Miracle-dz/UniFAR-FTUs huggingface dataset:
SNR Estimation of UAV Control Signal Based on Convolutional Neural Network

The signal-to-noise ratio (SNR) is an effective evaluation index for channel status and communication quality, and plays an important role in signal analysis. Under the gradual complexity of the unmanned aerial vehicle (UAV) remote control signal environment and the rapid development of neural network models in deep learning, this paper proposes a convolutional neural network (CNN) model-based SNR estimation method of UAV remote control signal environment. We construct a simulation dataset of UAV remote control signal with different SNRs, then train the model and its parameters, save the model with better performance and use the test set to verify the performance of the algorithm finally. The experimental result shows that the performance of the algorithm is improved compared to the two known algorithm.
A High-Efficiency Two-Stage GaAs HBT Doherty Power Amplifier With Thermal Compensation for WLAN Application

In this paper, a high-efficiency Doherty power amplifier (DPA) using an InGaP/GaAs heterojunction bipolar transistor (HBT) process is presented. The DPA aims for the application of 2.4 GHz wireless local area network (WLAN) systems with a high peak-to-average power ratio (PAPR). The traditional bulky quarter-wavelength line is realized using a low-pass n-type network for miniaturization and low loss. A thermally compensated dynamic bias circuit is applied to increase thermal stability. Under the excitation of a 40-MHz 64-quadratic-amplitude modu-lation(QAM) signal, the proposed DPA achieves a gain of 29 dB gain, an average power of 26 dBm, and an average power added efficiency (PAE) up to 21%. Good error vector magnitude (EVM) and adjacent channel leakage ratio (ACLR) performance are also measured, which demonstrates EVM and ACLR after digital pre-distortion (DPD) are −42 dB and −50.7 dBc. respectively.
External Validation of Deep Learning Algorithms for Cardiothoracic Ratio Measurement

Recent advances in machine learning have made it possible to create automated systems for medical image diagnosis. Cardiothoracic ratio (CTR) measurement, a common procedure for assessing cardiac abnormality in chest radiographs, has been investigated by several deep learning studies aiming to automate the process. However, of key consideration is whether automated CTR measurements by machine learning models can yield CTR values as accurately and consistently as trained human technicians on unseen data and thereby be considered trustworthy for clinical application. To assess this, we performed external validation of automated CTR algorithms on a dataset of 7,517 images, comparing four variants of U-Net architecture in heart and lung segmentations and CTR calculations: VGG-11, VGG-16, SegNet, and AlbuNet. We then benchmarked their performance against two human experts manually measuring CTR on the same images in a clinical setting such that we could equitably compare model-to-human variation against human-to-human variation. Our analysis shows that AlbuNet demonstrates human-level performance in CTR measurements, achieving MAPE of 2.38%, which is on par with human-to-human inter-rater variability (2.53%) when using the manual measurement method. The other three U-Net variants, particularly VGG-16, also performed similarly well. Additionally, we conducted an extreme outlier analysis on each model architecture, assessing the percentage of samples with higher measurement errors than the maximum error from the manual method. AlbuNet outperformed other architectures with only 0.35% occurrence of extreme outliers, while the other three U-Net variants ranged from 0.64% to 1.06% occurrence. Overall, the deep-learning-based algorithm was demonstrated to be as reliable as the manual method and shows strong potential for assisting radiologists in the CTR measurement process in clinical practice.
A Multilayer Perceptron-based Carotid-to-Femoral Pulse Wave Velocity Estimation using PPG Signal

Cardiovascular diseases (CVDs) are the primary cause of death in the world. The development of easy-to-use and non-invasive monitoring CVDs’ diagnosis methods is crucial. Among the key parameters in the cardiovascular system is arterial stiffness. An increase in arterial stiffness is considered a primary risk factor for CVDs. Although arterial stiffness can be assessed non-invasively by measuring the carotid-to-femoral pulse wave velocity (cf−PWV), which is considered as a gold standard for arterial stiffness measurement, the clinical process of assessing this parameter is very intrusive and complicated. This paper investigated the potential of estimating (cf−PWV) from distal photoplethysmogram (PPG) waveforms using regression technique based on a multilayer perceptron. Functionally, PPG offers a simple, reliable, low-cost technique to measure blood volume change and hence assess cardiovascular function. In this work, we identify and select features from the timing fiducial points-based PPG, its first, second, and third derivative waveforms. The in-silico validation shows promising results and satisfactory accuracy. It demonstrates good estimation performances with an R2 (correlation coefficient) around 0.95 and MAPE (mean absolute percentage error) less than 2.22% based on features extracted from PPG at the brachial artery level, an R2 around 0.98 and MAPE less than 1.71% based on features extracted from PPG at the radial artery level and R2 around 0.97 and MAPE less than 1.88% based on features extracted from PPG at the digital artery level.
A Photon-Counting 4Mpixel Stacked BSI Quanta Image Sensor with 0.3e- Read Noise and 100dB Single-Exposure Dynamic Range

This paper reports a 4Mpixel, 3D-stacked backside illuminated Quanta Image Sensor (QIS) with 2.2um pixels that can operate simultaneously in photon-counting mode with deep sub-electron read noise (0.3e- rms) and linear integration mode with large full-well capacity (30k e-). A single-exposure dynamic range of 100dB is realized with this dual-mode readout under room temperature. This QIS device uses a cluster-parallel readout architecture to achieve up to 120fps frame rate at 550mW power consumption.
Using MajorClust Algorithm for Sandbox-based ATM Security

Automated teller machines are affected by two kinds of attacks: physical and logical. It is common for most banks to look for zero-day protection for their devices. The most secure solutions available are based on complex security policies that are extremely hard to configure. The goal of this article is to present a concept of using the modified MajorClust algorithm for generating a sandbox-based security policy based on ATM usage data. The results obtained from the research prove the effectiveness of the used techniques and confirm that it is possible to create a division into sandboxes in an automated way.
3D Heterogeneous Integration Strategy for Physically Flexible CMOS Electronic Systems

Flexible and stretchable electronics has emerged as a promising field in the past decade, especially towards healthcare and environmental monitoring applications. We have witnessed significant progress in developing flexible sensors, logic/memory devices (transistors), communication modules, energy sources, and alternate materials to enhance performance with added physical conformity. However, there are unaddressed challenges in matching status-quo CMOS-based electronics, packaging, reliable flexible interconnects, and lack of pragmatic integration schemes to readily complement the existing state-of-the-art technology. Moreover, often rigid ICs are used on flexible polymeric substrates placed sparsely, making the system bendable with a caveat of reduced area efficiency. Hence, we present a CMOS compatible heterogeneous integration scheme with a unique 3D coin architecture to obtain truly flexible electronic systems. This integration sequence has multiple polymeric layers that resemble a coin with two faces and a sandwich layer. The bottom face of the coin hosts flexible sensors while the top face is reserved for communication (antenna) and energy harvester (flexible solar cell), and the associated electronic ICs (microcontroller, RF IC, solid-state micro-batteries) are embedded in the sandwich layer (all components in the flexible form). We use the modular-LEGO approach for the assembly of bare die ICs like a lock-and-key mechanics in plug and play manner to obtain reliable, flexible interconnects and accurate assembly. These multiple layers are connected electrically using vertical through-polymer-via (TPV) that shows less than 4% overall change in the resistance across the longest metallic interconnect layer trace. This vertical TPV interconnection helps in maximizing area usage, thereby increasing area efficiency. We transformed the commercial CMOS based 16 MHz microprocessor bare die and an array of micro-batteries with a rating of $\mathrm{5}\ \mu\text{Ah}$ @ 3.9V into ultrathin ($< \mathrm{20}\ \mu\mathrm{m}$) flexible form after assembly of thicker dies ($\mathrm{200}-\mathrm{300}\ \mu\mathrm{m}$) into the LEGO sites. It can integrate a solar cell on top face after following the corrugation approach of converting rigid solar cells into a flexible one. At the same time, the antenna for communication and the TPV are fabricated using electrochemical deposition technique. The overall thickness of the 3D-coin system is less than 1 mm and free from any rigid component making it flexible up to a bending radius of ∼1 mm. Soft-polymeric encapsulation provides mechanical strength, reduces stresses, and improves ruggedness for the stand-alone electronic system. Thus, we believe our 3D heterogeneous integration strategy offers a pragmatic approach to obtaining truly flexible electronic systems without compromising next-generation electronic systems' performance aspects.
Deep Learning for SWIPT: Optimization of Transmit-Harvest-Respond in Wireless-Powered Interference Channel

In this paper, we consider a wireless-powered two-way communication, called transmit-harvest-respond, with co-channel interference. The two-way communication considered here comprises three steps: i) transmitters send data signals, ii) receivers decode information and harvest energy simultaneously from the received signals using a policy of time switching (TS) or power splitting (PS), and iii) receivers transmit responses back to transmitters using this harvested energy. We aim to find the transmit power and energy harvesting ratios that maximize the sum rate of the forward links while ensuring a minimum rate requirement for each backward link. Due to the non-convexity and NP hardness of the optimization problem considered here, we first derive suboptimal solutions using an iterative algorithm (IA) on the basis of asymptotic strong duality. In view of the high computation time of the IA, we then design an efficient deep neural network (DNN) framework and novel training strategy as a means of combining supervised and unsupervised training. Specifically, DNNs are pre-trained using the suboptimal solutions obtained by the IA in a supervised manner, as a means of initialization; further training is then applied to DNNs using a well-designed loss function in an unsupervised manner to enhance performance. Simulation results reveal that the pre-training technique using IA solutions is beneficial for improving the performance of the DNN. The proposed hybrid scheme thus achieves near-optimal performances with a lower computation time, compared with the use of IA or DNN alone.
A survey on the development of intelligent robots in speech emotion recognition

Speech emotion recognition, an important branch of affective computing, has attracted much attention recently, which is of great significance for the realization of natural and harmonious human-robot interaction. Many researches have been carried out and remarkable results have been achieved in this field. At the same time, there are still many problems to be tackled urgently. Therefore, it is necessary to summarize the previous works and find out the problems in speech emotion recognition, so as to provide guidance for further research. This paper systematically summarizes the general process of speech emotion recognition, including speech emotional databases and various leading emotion classification models. By listing some outstanding works of speech emotion recognition in recent 20 years, this paper compares and analyzes the highlights and shortcomings of these works. It can be seen that people show more interest in deep learning in which features are mostly extracted automatically than the traditional machine learning methods. Finally, the main problems in the field of speech emotion recognition and the direction of further exploration are summarized in order to promote speech emotion recognition to a new stage.
Investigating binary EAs for Passive In-Building Distributed Antenna Systems

A passive in-building distributed antenna system (IB-DAS) is often used to enhance indoor mobile data coverage by introducing indoor antennas inside buildings. Such systems are created to ensure that traffic generated indoors does not heavily depend on base stations installed outdoor, as penetration issues of wireless signals can affect the quality of connection. The focus of this paper is on the automation of IB-DAS design. Particularly, it provides an extensive analysis of the performance of four binary Evolutionary Algorithms (EAs) for this problem and shows that the two tested Estimation of Distribution Algorithms (EDAs) performs well on this problem. Furthermore, it investigates the effect of different genetic operators on the performance of the considered EAs. The practice outcome of this is to select the best algorithm among others to be implemented in a DAS network planning tool and to help our industrial partner reduce both the design time and deployment cost.
A General Framework Based on Walsh Decomposition for Combinatorial Optimization Problems

In this paper we pursue the use of the Fourier transform for a general analysis of combinatorial optimization problems. While combinatorial optimization problems are defined by means of different notions like weights in a graph, set of numbers, distance between cities, etc., the Fourier transform allows to put all of them in the same framework, the Fourier coefficients. This permits its comparison looking for similarities and differences. Particularly, the Walsh transform has recently been used over pseudo-boolean functions in order to design new surrogate models in the black-box scenario and to generate new algorithms for the linkage discovery problem, among others, presenting very promising results. In this paper we focus on binary problems and the Walsh transform. After presenting the Walsh decomposition and some main properties, we compute the transform of the Unconstrained Binary Quadratic Problem and several particular cases of this problem such as the Max-Cut Problem and the Number Partitioning Problem. The obtained Walsh coefficients not only reinforce the similarities and differences among the problems which are known in the literature, but given a set of Walsh coefficients we can say whether or not they are produced by any of the problems analyzed. Finally, a geometrical interpretation of the space of Walsh coefficients with maximum order 2 and the subspace of each analyzed problem is presented.
Compósitos de polímero ramificado/óxidos metálicos para aplicações em fotocatálise heterogênea e agentes bactericidas

O polipirrol (PPy) e um polimero condutor com grande interesse tecnologico que, sintetizado atraves de crescimento ramificado, adquire grande area superficial em comparacao ao PPy convencional. Nesta tese foram preparados nanocompositos a base do PPy ramificado, para que pudesse, a depender da fase dispersa particulada, assumir um carater fotocatalitico ou antibacteriano. A matriz polimerica sintetizada nesse trabalho possui uma area superficial de 97,38m2/g, em torno de quatro vezes maior que o PPy convencional encontrado na literatura. Quando polimerizado na presenca de nanoparticulas de oxido de zinco (ZnO) e dioxido de titânio (TiO2), o material resultante possui uma otima atividade fotocatalitica frente a corantes orgânicos, como a rodamina B (80 a 85%) e rodamina 6G (90%). Nesse estudo verificou-se que a sinergia entre a matriz ramificada e as nanoparticulas de ZnO e TiO2 possibilita a absorcao de fotons na regiao do visivel e uma taxa de recombinacao eletron/lacuna menor em funcao da minima agregacao entre as particulas na matriz, sendo responsavel pela alta eficiencia fotocatalitica dos corantes usados. Nao obstante ao seu comportamento fotocatalitico sob a forma de composito, o polipirrol ramificado tambem possui um carater antibacteriano que o torna um material promissor diante da contaminacao de ambientes aquaticos por bacterias, inclusive multirresistentes, como as bacterias Escherichia coli, Staphylococcus aureus e Klebsiella pneumoniae. Atraves deste trabalho pode-se avaliar a atividade bactericida do polipirrol ramificado em funcao dos seus parâmetros estruturais, como morfologia e area superficial, incorporacao de aditivos, como as nanoparticulas de prata (AgNPs), e dos niveis de agregacao destas na matriz polimerica. Quando em contato com as bacterias, o composito PPy/Ag adere eletrostaticamente a parede celular bacteriana carregada negativamente, promovendo a adsorcao do polimero e difusao das nanoparticulas ao longo da parede celular, estas se ligam a membrana citoplasmatica permitindo a liberacao de componentes intracelulares e perda de ions de potassio (K+), resultando na morte do microrganismo. Assim, o polipirrol ramificado, e sua combinacao na forma de compositos com nanoparticulas de oxido metalico, se apresenta como um material com grande potencial para futuras aplicacoes como filtros antibacterianos e esponjas para fotodegradacao de corantes.
Residual Transformer Network for 3D Objects Classification

Three-dimensional point cloud is an efficient and flexible representation of three-dimensional structures. Recently, neural network algorithm has shown superior performance in the tasks of three-dimensional point cloud classification and segmentation. However, the good results shown in these tasks were experimentally obtained using complete and aligned 3D point cloud data, whereas real-world 3D point cloud data is missing and unaligned. The key to the difficulty of learning non- aligned point cloud data is how to obtain invariance of geometric transformation. To solve this problem, we propose a new spatial transformation network named residual space transformation network to process point cloud data. Different from the existing spatial transformation networks, the network is inspired by traditional image and point cloud alignment algorithms and residual learning, and predicts the three-dimensional space transformation through a multi-step estimation method. The experiment shows that compared with the baseline, the network performs better in the classification and attitude prediction of missing point cloud data.
Centralized Vehicle Coordination Strategies Based on Improved Age of Information

Since multiple roads merge at intersections, intersections are areas with high incidence of traffic jams. Proper vehicle coordination at intersections is of great importance for modern intelligent transportation systems (ITS). In this paper, we try to give an optimal scheduling framework in the presence of uncertainties in communications, control, and sensing, based on the model predictive control (MPC) control model. Based on a typical V2I communication model, we introduced the impact of CCH length and the number of vehicles on 802.11p communication. Due to the limitation in communications and computing resources, we give an improved age of information (AoI) based resource allocation method, to maximize the traffic throughput at intersections. Numerical results are provided and evaluated.
Interactivity in the Generation of Test Cases with Evolutionary Computation

Test generation is a costly but necessary testing activity to increase the quality of software projects. Automated testing tools based on evolutionary computation principles constitute an appealing modern approach to support testing tasks. However, these tools still find difficulties to detect certain types of plausible faults in real-world projects. Besides, recent studies have shown that, in general, automatically-generated tests do not resemble those manually written and, consequently, testers are reluctant to adopt them. We observe two key issues, namely the opacity of the process and the lack of cooperation with the tester, currently hampering the acceptance of automated results. Based on these findings, we explore in this paper how the interaction between current tools and expert testers would help address the test case generation problem. More specifically, we identify a number of interaction opportunities related to the object-oriented test case design driven to boost their readability and detection power. Using EvoSuite as base implementation, we present a proof of concept focused on the possibility to integrate readability assessment of the most promising test suites into a genetic algorithm.
Toward Unveiling How SAFe Framework Supports Agile in Global Software Development

Current research has shown that global software development (GSD) organizations rely on agile methods to guarantee better communication, coordination, and improved productivity. Moreover, as organizations continue to grow, they are facing large-scale global project challenges and complexity, leading to an urge to scale agile practices for better coordination. However, it has been surprising to find that approaches in the literature on scaling agile for large and global projects remain unclear. In addition, to identify and highlight which agile scales practices reported in studies on Agile GSD (AGSD) are related to SAFe practices, we conducted a systematic literature review. Besides, we mapped AGSD practices to the SAFe 5.0 framework to provide bases for those starting on such a journey. In our study, we found 24 articles related to the scaling of agile practices in a global environment, and 44 scaling agile practices that embrace 95 SAFe 5.0 components, including “agile coaching,” “agile architecture,” and “system demo”. This study provides an in-depth scientific investigation on how SAFe can be associated with global teams applying GSD. The results and findings will benefit researchers and practitioners in conducting further research and provide more dedicated solutions for challenges to scaling agile in the context of GSD.
Comparison of Heat Transfer Characteristics of Flow of Supercritical Carbon Dioxide and Water inside a Square Microchannel

Water is a common coolant for many heat transfer applications, yet, it has many disadvantages relative to other liquid coolants, such as high corrosiveness, high electrical conductance, and high pressure drop characteristics. Carbon dioxide slightly above its supercritical point — known as supercritical carbon dioxide (sCO2) — exhibits excellent thermo-physical properties, such as high specific heat, thermal conductivity, and high heat capacity than many other gases, such as air. Also, sCO2 is less corrosive and experience less pressure drop than water. In this study, heat transfer characteristics of sCO2 and water were compared for a 300-μm square microchannel. Computational studies were made for both laminar and turbulent flow regimes, and it was found that at a mass flux of 5,555 kg/m2s, sCO2 experienced a pressure drop of 27.81 kPa compared to 51.77 kPa for water. Nusselt numbers were compared with available empirical correlations, such as Dittus- Boelter and Gnielinski, for both water and sCO2, and it was found that they failed to accurately predict the Nusselt number for sCO2 obtained through CFD simulation. At higher mass fluxes the local Nusselt number for sCO2 were higher than those for water as the flow became turbulent for sCO2 due to its low dynamic viscosity.