Miracle-dz/UniFAR-FTUs huggingface dataset:
Transcriptional Responses of Beauveria bassiana Blastospores Cultured Under Varying Glucose Concentrations

Culturing the entomopathogenic fungus, Beauveria bassiana, under high glucose concentrations coupled with high aeration results in a fungal developmental shift from hyphal growth to mostly blastospores (yeast-like cells). The underlying molecular mechanisms involved in this shift remain elusive. A systematic transcriptome analysis of the differential gene expression was preformed to uncover the fungal transcriptomic response to osmotic and oxidative stresses associated with the resulting high blastospore yield. Differential gene expression was compared under moderate (10% w/v) and high (20% w/v) glucose concentrations daily for three days. The RNAseq-based transcriptomic results depicted a higher proportion of downregulated genes when the fungus was grown under 20% glucose than 10%. Additional experiments explored a broader glucose range (4, 8, 12, 16, 20% w/v) with phenotype assessment and qRT-PCR transcript abundance measurements of selected genes. Antioxidant, calcium transport, conidiation, and osmosensor-related genes were highly upregulated in higher glucose titers (16-20%) compared to growth in lower glucose (4-6%) concentrations. The class 1 hydrophobin gene (Hyd1) was highly expressed throughout the culturing. Hyd1 is known to be involved in spore coat rodlet layer assembly, and indicates that blastospores or another cell type containing hydrophobin 1 is expressed in the haemocoel during the infection process. Furthermore, we found implications of the HOG signaling pathway with upregulation of homologous genes Ssk2 and Hog1 for all fermentation time points under hyperosmotic medium (20% glucose). These findings expand our knowledge of the molecular mechanisms behind blastospore development and may help facilitate large-scale industrial production of B. bassiana blastospores for pest control applications.
Task Scheduling of High Dynamic Edge Cluster in Satellite Edge Computing

In a traditional satellite mission processing mode, the data is transmitted to the ground data center for processing. However, with the expansion and enrichment of satellite services, the huge amount of data leads to longer data transmission time and greater bandwidth pressure. Furthermore, time-sensitive satellite missions do not respond in a timely manner. In recent years, the emergence of mobile edge computing (MEC) provides a new mode for users to utilize the computing power of network edge side nodes for data processing. At the same time, the satellite information network has realized the interconnection of low-orbit satellites, realized the integration of computing power of dispersed satellites, and provided enough computing power to assist the processing of satellite big data tasks and time-sensitive tasks. It can be seen that introducing the mobile edge computing model into the satellite information network has become an effective way to solve the above problems. In this paper, we first introduce a satellite moving edge computing framework and analyze the particularity of the scene. Secondly, we modeled the task scheduling problem of mobile edge computing clusters in the satellite dynamic environment and proposed a satellite task time optimization scheduling algorithm based on dynamic priority queue (SDPLS). Experiments show that the algorithm can adapt to highly dynamic satellite network conditions, shorten the task response delay, and realize the efficient utilization of satellite cluster computing resources.
Speed Ripple Minimization for PMSM Based on Complex Vector Analysis and Iterative Learning Control

Inevitable speed and torque ripples caused by flux harmonics and current sampling errors affect the control performance of PMSM. In this paper, a closed-loop current ripple analysis method is first proposed with complex vector. Based on this, the transfer functions are derived. The results are analyzed under different conditions and compared with the traditional method. Then, a rotor position based P-type iterative learning control (ILC) with forgetting factor is utilized to minimize the ripples. Moreover, an online zero-phase-lag filter is designed to reduce the influence of high frequency harmonics and noise in the sampled signals. The experimental results validate the effectiveness of the proposed method.
Symbolic Execution for Network Functions with Time-Driven Logic

Symbolic Execution is a commonly used technique in network function (NF) verification, and it helps network operators to find implementation or configuration bugs before the deployment. By studying most existing symbolic execution engine, we realize that they only focus on packet arrival based event logic; we propose that NF modeling language should include time-driven logic to describe the actual NF implementations more accurately and performing complete verification. Thus, we define primitives to express time-driven logic in NF modeling language and develop a symbolic execution engine NF-SE that can verify such logic for NFs for multiple packets. Our prototype of NF-SE and evaluation on multiple example NFs demonstrate its usefulness and correctness.
Programmable Dictionary Code Compression for Instruction Stream Energy Efficiency

We propose a novel instruction compression scheme based on fine-grained programmable dictionaries. In its core is a compile-time region-based control flow analysis to selectively update the dictionary contents at runtime, minimizing the update overheads, while maximizing the beneficial use of the dictionary slots. Unlike in the previous work, our approach selects regions of instructions to compress at compile time and changes dictionary contents in a fine-grained manner at runtime with the primary goal of reducing the energy footprint of the processor instruction stream. The proposed instruction compression scheme is evaluated using RISC-V as an example instruction set architecture. The energy savings are compared to an instruction scratch pad and a filter cache as the next level storage. The method reduces instruction stream energy consumption up to 21 % and 5.5 % on average when compared to the RISC-V C extension with a 1% runtime overhead and a negligible hardware overhead. The previous state-of-the-art programmable dictionary compression method provides a slightly better compression ratio, but induces about 30 % runtime overhead.
Optimization of Hoop Layouts for Reducing Vibration Amplitude of Pipeline System Using the Semi-Analytical Model and Genetic Algorithm

The pipeline system on the outside of aero-engine needs to work in resonance environment in some cases, therefore, it is necessary to reduce the vibration amplitude of pipeline systems to improve the reliability in the dynamic design stage. In this paper, a single pipeline system with multi-hoop supports was taken as the object and a method based on genetic algorithm to realize the layout of hoops and effectively reduce the resonance amplitude of pipeline system was proposed. Considering that the system belongs to the statically indeterminate structure, a new semi-analytical model was developed, that is, the pipeline is modeled under free boundary conditions firstly, and then the hoop is introduced into the pipeline system in the form of spring-damping structure. Meanwhile, in the process of modeling, to improve the analysis accuracy of the model, the non-uniform distribution springs were used to simulate the support stiffness of hoops, and the uniformly distributed dampers were used to simulate the support damping of hoops. Taking the position of the hoop as the design variable, the optimization model of the hoop layout with minimizing the maximum vibration amplitude of pipeline systems as the optimization objective was established, furthermore the optimization solution process of the hoop layout based on genetic algorithm was given. Finally, a case study was carried out to verify the rationality of the proposed semi-analytical model and the optimal hoop support positions were obtained by the proposed optimization model and method.
CMOS Transformer Design for X-band Power Amplifier Applications

This paper presents transformer models to facilitate matching network design for power amplifiers (PAs). Both T-model based and magnetically coupled resonator (MCR) based transformer matching network design techniques are introduced for X-band PA applications. Using these two techniques, transformers for input/output balun and inter-stage matching are implemented using a standard CMOS technology. A fully integrated X-band PA is designed to verify the proposed design techniuues.
A 1-D Electronic Beam-Scanning Monopulse Antenna Array Based on a Compact Stripline Phase-Shifter

A completely passive method of enabling 1-D beam-scanning for a monopulse antenna array with controllable sidelobe levels (SLLs) is presented in this letter. To achieve this unique function, a compact frequency-dependent phase-shifter composed of stripline resonators is proposed. This proposed phase-shifter provides similar sensitivity as conventional meander striplines, while its size is much smaller, enabling a successful implementation into a highly integrated feeding network with monopulse functions. The validation is provided by a 4 × 16 monopulse antenna array operated at X-band. A good correspondence is achieved between simulation and measurement for both S-parameter and far-field results. Measurement results of the prototype indicate a scanning range from −54° (10.5 GHz) to 48° (11.5 GHz) can be covered. Within the entire operational frequency range, the sidelobe levels are smaller than −15 dB for the sum channel, while a null depth better than −20 dB is observed in the difference channel. The proposed method is characterized by control simplification and very suitable to be deployed in cost-effective tracking systems.
HelipadCat: Categorised Helipad Image Dataset and Detection Method

We present HelipadCat, a dataset of aerial images of helipads, together with a method to identify and locate such helipads from the air. Based on the FAA’s database of US airports, we create the first dataset of helipads, including a classification by visual helipad shape and features, which we make available to the research community. The dataset includes nearly 6,000 images with 12 different categories.We then train several Mask-RCNN models based on ResNet101 using our dataset. Image augmentation is applied according to learned augmentation policies. We characterize the performance of the models on HelipadCat and pick the best-performing configuration. We further evaluate that model on the metropolitan area of Manila and show that it is able to detect helipads successfully, with their exact geographical coordinates, in another country. To reduce false positives, the bounding boxes are filtered by confidence score, size, and the presence of shadows. Dataset and code are available for download.
Historical Places & Monuments Identification System

Sri Lanka, which is known as “the pearl of the Indian ocean” provides great survival and civilization history dating back to the 3rd century. Most of the archaeological sites are attracted by not only Sri Lankans but also by tourists. When searching for the information about the archaeological sites, there are lack of trusted information sources and smart online platforms. Even though some information is available, no convenient and efficient ways to retrieve them. When trusted information is provided in a user-friendly manner, the value will be added to the Sri Lankan economy. Since the world is driving towards the "E-Era", everything is involved with Information Technology. The proposed system contributes to solve the above problems with Artificial Intelligence & Machine Learning concepts. The system is assisted using four major components namely, image identification, community platform, conversational bot, and image visualization. The image Identification component identifies the archaeological sites using image processing techniques. The community platform gathers trusted information from archaeologists and deep learning techniques are used to deliver that content to the users. The artificial intelligence conversational bot is established to communicate and retrieve available information in a convenient manner. The image visualization component is used to provide reality visualization on archaeological sites using the augmented reality techniques. The techniques and the algorithms are evaluated to deliver better performance with brilliant user experience.
ELMs and sawteeth with ICRF heating on ASDEX Upgrade

The ASDEX Upgrade ICRF system has recently been completely equipped with 3-dB couplers. They reduce, in a purely passive way, the sensivity of the ICRF system to fast coupling changes as occur in the presence of ELMs. They also make the matching less critical and allow variations of the plasma conditions, even with fixed matching during a discharge. The 3-dB couplers have operated as expected and permitted full power operation of the system (5.7 MW at the antenna, 6.8 MW generator output) as well as routine operation at 4 MW under a variety of conditions. Using those new capabilities, it was possible to compare, at high power, the ELM and sawteeth behavior of various ICRF heating scenarios (minority heating, direct electron heating and mode conversion heating) and neutral injection heating. Large differences were found in the sawtooth behavior, but no differences in the ELM behavior. The sawteeth are very sensitive to the localization of the power deposition as well as to the distribution of power between electrons and ions, while ELMs seems to be sensitive to the total power only.
Comparative Analysis of Anthropometric Variables in Female Athletes and Non-athletes

Background:Anthropometry refers to the study of physical characteristics and measurements of human body. The objective of this study is to find the anthropometric differences between Indian female athletes and female non-athlete. It also portrays how sports bring changes in a female’s body composition and build up.Methods: Primary data was collected with the help of a set of questions from 40 female athletes and 40 female non-athletes. All women taken for this study is between 18 – 30 years. Seven variables were considered for collection and comparison of data; these are height, weight, BMI, body fat percent, waist, hip and chest circumference.Conclusion:Comparative study between female athletes and female non-athletes show how sports changed their body composition. The analysis was done by using t test for height, hip, chest and Mann Whitney U test was used for analyzing weight and waist. The study showed that female athletes are much fitter and less prone to physical and mental problems.
Opera Singing Voice Synthesis Considering Vowel Variations

In order to improve a synthesized opera singing voice that is obtained using deep neural networks (DNN), we focus on the vowel variations of the opera singing voice. Variations in the F0 of the vowels of the opera singing voice are analyzed using k-means clustering. Then, from the analysis results, a new score feature for a DNN-based voice synthesis system is introduced as a one-hot vector. The objective evaluation results show that the use of the new score feature improves the quality of the synthesized opera singing voice.
Hybrid Nonisolated Active Quasi-Switched DC-DC Converter for High Step-up Voltage Conversion Applications

This paper deals with a hybrid non-isolated active quasi-switched dc-dc converter with a high-boost voltage gain, which is applicable for the high step-up low power applications. By using fewer number of components in circuit topology, the proposed converter can provide higher-gain voltage with a small duty cycle, which can reduce the voltage stress and conduction loss on power switches. In addition, it draws continuous input current, has lower diode voltage stress and lower passive component voltage ratings. The operating principles and key waveforms in continuous conduction mode and discontinuous conduction mode are presented. Small-signal dynamic analysis, parameter design guideline, power loss calculation, and characteristics comparison with other non-isolated converters have been completed. Finally, a 250W hardware prototype is constructed and the experimental results are presented to verify the feasibility of the proposed converter.
Predicting Remaining Useful Life with Uncertainty Using Recurrent Neural Process

Recently deep learning based remaining useful life (RUL) prediction approaches have gained increasing attention due to their scalability and generalization ability. Although deep learning based approaches can obtain promising point prediction performance, it is not easy for them to estimate the uncertainty in RUL prediction. In this paper, a recurrent neural process model is proposed to address the prognostics uncertainty problem based on deep learning. Compared with the original neural process model, a recurrent layer is added to extract sequential information from input sliding windows. The RUL prediction problem can be considered as finding a regression function mapping the sliding window input to its corresponding RUL. By obtaining the distribution over the regression functions, the recurrent neural process is able to model the probability distribution of the RUL. As a probabilistic model, stochastic variational inference and reparameterization trick is applied to learn the parameters of the model. The proposed method is validated through the C-MAPSS turbofan engine dataset.
Alignment-Free Offline Calibration of Commercial Optical See-Through Head-Mounted Displays With Simplified Procedures

Despite the growing availability of self-contained augmented reality head-mounted displays (AR HMDs) based on optical see-through (OST) technology, their potential applications across highly challenging medical and industrial settings are still hampered by the complexity of the display calibration required to ensure the locational coherence between the real and virtual elements. The calibration of commercial OST displays remains an open challenge due to the inaccessibility of the user’s perspective and the limited hardware information available to the end-user. State-of-the-art calibrations usually comprise both offline and online stages. The offline calibration at a generic viewpoint provides a starting point for the subsequent refinements and it is crucial. Current offline calibration methods either heavily rely on the user-alignment or require complicated hardware calibrations, making the overall procedure subjective and/or tedious. To address this problem, in this work we propose two fully alignment-free calibration methods with less complicated hardware calibration procedures compared with state-of-the-art solutions. The first method employs an eye-replacement camera to compute the rendering camera’s projection matrix based on photogrammetry techniques. The second method controls the rendered object position in a tracked 3D space to compensate for the parallax-related misalignment for a generic viewpoint. Both methods have been tested on Microsoft HoloLens 1. Quantitative results show that the average overlay misalignment is fewer than 4 pixels (around 1.5 mm or 9 arcmin) when the target stays within arm’s reach. The achieved misalignment is much lower than the HoloLens default interpupillary distance (IPD)-based correction, and equivalent but with lower variance than the Single Point Active Alignment Method (SPAAM)-based calibration. The two proposed methods offer strengths in complementary aspects and can be chosen according to the user’s needs. We also provide several update schemes for the two methods that can be integrated for an on-line viewpoint-dependent refinement of the calibration parameters. Both methods have been integrated into a Unity3D-based framework and can be directly applied to Unity-assisted devices.
Computer Aided Classification of Benign and Malignant Breast Lesions using Maximum Response 8 Filter Bank and Genetic Algorithm

Breast Cancer has very low survival rate when detected at later stages. Hence, Computer Aided Diagnostic Models can assist the medical practitioners by diagnosing reports without human intervention. In this paper the sample size considered is 401(200 benign and 201 malignant images) and has been acquired from the Digital Database for Screening Mammography. This paper propose a novel method to detect breast malignancies using texton based analysis. The filter bank that has been used here to obtain the texton based response is the Maximum Response 8 Filter Bank. Further Haralick’s features from the Gray Level Co-occurence Matrix , histogram based features from Local Binary Pattern and statistical features namely skewness and kurtosis are extracted from each filter response. Genetic Algorithm and Linear Discriminant Analysis has been used for feature selection and feature reduction respectively. Classification is performed using three classifiers namely Naive bayes, Logistic Regression and Linear SVM. The proposed algorthm exhibit an Accuracy of 87.5% and Area under Curve of 0.95 using Logistic Regression classifier.
Myocardial Infarction Localization and Blocked Coronary Artery Identification Using a Deep Learning Method

Myocardial Infarction (MI) is a fatal heart attack that causes millions of death in the world every year and the mortality of this disease is extremely high. Therefore, the precise diagnosis and the instant alert of MI is vital for the patients in hospital. In this paper, we have provided a deep learning method using Lenet to determine whether there is MI in the patient and the necrosis region of MI, from which the model can also provide the prediction of the blocked coronary artery. The accuracy of the model using single lead of ECG independently can achieve more than 99% and even the precision, sensitivity and specificity can exceed more than 99%, which outperforms most of the algorithms.
Design Approach for a Microstrip Yagi Antenna With a Switched Beam Using Resonant TM10 and TM20 Modes

In this paper, a design approach for a microstrip Yagi antenna with a switched beam using resonant TM10 and TM20 modes is proposed. The antenna consists of a driven patch and two parasitic patches. Initially, a driven patch radiator with two symmetric probes is investigated to determine whether TM10 or TM20 mode of this patch can be generated by properly exciting the dual feeding ports. The operating bands of the dual modes are reallocated in close proximity to overlap with each other by introducing the shorting pins and narrow slots. Then, two smaller-sized parasitic patches are placed on the right side of the driver to achieve switchable tilted-beam radiation patterns. The results demonstrate that the smaller-sized parasitic patches act as either directors or reflectors when their TM10 mode or TM20 mode is excited, respectively. Therefore, the quasi-end-fire beams can be satisfactorily generated and steered in the E-plane by switching these two radiative modes by feeding probes. Finally, a proposed antenna is designed and measured. The measured results show that the antenna operating in TM10 and TM20 modes can achieve a common band of 3.03–3.19 GHz, and their beams are oriented at the directions of +27° and −42° in E-plane patterns, respectively. Most importantly, no RF switches are introduced in our design, thus significantly reducing the complexity and cost of the proposed antenna.