the synthetic T1w images with 10% noise level, our method performed better in terms of reaching a high structural similarity index (SSIM) at 0.9489 while that of the next best method was only 0.7485. Moreover, when the image noise level was increased from 1% to 10%, our method was more stable that the SSIM only dropped about 3.2% while that of the next best method dropped about 23.7%. Simulation results demonstrate that the proposed method is more robust and outperforms the conventional methods in both the denoising level and preservation of the anatomical structures and defined contrast.
Emotion Recognition in Singing using Convolutional Neural Networks

With the development of deep learning, convolution neural network (CNN) has been widely applied in the field of emotion recognition. The vital to enhance the performance of singing emotion recognition system is to select a suitable feature and establish reliable models. The feature of Mel Frequency Cepstral Coefficient (MFCC) method has been proved to be effective in recognizing emotions. Therefore, in this paper, CNN is used to build a model of singing emotion recognition system, and MFCC method is used in feature extraction. For improving the accuracy of this system, the feature matrices have been segmented into small slices, and the method of majority vote has been used in the test part to identify the emotion. To verify the generalization of this system, this paper provides two approaches in model building part. One approach distinguishes male and female speakers separately. The other one is to build a mixed model. The accuracy of the singing emotion recognition system has been improved in both approaches and is not influenced by using separate model or mixed model.
Audio similarity detection algorithm based on Siamese LSTM network

The key technology of audio signal similarity detection lies in the selection of audio signal features and feature matching model. In order to improve the accuracy of the similarity calculation of audios, a method of using LSTM in the basic network part of the Siamese network is proposed. First of all, we extract the Filter banks features of the two audio signals. Then, two feature matrices are input into the network to calculate the audio similarity. Experiments show that the Siamese LSTM network using FBank features can accurately detect the similarity of two audio segments.
Game Theory-Based Parameter-Tuning for Path Planning of UAVs

Modern UAV s are incredibly complex systems with numerous tunable knobs such as the battery capacity, camera settings, sampling rate, constraints on the route, etc. The area of theoretical exploration of the optimization problems that arise in such settings is dominated by traditional approaches that use regular nonlinear optimization often enhanced with AI-based techniques such as genetic algorithms. These techniques are sadly rather slow, have convergence issues, and are typically not suitable for use at runtime. In this paper, we leverage recent and promising research results that propose to convert the optimization problem into a game and then find the set of equilibrium strategies of different players. The strategies can then be mapped to the optimal values of the tunable parameters. With simulation studies in virtual worlds, we show that our solutions are 5–21% better than those produced by traditional methods, and our approach is 10–100 times faster.
Dataflow-Aware Macro Placement Based on Simulated Evolution Algorithm for Mixed-Size Designs

This article proposes a novel approach to handle macro placement. Previous works usually apply the simulated annealing (SA) algorithm to handle this problem. However, the SA-based approaches usually have difficulty in handling preplaced macros and require longer runtime. To resolve these problems, we propose a macro placement procedure based on the corner stitching data structure and then apply an efficient and effective simulated evolution algorithm to further refine placement results. In order to relieve local routing congestion, we propose to expand areas of movable macros according to the design hierarchy before applying the macro placement algorithm. Finally, we extend our macro placement methodology to consider dataflow constraint so that dataflow-related macros can be placed at close locations. The experimental results show that our approach obtains a better solution than a previous macro placement algorithm and a tool. Besides, placement quality can be further improved when the dataflow constraint is considered.
Drastic reliability improvement using H2O2/UV treatment of HfO2 for heterogeneous integration

For heterogeneous integration, the heat cycle constraint limits the available number of options for the process of fabricating high-quality reliable high-k dielectrics, for example, post-deposition annealing and high-temperature deposition. To solve this problem, we examine the effects of H2O2/UV treatment on the reliability characteristics of low-temperature-grown HfO2 via atomic layer deposition, wherein it is treated with a minimal post-deposition heat cycle. The leakage current and time zero dielectric breakdown characteristics of the H2O2/UV treated Hf02 are drastically improved without having applied the post-deposition heat cycle, compared with those of the control group that undergoes O2 post-deposition annealing. The proposed process is a promising method to improve the quality of dielectrics for heterogeneous integration.
Interpreting Mueller matrix of anisotropic material

The polarization imaging methodology has evolved to accept increasingly complex parametric measurements. The Mueller matrix is now generally calculated using a polarimetry technique. In our manuscript, we study the anisotropic wood sample using polarization imaging techniques. Herein, We attempt to study the difference between wood horizontal and vertical fibers. We calculated 3 × 3 Mueller matrix elements, which can be used to described an intuitive overview of the anisotropic material characteristics. We interpreted experimental results of Mueller matrix coefficients in terms of a graphical representation.
AI-Aided Game: Enhancing the Defense Performance of Scale-Free Network via Deep Reinforcement Learning

Scale-free network, as a type of complex network, is prone to various target attacks. Some researches characterize the attack and defense problem of complex network as a game problem, and design the optimal defense strategy from the perspective of achieving the game equilibrium. However, such a game problem in the scale-free network space is very difficult to solve because the strategy space is too large. An effective way to achieve the equilibrium is to train a growing population of player strategies and use heuristic methods to guide the population evolution. The deep reinforcement learning (DRL) has been used to guide the population evolution due to its strong ability to explore new strategies. Inspired by this fact, in this paper we apply an AI-aided game framework, Policy Space Response Oracle (PSRO), to solve the above game problem. We first abstract the scale-free network as a game environment and design a set of game rules. The confrontation game occurs between two agents acting as the attacker and the defender in the network, respectively. We then use the convolutional neural network (CNN) to evaluate values of actions in deep Q-network (DQN) and extract game states into tensor forms as CNN’s inputs. To tackle the targeted attacks against the scale-free networks, we carefully design a specific action value for the defender combining the node weight and network connectivity parameters of a node in CNN. The simulation results show that the defender agent using CNN as strategies can delay the attacker’s intrusions under equilibrium, and achieve a balance between protecting high-weight nodes and keeping the network connectivity.
Towards Semantic Travel Behavior Prediction for Private Car Users

The urban private car as convenient transportation plays an essential role in daily human life, which accordingly produces massive trajectory data by built-in GPS tracking devices. These data offer a new opportunity to mine and explore travel behavior for private car users. Existing works mining travel behavior mainly focus on modeling the sequential contexts while seldom considering the semantic information of the travel behavior, which led to a shallow understanding of users’ travel regularity. To capture valuable information on users’ travel mode, we design a semantic-aware method named as Semantic Long Short-Term Memory(Sem-LSTM). Specifically, we exploit an LSTM network as the foundation of a unified travel behavior prediction framework and introduce two types of semantic information, including area of interest (AOI) properties and user interests. We aim to explore individual travel behavior for a single private car user and conduct extensive experiments on real-life private car datasets. The experimental results demonstrate that Sem-LSTM is very suitable for capturing semantic content and improve prediction performance on private car users. In detail, for the travel behavior prediction, achieve average prediction accuracy of 0.82, recall of 0.80 and F1-score of 0.81.
Low-Cost Six-Port for High-Volume Frequency Measurement Systems in the 2.4 GHz ISM-Band

Due to the proceeding automation in industry, new low-cost sensor systems are gaining more importance for every sector. Measurands like strain, pressure or temperature have to be determined in many applications and at best remotely. For this purpose one approach are frequency measurement systems for resonant surface acoustic wave (SAW) sensors that can be interrogated wirelessly as well. Six-port based sensor systems are already much more cost-effective than others since high performance calculation units are not necessary. However in many industries every saving gives a system a further edge over others. Due to this, in the following publication a six-port for frequency measurement systems is proposed which is reduced in cost and size by only using one analog output channel.
Millimeter-Wave 5G Antenna-In-Package for Mobile Devices Featuring Intelligent Frequency Correction Using Distributed Surface Mount Technologies

A new class of frequency adaptive 5G front-end architecture incorporating three function blocks is proposed, analyzed, and verified using surface-mount technology (SMT) for a millimeter-wave (mmWave) 5G mobile devices. At high frequencies, such as mmWave spectrum, antenna systems often times suffer from unexpected interference by internal or external factors. Therefore, a tunable matching network (TMN) topology is proposed using active components (multi-throw switching chip), which is applicable to feedline of antenna featuring any arbitrary input impedance. The distributed SMT is exploited for compatibility with the current mmWave phased array 5G architectures. The measured results demonstrate that the fabricated 5G AiP can radiate maximum depending on the switch state covering at 27 GHz and 28 GHz, respectively. As a result, the proposed architecture exhibits the peak gain of more than 12 dBi at both states with an output power of 5 dBm/channel.
Low-Profile CTS Array in PCB Technology for K/Ka-Band Applications

In this paper, an ultra-low-profile wideband continuous transverse stub (CTS) array is presented. The antenna module is fully realized in multilayer printed circuit board (PCB) technology, comprising 9 dielectric substrates. Particularly, the radiating part consists of long slots etched on the top of the eighth dielectric substrate. The slots are parallel-fed by a corporate feed network made of vertical parallel-plate waveguides (PPW), using via-fences. A pillbox system, embedded into two substrates, is employed to feed the slots with a line source. The antenna module is low cost and presents a very low form factor. A prototype was fabricated and measured in the K/Ka-band. The antenna array provides a pencil-beam radiation for angles of scanning as far as ±24° along the azimuth plane parallel to the slots. The input reflection coefficient is below -10 dB in the frequency range 19-31 GHz (48% of relative bandwidth). The maximum realized gain is 21 dB at 29 GHz.