Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of Additively Printed Sustainable Aqueous Silver Inks using Aerosol Jet Printing and Investigation of Circuit Repairability

Increased consumer demand for wearable electronics has fueled the growth of flexible electronics technology and compatible circuitry. Inkjet printing, direct ink writing, and aerosol jet printing (AJP) are the popular processes used in additive printing of flexible electronics. Conductible ink is the fundamental element of all flexible circuits produced by these technologies, generally a solution of metal nanoparticles with a mixture of various solvents. The solvents used for formulating inks are usually chemicals which are harmful for release into the environment or contain volatile organic compounds (VOCs) which have negative health effects. The movement towards using sustainable components in products would need these inks to have reduced amounts of such harmful solvents. In this study, a commercially available aqueous silver ink is used for printing full wave rectifiers and voltage controlled oscillators using the pneumatic atomizer of the aerosol jet printer. Electrically conductive adhesive (ECA) has been used for component attachment. The performance of these additively printed circuits is compared with the theoretical values. Furthermore, same circuits were printed using non-aqueous silver ink, and its performance was compared with the aqueous one. Finally, a repairability investigation is performed on the printed circuits to further investigate product sustainability. Some of the attached components on the printed circuit were popped off and reattached using the two interconnect technologies mentioned earlier. The performance of the pristine and repaired circuit fabricated using both the aqueous and non-aqueous inks was ultimately compared.
Restricted String Representations

A string representation of a graph assigns to every vertex a curve in the plane so that two curves intersect if and only if the represented vertices are adjacent. This work investigates string representations of graphs with an emphasis on the shapes of curves and the way they intersect. We strengthen some previously known results and show that every planar graph has string representations where every curve consists of axis-parallel line segments with at most two bends (those are the so-called B2-VPG representations) and simultaneously two curves intersect each other at most once (those are the so-called 1-string representations). Thus, planar graphs are B2-VPG 1-string graphs. We further show that with some restrictions on the shapes of the curves, string representations can be used to produce approximation algorithms for several hard problems. The B2-VPG representations of planar graphs satisfy these restrictions. We attempt to further restrict the number of bends in VPG representations for subclasses of planar graphs, and investigate B1-VPG representations. We propose new classes of string representations for planar graphs that we call “order-preserving. ” Order-preservation is an interesting property which relates the string representation to the planar embedding of the graph, and we believe that it might prove useful when constructing string representations. Finally, we extend our investigation of string representations to string representations that require some curves to intersect multiple times. We show that there are outer-string graphs that require an exponential number of crossings in their outer-string representations. Our construction also proves that 1-planar graphs, i.e., graphs that are no longer planar, yet fairly close to planar graphs, may have string representations, but they are not always 1-string.
Large‐scale flows under location uncertainty: a consistent stochastic framework

Using a classical example, the Lorenz‐63 model, an original stochastic framework is applied to represent large‐scale geophysical flow dynamics. Rigorously derived from a reformulated material derivative, the proposed framework encompasses several meaningful mechanisms to model geophysical flows. The slightly compressible set‐up, as treated in the Boussinesq approximation, yields a stochastic transport equation for the density and other related thermodynamical variables. Coupled to the momentum equation through a forcing term, the resulting stochastic Lorenz‐63 model is derived consistently. Based on such a reformulated model, the pertinence of this large‐scale stochastic approach is demonstrated over classical eddy‐viscosity based large‐scale representations.
Reflection type Metasurface based Reconfigurable Linear to Circular Polarisation Converter for 5G/Sateilite Communication

A reconfigurable polarization converter is presented in this article. This is a single layer metasurface based broadband polarizer which is designed over an inexpensive substrate that is FR-4 and the unit cell dimension is $14\ \text{mm} \times 14$ mm. The proposed method follows transmission line model and the simulation is based on full-wave Floquet analysis. A meander-line based unit cell is chosen for this polarization converter design due to its simplicity and flexibility from design point of view. This polarizer provides similar conversion capacity for TE and TM modes. A PIN diode is used to switching the frequency of polarization band. When the diode is ‘Off’, the conversion bandwidth reduced to 1.16 GHz (5.83 GHz to 6.99 GHz) and at ‘On’ condition of diode, the conversion bandwidth is 3.37 GHz expanding from 3.21 GHz to 6.58 GHz. The above bandwidth is considered over 90% Polarization Conversion Ratio (PCR) value. This polarizer can be useful in the C band satellite communication for LP-CP conversion at source antenna. Due to the reconfigurability, it can also be used in different bands of 5G communication.
Integrated Front-End Approaches for Wireless 100 Gb/s and Beyond: Enabling Efficient Ultra-High Speed Wireless Communication Systems

Wireless communication faces ever-growing demand for higher data rates, which has driven the need for modem implementations that are capable of providing single-link data rates on the order of 100 Gb/s and beyond for extremely fast data transmission. In recent years, the 60-GHz band has been employed to significantly increase wireless local area network data rates in IEEE’s 802.11ad standard targeting multigigabit modems. Its extension 802.11ay was finalized in 2021, targeting data rates toward 100 Gb/s in the same bands by increasing spectral efficiency and combining up to four channels with more than 8-GHz bandwidth in total <xref ref-type="bibr" rid="ref1">[1]</xref>. This approach has the advantage of existing unlicensed regulatory allocations. Nevertheless, options for higher bandwidth have increasingly been investigated, also driven by the publication of the IEEE 802.15. 3d <xref ref-type="bibr" rid="ref2">[2]</xref> physical layer standard for a frequency range between 252 and 325 GHz, which resides in an atmospheric window that enables reasonable long-range communication links on the order of a few kilometers. In this frequency range, a substantially higher bandwidth is achievable. Specific regulatory allocations are still missing; nevertheless, the conditions to use this frequency range have already been discussed and described during the 2019 World Radiocommunication Conference <xref ref-type="bibr" rid="ref3">[3]</xref>. Although the first demonstrations of 100-Gb/s modems in this frequency range were at least partially based on photonic technologies <xref ref-type="bibr" rid="ref4">[4]</xref>, integrated front ends in different semiconductor technologies have been demonstrated <xref ref-type="bibr" rid="ref5">[5]</xref> and are discussed in the present article in more detail together with the major remaining challenges for practical applications.
Source Localization Based on Hybrid AOA, TDOA, and RSS Measurements

In this article, a constrained weighted least-squares (CWLS) problem with two quadratic constraints for hybrid angle of arrival (AOA), time difference of arrival (TDOA), and received signal strength (RSS) localization is formulated. The Lagrange multiplier method cannot be efficiently used in this situation, so we provide four methods to solve the CWLS problem: 1) weighted least-squares (WLS); 2) iterative CWLS (ICWLS); 3) Lagrange multiplier method combined with Newton’s method (LMM+NM); and 4) semidefinite programming (SDP). Furthermore, to obtain a good balance between accuracy and computational burden, we develop a fixed point iteration (FPI)-based algorithm that is derived from an approximate maximum likelihood estimator (MLE). Simulation results show that the accuracy of the proposed FPI method is better than the other four methods; besides, its computational burden is one in ten of the SDP algorithm.
Artificial Intelligence in Software Engineering: Perspectives and Challenges

Artificial Intelligence in Software Engineering is the hottest trend in the rapidly growing software world. AI techniques are powerful and easy to use as it can be easily deployed as key components of the system. The fusion of both these techniques emerges four new innovative research areas which require extensive research in the future. The application of AI techniques in the Software Engineering field poses many opportunities and risks for software organizations and handling these issues is essential before the selection of appropriate techniques. In this paper, we present an introduction, new emerging fields with the fusion of Artificial Intelligence with Software Engineering, challenges, and a conclusion.
Research on the influencing factors of ship detection accuracy based on Deep Learning

In order to explore the influence of different camera observation parameters on the detection accuracy of ships, this paper adopts virtual simulation technology to establish a ship data set, trains a ship detection model based on the YOLOv5 algorithm, detects ship motion video samples under different observation distances, heights and angles, obtains the corresponding detection confidence, and draws the correlation between observation parameters and detection accuracy through statistical analysis. A total of 2220 data including 6 heights, 10 distances and 37 angles were analyzed in this paper. The results show that under the condition that the observation distance increases from 50m to 500m, the average detection confidence of ships decreases linearly from 0.87 to 0.62, and detection failures occur at 350m. As the observation distance increases from 350m to 500m, the number of detection failures increases from 5 to 63. It can be defined that a certain corresponding distance between 300m and 350m is the limit detection distance under these experimental conditions. When the observation height changes from 10 meters to 60 meters and the observation angle changes from 0° to 180°, the average detection confidence fluctuates between 0.79 and 0.83 and between 0.77 and 0.84. The study shows that the detection accuracy decreases with increasing distance, while the observation height and angle have no regular effect on the ship detection accuracy.
Narrowband Spectrum Sensing: Fuzzy Logic Versus Deep Learning Systems

The motivation for this work was to investigate the advantages and disadvantages of two promising techniques for narrowband spectrum sensing: fuzzy logic and deep learning which can be useful for future users. To this end, we present three fuzzy logic systems and four deep learning-based systems for narrowband spectrum sensing. The fuzzy logic systems include triangular and Gaussian membership functions, multiple implications, and aggregation methods. The deep learning systems are based on three basic architectures, including convolutional neural networks (CNN), long short-term memory (LSTM), and fully connected (FC) layers. Simulation results show that deep learning techniques provide a higher probability of detection in a wider SNR range than fuzzy logic techniques. However, fuzzy logic utilizes simpler hardware-friendly detectors, than deep learning.
Operand-Variation-Oriented Differential Analysis for Fuzzing Binding Calls in PDF Readers

Binding calls of embedded scripting engines introduce a serious attack surface in PDF readers. To effectively test binding calls, the knowledge of parameter types is necessary. Unfortunately, due to the absence or incompleteness of documentation and the lack of sufficient samples, automatic type reasoning for binding call parameters is a big challenge. In this paper, we propose a novel operand-variation-oriented differential analysis approach, which automatically extracts features from execution traces as oracles for inferring parameter types. In particular, the parameter types of a binding call are inferred by executing the binding call with different values of different types and investigating which types cause an expected effect on the instruction operands. The inferred type information is used to guide the test generation in fuzzing. Through the evaluation on two popular PDF readers (Adobe Reader and Foxit Reader), we demonstrated the accuracy of our type reasoning method and the effectiveness of the inferred type information for improving fuzzing in both code coverage and vulnerability discovery. We found 38 previously unknown security vulnerabilities, 26 of which were certified with CVE numbers.
Robust Stability Region for Time-Delayed Single-Area LFC-EV System

The share of renewable energy sources (RES) in power systems is rising due to environmental concerns. This increment leads to the lack of inertia and insufficient damping which pose a significant threat for frequency stability and dynamic performance of load frequency control (LFC) systems. Various methods have been employed to overcome this drawback. One of these is the utilization of electric vehicles (EVs) for frequency regulation. Furthermore, in practice, systems do not always operate under identical conditions. Therefore, parametric uncertainties should be considered in controller design. This paper applies Kharitonov’s theorem and stability boundary locus method to determine a set proportional-integral (PI) controller gains defined as a robust stability region that ensures the stability of the time-delayed LFC system with EV aggregator having uncertainties in system parameters. The impacts of the level of uncertainties and time delay on the robust stability region are investigated. Results indicate that robust stability regions shrink as the level of uncertainties and time delay increase.
A Computational Friendly Improved LIF Model Considering Refractory Period

In the past few years, spike neural networks (SNN) have been called the third generation neural networks. Compared with traditional neural networks, SNN has been widely concerned because of its space-time characteristics, diverse coding methods, and character of event driven. In order to make the spiking neural network show more complex behaviors, this paper improved the leakage integrity fire (LIF) model, considered the refractory period, proposed the refractory leakage integrity fire (RLIF) model, and simulated the neural network behavior. Through the regulation of various factors of SNN, the experiment is carried out to observe the different behaviors emerging from SNN, so as to analyze the influence of various factors in the model on SNN emerging behavior, and provide basis for discovering the intelligent behavior of SNN and applying it. The following research can further analyze the impact of neural network structure and training methods of spiking neural network based on RLIF model.
Path Texture Detection for Visually Impaired using Transfer Learning

The computer vision technique known as object detection is used to determine the location of an object in an image or video. Recent advances in a number of areas, including computer vision, speech recognition, and natural language processing have been made using deep learning approaches. Convolution neural networks (CNNs), which have several layers, are used to detect objects. A deep learning model that uses CNN to identify various surface regions has been suggested. The study of tracks, walkways, playgrounds, highways, and muddy areas is a pertinent and significant aspect of object detection for Visually Impaired (VI) individuals. In addition to adding indirect vision to the blind individual, this model recognizes surface regions and alerts them using text-to-sound conversion API. In this study, transfer learning was carried out utilizing Faster RCNN (Region Based Convolutional Neural Networks) with Inception V2 and ResNet50 model weights. The accuracy of both trained models was the same, while Faster RCNN Inception V2 had the lowest loss (0.02).
XAI for Securing Cyber Physical Systems

Machine Learning (ML) is heavily leveraged in various fields, specially in Cybersecurity to detect attacks and anomalies. Although these models are capable of achieving high prediction accuracies, they are not interpretable. In fields like medical sciences and cybersecurity, it is essential for the model's predictions to be understandable so that informed decisions can be taken accordingly. A Cyber Physical System (CPS) is controlled or monitored by computer-based algorithms. A CPS is divided into three layers, application control layer, data transmission layer and perception execution layer. Attacks can be performed in any of these layers. Network attacks, cryptographic attacks, physical threats and malicious software injection are some of the possible attacks. In the present work, we used a dataset containing Network Attacks and Physical Threats. The aim was to detect these attacks using ML models and interpret the decisions using Explainable Artificial Intelligence (XAI). By extracting explanations from the model, XAI helps build trust on the model outcome.
Facial Expression Recognition Model Based on Improved VGGNet

Facial expression recognition is of great significance for human-machine interaction and autonomous driving. In order to achieve simple extraction of facial emotional features and accurate classification of facial expressions, mainstream convolutional neural networks were studied, and an improved VGG-16 convolutional neural network based facial expression recognition model was proposed. This model is improved on the basis of VGG-16 network model, optimizing the number of convolution layers in the third and fourth convolution blocks, replacing the SoftMax classifier in the original VGG-16 network with the 7-tag SoftMax classifier, and replacing the original Relu activation function with the LeakeyRelu activation function. The experiment was conducted on the FER2013 dataset, and the final accuracy reached 72.42%, which is higher than the accuracy of unimproved VGGNet (66.31%) and ResNet (71.3S%). The experimental results indicate that the model has certain application capabilities in facial expression recognition.
Data Center Energy Reduction by Lowering Chip-to-Supply Thermal Resistance

Data centers are estimated to consume up to 2% of current US electricity production. With increased processor power, rising ambient temperatures, and reduced water availability cooling of data centers is becoming a greater challenge. All electrical power entering a data center must be rejected to the environment as heat. This is typically done via primary and secondary cooling loops. The secondary loop transfers heat from the servers to the facility cooling system. The primary cooling loop rejects heat from the facility cooling system to the environment. The energy cost of rejecting heat to the environment is often high because primary facility coolants are typically kept at 20-30°C, which is relatively close to typical ambient climate conditions. Evaporative cooling or chillers are therefore often used to facilitate heat rejection and maintaining the compute room at acceptable temperatures. As chipsets and electronics can often operate at higher temperatures (typically at 70-90°C), it is here explored how reducing compute room thermal resistance (thermal resistance of the secondary loop) can impact the energy of the cooling plant of the data center as a whole. This paper explores the benefits of lowering the chip-to-coolant thermal resistance and raising the primary loop water supply temperature in data centers and its effect on cooling plant energy and water usage. A simplified Python model of a data center primary and secondary loop was constructed. Hourly data from U.S. Climate Reference Network weather stations in each US ASHRAE climate zone was then used to drive a dispatching algorithm that decided whether dry coolers, evaporative towers or chillers could be used to reject heat to the environment each hour of the year. This dispatching algorithm was optimized to produce results comparable to those published by cooling system manufacturers for a set of six test cases. The optimized model was then used to predict data center energy and water usage in each US climate zone for a wide range of primary loop water supply temperatures. The model results show that lowering the chip-to-coolant thermal resistance allows the water supply temperature to be increased, leading to an increase in the number of hours that dry coolers can be used in every climate zone throughout the year. This in turn leads to an opportunity to reduce cooling energy consumption, operational CO2 footprint, and water consumption.
Distribution Network Topology Identification Using Smart Meter Data and Considering the Same-Bus-Different-Feeder Condition

Due to the rapid growth of distribution systems in urban areas, the increasing complexity of these distribution systems brings challenges to accurate topology identification. The collected voltage data from smart meters have been proven effective in topology identification applications. However, when multiple feeders are connected to the same bus, the accuracy of existing voltage-correlation-based topology identification can be degraded significantly. To address this challenge, a comprehensive inference method is proposed in this article to identify the on/off switch state in the distribution system considering the same-bus-different-feeder condition. The voltage-power-dependence principle among connected nodes is revealed. Based on this theory, a physical probabilistic network model is proposed to represent the causal relationships between the switch states and the voltage-power dependence in a distribution network. The belief propagation algorithm is introduced to deduce the topology identification model, which can reduce the time consumption of the inference. The performance of the proposed method and its advantage over the existing methods are verified in case studies.
Absolute Bandwidth-stabilized S-band Tunable Bandpass Filter

This paper reports on a frequency tunable bandpass filter with a constant absolute bandwidth that operates in the s-band. Two half-wavelength resonators loaded with varactor diodes are controlled by the same bias. Hence, the control circuit is user-friendly and has high engineering applicability. In addition, the use of a coupling structure to feed the resonator reduces insertion loss. The coupling coefficient between the two resonators is determined by a combination of electrical and magnetic coupling to achieve the requirement that the absolute bandwidth of the passband remains constant during frequency tuning. According to the measured data, the filter achieves -3dB absolute bandwidth of 140MHz ± 3MHz in the passband. adjustable range is 2.49GHz~1.99GH. in the tuning process, the insertion loss is better than 2.82db and the return loss is greater than 10db.