Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling and Simulation of a Low Voltage Electroosmotic Micropump for Non-Newtonian Fluids

Non-mechanical micropumps, which do not require moving parts, have prominent role in several biomedical microsystems such as drug delivery, and lab-on-a-chip. Electroosmotic micropump is a non-mechanical micropump which can pump working fluids with wide range of electrical conductivity by the application of an electrical field across a channel. In this study, a low voltage electroosmotic micropump with blood as the working fluid is modeled and simulated. In addition, the effect of changing the depth (D) and the length (L) of the electroosmotic micropump on the free flow velocity and the pressure drop of the fluid are examined to assist in designing a low voltage electroosmotic micropump for blood transport. As a result, a 20V electroosmotic micropump with depth of 50 $\mu \mathrm{m}$, length 100 $\mu {\mathrm {m}}$ and width of 1 mm is designed which provides a flow rate of ${\mathrm {42\mu L/m\dot{m}}}$ and a static pressure of 14 Pa.
Lepidoptera Classification through Deep Learning

Deep learning for image recognition has received a lot of attention in recent years. In this paper we present a case study using two state-of-the-art deep learning libraries for image classification based on single phase (Single Shot Detection - SSD) and two-phase (Faster Region-based Convolutional Neural Network – Faster-RCNN) deep learning technologies. The case study is based on classification of lepidoptera: an order of species that includes butterflies and moths. We describe the data that was collected that underpinned this work. We also present the results and discuss the challenges with the work. Finally, we outline the implementation of a mobile application used as the client interface to the final solution.
Fundamental Bounds for Multi-Port Antennas

Current-density fundamental bounds are formulated for N-port radiators utilizing a port mode basis. A prescription is given for the transformation of full-wave method-of-moments matrix operators into port matrices. Fundamental bound on the total active reflection coefficient is presented as an example. Significant reduction of size of underlying matrices allows for simultaneous optimization of ports’ placement and excitation amplitudes.
SiegeCannon: Detecting Malicious Infrastructures by Analyzing Integral Characteristics of Involved Server Flocks

Nowadays, cybercriminals tend to leverage dynamic infrastructures consisting of multiple controlled servers to carry out malicious campaigns due to the resistance against security enforcement. Though a number of researchers have dedicated to combating with vicious domains or servers, they typically focus on a single point rather than the entire infrastructure, such that can be circumvented by simply throwing away the detected node and recruiting a new one.In this paper, we propose a novel system, SiegeCannon, that extracts and classifies malicious infrastructures by analyzing the anomalies of both internal relationships and external traffic patterns. This is motivated by that malicious infrastructures are formed by a set of orchestrated servers and domains which requested only by the compromised client in most cases. SiegeCannon employs a dependence graph to model the interactions among servers in a monitored network. Then, a pruning algorithm is implemented to extract flocks of intimate servers as candidate infrastructures for further investigation. At last, SiegeCannon trains a classification model based on three types of features from the aspects of graph structure, node traffic and flock behavior, respectively. We build a prototype system of SiegeCannon and evaluate it with traffic from a large ISP network. The result shows the efficacy and effectiveness.
Optimizing Energy consumption and Latency based on computation offloading and cell association in MEC enabled Industrial IoT environment

Mobile edge computing emerges as a promising technology for the industrial internet of things (IIoT). It provides more opportunities and efficient computing resources for end-users when edge nodes are deployed at the nearest IoT devices (IDs). However, IDs have limited computing capability and battery life in the IIoT environment due to their high computational tasks. The IDs can offload the computational tasks to edge nodes to achieve low latency and energy consumption. Our proposed work examines the cell association and computational offloading problem in the MEC-enabled IIoT environment. This problem is formulated as a cost execution problem (total sum of energy consumption and latency). Three different computing modes (full-local, full-MEC, and partial) are used for task execution, where the end-user can choose one of them. To achieve the optimal solution Khun-Munkres algorithm and extensive search method are deployed. Experimental results demonstrate the better performance of the proposed method in latency and energy consumption.
Android based Emotion Detection Using Convolutions Neural Networks

With the advent of improved mobile processing capabilities we have seen many novel and useful applications. Among other usecases is the utilization of graphics capabilities of on-board computing capabilities. The study evaluates a new trend of functionality that has been considered in the emotion detection field. The proposed study uses Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) to conduct a comparison of which deep learning technique works best for emotion recognition. Both neural network are trained using FER2013 dataset of Kaggle with seven emotion classes. The trained models are evaluated where CNN attains the accuracy of 65% and RNN lack behind with the accuracy of 41%. The trained models are then applied using music player based on one’s facial expressions . The user gets the music according to the mood in two forms. Thus with the application user is provided with new and interactive way of getting the music that provides new and latest music and gets an entertaining music app. The Final Product has great scope as the end product can be modified and expanded where music recommendation can be exchanged with other recommendation systems like news, content etc. according to the emotion fetched.
Unsafe image classification using convolutional neural network for brand safety

Contextual advertising is an advertising form in which the advertisements are served by automated systems based on the context of the host websites. In contextual advertising, deciding the matching level between a brand and a website context is crucial. Brand safety is a strategy to keep a brand’s reputation safe by avoiding advertisement placements on inappropriate websites. In this paper, we approach brand safety as an image classification issue to classify and filter websites containing inappropriate images. We propose an image multi-class classification method in order to recognize some categories using for brand safety purpose. We also apply pruning techniques in order to reduce model complexity. In addition, we create a dataset including 56,501 images in six categories: adult, gambling chip, gore, gun, knife and safe. Experimental results show that our proposed method achieves better performance compared to some original methods.
Does the Threshold Voltage Extraction Method Affect Device Variability?

The gate-all-around nanowire FET (GAA NW FET) is one of the most promising architectures for the next generation of transistors as it provides better performance than current mass-produced FinFETs, but it has been proven to be strongly affected by variability. For this reason, it is essential to be able to characterize device performance which is done by extracting the figures of merit (FoM) using data from the IV curve. In this work, we use numerical simulations to evaluate the effect of the threshold voltage (<inline-formula> <tex-math notation="LaTeX">$\mathrm {V_{TH}}$ </tex-math></inline-formula>) extraction method on the variability estimation for a gate-all-around nanowire FET. For that, we analyse the impact of four sources of variability: gate edge roughness (GER), line edge roughness (LER), metal grain granularity (MGG) and random discrete dopants (RDD). We have considered five different extraction methods: the second derivative (SD), constant current (CC), linear extrapolation (LE), third derivative (TD) and transconductance-to-current-ratio (TCR). For the ideal non-deformed device at high drain bias, the effect of the extraction technique can lead to a 137 mV difference in <inline-formula> <tex-math notation="LaTeX">$\mathrm {V_{TH}}$ </tex-math></inline-formula> and an 89 mV/V difference in the drain-induced-barrier-lowering (DIBL), and when considering GER and LER variability, the influence of the extraction method leads to differences in the standard deviation values of the <inline-formula> <tex-math notation="LaTeX">$\mathrm {V_{TH}}$ </tex-math></inline-formula> distribution (<inline-formula> <tex-math notation="LaTeX">$\sigma \mathrm {V_{TH}}$ </tex-math></inline-formula>) of up to 2.3 and 3.7 mV respectively, values comparable to intrinsic parameter variations. Therefore, the <inline-formula> <tex-math notation="LaTeX">$\mathrm {V_{TH}}$ </tex-math></inline-formula> extraction technique presents itself as an additional parameter that should be included in performance comparisons as it can heavily impact the results.
Resilient Communications for the First Responders at the Field

Rescue operations in both small-scale emergencies and major natural or man-made disasters are very challenging. The first responders are requested to explore unknown and potentially hazardous environments, risking their own well-being in order to save others. New innovative technologies are essential to support the first responders in their tasks, ensuring their safety and the effectiveness of their operations. These technologies, that may include wearable devices, automated vehicles and drones or back-end services require communications in order to operate in full capacity. Available infrastructure often fails in cases of emergency, while some operational environments may not even support them to begin with. Aiming to alleviate this barrier, a resilient, field deployable system, that can support the communication between all the equipment deployed at the field and multiple backhaul networks is presented here. The design of the communications, the hardware solutions that support the design as well as the selected configurations are discussed in detail.
Conductance variations and their impact on the precision of in-memory computing with resistive switching memory (RRAM)

This work addresses the reliability of RRAM, with a focus on conductance variation and its impact on in-memory computing (IMC). The core advantage of IMC is the ability to execute matrix-vector multiplication (MVM) in one step in crosspoint memory arrays, which can significantly accelerate data-intensive computing tasks, such as the inference and training of deep neural networks (DNNs). Since MVM is executed in the analogue domain, the imprecision of weight parameters stored in the memory array can result in errors which can affect the accuracy of the computation. By referring to a typical IMC device, that is the resistive switching memory (RRAM), we describe the conductance variations and stability with time, highlighting their impact on IMC accuracy. Then we discuss various options for mapping coefficients in the memory device, including multilevel, binary, unary, redundancy and slicing schemes, and their robustness with respect to conductance errors. It turns out that a tradeoff exists between accuracy and memory area occupation in the IMC circuit. Accurate IMC circuits thus must rely on the co-design of highly-precise, highly-stable devices and error tolerant mapping/computing schemes.
Causal Convolutional Neural Network-Based Kalman Filter for Speech Enhancement

Speech enhancement using Kalman filter (KF) suffers from inaccurate estimates of the noise variance and the linear prediction coefficients (LPCs) in real-life noise conditions. This causes a degraded speech enhancement performance. In this paper, a causal convolutional neural network (CCNN) model is used to more accurately estimate the noise variance and LPC parameters of the KF for speech enhancement in real-life noise conditions. Specifically, a CCNN model gives an instantaneous estimate of the noise waveform for each noisy speech frame to compute the noise variance. Each noisy speech frame is pre-whitened by a whitening filter, which is constructed with the coefficients computed from the estimated noise. The LPC parameters are computed from the pre-whitened speech. The improved noise variance and LPCs enables the KF to minimize residual noise as well as distortion in the enhanced speech. Objective and subjective testing on NOIZEUS corpus reveal that the enhanced speech produced by the proposed method exhibits higher quality and intelligibility than some benchmark methods in various noise conditions for a wide range of SNR levels.
Direct Synthesis of Accurate Angular Filtering Patterns using Holographic Leaky-Wave Antennas

We propose a new synthesis technique of angular filtering radiation patterns, using holographic leaky-wave antennas. The synthesis is based on the association of a set of N leaky waves with the response of a passband filter of order N. Compared to previous broad-beam synthesis techniques, the novel method provides accurate angular filtering specifications with demanding restrictions (ripple and rejection levels), and without numerical optimization.
Corporate Innovation and Dividend Payout Policy: Evidence From China’s Listed Companies

Using the sample of China’s listed companies, this study investigates the effect of corporate innovation on dividend payout policy. We expect that managers from more-innovative firms would have more confidence in future corporate performance, and hence, be more motivated to signal future profitability in stock market through distributing cash dividends. Employing the number of effective patents to measure corporate innovation, we find that the propensity to pay cash dividends is higher among more-innovative firms. Moreover, more-innovative firms usually pay more cash dividends to their shareholders than that of less-innovative firms. The empirical results are robust to different model specifications and to various subsamples based on firm size. This study emphasizes the importance of stimulating corporate innovation in both domestic economic growth and shareholder protection. This study sheds light on the role of corporate innovation in major corporate decisions in China, which is the largest emerging economy in the world. Moreover, the empirical findings of this study also have a policy implication to Chinese regulatory bodies who have been urging firms listed in domestic stock exchanges to pay cash dividends. Regulatory bodies may spend more effort on encouraging innovation, as corporate innovation motivates the managers of listed firms to distribute profits to shareholders.
“I Tried to Breastfeed but…”: Exploring Factors Influencing Breastfeeding Behaviours Based on Tweets Using Machine Learning and Thematic Analysis

Social media is a growing platform for health-related discourse, opinion and experience sharing, including breastfeeding. For instance, nursing mothers share their personal experiences and opinions about breastfeeding on social networks, such as Facebook and Twitter. Unravelling the sentiments behind these experiences will promote adequate knowledge of many challenges, benefits, and factors influencing breastfeeding behaviours. To achieve this, we mine breastfeeding-related tweets and then perform sentiment analysis of the tweets using lexicon-based and machine learning (ML) techniques with the aim of detecting their sentiment polarity (i.e., positive or negative). Specifically, we implement and compare four lexicon-based sentiment classifiers, as well as five ML-based classifiers. Our results show that VADER-EXT (our extended version of VADER) performed best with an overall F1-score of 82.4%, compared to the other lexicon-based classifiers. On the other hand, Support Vector Machine (SVM) outperformed the other four ML-based classifiers with an overall F1-score of 73.7%. The overall best performing classifier is then used in determining the sentiment polarity of tweets. Next, we conduct thematic analysis of both positive and negative tweets to identify the factors influencing breastfeeding behaviours either positively or negatively. Our findings reveal various health-related factors (such as lactational issues, medical issues, and nutritional issues), social factors, psychological factors, and situational factors affecting breastfeeding behaviours negatively. Also, perceived benefits, maternal self-efficacy, social support, and education and training support emerged as the positive factors influencing breastfeeding behaviours. Finally, we reflect on our findings and recommend interventions that address the negative factors to promote positive breastfeeding behaviours.
Effective SSB Beam Radiation Pattern for RF-EMF Maximum Exposure Assessment to 5G Base Stations Using Massive MIMO Antennas

Current extrapolation procedures for the assessment of theoretical maximum RF-EMF exposure to 5G massive MIMO base stations consider the antenna gain difference between SSB and traffic beams. The procedure strongly relies on the provided antenna patterns obtained under ideal conditions, but being distorted and smoothed in practice. In this paper, we investigate the impact of ground reflections (for different ground materials) as well as the vertical sweeping of the measurement antenna (in order to find the local maximum exposure) on the effective base station antenna pattern. It shows that for base station antenna heights ≥30 m, ground reflection dominates the impact and leads to an increase of null levels in the normalized antenna pattern to around -30 dB in case of concrete ground and furthermore, to a slight increase of side lobe levels. Both ground reflection and vertical antenna sweeping additionally lead to a broadening of the side lobes.
MEO Satellite Ka-band Receiving Stations for Tropospheric Propagation Impairment Analysis: Design, Architecture and Preliminary Measurements

The evolution of telecommunication systems is shifting towards the higher portion of the microwave spectrum to achieve higher data rate as well as to non-geosynchronous orbit (non-GSO) satellites. The use of frequency at Ka band and above can be very challenging due to the non-negligible effects of the atmosphere, but also the exploitation of medium-Earth orbit (MEO) and low-Earth-orbit (LEO) satellite needs to tackle the dynamical angular behavior of the channel itself. This paper describes the architecture and preliminary measurements of the Italian receivers for the ESA-funded MEKaP (MEO Ka-band Propagation) experimentation. The activity involves several partners, such as Politecnico di Milano, Sapienza Università di Roma, Instituto de Telecomunicações-Aveiro Pole, and Thales Alenia Space - Italia. A total of four receivers (three in Italy and one in Portugal) will create a network of Ka-band shared data with the main objectives of characterizing the MEO-satellite tropospheric propagation impairments for co-polarized as well as cross-polarized channels. We describe the detailed architecture of the MeKaP twin receiving stations in Rome and Milan, sharing the same design, and on the preliminary measurements acquired and post-processed thanks to the overall effort of the MaKaP international research group.
Efficient and Accurate Modeling of EMC Anechoic Chambers using a Combined Discrete Complex Image and Raytracing Method

The traditional approach to simulate an EMC chamber is by using Geometric Optics (GO) and raytracing. Full wave methods are impractical because of the large amount of computer resources needed. Extending the asymptotic method such as GO to low frequency and in near field, as is required to model an EMC chamber, has long been questioned. In this paper, we describe an improved algorithm by combining GO with the Discrete Complex Image Method (DCIM). We treat the absorbers as multilayered media so that DCIM can be applied to solve for the first order reflections in a more rigorous manner. For higher order reflections where the GO model is adequate, we adopt a visibility tree approach for the raytracing. This approach allows raytracing in an arbitrarily shaped chamber. Several real world case studies are presented to show the accuracy, speed, and flexibility of this method.
Reconfigurable FSS Based on PIN Diodes for Shared-Aperture X/Ka-Band Antennas

This contribution presents a reconfigurable frequency selective surface (FSS) for shared-aperture satellite antennas in X and Ka bands. The FSS has been designed to be reflective at 20 and 30 GHz (transmit and receive frequencies in Ka-band) and to allow switching of transmission and reflection responses in X-band (between 9 and 12 GHz). The FSS unit cell comprises a multi-layer structure, and reconfigurability is achieved using four PIN diodes. An equivalent circuit model has been developed explaining the operating principle of the proposed FSS. The active FSS provides a stable performance for different polarizations under oblique incidence angles.
Deep Inference Localization Approach of License Plate Recognition: A 2014 Series Philippine Vehicle License Plate

License plates are the most reliable and cost-effective approach used for automobile verification purposes. With the advancement of technology, a different application that is related to the vehicle license plate such as license plate recognition has emerged and became a major area of research due to its diverse applications in many areas such as toll collection, road and traffic management, and for law enforcement. In the Philippines, due to the multiple versions of license plates, introducing such a system has made it difficult. To be able to adapt to this kind of system, this study proposed a system that uses a deep learning approach for detection or recognition of license plates focusing on the 2014 license plate format that can be used for different applications and purposes. The study utilized a simple but effective algorithm that is capable of detecting license plates accurately by generating a 100% accuracy as it can detect all the license plates in the video frames with 40%-60% precision.
Online Yarn Breakage Detection: A Reflection-Based Anomaly Detection Method

This article discusses an optical reflection-based method for equipment anomaly detection that enhances weak signals and has high sensitivity to abnormalities. Automatic warp knitting machine yarn breakage detection, which has become an acknowledged difficulty in the textile field, is achieved. To the best of our knowledge, this is the first time that visual inspection has been applied to yarn breakage detection in weaving. Furthermore, based on the periodicity of the yarn distribution and the periodic motion law of the machine, the combined wavelet and Seasonal and Trend decomposition using locally weighted regression (LOESS) (STL) decomposition method is proposed for yarn breakage detection. Finally, the efficiency and accuracy of the proposed method are verified experimentally. Our research is a successful application of reflection characteristics to the anomaly detection of non-Lambertian objects, which has implications for the high-precision anomaly detection of precision equipment.
Recognition algorithm of e-commerce click farming based on K-means technology

This paper proposes a clustering technology based on K-means big data to design an online shop order recognition algorithm applied in the field of e-commerce. This algorithm mainly analyzes user consumption behavior based on user consumption data and finds out a user who is single suspected or who has behavior of click farming. This algorithm improves the unsupervised learning characteristics of the original K-means algorithm and is equipped with an outlier detection algorithm based on business experience, which greatly improves the recognition performance of online shop users.