Miracle-dz/UniFAR-FTUs huggingface dataset:
Scattering Effect on Optical Performance of Quantum Dot White Light-Emitting Diodes Incorporating SiO₂ Nanoparticles

The scattering effect plays an important role in improving the optical performance of the quantum dot (QD) white light-emitting diodes (LEDs). A majority of the previous studies have focused on the planar packaging structure only with respect to total internal reflection (TIR). In this study, SiO2 nanoparticles are incorporated into QD-silicone encapsulation of LEDs with semi-spherical lens packaging (SSLP) to exploit their scattering effect. The results show that the radiant efficacy (31.35%@100 mA) and luminous efficacy (87.56 lm/W@100 mA) of QD white LEDs can be optimized using SiO2 nanoparticle concentration (0.1 wt%) and that they increase by 5.04% and 11.08%, respectively, as compared to the conventional structure. A comprehensive ray-tracing simulation validates that the nanoparticles in the SSLP structure lead to severe loss of chip light. On the contrary, the fluorescence light increases due to the enhancement of conversion by QDs. The transmission electron microscopy images and the finite-difference time-domain simulation have been introduced to investigate the surface adsorption of SiO2 nanoparticle. This study indicates that SiO2 incorporation is an effective method to improve the efficiency of QD white LEDs, and provides a better understanding on the scattering effect based on TIR, color conversion, and surface adsorption.
Improving Semantic Segmentation of 3D Medical Images on 3D Convolutional Neural Networks

A neural network is a mathematical model that is able to perform a task automatically or semi-automatically after learning the human knowledge that we provided. Moreover, a Convolutional Neural Network (CNN) is a type of neural network that has shown to efficiently learn tasks related to the area of image analysis, such as image segmentation, whose main purpose is to find regions or separable objects within an image. A more specific type of segmentation, called semantic segmentation, guarantees that each region has a semantic meaning by giving it a label or class. Since CNNs can automate the task of image semantic segmentation, they have been very useful for the medical area, applying them to the segmentation of organs or abnormalities (tumors). This work aims to improve the task of binary semantic segmentation of volumetric medical images acquired by Magnetic Resonance Imaging (MRI) using a preexisting Three-Dimensional Convolutional Neural Network (3D CNN) architecture. We propose a formulation of a loss function for training this 3D CNN, for improving pixel-wise segmentation results. This loss function is formulated based on the idea of adapting a similarity coefficient, used for measuring the spatial overlap between the prediction and ground truth, and then using it to train the network. As contribution, the developed approach achieved good performance in a context where the pixel classes are imbalanced. We show how the choice of the loss function for training can affect the final quality of the segmentation. We validate our proposal over two medical image semantic segmentation datasets and show comparisons in performance between the proposed loss function and other pre-existing loss functions used for binary semantic segmentation.
Cascaded Modular Converter With Reduced Output Voltage Ripple

In this paper, two cascaded modular converter configurations are compared: CMC and MLC. The non-isolated version offers a simpler construction, control method, better modularity and better power management architecture. The isolated version is considered better in a photovoltaic (PV) application as it can handle the difference in irradiance conditions i.e. due to partial shading without affecting the symmetry of its output voltage and current. Combining the advantages of differential-mode converters and multilevel inverters, CMCs can produce naturally filtered output voltage and current with lower voltage stress across its components while allowing for better power management. Moreover, a modified switching pattern is introduced to further reduce the sizing of the passive components (LC filters). This is done by shifting the triangular carrier associated with each dc-dc module which results in much lower average output ripples. Thus, lower switching frequency and / or converter size can be achieved. A brief description of the converters construction and operation is introduced. Then, Matlab / Simulink model is constructed to validate the converters ability to produce lower output ripples.
Comparative Study of Computer Vision Models for Insect Pest Identification in Complex Backgrounds

Agriculture is considered the economic basis of countries around the globe, and the development of new technologies contributes to the harvesting efficiency. Autonomous vehicles are used in farms for seeding, harvesting and tasks like pesticide application. However, one of the main issues of any plantation is insect pest and disease identification, essential for pest control and maintenance of healthy plants. This work presents and compares three methods for insect pest identification using computer vision: Deep Convolutional Neural Network (DCNN), as a baseline; Hierarchical Deep Convolutional Neural Network (HD-CNN), in order to improve prediction of similar classes; and Pixel-wise Semantic Segmentation Network (SegNet). They were tested for two kinds of culture, soybean and cotton. SegNet outperformed both approaches by a wide margin: the methods had respective accuracies of 70.14% DCNN, 74.70% HD-CNN and 93.30% SegNet.
A $43.8\mu \mathrm{W}$ per Channel Biopotential Readout System using Frequency Division Multiplexing with Cable Motion Artifact Suppression

This paper presents a fully-integrated biopotential readout system using frequency division multiplexing (FDM) for general purpose, multi-channel biopotential signal acquisition. FDM reduces the number of required cables between active electrode and back-end readout, and frequency translation prior to transmission mitigates low-frequency motion artifacts and mains interference in the cable. The 4-channel EMG/ECG architecture carries all channels over a 3-wire interface and attenuates low-frequency cable motion artifacts by 15X and 60 Hz mains noise coupled into the cable by 62X. The IC is fabricated in 180 nm CMOS, including both front-end active electrode and back-end demodulation architectures; each 1 Hz-150 Hz, differential active electrode channel occupies 0.75 mm2 and consumes 43.8 $\mu \mathrm{W}$.
Determinants of learning motivation from the perspective of Self determination theory

From the perspective of Self - determination theory, student engagement is considered self-regulated learning since it manifests behaviourally through a permanent focus upon the main objectives, attendance, concentration, time management, positive collaboration with teachers and peers. The present research is an application of Self-Determination Theory (SDT) through which we try to identify the relation between students' motivation (students’ motivation being conceptualized as a continuum having five aspects: amotivation, external, introjected, identified and intrinsec), individual traits (self-efficacy) and the characteristics of the learning environment: perception upon the learning environment (support of autonomy, support of competence and social relatedness).
Optimal Prosumer Asset Planning Considering Energy Storage and PV Units

The use of renewable energy sources and battery energy storage system (BESS) has increased among customers in the distribution system, transforming traditional loads into active prosumers. Therefore, methodologies are needed to provide prosumers with tools to optimize their investments and increase business opportunities. In this paper, a mixed integer linear programming (MILP) formulation is proposed to solve for optimal sizes of prosumer assets, considering the use of a BESS and photovoltaic (PV) units. The objective is to minimize the total cost of the system which is defined as the combination of solar PV system investment, BESS investment, maintenance costs of assets, and the cost of electricity. The developed method defines optimal sizes of PV units, power and energy capacities of the BESS, and the optimal value for initial energy stored in the BESS. The proposed model was tested for three cases. The three cases provide a varying combination of the use of grid power and the use of prosumer assets. The optimal values from each case were compared with each other and the results show that there is potential to achieve more economic plans for prosumer asset sizes.
Analyzing Indoor/Outdoor Environmental Effects with Varying Cell Size on 5G Millimeter-Wave Propagation

In this paper the millimeter-wave cellular communication is analyzed and simulated for the fifth-generation (5G) communication using broadband statistical spatial channel model (SSCM). This model is simulated in NYUSIM developed by research group in New York University which support the wideband carrier frequencies from 500 MHz to 100 GHz with bandwidth in the range of 0 MHz to 800 MHz. Microcells, beamforming and other similar techniques are needed to reduce the losses as the millimeter-wave are unable to travel over longer distances. Millimeter-wave frequency of 28 GHz is initially considered here and its effects on communication due to different environmental parameters such as temperature, relative humidity and rain rate in cities like Hyderabad, Karachi and Islamabad are observed while varying cell sizes. Parameters like received power, pathloss and pathloss exponent are analyzed to predict best suitable cell size in different cities environments.
Cascade U-ResNets for Simultaneous Liver and Lesion Segmentation

In recent years, several deep learning networks are proposed to segment 2D or 3D bio-medical images. However, in liver and lesion segmentation, the proportion of interested tissues and lesions are tiny when contrasting to the image background. That is, the objects to be segmented are highly imbalanced in terms of the frequency of occurrences. This makes existing deep learning networks prone to predict pixels of livers and lesions as background. To address this imbalance issue, several loss functions are proposed. Since no researches are having made a comparison among those proposed loss functions, we are curious about that which loss function is the best among them? At the same time, we also want to investigate whether the combination of several different loss functions is effective for liver and lesion segmentation. Firstly, we propose a novel deep learning network (cascade U-ResNets) to produce liver and lesion segmentation simultaneously. Then, we investigate the performance of 5 selected loss functions, WCE (Weighted Cross Entropy), DL (Dice Loss), WDL (Weighted Dice Loss), TL (Teverskry Loss), WTL (Weighted Teversky Loss), with our cascade U-ResNets. We further assemble all cascade U-ResNets trained with different loss functions together to segment livers and lesions jointly on the liver CT (Computed Tomography) volume. Experimental results on the LiTS dataset1 showed our ensemble model can achieve much better results than every individual model for liver segmentation.1https://competitions.codalab. org/competitions/17094#
Flexible Circuits Based on Aluminum Conductor and Nonwoven Substrate

Electronic textiles, integrating functional electronics circuits into fabric materials, are emerging as an important branch of flexible circuits. In this paper, we introduce a novel material combination for electronic textiles that can be used in implementing hybrid electronics. This type of circuits is fabricated by laminating patterned aluminum foils onto a nonwoven substrate in a high-speed roll-to-roll method. An isotropic conductive adhesive and an anisotropic conductive adhesive are used to assemble standard surface mount device components onto the fabricated circuits. The surface mount techniques are characterized by means of contact resistance measurements, component bonding strength tests, circuit bending tests, and scanning electron microscopy. An NFC tag with relative humidity sensing functionality is fabricated to evaluate the fabricated circuits to an electronic system level.
A Parametric Technique for Trap Characterization in AlGaN/GaN HEMTs

A new parametric and cost-effective technique is developed to decouple the mechanisms behind current degradation in AlGaN/GaN high-electron mobility transistors (HEMTs) under a normal device operation: self-heating and charge trapping. Our unique approach investigates charge trapping using both source (<inline-formula> <tex-math notation="LaTeX">${I}_{\text {S}}$ </tex-math></inline-formula>) and drain (<inline-formula> <tex-math notation="LaTeX">${I}_{\text {D}}$ </tex-math></inline-formula>) transient currents for the first time. Two types of charge-trapping mechanisms are identified: 1) bulk charge trapping occurring on a timescale of less than 1 ms and 2) surface charge trapping with a time constant larger than a millisecond. Through monitoring the difference between <inline-formula> <tex-math notation="LaTeX">${I}_{\text {S}}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">${I}_{\text {D}}$ </tex-math></inline-formula>, a bulk charge-trapping time constant is found to be independent of both drain (<inline-formula> <tex-math notation="LaTeX">${V}_{\text {DS}}$ </tex-math></inline-formula>) and gate (<inline-formula> <tex-math notation="LaTeX">${V}_{\text {GS}}$ </tex-math></inline-formula>) biases. Surface charge trapping is found to have a much greater impact on slow degradation than bulk trapping and self-heating. At a short timescale (<1 ms), the RF performance is mainly restricted by both bulk charge-trapping and self-heating effects. However, at a longer time (>1 ms), the dynamic ON-resistance degradation is predominantly limited by surface charge trapping.
Desert Plants Recognition by Bark Texture

Recognition of the desert plants is a challenging task for human as well as computers due to the similarities between these plants. We propose a novel method for recognizing of desert plants by the images of the bark. We extract the features of the texture of the bark using Weber Local Descriptor (WLD), we build a dataset of bark images for desert plants, this dataset consists of 1660 bark images for five species of the desert plants, these species are Palm Dates, Mimosa Scabrella, Sidr, Lemon and Pomegranate. We test three classifiers ANN, SVM and KNN on this dataset and the resulted accuracies are 99.7%, 98.8% and 98.0%, respectively. Performance of ANN is very high when compared to SVM and KNN classifiers, hence ANN can be adapted for recognition of the desert plants.
Comparing and analysing binary classification algorithms when used to detect the Zeus malware

The Zeus banking malware is one of the most prolific banking malware variants ever to be discovered. This paper examines and analyses the Support Vector Machine (SVM), Decision Tree and Random Forest machine learning algorithms when used in conjunction with a manual feature selection process to detect Zeus network traffic. Selecting the features manually provides the researcher with more control over which features that can and should be selected. The manual feature selection process will also allow the researcher to analyze the impact of the various features and then select the features that provide the best accuracy results during the classification and detection of Zeus. The algorithms in scope for this research are the Decision Tree algorithm, Random Forest algorithm and the SVM algorithm.
Analysis of activity of muscles acting on ankle joint in sitting and standing from chairs of different heights

When a person sitting on a chair stands and sits from the standing position the weight transfers from spine to ankle and from ankle to spine. Most significant joint loads occur during stand to sit and sit to stand movement than any other activity. Slight changes in loads on the knee joint, ankle joint and the lumbar spine are linked with greater angular displacement at knee, spine and hip joints. The movement of body segments with respect to each other moves the body center mass which causes greater forces in the joints. Incorrect simple activities contribute to increased loads which lead to increase in strains and inflammation. Tibialis anterior - dorsi- flexor of the ankle and soleus - plantar-flexor of the ankle are the important muscles required for all leg activities. The activity of these two muscles were monitored in this study. The subjects were asked to sit and stand from a chair of height 17.5 cm and a podium of height 12.5 cm which can be considered as a chair shorter than an ideal chair. An accelerometer mounted on the cervical spine was used to determine the position of the subjects. The EMG data epochs were classified using the accelerometer signal. Our study result shows that there is a significant difference in muscle activity between chair and podium while performing sit to stand actions and stand to sit actions observed from EMG signal analysis.
Tunable Multi-Modal Locomotion in Soft Dielectric Elastomer Robots

Soft robots require strong, yet flexible actuators for locomotion and manipulation tasks in unstructured environments. Dielectric elastomer actuators (DEAs) are well suited for these challenges in soft robotics because they operate as compliant capacitors and directly convert electrical energy into mechanical work, thereby allowing for simple design integration at a minimal footprint. In most demonstrations, DEA-based robots are limited to a single mode of locomotion, for example crawling, swimming, or jumping. In this work, we explored a range of actuation patterns in combination with a novel actuator design to enable multi-modal locomotion, whereby an actuation pattern is defined by an actuation voltage (proportional to the applied electric field) and frequency (the actuation rate). We present a DEA robot capable of three different gaits including crawling, hopping, and jumping. In addition, our robot can set itself upright by performing a roll, for example to prepare for the next jump after landing on its side. These results demonstrate that DEAs can be used as versatile experimental devices to validate locomotion models, in both natural and engineered systems.
Transport Mechanism of B/P Codoped Armchair Graphene Nanoribbons

Based on the nonequilibrium Green’s function method combined with density functional theory, a nanoscale tiny diode is designed by doping B and P in armchair graphene nanoribbons (AGNRs). Moreover, the mechanism of its rectification may be explored by the analysis of transmission spectrum and energy band. The analysis of simulation data shows that the relationship between rectification and transmission peak at −0.25 eV is very close, and the broadening and disappearance of the peak may be explained by the local device density of states (DOS) and electrostatic difference potential. In addition, the eigenstates of the highest occupied molecular orbital and the lowest unoccupied molecular orbital under different bias voltages also explain the change of the transmission spectra. Moreover, the occurrence of rectification also can be ascribed to the difference of effective mass between <inline-formula> <tex-math notation="LaTeX">${E}_{v}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">${E}_{c}$ </tex-math></inline-formula> and distinctive position of <inline-formula> <tex-math notation="LaTeX">$\pi $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\pi ^\ast $ </tex-math></inline-formula> in band structure.
A Novel Approach for Early Prediction of Drought

Droughts are prolonged period of deficient rainfall leading to shortages of water in a region. Droughts are declared when the total rainfall received in a region is less than 50% of average rainfall. Hence, it is necessary to predict such a natural hazard which affects 42% of the country's land area and affects 300 million people in India. This system aims to provide an early prediction of drought in regions of Maharashtra and their preventive suggestions. The most affected regions of Maharashtra are Marathwada and Vidarbha. However, currently no such system exists for drought prediction in Maharashtra. This system aims to increase the accuracy of predictions. This study uses precipitation data to compute Standard precipitation index (SPI) which is a probability index used to ascertain abnormal wetness or dryness in a region. The system will predict whether drought will occur or not in a particular region and also aims to provide suggestions for drought management.
Current Status and Future Trends of GaN HEMTs in Electrified Transportation

Gallium Nitride High Electron Mobility Transistors (GaN HEMTs) enable higher efficiency, higher power density, and smaller passive components resulting in lighter, smaller and more efficient electrical systems as opposed to conventional Silicon (Si) based devices. This paper investigates the detailed benefits of using GaN devices in transportation electrification applications. The material properties of GaN including the applications of GaN HEMTs at different switch ratings are presented. The challenges currently facing the transportation industry are introduced and possible solutions are presented. A detailed review of the use of GaN in the Electric Vehicle (EV) powertrain is discussed. The implementation of GaN devices in aircraft, ships, rail vehicles and heavy-duty vehicles is briefly covered. Future trends of GaN devices in terms of cost, voltage level, gate driver design, thermal management and packaging are investigated.
Geospatial Technology: The Rising Sun on Banking and Economic Horizon

Geospatial Technology (GT) has come to be deeply rooted in a civilized society. Right from helping the common commuter to bypass traffic in rush hours [13] to weather forecasts, spotting hidden natural wealth, and to measure the level of economic activity, it has broadened the horizon of possibilities and provided interpretation to obscure secrets of nature. Over the last decade and a half, economists have sizably grown in number as a class of the users of geospatial data to innovate, evolve and design new ways of measuring and managing growth. Their growing understanding and application of geospatial technology has revolutionized banking and the economic world. Sensory images from satellites are easily available in public domain and there has emerged a class of sophisticated remote sensing users in the field of economics. The development is welcome as it goes deeper and enables a renewed look at the growth dynamics with an eye on discovering some fresh, though dormant,latentwith the potential to reenergize the global economy particularly when the traditional growth drivers seem exhausted and fading out. However, the data explosion brought in by the surge in geospatial technology will remain largely untapped in the absence of a parallel growth in the art of knowledge management particularly in converting the raw data to useful information and applicable knowledge. Thecapability of customizing and decodinggeospatial input and output, prescribing solutions and making calculated predictions for the future is yet to bloom to its potential.