Miracle-dz/UniFAR-FTUs huggingface dataset:
Sum-Rate Maximization with Joint Power Allocation and Mode Selection in D2D-Enabled 5G Cellular Networks

Device-to-Device (D2D) communication is a potential candidate which satisfies the fundamental requirements of the upcoming 5th generation (5G) technology. In underlay D2D communication, the cell sum-rate of the system increases but it also induces interference to the primary cellular users which may degrade the performance of the system. This paper proposes a scheme to mitigate the interference generated by the D2D user equipment (DUE) to the cellular user equipment (CUE) with the help of power control of DUE, and also by selecting proper mode of operation. DUE's mode of operation is selected on the basis of channel gain threshold. DUEs power is controlled such that the interference generated by DUEs is within the certain limits and CUEs performance does not degrade below the certain threshold. Performance of the scheme is simulated in the mobile and wireless communication enablers for twenty-twenty information society (METIS-2020), International Telecommunication Union Radiocommunication Sector (ITU-R) and WINNER-II channel environments. Instantaneous channel gains and receiver sensitivity are assumed to be known to the enhanced Node-B (eNB) for simulation. Simulation results show that cell sum-rate of the system increases by a factor of 32 when DUEs are in dedicated mode and by a factor of 6, when DUEs are in reuse mode, by allowing underlaying D2D communication at the cost of interference to the primary users.
Vertical 1.2kV SiC Power MOSFETs with High-k/Metal Gate Stack

We demonstrate the first integration of high-k/metal gate stacks in vertical 1.2kV SiC power MOSFETs including static and dynamic characterization as well as safe operation area (SOA). The high-k/4H-SiC MOS interface exhibits a remarkably low interface defect state density and improved threshold voltage stability compared to conventional gate stacks based on SiO2. Moreover, we achieved an impressive performance boost in terms of on-resistance due to this low-defective interface and increased gate capacitance. Compared to vertical devices with SiO2/poly Si gate stacks these devices exhibit a negligible hysteresis.
Capacity Results on the Action-Dependent Dirty Paper Wiretap Channel

The dirty paper wiretap channel, also referred to as the Gaussian wiretap channel with noncausal channel state at the transmitter, plays an important role in wireless physical layer security. In this paper, the dirty paper wiretap channel is revisited by considering actions on the channel state, i.e., the transmitter generates an action sequence that affects the formation of the channel state. Lower and upper bounds on the secrecy capacity of this model are proposed, and numerical results indicate that the actions on the state help to increase the achievable secrecy rate of the dirty paper wiretap channel. The study of this paper offers a new option for enhancing the secrecy rates of the state-dependent wiretap channel models.
A Decision Support Method to Increase the Revenue of Ad Publishers in Waterfall Strategy

Online advertising is one of the most important sources of income for many online publishers. The process is as easy as placing slots in the website and selling those slots in real time bidding auctions. Since websites load in few milliseconds, the bidding and selling process should not take too much time. Sellers or publishers of advertisements aim to maximize the revenue obtained through online advertising. In this paper, we propose a method to select the most profitable ad network for each ad request that is built upon our previous work [1]. The proposed method consists of two parts: a prediction model and a reinforcement learning modeling. We test two strategies of selecting ad network orderings. The first strategy uses the developed prediction model to greedily choose the network with the highest expected revenue. The second strategy is a two-step approach, where a reinforcement learning method is used to improve the revenue estimation of the prediction model. Using real AD auction data, we show that the ad network ordering obtained from the second strategy returns much higher revenue than the first strategy.
Automated Quantification of Amyloid Fibrils Morphological Features by Image Processing Techniques

Image analysis of protein fibrils is a key step for a quantitative understanding of the mechanisms responsible for aggregation and deposition of amyloids. In this paper, we develop a fully automated method to detect and measure all characteristics of fibrils. After detecting the fibrils centerline, we propose a tracking-based method to estimate the local width, length and curvature of fibrils. We also discriminate between branches and overlapping of fibrils by comparing the orientations of the fibril segments at their intersection point. We test our method on Transmission Electron Microscopy (TEM) images of human, bank vole, and ovine PrP (prion protein) fibrils, and analyze the result to evidence structural differences between different types of fibrils. The proposed method has high estimation accuracy in detections and is further compared with a state-of-art method.
Automatic impedance matching of an active helical antenna near a human operator

This paper, presents an automatic impedance matching technique for normal mode helical antenna technique for a normal mode helical antenna near a human operator in the 150 MHz band. The helical antenna incorporating varactor diodes for changing impedance maintains a good matching condition regardless of the distance between the helical antenna and the operator. This eliminates a power loss caused by the impedance mismatch due to the human body. The technique makes use of the steepest gradient algorithm that can achieve an adaptive control algorithm of the impedance condition.
Distributed Resource Allocation for D2D-Assisted Small Cell Networks With Heterogeneous Spectrum

Device-to-device (D2D) communication with increased spectral efficiency and reduced communication delay has undoubtedly become a general trend in future wireless networks. However, when D2D communication is incorporated into small cell networks (SCNs) with large number of randomly overlapped small cells, the co-channel interference between small cell users (SUEs) and D2D users is an inevitable challenge, especially with the heterogeneous spectrum, i.e., licensed spectrum bands and unlicensed spectrum bands. In this paper, we study the downlink channel allocation in D2D-assisted small cell networks with heterogeneous spectrum bands. By taking the required data rate of users and the interference constraint of SUEs into account, we formulate a channel allocation problem integrating channel selection and channel sharing to maximize the network utility, which is the service satisfaction of all users. To derive the solution, we decompose the optimization problem into two games: a potential game and a coalition game. Then, a potential game-based scheme using an interference graph and a coalition scheme with D2D user transferring is proposed to solve these two games, respectively. Based on these schemes, a two-stage distributed channel allocation algorithm is proposed and can converge with low computational complexity. Moreover, the simulation results reveal that the proposed algorithm could achieve high system throughput and network utility.
A Metaheuristic Strategy for Feature Selection Problems: Application to Credit Risk Evaluation in Emerging Markets

As countries develop digital financial infrastructure, a wide range of economic activities expand and grow in importance: from personal loans, to the rapidly developing networked microfinance industry, to mobile telephone services and real estate transactions and so on. Personal credit is also a foundation of trust for facilitation of integrated societal transactions more generally. In emerging markets there is, however, a gap between the requirement for establishing a credit or trust rating and the lack of a credit record. The development of methodologies for greater financial integration of growing economies has the potential to have a significant impact on increasing the GDP of developing economies (4-12% according to a recent McKinsey Global Institute report). In this paper, we develop and test a methodology for feature selection and test its in standard datasets from large institutions in mature market economies, and a recent dataset which illustrates characteristics of emerging markets. The results show performance in classification can be maintained while runtime can be reduced when using a GA for feature selection in a range of machine learning techniques.
On the Performance of NOMA and Coded Multicasting in Cache-Aided Wireless Networks

Coded multicasting has been widely studied as an effective content delivery solution for wireless caching at user equipment. Recently, non-orthogonal multiple access (NOMA) has been studied as an alternative delivery approach in cache-aided network. Both schemes allow multiple users to access the same frequency/time/code resource and thus offer enhanced spectral efficiency. By exploiting the information available in the cache, the users can successfully decode the requested message through cache-enabled interference cancellation (CIC) in NOMA or XOR decoding in coded multicasting. These techniques require user pairing/clustering in order to address the complexity issue arising from handling large number of users. Nevertheless, it is not known which delivery technique performs better under different user pairing scenarios. Based on two-user pairing, we study the performance of NOMA and coded multicasting in terms of probability of sum rate comparison and outage probability under the situation in which the users are randomly located in a circular cell. Both analytical and simulation results show that NOMA is better than coded multicasting when the channel gains of the paired users are highly distinctive while coded multicasting is preferable when the users' channel gains are similar.
A New Technology to Reduce Harmonic Emission in Distribution Networks: Addressing IEC 61000-3-12

Current harmonics generated by Adjustable Speed Drives (ASDs) due to the use of power electronics devices are one of the main power quality concerns in distribution networks. These harmonics affect the grid quality and lead to power losses in the grid. Therefore, different harmonic mitigation strategies using active front end had been proposed to tackle this issue. However, due to the cost effectiveness, conventional front end such as Diode Rectifier is still recommended in industries for a broad range of three phase power electronics systems. Thus, in this paper, a new grid harmonic elimination approach is proposed using three-phase diode rectifier with controlled DC link current (boost converter). Simulation results validates the proposed concept that a selective harmonic can be eliminated from the grid using the developed strategy.
Adapting the Concept of Artificial DNA and Hormone System to a classical AUTOSAR Environment

Embedded systems are growing very complex because of the increasing chip integration density, larger number of chips in distributed applications and demanding application fields e.g. in autonomous cars. Bio-inspired techniques like self-organization are a key feature to handle the increasing complexity of embedded systems. In biology the structure and organization of a system is coded in its DNA, while dynamic control flows are regulated by the hormone system. We adapted these concepts to embedded systems using an artificial DNA (ADNA) and an artificial hormone system (AHS). Based on these concepts, highly reliable, robust and flexible systems can be created. These properties predestine the ADNA and AHS for the use in future automotive applications. However, computational resources and communication bandwidth are often limited in automotive environments. Furthermore, in many critical areas, classical AUTOSAR in combination with CAN bus is used as a static operating system. Nevertheless, in this paper we show that the dynamic concept of ADNA and AHS can be successfully applied to a static system like AUTOSAR and the available computational resources are more than sufficient for automotive applications. The major bottleneck becomes the CAN bus communication when implemented on top of AUTOSAR's communication stack as this limits the maximum achievable throughput for a single device to provide bandwidth for numerous different participants. Implementing the CAN bus communication directly through AUTOSAR's CAN driver mostly removed this problem. Keywords: Artificial DNA, artificial hormone system, self-organization, automotive, CAN bus, AUTOSAR
Estimating Brain Connectivity Using Copula Gaussian Graphical Models

Electroencephalogram (EEG) has been widely used to study cortical connectivity during acquisition of motor skills. Previous studies using graphical models to estimate sparse brain networks focused on time-domain dependency. This paper introduces graphical models in the spectral domain to characterize dependence in oscillatory activity between EEG channels. We first apply a transformation based on a copula Gaussian graphical model to deal with non-Gaussianity in the data. To obtain a simple and robust representation of brain connectivity that explains most variation in the data, we propose a framework based on maximizing penalized likelihood with Lasso regularization utilizing the cross-spectral density matrix to search for a sparse precision matrix. To solve the optimization problem, we developed modified versions of graphical Lasso, Ledoit-Wolf (LW) and the majorize-minimize sparse covariance estimation (SPCOV) algorithms. Simulations show benefits of the proposed algorithms in terms of robustness and accurate estimation under non-Gaussianity and different structures of high-dimensional sparse networks. On EEG data of a motor skill task, the modified graphical Lasso and LW algorithms reveal sparse connectivity pattern among cortices in consistency with previous findings. In addition, our results suggest regions over different frequency bands yield distinct impacts on motor skill learning.
High-Efficiency Single-Input Triple-Outputs DC-DC Converter With Zero-Current Switching

This study mainly develops a high-efficiency single-input triple-outputs dc/dc converter (HSTDC) with a soft-switching technique for high step-up applications. In general, multiple output voltages with only one single input power source is always required for the applications of renewable energy or electric vehicles. At the high step-up operation, the proposed HSTDC has a high voltage conversion ratio to sustain the high-voltage dc bus for the utilization of dc/ac inverter. The proposed HSTDC uses a coupled inductor with lower turn ratios to achieve the high step-up voltage gain and adopts an auxiliary inductor to adjust the voltage of the auxiliary output terminal indirectly. Moreover, the utilization of voltage clamping and zero-current switching (ZCS) in the proposed HSTDC is helpful for accomplishing the goal of high-efficiency power conversion and three output voltage levels. In addition, the effectiveness of the proposed HSTDC is verified by rich experimental results.
Ternary Logic Synthesis with Modified Quine-McCluskey Algorithm

Logic synthesis has been increasingly important to accelerate the development of high-level systems. However, in multi-valued logic, logic synthesis methods that can process emerging devices are deficient. We propose and automate a method to synthesize ternary logic circuits. Our design of ternary logic circuits is based on static gate design, and exploits carbon nanotube field-effect transistors. We optimize ternary logic circuits by minimizing the number of transistors with a modified Quine-McCluskey algorithm. Our proposed method has improved power-delay product by 52.72 % over the state-of-the-art method for a ternary half adder, and by 68.06 % for a ternary multiplier. We also have improved power-delay product by 37.30 % over the state-of-the-art method for a ternary full adder that has high load capacitance. Our design has an average of 42.43 % fewer transistors than the existing design for circuits that have large number of inputs. As circuits become larger, the improved power-delay product and reduced transistor count are advantageous.
MEGC 2019 – The Second Facial Micro-Expressions Grand Challenge

Automatic facial micro-expression (ME) analysis is a growing field of research that has gained much attention in the last five years. With many recent works testing on limited data, there is a need to spur better approaches that are both robust and effective. This paper summarises the 2nd Facial Micro-Expression Grand Challenge (MEGC 2019) held in conjunction with the 14th IEEE Conference on Automatic Face and Gesture Recognition (FG) 2019. In this workshop, we proposed challenges for two micro-expression (ME) tasks— spotting and recognition, with the aim of encouraging rigorous evaluation and development of new robust techniques that can accommodate data captured across a variety of settings. In this paper, we outline the evaluation protocols for the two challenge tasks, the datasets involved, and an analysis of the best performing works from the participating teams, together with a summary of results. Finally, we highlight some possible future directions.
E-Government Ecosystem: A new view to explain complex phenomenon

Different kinds of e-governmental services have taken into use in several countries all over the world. The transition is often driven by the seen benefits in, e.g., efficiency, money savings as well as empowerment of citizens. Different e-governmental systems have been studied and analysed by using different theories and models; however, the current models often do not succeed to take a citizen—the customer—into account enough. This study propose of using an ecosystem viewpoint to define and explain this phenomenon and presents the concept of ‘e-government ecosystem’. The concept is based on philosophical foundation on citizenship. In addition, an existing maturity model is extended and future research areas proposed.
Automated Quantification With Sub-Micrometer Scale Precision In Volumetric Multicolor Multiphoton Microscopy Images

Recent multicolor multiphoton microscopy schemes are opening the way to high quality fluorescence color imaging at near-micrometric resolution over virtually unlimited volumes of brain tissue. However, unavoidable artifacts related to depth, chromatic aberration or anisotropic resolution affect the resulting images and can bias measurements and quantifications. Here, we prove the possibility of performing precise automated quantitative measurements on such tridimensional multicolor images, taking as a model fluorescently labeled axons belonging to an auditory tract in the mouse brainstem. We present an analysis pipeline to compute the diameter of these axons based on the calculation of distance in HSV color space, its binarization using a random walker algorithm and skeleton extraction by distance transform. We measure a biologically meaningful difference of about 500 nm before and after the axons enter their target nucleus where they form synaptic connections, proving the robustness of our pipeline with respect to the aforementioned limitations. This demonstrates the ability of sub-micrometric measurements across tissue depths of 0.5 mm with multicolor multiphoton microscopy.
Building A Reduced Dictionary Of Relevant Perfusion Patterns From Ceus Data For The Classification Of Testis Lesions

Radical orchifunicolectomy has traditionally been the main clinical treatment for small testicular masses (STMs); however STMs represent a constantly increasing and often incidental finding. Since many of them result benign, a more conservative testis-sparing surgery was proposed, but it requires a preliminary differentiation between benign and malignant masses: this however remains challenging. Although common understanding in radiology and oncology is that perfusion patterns might provide a useful information about the type of masses, no guidelines or consensus is available for the differentiation of STMs. We propose to build a dictionary of relevant perfusion patterns, extracted using non-negative matrix factorization on pixel-wise time-intensity curves from contrast-enhanced ultrasound data. When data from a lesion are reconstructed using this dictionary, a vector containing the frequency of utilization of each pattern can be used as a tissue signature. Using this signature, a support vector machine classifier has been trained, and the cross validated accuracy reached 100% in our pilot cohort.
Approaches to metagenomic classification and assembly

Microbiome is an ecological community of commensal, symbiotic, and pathogenic microorganisms that share the same environment. The study of microbiome, i.e., genetic material sampled directly from environmental samples is called metagenomics. In recent years, genome sequencing methods have dramatically improved and the number and variety of sequenced genomes has rapidly increased. New technology has significantly increased the variety and complexity of the microbiome research and ever-larger datasets present new challenges in analysis of metagenomic data. Two main tasks in metagenomic analysis are classification of sequenced metagenomic data into taxonomic group of any rank, such as a species, family, or class, and assembly of the data into longer contiguous sequences. The final aim of both tasks is to correctly identify species presented in the metagenomic sample. This has various applications in medicine (e.g. infectious disease diagnosis), development of biofuels, biotechnology, agriculture, and many other areas. In this paper, we present a description of common procedures and methods for metagenomic data analysis and challenges facing these procedures. We give an overview of existing software tools and a review of public genome databases used for metagenomic analysis. Finally, we explore possible improvements to the existing methods for metagenomic classification and assembly.
IdenNet: Identity-Aware Facial Action Unit Detection

Facial Action Unit (AU) detection is an important task to enable the emotion recognition from facial movements. In this paper, we propose a novel algorithm which utilizes identity-labeled face images to tackle the identity-based intra-class variation of AU detection that the appearances of the same AU vary significantly among different subjects, which makes existing methods generate low performance under cross-domain scenarios in case that the training and test datasets are dissimilar. The proposed method is based on network cascades consisting of two sub-tasks, face clustering and AU detection. The face clustering network, trained from a large dataset containing numerous identity-annotated face images, is designed to learn a transformation to extract identity-dependent image features, which are used to predict AU labels in the second network. The cascades are jointly trained by AU- and identity-annotated datasets that contain numerous subjects to improve the method’s applicability. Experimental results show that the proposed method achieves state-of-the-art AU detection performance on benchmark datasets BP4D, UNBC-McMaster, and DISFA.