Miracle-dz/UniFAR-FTUs huggingface dataset:
Two-Probe Waveguide Orthomode Transducer With Twofold Rotational Symmetry

A novel (patent pending) waveguide orthomode transducer (OMT) design is presented. The use of a common waveguide cross section with twofold rotational symmetry is proposed to mitigate the typical drawbacks of a two-probe OMT geometry. In particular, the cross-polarized electric field component may be reduced by introducing a twofold rotationally symmetric cross-sectional design with slant axes in the reference frame defined by the directions of the two coupling probes. Interestingly, it is also demonstrated that this solution may be used directly in the probing area to either reduce the cross-polarized field component or to reduce the probe-to-probe coupling. The aspect ratio of the required asymmetric section is close to 1, and the design has limited impact on other signals when used as an orthomode junction (OMJ) in multifrequency feed systems. Two designs, one with dual-linear operation and one with dual-circular operation, are optimized in K-band (17.7–20.2 GHz). Good agreement is found between simulated and measured data, validating the concept. This solution is of interest for the design of future multifrequency feed systems onboard broadband communication satellites.
Four-Element Printed Dipole Array Antenna for UAV Applications

In this paper, the design of four-element printed dipole antenna providing 360 degree coverage on roll axis of Unmanned Aerial Vehicle (UAV) for the detection of objects that may approach UAV systems is discussed. The gain requirement of the antenna is minimum 7 dB. The suitable power divider design to feed the antenna is also performed in this paper. The four-way power splitter is attained using the T-junction power divider. The antenna is designed and simulated in Ku-band.
SOME CONSEQUENCES OF 
${\mathrm {TD}}$
 AND 
${\mathrm {sTD}}$

Abstract Strong Turing Determinacy, or ${\mathrm {sTD}}$ , is the statement that for every set A of reals, if $\forall x\exists y\geq _T x (y\in A)$ , then there is a pointed set $P\subseteq A$ . We prove the following consequences of Turing Determinacy ( ${\mathrm {TD}}$ ) and ${\mathrm {sTD}}$ over ${\mathrm {ZF}}$ —the Zermelo–Fraenkel axiomatic set theory without the Axiom of Choice: (1) ${\mathrm {ZF}}+{\mathrm {TD}}$ implies $\mathrm {wDC}_{\mathbb {R}}$ —a weaker version of $\mathrm {DC}_{\mathbb {R}}$ . (2) ${\mathrm {ZF}}+{\mathrm {sTD}}$ implies that every set of reals is measurable and has Baire property. (3) ${\mathrm {ZF}}+{\mathrm {sTD}}$ implies that every uncountable set of reals has a perfect subset. (4) ${\mathrm {ZF}}+{\mathrm {sTD}}$ implies that for every set of reals A and every $\epsilon>0$ : (a) There is a closed set $F\subseteq A$ such that $\mathrm {Dim_H}(F)\geq \mathrm {Dim_H}(A)-\epsilon $ , where $\mathrm {Dim_H}$ is the Hausdorff dimension. (b) There is a closed set $F\subseteq A$ such that $\mathrm {Dim_P}(F)\geq \mathrm {Dim_P}(A)-\epsilon $ , where $\mathrm {Dim_P}$ is the packing dimension.
Intelligent Tidal Lane System Based on Vehicle Attribute Recognition Algorithm and Related Laws and Regulations

In this paper, an intelligent tidal lane combined with artificial intelligence technology which can adapt to the change of the number of vehicles is designed. The recognition of vehicle position and the direction of vehicle front and rear are recognized by using the vehicle attribute recognition technology based on artificial intelligence. In order to alleviate the morning and evening peak of traffic flow, holiday travel or emergencies caused by two-way lane traffic imbalance, the tidal lane can automatically switch the direction of tidal lane according to the number and direction of vehicles on the two-way road. In addition, we study and analyze the construction of laws and regulations of tidal lane, explore the problems and put forward some suggestions to protect the interests of travelers and maximize social benefits.
Design and Construction of a Low Cost All-Digital Phase Locked Loop Based on Field Programmable Gate Array

The phase-locked loop (PLL) is a key element to capture the voltage phase of the grid in power systems with high permeability of new energy sources. An accurate phase information catcher which can be applied in the field can provide a strong support for the control process of the system. This paper constructs a low-cost all-digital PLL (ADPLL) on an embedded field-programmable gate array (FPGA) for real-time (RT) digital simulation. Compared with offline simulation, RT digital simulation can realize hardware-in-loop (HIL) testing of utility connected devices to validate their performance in every working condition, which would not be possible using only experimental tests. Meanwhile, an improved simulation architecture construction method based on Algorithm Architecture Adequation (A-A-A) is presented in this paper. This method further saves the internal hardware resources on the basis of making full use of the parallel computing capability of FPGA. Analysis and experimental results made based on an FPGA ALTERA Stratix V show that the proposed modeling algorithm can achieve both efficiency and accuracy.
Correlation Power Analysis of Lightweight Block Cipher Algorithm LiCi

The lightweight block cipher algorithm LiCi processes the input plaintexts into high 32-bits and low 32-bits respectively in Feistel structure, and the corresponding keys of these two parts are not related. Therefore, keys of the first round can be cracked independently, and the computational complexity can be reduced from 264 to 232. After studying the key update strategy, this paper proposed that the computational complexity of cracking the second round keys can reduce from 264 to 213 by using the key obtained in the first round attack. When cracking the sixth round, the complete 128-bit key can be recovered. In this paper, the LiCi encryption algorithm was deployed on FPGA, the power consumption curves were measured with Tektronix MSO5204B oscilloscope which are used to recover the key in the LiCi encryption algorithm. we choose 8 bits keys as attack goal and get the true keys.
IDENTIFICATION AND VERIFICATION OF VEHICLE DRIVE AND STEERING PERFORMANCE

Through the analysis of vehicle driving characteristics and steering dynamics, the system identification of vehicle driving performance and steering performance and the identification of driver types are obtained. Using MATLAB software, by analyzing the relationship between speed and engine speed, the specific gear selected by the driver at a certain time is determined, and the throttle percentage and speed amplitude-frequency characteristic curve of the gear are estimated by TFESTIMATE. The driving characteristics of each gear of the experimental vehicle are obtained. According to the values of steering wheel angle, vehicle lateral acceleration and real-time speed, the corresponding law of vehicle lateral acceleration changes and steering wheel angle changes under different speed is obtained by using transfer function identification.
EH-GPSR: An Energy Harvesting Protocol for IoT-based Wireless Sensor Networks

Energy harvesting is a promising paradigm in the Internet of things based on wireless sensor networks (IoT-based WSNs) for emerging applications such as smart cities, healthcare, and farming. Although the amalgamation of energy harvesting in WSNs (EH-WSNs) fostered many new opportunities, they still face challenging requirements to achieve high sustainability. Nowadays, most research efforts are focused on curtailing the limitation of the energy supply of IoT sensors, which strongly impacts their design. Therefore, new efficient energy harvesting routing protocols need to be developed, while ensuring network reliability and continuity. In this paper, we proposed a new energy harvesting greedy perimeter stateless routing protocol (EH-GPSR) to address the issue of limited sensor energy. EH-GPSR employs an EH rate in a cost function, derived from the randomized minimum path recovery time (R-MPRT) algorithm, to measure the harvested energy. Farther, it improves the greedy routing mechanism. Specifically, it uses a weighted function that combines the destination distance information with the EH rate-based cost to adaptively select the next hop. The simulation results showed that our protocol EH-GPSR extends the network lifetime and allows a better packet delivery rate by more than 20% compared to the GPSR protocol.
Building Healthy Ageing: The Dynamics of Self-fulfillment in Old Age

This study examines the achievement of self-fulfillment in the conceptualization of good old age by providing theoretical and cultural implications for elder's technology development. The method employed interviews with 30 people aged from 70 to 80. The findings show the relationship and dynamics in the self-fulfillment process, drawing on a research conceptual framework and propositions for the future development and study in aging issues. A more comprehensive framework and the future development of wellbeing toward elderly is discussed.
FlitZip: Effective Packet Compression for NoC in MultiProcessor System-on-Chip

Applications running on Network on Chip (NoC) based multicore systems demand increased on-chip network bandwidth that can cater to the need for intensive communication among the cores and caches. Due to strict area and power budget, the bandwidth offered by NoC is very limited. Data-intensive and communication-centric applications on encountering a cache miss lead to a considerable burden on the underlying network for transferring blocks from multiple cache hierarchies to the requesting core as packets. This increases the packet transmission latency, thereby slowing down the system performance. Also, NoC being the highest component of power consumption after the cores, an increase in packets increases the dynamic power consumption of NoC. The article proposes <italic>FlitZip</italic> that addresses the problem by reducing on-chip traffic through compressing network packets. Hence, the compressed packet requires less bandwidth during its transfer, reducing the network’s power consumption. Experimental analysis shows that <italic>FlitZip</italic> achieves a better compression ratio of 52 percent, reduces packet latency and bandwidth utilization by 19.28 and 27 percent, respectively. It also reduces the area and power consumption of the de/compression units by 53.33 and 62.3 percent, respectively, compared to the state-of-the-art packet compression technique, No<inline-formula><tex-math notation="LaTeX">$\Delta$</tex-math><alternatives><mml:math><mml:mi>Δ</mml:mi></mml:math><inline-graphic xlink:href="deb-ieq1-3090315.gif"/></alternatives></inline-formula>.
Autonomous Cave Exploration using Aerial Robots

In this paper we present the complete system design for an aerial robot capable of autonomous exploration inside natural cave environments. Cave networks involve diverse and complicated topologies, complex geometries and degraded conditions rendering the process of robotic mapping a particularly daunting adventure. In response to these challenges, we outline the core algorithmic modules relating to localization and mapping, exploration path planning and control, alongside the developed perception and computing solutions onboard an aerial robot tailored to undertake such complex tasks given no prior information for the cave environments in which it is deployed. A set of extensive results is presented including both simulation studies in multi-branching and maze-like cave environments, as well as field experiments inside the Moaning Caverns natural cave environment in California, US.
Hybrid Wavelet Domain Features For Dermoscopic Image Classification

In this paper, Skin Cancer Diagnostic System (SCDS) using hybrid wavelet domain features is developed. The objective of this study is to improve the performance of the SCDS by hybridizing features of two-dimensional wavelet and three-dimensional wavelet features and also to compare them with their individual counterpart, and then to show their applicability in skin cancer diagnosis. Textural information is the major concern of the study. To show how textural patterns can be well described by texture specification, co-occurrence wavelet features are extracted. The experimental results on the PH2 database show the general usefulness of Co-Occurrence Features (COF) with Multi Layer Perceptron (MLP) for aiding skin cancer diagnosis. Among the wavelet features, three-dimensional wavelet features (accuracy of 84%) perform substantially better than the two-dimensional wavelet features (accuracy of 78%). Also, the combination of features appears the best, with an accuracy of 93%.
Choosing an Open Source License Based on Software Dependencies

License selection for new software system is essential for further use and distribution. However, it’s hard for developers to choose an appropriate open source software license for their projects due to lack of sufficient knowledge in this field. Some works have been done to address the license selection problem, but a key factor – software dependencies -- was ignored in previous work. Software dependencies can impose restrictions when choosing license for new software systems. To this end, an open source license selection approach is proposed in this paper. How license selection can be affected by software dependencies and license compatibilities are analyzed. In the proposed approach, the open source project dependencies data are used to construct a dependencies network and calculate project similarities. A process utilizes a modified license compatibilities graph and user project’s dependencies to detect license violations. We evaluate the license selection model using test set. The results demonstrate the validity of this model in selecting the applicable licenses.
High Efficiency Medium Voltage to Low Voltage Wireless Power Transformation for Data Centers

Wireless power transfer (WPT) techniques have been widely explored for various applications including for electric vehicles (EVs) and mobile phones. Less attention has been paid to the use of WPT in power distribution applications. An isolated wireless power link enables a touch safe interface without electrically energized contacts eliminating any arc flash or electrocution hazards. This concept will for example enable safe medium voltage distribution inside data centers eliminating low voltage distribution with its large bus bars and cables and bring voltages larger than 1kV directly to the server rack. It has been demonstrated that a converter using a planar contactless transformer can directly convert the 1kV DC input to 48V over a wide load range. In this paper, the planar contactless transformer is optimized with 3D printed bobbins to improve the manufacturability and technology readiness level. A new synchronous rectifier architecture is developed with MOSFET drain-to-source voltage-sensing for an H-bridge rectifier to improve the DC-to-DC efficiency. An adaptive dead time control method is used to compensate for layout parasitic inductances, to maximize the synchronous rectifier efficiency, and to avoid premature turn-off of MOSFETs. The DC-to-DC power transfer efficiency and loss distribution are analyzed.
Multipath In-band Network Telemetry

Multicast is a popular way of data dissemination. How to monitor the multicast traffic is essential to tackle network bottlenecks and performance woes. The original In-band Network Telemetry (INT) provides a low-latency and fine-grained monitoring solution. However, to apply INT directly to multicast traffic causes problems like telemetry data redundancy and network bandwidth waste since the telemetry data collected from the same device may be duplicated several times. In this work, we propose MPINT, a telemetry framework dedicated to multicast traffic monitoring, which is cost-effective with minor bandwidth overhead. Evaluation shows, compared with the original INT, MPINT reduces the INT bandwidth overhead by 80% and eliminates 50% of the telemetry data uploaded to the controller. In addition, it takes less than 0.5s to complete the entire multicast tree recovery and packet loss estimation on 180 devices.
Continuous Non-Invasive Blood Glucose Level Measurement using Near-Infrared LEDs

Constant blood glucose level monitoring is important especially to diabetic patients. However, the current methods available are invasive in nature and cause pain while also being costly. In addition, there is also the risk of contracting infections with improper hygienic practice. Therefore, in this project, a near-infrared (NIR) based LED sensor circuit has been developed to achieve a non-invasive method as an alternative for measuring blood glucose level. The system used 940 nm, 1450 nm, and 1550 nm wavelength NIR LEDs. The emitters and receiver were placed in a black box to reduce noise interference. The analog signal was transmitted through a filter and an amplifier circuit. Then, it was converted to a digital signal where the output voltage was measured, and the glucose level was calculated using an Arduino UNO. In the in vitro test, it was observed that the output voltage changes with changing concentrations of glucose solution, and there was a good correlation between the two variables, and this motivated the in vivo test. Measurement in transmittance yielded a 0.74 for coefficient of determination and 0.72 for reflectance. It was found that the combination of two spectroscopic measurement techniques improved the root mean square error to 16.02 mg/dL and reduced the percentage error to 9.56% compared to individual results. Thus, this non-invasive method can be an alternative for current invasive glucose measuring devices.
Monitoring and Alarming Activity of Islamic Prayer (Salat) Posture Using Image Processing

This paper introduced a Salat Inspection and Training System based on Machine Vision and Image Processing Subject. In Islam, prayer (i.e., Salat) is the second pillar of Islam. It is the most important and fundamental worshipping activity that believers have to perform five times a day. From gestures’ perspective, there are predefined human postures that must be performed in a precise manner. There are lots of materials on the Internet and social media for training and correction purposes. However, some people do not perform these postures correctly due to being new to salat or even having learned prayers incorrectly. Furthermore, the time spent in each posture has to be balanced. To address these issues, we propose to develop an assistive intelligence framework that guides worshippers to evaluate the correctness of their prayer’s postures. Many features of images are being extracted and analyzed. Methods for image comparison and pattern matching are used to study the system’s effectiveness by using several combining algorithms, such as Euclidean Distance, Template Matching and Grey-Level Correlation, to compare the images of the user and the database. The experiments’ results, both correct and incorrect salat performances, are shown via pictures and graph for each of the postures of salat. Limitations of the system, such as lighting, is discussed regarding how it affects the system’s performance.
Grape Diseases and Pests Named Entity Recognition Based on BiLSTM-CRF

Named entity recognition (NER) is one of the foundational and key tasks of knowledge graph construction. Traditional entity recognition methods of diseases and insect pests highly rely on artificial design features. To solve this problem, this paper constructs a network model combining bidirectional long short-term memory network (BiLSTM) and conditional random field (CRF). In addition, BIOES labeling strategy is used to solve the problem that the boundary of diseases and pests’ entities is not clear. First, the text data of grape diseases and insect pests were annotated in sequence with BIOES method. Secondly, the labeled text sequence was vectorized at character level by word vector technology. The BiLSTM network was used to extract the context feature information of the text sequence. Finally, the CRF model was used to constrain the label rules at the sentence level to identify entities. The experimental results show that the BiLSTM-CRF model has a good effect on grape disease entity recognition, and the F1 value is as high as 95.44%. Compared with other comparison models, the recognition effect of the BiLSTM-CRF model is relatively stable, which to a certain extent verifies that the model has better recognition performance in the task of identifying grape diseases and pests’ entities.
Stock Price Analysis with Deep-Learning Models

Novel artificial intelligence prediction algorithms use deep learning techniques, i.e., recurrent neural networks and convolutional neural networks, to predict financial time series. Also, autoencoders have gained notoriety to extract features from latent space data and decode them for predictions. This paper compares several deep learning architectures with different combinations of long short-term memory networks and convolutional neural networks. Autoencoders are implemented within these networks to find the best model performance for financial forecasting tasks. Four different architectures were trained with stock market data of four companies (AMD, ResMed, Nvidia, and Macy’s) from 2010 to 2020. Without autoencoder, the long short-term memory network architecture achieved the best performance for all companies, obtaining a mean squared error of 0.004 for AMD stocks by applying 10-fold nested cross-validation. The results show that long short-term memory networks are very well suited for prediction tasks using a simple deep-learning architecture.
Analysis of Automotive Radar Interference in Spatial Domain

Mutual interference between automotive radars is a major obstacle currently faced by manufacturers in the widescale deployment of these radars. A thorough understanding of interference behaviour along the signal processing stages of victim radar's receiver is essential to devise robust interference detection and mitigation strategies. This paper aims at investigating the behaviour of various types of FMCW automotive interferences in the spatial domain. An extensive analysis of interference using experimentally verified modelling results, after fast-time Fourier transform and beamforming at the receiver is performed in the presented details.
Blind Hyperspectral Inpainting Via John Ellipsoid

Hyperspectral inpainting (HI) is a signal processing technique for recovering the complete hyperspectral imaging data cube from its incompletely acquired version. Some benchmark methods either rely on big data or the plug-and-play learning strategy. In this paper, we introduce John ellipsoid (JE), a key topology in functional analysis, to design a blind HI algorithm. JE criterion holds strong endmember identifiability like the well known (non-convex) minimum-volume simplex criterion in hyperspectral remote sensing, but just requires solving a convex optimization problem bringing it an advantage in computational aspect. As revealed in recent literature, comparing to widely adopted simplex topology, JE is robust against both low purity of hyperspectral data and ill-conditioned endmember matrix. Such robustness does bring us advantage in HI performance, as illustrated by experimental results on benchmark dataset.