Miracle-dz/UniFAR-FTUs huggingface dataset:
Design of Low-Noise X-band Frequency Source Based on DDS-PLL

This paper proposes a DDS directly excites PLL frequency source, which is small, portable, stable, low-phase noise and fast frequency hopping. In the design, the loop bandwidth, phase margin, and phase detection frequency are set rationally through theoretical calculations, while coplanar waveguide, horseshoe-shaped etching, and continuous impedance are used to improve system performance. The frequency range of PLL is from 8.5GHz to 9.5GHz. The phase noise is −115dBc at 100KHz frequency offset, the phase locked time is 580ns when the PLL operating at 9.0GHz.
Design of a Polymer Based On-Skin Temperature Sensor

The emergence of polymer-based electronic devices has gained a lot of reputation in the research community throughout the world. These types of on-skin electronic devices can be used for biomedical purposes, i.e. biophysical sensors, biochemical sensors. These sensors are usually developed with the layers of different types of materials drawn on a piece of paper that can be placed on the skin of a human body. This paper represents the development of a novel on-skin thin polymer (paper substrate) based temperature sensor. The conductor model of the sensor has been designed using graphite and copper. This sensor can detect the temperature of a human body when attached to the skin and the use of thin polymer makes it cost-efficient. The developed sensor can be easily fabricated and integrated as a smart biological data collection sensor. The paper also presents a detailed simulation of the sensor to evaluate its efficiency and performance. The work shown in this paper is the initial stage of broad-scale research and the methodology followed in this research can be used to develop different biomedical sensors using other materials.
Multi-attention DeepEMD for Few-Shot Learning in Remote Sensing

Currently in the field of remote sensing (RS), scene classification is a crucial research subject. The usual method is to gather plenty of scene classification data manually annotated by experts in the field, and then use the dataset to train a scene classification model. Typically, the better the deep learning model requires more labeled data. Therefore, how to obtain a model with good classification performance when there is only a small amount of labeled data, this becomes a well-known few-shot classification problem. In this study, a new few-shot learning scheme is developed for RS scene classification. Our method is based on deepEMD with spatial and channel attention modules(CBAM) to capture subtle but distinguishing features. To get the significant weights of pixels in the EMD formulation, an attention-reference mechanism is designed, which can sufficiently leverage subtle local features and minimize the influence posed by the cluttered background. To avoid overfitting, label smoothing that can improve the generalization performance of the model is introduced. Comprehensive experiments are conducted to validate the method and achieve new state-of-the-art performance on three popular RS scene datasets, namely UC Merced, AID and OPTIMAL-31. Furthermore, two ablative studies also indicate that the components are conducive to fast convergence and high accuracy.
A 0.5-to-1.2V, 310nA Quiescent Current, 3fs-FoM Time-Domain Output-Capacitorless LDO with Propagation-Delay-Triggered Edge Detector

A time-domain output-capacitorless LDO for low power IoT application is presented in this paper. A fully differential voltage-controlled oscillator (DVCO) comparator converts the LDO output error voltage to time-domain signal and then processed by a frequency comparator (FC) with propagation-delay-triggered edge detectors to control the LDO pass transistor. Fabricated in a standard 65nm CMOS process, the LDO achieves a 0.003ps Figure of Merit (FOM) with the lowest reported quiescent current of 310nA and the largest load dynamic range (1,250,000) compared to the recent prior-arts.
Forensic Fingerprinting of Biomarkers for Oil Spill Characterization and Source Identification

Biomarkers are one of the most important hydrocarbon groups in petroleum. Biomarkers can be detected in low quantities (ppm and sub-ppm level) in the presence of a wide variety of other types of petroleum hydrocarbons by the use of the gas chromatography/mass spectrometry (GC/MS). Relative to other hydrocarbon groups in oil such as alkanes and most aromatic compounds, biomarkers are more degradation-resistant in the environment. Furthermore, biomarkers formed under different geological conditions and ages may exhibit different biomarker fingerprints. Therefore, chemical analysis of biomarkers generates information of great importance to environmental forensic investigations in terms of determining the source of spilled oil, differentiating and correlating oils, and monitoring the degradation process and weathering state of oils under a wide variety of conditions. This article briefly reviews biomarker chemistry, biomarker characterization and quantification, biomarker distributions, weathering effects on biomarker composition, bicyclic biomarker sesquiterpanes and diamondoids, diagnostic ratios and cross-plots of biomarkers, unique biomarkers, application of biomarker fingerprinting techniques for spill source identification, and application of multivariate statistical analysis for biomarker fingerprinting.
Sparse decomposition of pressure pulse wave signal based on time frequency analysis

In traditional Chinese medicine (TCM), wrist pulse is of great significance to help doctors in diagnosis. With the development of sensing technology, the computerized wrist pulse analysis has been attracting more attention in modern medicine for its non-invasive and convenient. Considering the TCM pulse diagnosis theory, it is necessary to develop effective feature extraction methods for computerized diagnosis. In this paper, we decompose the pressure pulse waveform of the radial artery to several components by sparse decomposition with Gabor function. In order to better represent the pulse waveform signal, we use an Gabor function based on the characteristics of the pulse waveform to generate a time-frequency dictionary. Compared with the conventional representation methods, the shape of the Gabor function is more variable, which can better represent both the contour and specific peaks. In addition, due to the limitation of the windowing, the Gabor function can reduce the influence on other positions when representing specific position. The feature vector composed of the decomposed components can be used for the computerized pulse signal analysis and disease diagnosis. The experimental results show that the proposed method can exhibit superior performance in distinguishing between the signals collected from patients and healthy individuals.
Homophily and Transitivity in Bot Disinformation Networks

Many disinformation and propaganda campaigns on social media platforms deploy social bots-artificial accounts that pose as humans-to disseminate political propaganda. To understand the effects of the presence of bots as conduits for information transference in online social networks, we use Exponential Family Random Graph Models (ERGMs) to examine the structure of a bot network during a propaganda campaign in Ecuador in October 2019. We find heterophily and transitivity between bot and human actors, but that bots are less likely to engage with humans than with other bots. This may represent a tactic deliberately deployed to maximize influence by exploiting Twitter's algorithm for showing content. The use of ERGMs produces greater insight into this Twitter bot network, and we believe that this methodology should be extended in future work.
Multisensor Fusion Using Fuzzy Inference System for a Visual-IMU-Wheel Odometry

This article presents an adaptive fusion method for a multisensor odometry using a fuzzy inference system and applies it to a visual-inertial measurement unit (IMU)-wheel odometry. This article first presents the mechanism of the multisensor odometry with an error state Kalman filter and points out that the assumption of the constant covariance in the prediction and measurement models are invalid due to dynamical interior and exterior abnormities. The previous work of fuzzy logic Kalman filter can only deal with uncertain uncertainty in the measurement model and thus not suitable for this double stage variant uncertainty problem. This article provides a solution using adaptive network fuzzy inference systems. The abnormal working conditions of the two models can be detected online separately, and the fusion gains are tuned accordingly. The training method of the networks is also provided to further improve the fuzzy inference system when the ground truth of the trajectories is available. Experimental results show that the proposed adaptive fusion method can significantly improve the robustness and accuracy of the fused trajectory estimation, even under severe working conditions.
Performance and Capacity Consistency Analysis of A Wireless UAV-Blockchain System

Blockchain is widely applied in the field of Unmanned Aerial Vehicles (UAVs) for its characteristics of high distribution, secure data exchange and storage. Different from the current blockchain systems that are mostly deployed on a wired network, this paper proposed a wireless UAV-blockchain system. Each UAV node acts as a blockchain node and has onboard functionality of performing transactions interactions and consensus mechanisms via wireless ad-hoc channels. We study the interactions between the UAV ad hoc network capacity and the blockchain system, and evaluate the performance (transactions per second) under corresponding all possible network coverage probabilities. Performance evaluations are presented to testify the theoretical analysis, which shows how the UAV-blockchain system throughput is affected by the block arrival rate and UAV network scale.
Sensor-Based Human Activity Recognition: A Comparative Study of Machine Learning Techniques

Human activity recognition (HAR) is a wide field of study which identifies a person’s specific movement or behavior based on sensor data. Recognition of human behavior is the origin of many technologies, such as those concerned with personal biometric signatures, sports training, digital computing, security, health and fitness tracking, ambient-assisted living and management. Studying recognition of human activity shows that researchers are mostly interested in human everyday activities. HAR models input is the reading of the raw sensor data, and output is the prediction of the movement activities of the user. The HAR framework is becoming an evolving discipline in intelligent computing applications in the field of pervasive computing. In our study, we applied several machine learning algorithm along with some preprocessing techniques to identify which algorithm performs better in dataset acquired from the WISDM laboratory, which is available in public domain. The experiment shows that the highest accuracy is achieved in phone accelerometer data using Principal Component Analysis (PCA) with Random Forest (RF) than any other algorithm and preprocessing techniques in terms of human activity recognition. This experiment will help perform more work on the basis of implementing classification and preprocessing techniques to identify human activities.
An Analysis of the Use of DNS for Malicious Payload Distribution

The Domain Name System (DNS) protocol is a fundamental part of Internet activities that can be abused by cybercriminals to conduct malicious activities. Previous research has shown that cybercriminals use different methods, including the DNS protocol, to distribute malicious content, remain hidden and avoid detection from various technologies that are put in place to detect anomalies. This allows botnets and certain malware families to establish covert communication channels that can be used to send or receive data and also distribute malicious payloads using the DNS queries and responses. Cybercriminals use the DNS to breach highly protected networks, distribute malicious content, and exfiltrate sensitive information without being detected by security controls put in place by embedding certain strings in DNS packets. This research undertaking analysed the use of the DNS in detecting domains and channels that are used for distributing malicious payloads. Passive DNS data which replicate DNS queries on name servers to detect anomalies in DNS queries was evaluated and analysed in order to detect malicious payloads. The research characterised the malicious payload distribution channels by analysing passive DNS traffic and modelled the DNS query and response patterns used during malicious payload distribution. The research found that it is possible to detect malicious payload distribution channels through the analysis of DNS TXT resource records.
Continuous Model Predictive Control of Current-fed Half-Bridge Isolated DC/DC Converter

This paper focuses on the control of the snubberless naturally-clamped soft-switching current-fed halfbridge isolated DC/DC converter. The average state-space model for the converter is established and linearized. Adopting the discrete-time linear predictive model, a continuous control set model predictive controller (CCS-MPC) with fixed switching frequency is firstly proposed for this converter control to improve the converter dynamic performance. Meanwhile, in order to solve the model-mismatch to achieve offset-free tracking, the extended-Kalman observer is designed to obtain the voltage error and the integrated error for dynamically compensating the current reference calculation as a feedforward compensator. Finally, the proposed control strategy is validated by the simulation of MATLAB/Simulink, and the proposed approach can stabilize the output voltage with good steady-state performance and strong robustness against the input voltage and load variations.
Differential Growth Response of Wheat Genotypes to Ammonium Phosphate and Rock Phosphate Phosphorus Sources

ABSTRACT A solution culture experiment was conducted to determine the response of 15 wheat genotypes for growth, phosphorus (P) uptake, and P utilization efficiency, and their adaptability to P stress conditions using adequate [250 μM P in nutrient solution as ammonium phosphate (NH4H2PO4)] and stress (powdered rock phosphate suspended in nutrient solution) P supply levels. Shoot dry matter (SDM) and total plant DM (shoot + root) and P uptake were generally higher for most genotypes in adequate P than stress P level treatment, but the opposite was true for root dry matter (RDM), root: shoot ratio (RSR), and root P uptake. Relative reduction in SDM due to P deficiency stress ranged from none to 54%. Genotypes Kohinoor 83, PB 85, Parvaz 94 and 4770 did not respond to P deficiency stress for SDM production, while genotypes FSD 83, Chakwal 86, Pasban 90, 4072, 4943, 5039, 6529-11, and 6544-6 were highly responsive to P application for SDM. Shoot P uptake in genotypes at adequate P level was about 3-times higher than those genotypes grown at stress P level. Differences in P concentration of shoot ranged between 2.00 to 3.06 mg P g−1 in stress P level treatment, and had a significant positive correlation with P harvest index (PHI) (r = 0.558*, P < 0.05) and root efficiency ratio (RER) (r = 0.611*, P < 0.05) and negative correlation with P efficiency ratio (PER) (r = −0.909**, P < 0.01). A significantly positive correlation of P utilization index (PUI) and SDM (r = 0.784**, P < 0.01) and non-significant negative correlation (r = −0.483) of PUI with P concentration in shoot implies that wheat genotypes with higher PUI may be selected for P deficient milieu. Genotypes with higher PUI (>0.8 g mg−1 P) in rook phosphate treatment were Inqlab-91, Pak-81, Lu 26s, Parvaz 94, 4072, 4770, 4943, and 5039. There was no interrelationship observed between shoot P uptake and P efficiency in stress P level treatment. However, highly significant and positive correlation (r = 0.720**, P < 0.01) between PHI and RER suggested that shoot P uptake depended upon root efficiency and it increased with the increase in P uptake per unit RDM. Consequently, this resulted in increased SDM which is evident from the significant positive correlation (r = 0.833**, P < 0.01) between SDM and shoot P uptake. In summary, the findings suggest that PUI and RER may be used for selecting P efficient wheat genotypes (e.g., 4072, 4770, 4943, Pak 81, and Inqlab 91) for dry matter production and P use.
A Multiband Navigation Stacked Patch Antenna for GNSS Receiver Measurement

In this paper, a circularly polarized (CP) stacked antenna operating at Global Navigation Satellite System (GNSS) frequencies is proposed. Three circular air microstrip patches with difference are stacked as radiator operating at three frequency bands, two pairs of long probes is adopted to feed the top patch for improved combiner loss, and a 3dB bridge combing with phase shifter is employed to produce a 90° phase difference for circular polarization. Simulation and experiment results show that the left hand CP(LHCP), right hand CP (RHCP) and the axial ratio (AR) are acceptable at each operation frequency.
Power conditioning of standalone Photo-voltaic system with BLDC motor by Negative-Output Luo Converter

The power electronic devices have evolved the drive circuits for machines over the last decade. The introduction of many DC-DC topologies has led to different functionality in power conditioning. Luo converter is one such DC-DC converter that is embedded by the voltage-lift techniques. The power transfer efficiency and the output voltage of Luo converter are limited by the limits on the parasitic elements in DC-DC converters. The Negative Output Luo converter is one wherein the DC power conditioning is done in the second quadrant with minimum passive elements. This helps in achieving reduced ripples, and high voltage transfer gain. The DC input can be harnessed from renewable energy resources like Solar energy through Photo-voltaic panels. The PV integrated Negative Output Luo converter is backed up by a Maximum Power Point Tracker with Perturb and Observe algorithm to extract maximum power at varying irradiance. In this paper, the Negative Output Luo converter is designed to control the speed of high power-to-weight ratio motors like BLDC motor. The characteristics of the proposed system under various irradiance have been analysed and reported.
Design and Optimization of a Novel Dual-Rotors Flux-Switching Permanent-Magnet Machine

In this paper, a novel dual-rotors flux switching permanent magnet machine (DRFSPMM) is designed and optimized for low speed and high torque applications to improve torque ripple, torque and efficiency. The design concept and the operating principle are illustrated firstly. The structural parameters of the initial machine are optimized by finite element method (FEM), taking the torque, PM utilization, power factor (p.f.) into comprehensive consideration. The dominant parameter ranges are determined by single parameter scanning, and the optimal candidate is obtained by response surface method (RSM) optimization incorporating Latin hypercube sampling. The prediction results of the optimal candidate fit well with these of FEM with only slight discrepancy. The results indicate that the torque ripple is reduced from 6.32% to 3.77% while the output torque increases from 57.12 Nm to 63.18 Nm.
Influence of Ionic Strength and Lead Concentrations on the Adsorption of Aluminum by a Latosol

A Latosol was used to examine the effect of ionic strength on the adsorption of aluminum (Al), as well as the competitive adsorption between Al and lead (Pb). The adsorption of Al shown very little ionic strength dependence on the change in supporting electrolyte concentrations (0.001 M, 0.01 M, and 0.1 M) of NaNO3, which demonstrated that Al is adsorbed specifically by the soil. The observation that Al was adsorbed when the net surface charge is positive confirmed this opinion. The addition of 0.1 M Ca(NO3)2 resulted in a noticeable reduction of Al adsorption, a result of competition between Al and background salt, while this inhibitory effect gradually eliminated with the increase in pH. Competitive adsorption between Al and Pb at low concentration (0.1 mM) was noticeable at low pH and become less noticeable as pH increased from 3.5 to 5.5. Additionally, competition was enhanced throughout the pH range examined as the initial metal concentration was raised up to 1.0 mM. The observed affinity by the soil was Al>Pb consistent with the tendency of metal hydrolysis. The 0.1 mM Pb shown no influence on the 1.0 mM Al adsorption, and more important adsorption of Pb was not eliminated under the condition that the molar ratio of [Al]/[Pb] was as large as 10:1, which may reflect that some adsorption sites on surface of the soil show high selectivity for Pb.
A Comparative Study on Voltage Level Standard for DC Residential Power Systems

With the increasing demand for renewable energy integration and DC power consumption, the application and investigation of residential low voltage DC (LVDC) power system have attracted more attention in recent years. The residential DC power system has advantages on distributed generation integration and conversion cost reduction, which could provide high efficiency and high power quality power supply to consumers. However, the lack of voltage level standards challenges the commercial application of the LVDC power system. In this paper, a comprehensive study on the performance of different voltage levels is proposed, in order to provide a voltage level guidance for DC power supplier and appliance manufacturer. Existing voltage selections from applications and researches are compared with each other in terms of voltage compatibility, topology, power supply capability and electrical safety. Comparative simulation is conducted in the residential DC power system model of a community with four household subsystems. Based on the simulation and investigation, the comparison result indicates that: 240V and 380V is optimal for the unipolar residential system with low power demand and ±375V bipolar DC system should be used in heavy load scenarios.
Extraction of ROM Data from Bitstream in Xilinx FPGA

Recently, many researches have investigated efficient reverse engineering methods to restore Programmable Logic Points (PLPs) and Programmable Interconnect Points (PIPs) in SRAM-based Field Programmable Gate Arrays (FPGAs). However, the research on the restoration of Programmable Content Points (PCPs) such as memory data are rarely studied. In this paper, we propose an efficient reverse engineering method to recover Read Only Memory (ROM) data, which is essential for the implementation of modern digital circuits. First, we analyze the FPGA hardware resources mapped to Xilinx primitive library of ROM, and next the proposed reverse engineering process is explained using mapping relation between ROM data and hardware resources. As an example, XC3S50 FPGA of Xilinx Sparatan-3 family is utilized, and the process of restoring the SBOX of AES (Advanced Encryption Standard) is provided as a practical application.
License Plate Recognition Algorithm Based on Particle Swarm optimization

A license plate recognition algorithm is designed based on character feature vector and particle swarm optimization. By means of vertical projection method, adaptive threshold scheme and directional gradient histogram method, the feature vectors of license plate characters are segmented and extracted. A character recognition accuracy model is established based on character feature vector sample and support vector machine, and a license plate recognition algorithm is established based on particle swarm optimization algorithm. Comparative numerical experiments show that the algorithm can effectively improve the accuracy of license plate recognition, and character feature vectors have a great impact on license plate recognition.