Miracle-dz/UniFAR-FTUs huggingface dataset:
System Model for Average Downlink SINR in 5G Multi-Beam Networks

To study critical 5G mobility features like zero-millisecond interruption time and multi-connectivity, an average downlink Signal-to-Interference and Noise Ratio (SINR) is needed for radio link failure detection and throughput calculation. This paper presents an accurate approximation of the average downlink SINR with low computational complexity in 5G networks where the base station forms multiple beams simultaneously. To this end, geometry-based link budget is formulated first for both desired and interfering downlink signals. Then, a closed form expression of the average downlink SINR is derived for multi-beam scheduling system and approximated by Monte-Carlo experiment using beam scheduling probabilities. In addition, the SINR model is derived for both strict and opportunistic resource fair scheduler where the latter targets a higher utilization of radio resources. Results have revealed that with increasing number of scheduled beams, the average downlink SINR generally degrades while the network throughput improves. Moreover, it has been also shown that the opportunistic resource fair scheduler performs better than the strict in terms of utilizing available network resources.
Quantifying benefits of grid reinforcement measures to power system resilience against wet snow events

A major concern for Transmission System Operators (TSOs) is to effectively manage power systems during extreme weather events. This need calls for new methods and tools to support operational planning by forecasting critical conditions in the grid, and to quantify the benefits of the measures deployed to improve system performance under these events. For this reasons, the concept of resilience is particularly useful to build a proper assessment framework. This paper presents an in-depth resilience assessment methodology supporting the evaluation of the system response under different assumptions on threat intensity and component vulnerability, including the modeling of countermeasures aimed to improve system resilience. In particular the paper investigates the models for anti-torsional devices and ice-phobic coatings, i.e. two grid hardening measures against wet snow events. The case studies applied to a model of a realistic EHV/HV transmission system demonstrate the potentialities of the tool, which can be useful for a future quantitative techno-economic assessment of the portfolio of resilience boosting measures.
Intracavity-Pumped, Mid-Infrared Tandem Optical Parametric Oscillator Based on BaGa4Se7 Crystal

We have demonstrated a tunable mid-infrared tandem optical parametric oscillator (OPO) based on BaGa4Se7 (BGSe) crystal, which was intracavity-pumped by the tunable signal wave at ∼1 μm generated from a KTiOPO4-OPO (KTP-OPO) in doubly-resonant oscillator (DRO) configuration. Wavelength tuning of the mid-infrared tandem OPO was realized by tuning the pump wavelength at the fixed phase matching angle to reduce the effects of beam cutting and Fresnel reflection loss. Together with pump-tuning method, angle-tuning method was supplied to extend tuning range. A tuning range of 4.1–4.5 μm was obtained with just 5 individual phase matching angles of the BGSe crystal. The maximum output energy of 1.92 mJ/pulse was obtained at 4.26 μm with an estimated slope efficiency of 9.5%.
On the Performance of Tactical Communication Interception Using Military Full Duplex Radios

Advances in the design of Full-Duplex (FD) transceivers with low residual Self-Interference (SI) levels has led to their deployment in various wireless communication applications. One promising field that FD transceivers could play a major role in reshaping its dynamics is physical layer security. This paper investigates the performance of FD transceivers in the context of Military FD Radios (MFDR). Particularly, the adopted system model builds on recent investigations regarding the feasibility of deploying an MFDR in a scenario where it simultaneously jams a receiver node while intercepting the signal of a transmitter node, thus simultaneously disrupting and intercepting a tactical communication link of an opponent team. The secrecy performance of the MFDR is theoretically quantified by deriving its outage in interception probability expression. The results reveal that a well-performing SI cancellation scheme can increase the secrecy performance of an MFDR.
Constrained Optimization and Distributed Model Predictive Control-Based Merging Strategies for Adjacent Connected Autonomous Vehicle Platoons

Vehicle platooning has been a major research topic in recent years because of its ability to reduce fuel consumption, enhance road traffic safety and utilize the road more efficiently. A practical and applicable platoon merging maneuver is the key to forming new platoons while ensuring safety and economy. This study proposes merging strategies that consider both safe space and acceleration limitations for two adjacent platoons comprising connected autonomous vehicles (CAVs). The distributed model predictive control (DMPC) algorithm is adopted to design a DMPC2 controller, which includes 1) a space-making DMPC controller that controls the vehicles in one platoon, i.e. the target platoon, to make space for the vehicles in a second platoon, i.e. the merge platoon, and 2) a DMPC platoon controller that controls the merging vehicles to fill in the space in the target platoon. The former considers the explicit acceleration constraint of the vehicle, making the generated trajectory more feasible, and the latter controls the merge platoon to perform an overall mergence, which reduces the complexity of the merge problem. The low computation load of DMPC makes online computing and real-time control possible in practical scenarios. A simulation study is conducted with different scenarios and parameters, and the results demonstrate that the proposed strategy is more feasible and efficient, and less time-consuming than the existing state-of-the-art methods and have the advantages of taking safety distance and control input constraints into account.
Teaching Software Modelling in an Undergraduate Introduction to Software Engineering

In this article we present our design of an (otherwise completely ordinary) undergraduate introduction to software engineering with an emphasis on contemporary software modelling. A distinguishing aspect of our course is that we aim at a comprehensive introduction of modelling in two regards. Firstly, we introduce proper sub-languages of common modelling languages like UML class diagrams (rather than sampling examples or covering as many building blocks as possible) with a complete formal abstract syntax and semantics (so to give exact meaning to all models from the sub-language). Secondly, we emphasise issues arising from software models in the context of software engineering, e.g., that (formal) analysis results needs proper interpretation wrt. the considered software. We discuss our objectives wrt. formal modelling in software engineering, and outline the content of the course and the narratives that we use to reach these objectives. Evaluation results from four seasons of teaching the course give no indication of over-straining students wrt. level or workload.
Evaluating the Impact of Code Smell Refactoring on the Energy Consumption of Android Applications

Energy consumption of mobile apps is receiving a lot of attention from researchers. Recent studies indicate that energy consumption of mobile devices could be lowered by improving the quality of mobile apps. Frequent refactoring is one way of achieving this goal. We explore the performance and energy impact of several common code refactorings in Android apps. Experimental results indicate that some code smell refactorings positively impact the energy consumption of Android apps. Refactoring of the code smells 'Duplicated code' and 'Type checking' reduce energy consumption by up to 10.8%. Significant reduction in energy consumption, however, does not seem to be directly related to the increase or decrease of execution time. In addition, the energy impact over permutations of code smell refactorings in the selected Android apps was small. When analyzing the order in which refactorings were made across code smell types, it turned out that some permutations resulted in a reduction and some in an increase of energy consumption for the analyzed apps.
Evaluation of Influential Properties in Twitter

In this paper we describe a methodology for measuring the influence and impact of Twitter accounts, by combining their activity, social degree and disseminated content. Experimental results show that the accuracy of our methodology is comparable to an established commercial service, while the distribution of the assigned influence values and their properties are described by power law distributions.
Joint unmixing-deconvolution algorithms for hyperspectral images

This paper combines supervised linear unmixing and deconvolution problems to increase the resolution of the abundance maps for industrial imaging systems. The joint unmixing-deconvolution (JUD) algorithm is introduced based on the Tikhonov regularization criterion for offline processing. In order to meet the needs of industrial applications, the proposed JUD algorithm is then extended for online processing by using a block Tikhonov criterion. The performance of JUD is increased by adding a non-negativity constraint which is implemented in a fast way using the quadratic penalty method and fast Fourier transform. The proposed algorithm is then assessed using both simulated and real hyperspectral images.
CNN-based virtual microphone signal estimation for MPDR beamforming in underdetermined situations

In this paper, we propose a novel approach to virtually increasing the number of microphone elements between two real microphones to improve speech enhancement performance in underdetermined situations. The virtual microphone technique, with which virtual signals in the audio signal domain are estimated by linearly interpolating the phase and nonlinearly interpolating the amplitude independently on the basis of β-divergence, has been recently proposed and experimentally shown to be effective in improving speech enhancement performance. Furthermore, it has been reported that the performance tends to improve as the nonlinearity is improved. However, one drawback of this method is that the interpolation is employed in each time-frequency bin independently, in which the spectral and temporal structures of speech signals are ignored. To address this problem and improve the nonlinearity, motivated by the high capability of neural networks to model nonlinear functions and speech spectrograms, in this paper, we propose an alternative method of amplitude interpolation. In this method, we employ a convolutional neural network as an amplitude estimator that minimizes the mean squared error between the outputs of a minimum power distortionless response (MPDR) beamformer and the target speech signals. The experimental results revealed that the proposed method showed high potential for improving speech enhancement performance, which was not only superior to that of the conventional virtual microphone technique but also the performance in the corresponding determined situation.
Ultra-dense co-integration of FeFETs and CMOS logic enabling very-fine grained Logic-in-Memory

Ferroelectric field-effect transistors (FeFET) based on hafnium oxide offer great opportunities for Logic-in-Memory applications, due to their natural ability to combine logic (transistor) and memory (ferroelectric material), their low-power operation, and CMOS compatible integration. Besides aggressive scaling, dense integration of FeFETs is necessary to make electronic circuits more area-efficient. This paper investigates the impact of ultra-dense co-integration of a FeFET and an n-type selector FET, sharing the same active area, arranged in a 2TNOR memory array. The examined FeFETs exhibit a very similar switching behavior as FeFETs arranged in a standard AND-type array, indicating that the ultra-dense co-integration does not degrade the FeFET performance, and thus, paves the path to a very fine-grained, ultra-dense Logic-in-Memory implementation. Based on this densely integrated 2TNOR array we propose a very compact design of a 4-to-1 multiplexer with a build-in look-up table, thus directly merging logic and memory.
Survey on Internet of Things based Smart Home

Internet of Things (IoT) enables tiny sensors and actuators to be more accessible and responsive. Researchers have applied IoT techniques in different domains such as health monitoring, agriculture, food supply, smart city and so on. In these applications, IoT technologies enabled the end user to monitor and control different devices from remote places. To increase security and provide variant facilities in a home environment, researchers applied IoT to create a smart home. This paper contains a study of different IoT based smart home techniques, we have classified IoT based smart home techniques into two categories namely, IoT based Smart Home Automation and IoT based Smart Home Security Systems. In this study, we have discussed various sensors, doorways, and communication protocols used in IoT based smart home.
Edge Breakdown Suppression of Avalanche Photodiodes using Zn Diffusion and Selective Area Growth

Avalanche photodiodes are fabricated and characterized, using a single diffusion fabrication process with the surface patterned by selective area growth prior to diffusion. Raster mapping of the photocurrent near the breakdown voltage is used to characterize the electric field distribution in the multiplication layer. Devices fabricated with the precursor CBrCl3 introduced during selective area epitaxy show a smoother surface morphology and effective edge breakdown suppression, while those fabricated without CBrCl3 have a rougher surface morphology and exhibit evidence of premature edge breakdown at the corners of the device along specific crystal orientations. Comparison of the dark current — voltage curves below breakdown shows a reduction of dark current associated with the use of CBrCl3, as well as with the inclusion of floating guard rings in the device design.
Data Augmentation Using Generative Adversarial Network for Environmental Sound Classification

Various types of deep learning architecture have been steadily gaining impetus for automatic environmental sound classification. However, the relative paucity of publicly accessible dataset hinders any further improvement in this direction. This work has two principal contributions. First, we put forward a deep learning framework employing convolutional neural network for automatic environmental sound classification. Second, we investigate the possibility of generating synthetic data using data augmentation. We suggest a novel technique for audio data augmentation using a generative adversarial network (GAN). The proposed model along with data augmentation is assessed on the UrbanSound8K dataset. The results authenticate that the suggested method surpasses state-of-the-art methods for data augmentation.
The Increase of Brain Activity in Frontal Lobe After One Hour Methadone Intake

Narcotics is a very dangerous disease because it can damage the nervous system which change the abuser's personality becomes worse. Uncontrolled use of the drugs can lead to criminal acts, namely by destroying norms, public peace, and national security. Methadone is a synthetic opioid that is medically used as a substitution therapy for drug abusers. Giving doses must be done carefully to avoid the undesirable effect. So far, an instrument that can be used to help a medical treatment of doses methadone have not been found yet. In this study, an instrument for detection of drug effects on brain activity is developed and subsequently can be used as a tool for administering of methadone doses. To see the changes in the brain activity, an experiment is conducted in which the brain signals of 8 narcotics addicted subject is measured: just before, 10 minutes, and one hour after methadone intake. The results of data processing showed that the amplitude in the frontal lobe tended to increase for all subjects (except subject 3) after one hour of methadone intake are obtained.
Atrial myxoma-related embolism resulting in acute limb ischaemia in a critical care patient

This report presents an unusual case of limb ischaemia in the critical care setting, the cause of which was elucidated on echocardiography. Evaluation of the case highlights the importance of appropriate and timely investigation, in particular the role of bedside echocardiography. Although atrial myxomas are uncommon, a thorough investigation of patients presenting with acute peripheral ischaemic events should be undertaken to facilitate the diagnosis of this treatable condition.
Evaluation of the Next Birth After Caesarean (NBAC) clinic.

Background: Developing models of care that reduce the caesarean section (CS) rate has been a health care priority in Australia since the 1990’s. Research around vaginal birth after caesarean (VBAC) focuses on maternal and neonatal morbidity and mortality, and the psychological impact on women. Minimal evidence examines models of care that aim to nurture women’s emotional well-being after CS; as well as providing consistent evidence-based information and promoting safe and successful vaginal birth in the subsequent pregnancy. Furthermore the experiences of midwives working in these models have been overlooked. Aim: The aim of the study was to evaluate the Next Birth After Caesarean (NBAC) service at King Edward Memorial Hospital (KEMH). The service provides continuity of care by a small midwifery team within a supportive collaborative network. Design: The study used a multi-phased mixed methods approach. A comparative descriptive study design (pre / post-test) was used to evaluate the NBAC service postnatally following a CS and antenatally in a subsequent pregnancy in terms of childbirth fear, childbirth self-efficacy and confidence, knowledge of birth options, intention to VBAC and satisfaction with care. Simple descriptive statistics and chi square analysis was used test a number of formulated hypotheses relevant to both care delivery points. In addition, a small qualitative phase was undertaken to explore the midwives experiences of working within the service. Thematic analysis was used to analysis the data set. Given the complexity of the design this thesis has been constructed around the three individual phases of the study. Postnatal Phase: The NBAC postnatal group included 50 women who were visited by the NBAC midwives and a comparison group of 53 women who also experienced their first birth as a CS and received standard postnatal care. The hypothesis that women who received a visit from the NBAC midwives would have increased intention to birth vaginally in a subsequent pregnancy was not supported (p = 0.373) even though more women in the NBAC group indicated they intended to have a vaginal birth after caesarean (VBAC) in their next pregnancy (n = 33, 71.7%) compared to the comparison group (n = 25, 54.3%). The hypothesis that women who received a visit from the NBAC midwives would have reduced childbirth fear was not supported as the 12 week postnatal mean fear scores remained high for the comparison (86.27) and NBAC group (84.67). The hypothesis that women who
PREVALENCE OF METABOLIC SYNDROME AND HYPERHOMOCYSTEINEMIA IN DEPRESSION AND SCHIZOPHRENIA

Depression and schizophrenia are two disabling psychiatric diseases which are complicated by insulin resistance, metabolic syndrome and abnormal homocysteine metabolism. In the present study serum levels of homocysteine and insulin have been measured by ELISA using commercial kits and fasting blood glucose and lipid profile was estimated in Depression (n = 56), Schizophrenia (n = 53) and age and sex matched control subjects (n = 56). The data are compared by ANOVA and post-hoc analysis. The serum homocysteine is markedly elevated compared to control both in Depression and schizophrenia subjects, but to a significantly higher extent in the latter. HOMA-IR is increased in the depressive subjects only compared to control. Likewise, Total cholesterol and LDL cholesterol were significantly higher in both depression and schizophrenia compared to control group. Overall, 38.89% of men and 25% of women have satisfied criteria of metabolic syndrome among depression. On the contrary, prevalence of metabolic syndrome was higher in women (30.77%) compared to men (23.53%) in case of schizophrenia. The analysis of the present data and those published by others suggest that depression and schizophrenia is associated with higher prevalence of metabolic syndrome. Hypertension and dyslipidemia is very common in both groups, but they often remain undetected. This study also indicates that homocysteine metabolism a plays role with respect to depression as well as schizophrenia irrespective of development of metabolic syndrome.
Automatic playlist generation using Convolutional Neural Networks and Recurrent Neural Networks

Nowadays, a great part of music consumption on music streaming services are based on playlists. Playlists are still mainly manually generated by expert curators, or users, process that in several cases is not a feasible with huge amount of music to deal with. There is the need of effective automatic playlist generation techniques. Traditional approaches to the problem are based on building a sequence of music pieces that satisfies some manually defined criteria. However, being the playlist generation a highly subjective procedure, to define an a-priori criterion can be an hard task in several cases. In this study we propose an automatic playlist generation approach which analyses hand-crafted playlists, understands their structure and evolution and generates new playlists accordingly. We adopt Recurrent Neural Network (RNN) for the sequence modelling. Moreover, since the representation model adopted to describe each song is determinant and is also connected to the human perception, we take advantages of Convolutions Neural Network (CNN) to learn meaningful audio descriptors.
Assessing depression risk in Chinese microblogs: a corpus and machine learning methods

Depression is a serious challenge for personal and public health. Tens of millions of people suffer from depression every year, but only a small part of them can receive professional treatment. Because people usually avoid disclosing or discussing mental health conditions, which are considered as stigma or taboo. Social network, such as the most popular Chinese microblogging website Sina Weibo, provides a channel for people, including depressed patients, to share their thoughts. We may find depressed emotions from microblogs. In this paper, we randomly collected a set of microblogs and manually annotated them with depression risk at level 0-3 (from mild to severe). On this corpus, we compared different machine learning methods for depression risk prediction, and provided benchmark results. The machine learning methods are support vector machine (SVM), convolutional neural network (CNN), long short-term memory network (LSTM) and bidirectional encoder representations from transformers (BERT).