Miracle-dz/UniFAR-FTUs huggingface dataset:
Modified Orthogonal Matching Pursuit (MOMP)-based Multiuser Detector for Uplink Grant Free NOMA Systems

A multiuser detection (MUD) scheme, the modified orthogonal matching pursuit (MOMP)-based MUD is proposed for uplink grant free non-orthogonal multiple access (NOMA) systems. In the proposed MUD, the temporary correlation of active user sets between adjacent time slots is exploited in conjunction with the side information (the a-priori knowledge of the active user support set’s estimate). The detections of user’s activity and data are performed by the proposed MOMP-based MUD in a continuous time slots in divergence to how the traditional OMP-based MUD and some other MUDs considered in this paper perform similar detection in each time slot with assumption of invariant user’s set activity within a frame. Computer simulation results show that the proposed MOMP-based MUD achieves better performance in comparison with the performances of the traditional OMP-based MUD and others.
Statistics and Propagation Modeling of Atmospheric Lightning

A stochastic model of downward lightning propagation and attachment was developed based on existing models of the propagation of a negative leader as a gas diffusion problem. Attachment was modelled using the rolling sphere concept. The resulting simulation enables Monte Carlo methods of assessing the efficacy of geometric lightning protection schemes. Protection afforded to a 100 m by 50 m building was investigated to elucidate the relative impact of selected protection configuration parameters. The results were found to be insensitive to variations in building height for configurations of less than 30 m. Variation in footprint area found protection for large buildings generally exhibits worse performance relative to smaller structures. This trend was examined in detail for a regular, square arrangement of lightning rods. Variation in building aspect ratio was found to have a pronounced impact on protection performance and is attributed to non-preferential striking to the available geometries. A method for arriving at the expected rate of a given building being stuck is outlined which expects the aforementioned structure to be struck at a rate of about four times per 10 000 years.
Identifying Asthma genetic signature patterns by mining Gene Expression BIG Datasets using Image Filtering Algorithms

Asthma is a treatable but incurable chronic inflammatory disease affecting more than 14% of the UAE population. Asthma is still a clinical dilemma as there is no proper clinical definition of asthma, unknown definitive underlying mechanisms, no objective prognostic tool nor bedside noninvasive diagnostic test to predict complication or exacerbation. Big Data in the form of publicly available transcriptomics can be a valuable source to decipher complex diseases like asthma. Such an approach is hindered by technical variations between different studies that may mask the real biological variations and meaningful, robust findings. A large number of datasets of gene expression microarray images need a powerful tool to properly translate the image intensities into truly differential expressed genes between conditioned examined from the noise. Here we used a novel bioinformatic method based on the coefficient of variance to filter nonvariant probes with stringent image analysis processing between asthmatic and healthy to increase the power of identifying accurate signals hidden within the heterogeneous nature of asthma. Our analysis identified important signaling pathways members, namely NFKB and TGFB pathways, to be differentially expressed between severe asthma and healthy controls. Those vital pathways represent potential targets for future asthma treatment and can serve as reliable biomarkers for asthma severity. Proper image analysis for the publicly available microarray transcriptomics data increased its usefulness to decipher asthma and identify genuine differentially expressed genes that can be validated across different datasets.
Toward Automatic Building Footprint Delineation From Aerial Images Using CNN and Regularization

This study proposes an automatic building footprint extraction framework that consists of a convolutional neural network (CNN)-based segmentation and an empirical polygon regularization that transforms segmentation maps into structured individual building polygons. The framework attempts to replace part of the manual delineation of building footprints that are involved in surveying and mapping field with algorithms. First, we develop a scale robust fully convolutional network (FCN) by introducing multiple scale aggregation of feature pyramids from convolutional layers. Two postprocessing strategies are introduced to refine the segmentation maps from the FCN. The refined segmentation maps are vectorized and polygonized. Then, we propose a polygon regularization algorithm consisting of a coarse and fine adjustment, to translate the initial polygons into structured footprints. Experiments on a large open building data set including 181 000 buildings showed that our algorithm reached a high automation level where at least 50% of individual buildings in the test area could be delineated to replace manual work. Experiments on different data sets demonstrated that our FCN-based segmentation method outperformed several most recent segmentation methods, and our polygon regularization algorithm is robust in challenging situations with different building styles, image resolutions, and even low-quality segmentation.
A New Construction of Zero Correlation Zone Complementary Sequence Sets with Bounded Peak-to-Average Power Ratio

In this paper, a new direct construction of zero correlation zone complementary sequence sets (ZCZ- CSs) via generalized Boolean functions is proposed. The resultant ZCZ-CSs can meet the theoretical upper bound on the set size and have good peak-to-average power ratios (PAPRs). Boolean monomials of degrees more than 2 are considered in our construction, so more ZCZ-CSs can be obtained. The larger number of ZCZ-CSs constructed by our method will increase possible applications in practical communication systems.
Evaluating Cryptographic Algorithms in IEC 61850 Networks

We evaluate in this paper several cryptographic algorithms applied to the GOOSE protocol in IEC 61850 communication networks for electric power substations. International Electrotechnical Commission (IEC) in its standards suggests to use RSA algorithm for digital signature of GOOSE messages and at the same time defines a maximum communication latency of 3 ms for critical messages. In this context, the challenge is to answer if we can use RSA in practice to sign GOOSE critical messages. We perform several experiments with low-processing-power devices to emulate Intelligent Electronic Devices (IEDs) to answer this question. Our results confirm RSA cannot be used to sign critical GOOSE messages, as suggested by IEC, if we are using such kind of devices. The time to sign, transmit, receive, and recover the message is often much close to 3 ms in our experiments. On the other hand, we show that symmetric cryptography algorithms satisfy this time constraint. For example, with AES algorithm with CMAC, we can encrypt the entire payload of a 459-byte message, send, and receive it in much less time than 3 ms. The challenge that is still open in this context is how to distribute the keys securely to use symmetric cryptography.
On the Feasibility of Battery-Less LoRaWAN Communications Using Energy Harvesting

From the outset, batteries have been the main power source for the Internet of Things (IoT). However, replacing and disposing of billions of dead batteries per year is costly in terms of maintenance and ecologically irresponsible. Since batteries are one of the greatest threats to a sustainable IoT, battery-less devices are the solution to this problem. These devices run on long-lived capacitors charged using various forms of energy harvesting, which results in intermittent on-off device behaviour. In this work, we model this intermittent battery-less behaviour for LoRaWAN devices. This model allows us to characterize the performance with the aim to determine under which conditions a LoRaWAN device can work without batteries, and how its parameters should be configured. Results show that the reliability directly depends on device configurations (i.e., capacitor size, turn-on voltage threshold), application behaviour (i.e., transmission interval, packet size) and environmental conditions (i.e., energy harvesting rate).
Stock Market Behaviour Prediction using Stacked LSTM Networks*

Predicting the behavior of the stock market has been an area that has attracted the interest of many researchers particularly in the field of Machine Learning and time series analysis. The ability to analyse an entity such as the stock market that appears to lack synchronicity yet seemingly influenced by historic events remains an open issue in research. This study adopts a stacked Long Short Term Memory network model for predicting stock market behabiour. The data used is composed of historic stock market data from the American Stock Exchange, NASDAQ Composite (IXIC). Results obtained show that by making use of a stacked Long Stort Term Memory network model, future stock market behavior can be predicted.
Energy Efficiency Analysis of JT-CoMP Scheme in Macro/Femto Cellular Networks

5G Wireless Networks are expected to increase substantially data rates and quality of service the users will experience, with a similar or a lower power consumption as todays 4G networks. The Joint Transmission Coordinated MultiPoint (JTCoMP) is a promising scheme to enhance throughput by reducing the interference, especially for cell- edge users. However, some additional energy for hardware circuit and resource information is consumed by this technology. Meanwhile, the performance evaluation of energy efficiency (EE) in dense networks with JTCoMP approach becomes a hard task in terms of time expense to conduct simulations. To evaluate the EE metric in cellular networks with JT-CoMP scheme and to capture the major factors involved in the energy consumption process, representative and accurate models are needed. In this paper, we develop a tractable and efficient model of EE based on spatial fluid modeling when JT-CoMP is applied. Simulations results show that EE is improved with the raise of the number of coordinated BSs in case of a constant backhauling power cost. Similar EE improvement is also observed in case of variable backhauling power cost, while adding a new coordinated BS. Furthermore, the EE is significantly enhanced in femto cellular networks compared to macro cellular ones, making thereby, JT-CoMP scheme more effective in small cells.
Spectral Efficiency in Large Intelligent Surfaces: Asymptotic Analysis under Pilot Contamination

Large intelligent surfaces (LISs) have emerged as a new and promising wireless communication paradigm that relies on equipping man-made structures such as walls with a massive number of antennas. However, despite their potential benefits, a fundamental analysis on the performance limits of LIS systems is lacking. In this paper, the system spectral efficiency (SSE) of an uplink LIS system is asymptotically analyzed under a practical frame structure and LIS environment. In order to quantify the impact on the SSE of pilot contamination, the SSE of a multi-LIS system is asymptotically studied and a theoretical bound on its performance is derived. Simulation results show that the derived analyses are in close agreement with the exact mutual information in presence of a large number of antennas. Moreover, the results show that the achievable SSE is limited by the effect of pilot contamination and intra/inter-LIS interference through the line-of-sight path, even if the LIS is equipped with an infinite number of antennas.
A new approach to river flow forecasting: LSTM and GRU multivariate models

Hydroelectric power stations are responsible for renewable energy generation, especially in countries with many rivers such as Brazil. It is very important to have good estimates of the hydrological flow in order to determine whether thermoelectric power plants should begin operation, an event that would increase the costs of electricity and also have aterrible environmental impact. The monthly flow of a river was estimated using two recurrent neural networks techniques: Long-Short Term Memory (LSTM) and Gated Recurrent Unit (GRU). The results were compared with other articles that had the same structure and used the same data: the Rio Grande river in the Furnas and Camargos dam.
Machine Learning-Assisted Detection for BPSK-Modulated Ambient Backscatter Communication Systems

Ambient backscatter communication (AmBC), a green communication technology, is hampered by the continuously and extremely fast varying, strong and unknown ambient radio frequency (RF) signals. This paper presents a machine learning-assisted method for extracting the information of the AmBC device. The information is modulated on top of the unknown Gaussian-distributed ambient RF signals. The proposed approach can decode the binary phase shift keying backscatter signals encoded using Hadamard codes. This method extracts the learnable features for the tag signal by first eliminating the direct path signal and then correlating the residual signal with the coarse estimate of ambient signal. Thereafter, the tag signals are recovered by using the k-nearest neighbors classification algorithm. The recovered signals are decoded by a Hadamard decoder to retrieve the original information bits. We validate the performance using simulations to corroborate the proposed approach.
Trading Against the Random Expiration of Private Information: A Natural Experiment

Using data from a unique episode in which the SEC disseminated securities filings to a small group of private investors before releasing them to the public, we provide a direct test of the process through which private information is impounded into stock prices. Because the delay between the time when the filings were privately distributed and when the filings were made public was randomly distributed, our setting provides a rare natural experiment for examining how markets process new private information. We find that it takes minutes — not seconds — for informed traders to incorporate fundamental information into stock prices. We also show that the private investors who had early access to fundamental information profited more, and convey more information into stock prices, when the delay before the filings are released to the public is longer. More importantly, the rate at which information is impounded into stock prices is more correlated with the length of the predicted delay before public release than the actual delay, suggesting that informed investors trade strategically. Our study serves as the modern counterpart to Koudijs’s (2014a) study on insider trading on eighteenth-century stock exchanges — except, in our case, week-long sailing voyages have been replaced by modern electronic transmission as the conduit for information flows.
Litigation in Mergers and Acquisitions

Using hand-collected data, we examine the targeting of lawsuits in M&A transactions, the effect of these suits on offer completion rates and takeover premiums, and the factors that lead to positive settlement outcomes in these cases. Shareholder lawsuits form the vast majority of all lawsuits. We find that M&A offers that are subject to lawsuits are completed at a significantly lower rate than offers that are not subject to litigation, after controlling for selection bias, and for offer features, M&A financial and legal advisor reputation as well as industry and time fixed effects. However, litigation significantly increases the takeover premium in deals that are completed. Economically, the expected rise in the takeover premium more than offsets the fall in the probability of deal completion, so there is a rise in the expected takeover premium paid in offers that are subject to pre-deal-completion litigation. Examining the different types of lawsuits, suits challenging controlling shareholder squeeze-outs are significantly more likely to lead to settlement and the payment of cash settlements. Target lawsuits, generally designed to impede deal completion, are significantly associated with higher takeover premia in completed deals.
The impact of dental status on diet, nutrition and nutritional status in U.S.A. adults.

The hypothesis of this study was that dental status significantly affects diet, nutrition and nutritional status in U.S.A. adults. The objectives were to test whether significant relationships existed between numbers of teeth, of occluding pairs of teeth (OPs) and of posterior occluding pairs of teeth (POPs) amongst (1) those with a solely natural dentition and (2) those who had a dentition with a combination of replaced and natural teeth. In addition, to test whether complete denture wearers differed from fully dentate adults in relation to diet, nutrition and nutritional status. Data were for adults 25 years and older (n= 12,243) from the third U.S.A. National Health and Nutrition Examination Survey (NHANES III) 19881994, which used a stratified, multi-staged probability sample of the United States non institutionalized civilian population. The population had a full dental examination and dietary analysis and an assessment of blood analytes and anthropometric measures. Findings of multivariate analyses indicated that complete denture wearers had lower (1) consumption of most foods (2) dietary fibre and (3) levels of blood analytes than did the fully dentate. Significant relationships existed between numbers of teeth (and OPs and POPs) and most of the nutrition related outcomes amongst people with only natural teeth. Among people who had both natural and replaced teeth there were some differences in diet, nutrition and nutritional status between those with low numbers of teeth, OPs or POPs (whether natural or replaced) compared to fully dentate adults. On the other hand, there were few associations between diet and dental status in the rehabilitated group with more than 20 teeth. An inverse relationship was found between dental status and Body Mass Index across all typologies.
Identifying Potential miRNA-Disease Associations Based on an Improved Manifold Learning Framework

Adequate evidence has shown that miRNA-disease interactions are strongly involved in the pathological processes of complex human diseases. However, it is commonly time-consuming and labor-intensive to utilize laboratory biological experiments to reveal unknown miRNA-disease pairs. Since the previously proposed calculation model has more or fewer deficiencies, we developed the semi-supervised method called Hessian Regularized Non-negative Matrix Factorization Method for miRNA-disease Association prediction (HRNMFMDA). This model introduced Hessian regularization into the NMF framework to preserve the local manifold information and increased an $l_{2,1}$ -norm penalty term to ensure the feature selection of the coding matrix and an approximate orthogonal constraint to obtain discriminative information. In the model performance evaluation, HRNMFMDA outperformed eight existing models, achieving AUC values of 0.9074, 0.8618, and 0.9044+/−0.0080 in the global leave-one-out cross-validation (LOOCV), local LOOCV and 5-fold cross-validation, respectively. In addition, we applied HRNMFMDA to several high-incidence human carcinomas via three kinds of case studies. Almost all predicted miRNAs were confirmed by external databases derived from experimental literature. Therefore, the conclusion can be drawn that HRNMFMDA is reliable for revealing uncovered miRNA-disease pairs.
Combinatorial Auctions for Temperature-Constrained Resource Management in Manycores

Although manycore processors have plenty of cores, not all of them may run simultaneously at full speed and even some of them might need to be power-gated in order to keep the chip within safe temperature limits. Hence, a resource management technique, that allocates cores to application aiming at maximizing the system performance, will not be able to achieve its goal without taking into account the on-chip temperature and its impact on the availability of the chip's resources. However, considering a temperature constraint by the resource management will further increase its complexity, especially in manycores, and thus implementing it in a centralized scheme might lead to a computation bottleneck and a single point of failure. To avoid such scenarios, it is inevitable to distribute the computation required by the resource management technique throughout the chip. In this article, we propose a distributed resource management technique that considers temperature as an essential factor in allocating cores to applications and determining the power states of these cores and their voltage/frequency levels, while taking into account the performance models of the applications in order to maximize the overall system performance under a temperature constraint. Our proposed technique employs, for the first time, combinatorial auctions within an agent system to achieve the targeted goal in a distributed manner. The experimental evaluations show that our proposed technique achieves significant performance improvements with an average of 41% compared to several distributed resource management techniques.
Interfacial Resistance Characterization for Blade-Type Phase Change Random Access Memory

The blade-type phase-change random access memory (PCRAM) has recently attained considerable interest due to its potential for providing low programming current, while its interfacial resistance (IR) characteristics that play an important role in temperature and programming current for conventional PCRAMs is yet to be deeply studied. To achieve this, a completely 3-D electro-thermal and phase-transformation model with inclusions of thermal boundary resistance (TBR) and electrical IR (EIR) at different layered interfaces were developed to assess the influence of the IRs on phase-transformation kinetics. It was found that the TBR at the chalcogenide/insulation interface as well as interfacial size dominates the resulting programming current that is almost independent of the TBR and EIR at chalcogenide/heater interface. In this case, an optimized blade-type device having platinum silicide heater and superlattice insulated encapsulation was proposed, allowing for a 38% reduction on “SET” current and a 40% reduction on “RESET” current, respectively.
A Symmetric Modified Multilevel Ladder PMIC for Battery-Connected Applications

This article describes a symmetric modified multilevel ladder (SMML) converter that is capable of directly converting down from Li-ion battery voltage ranges to system-on-chip (SoC)-compatible voltage ranges while using low-voltage power MOSFETs in the power stage. Compared to conventional flying-capacitor multilevel converters, the SMML converter features reduced conduction losses, naturally balanced flying capacitors, and internal generation of all the supplies required for drivers and level shifters. A prototype shows that the converter achieves a peak efficiency of 90% and a peak power density of 0.52 W/mm<sup>2</sup> while providing up to 16.7<inline-formula> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> conversion ratios at still acceptable efficiencies.