Miracle-dz/UniFAR-FTUs huggingface dataset:
Fruit Tree Disease Recognition Based on Convolutional Neural Networks

In order to realize the rapid and accurate recognition of fruit tree diseases in orchard environment, this paper puts forward a deep learning model based on Convolution Neural Network to identify fruit tree diseases. In this paper, the data set is processed by the Sobel operator and image enhancement respectively. Then, the network depth, convolution kernel, feature maps, and fully connected layer in the Convolution Neural Network structure use different parameters and softmax classifier. Differently composition networks are used to train processed dataset. Convolution Neural Network models are used to predict test sets, and the results show that deeper Convolution Neural Networks and mean pooling for tiny features in the dataset are more accurate. It can achieve the disease recognition, which includes cab disease, black rot, rust of apple leaves and bacterial spot disease of peach tree leaves. The model has a good recognition function for disease identification of fruit trees and can help real-time monitoring of orchard diseases.
Wireless Power Transfer for Toys and Portable Devices

Powering electronic toys by replacing chargeable batteries or using wires are common technique, although these techniques are unsecure for the children. Providing an alternative source to power the toys is a pressing demand for kid safety. Transferring power to toys/portable devices wirelessly is an attractive and desirable technique due to eliminating the risks of replacing batteries or using cords for the toys. In this paper, a wireless power transfer (WPT) system is designed and built, to deliver power from a stationary source (primary coil) to a moving toy/portable device (secondary coil) via magnetic resonant coupling. The moving toy/device must be able to move freely, a novel primary coil design is proposed to cover the required space for the toys. Three-dimensional finite element analysis COMSOL software is used to investigate the magnetic flux density distribution of three different primary coil designs. The simulation results show the novel design of the primary coil that distributes the electromagnetic field uniformly at any location for the moving toy. A planar spiral PCB coil is designed as a secondary coil to provide the required power to the toys. Experiments are conducted to measure the received power by the secondary coil wirelessly and the maximum system efficiency is 4.9%. The transferred energy powers the electric motors of the car toy/portable devices.
Designing a Low-Cost and Portable Infusion Pump

This document describes the prototyping of a low-cost and portable infusion pump, which is used to provide a certain dose of a drug in certain time. This device uses the principles of electronics, mechanics and fluid mechanics to drive a syringe-based infusion pump, which delivers the prescribed amount of drug in required time to the patient. The designed device can deliver drug up-to 1 ml/hour with an adjustable range. The device detects and maintains flow of drug according to the input provided by the user. The most prominent feature of the device is its ability to detect bubble in the liquid. Whenever an air bubble is detected in the device it will instantly stop the flow. It is a low-cost and portable device which can be operated on a 12-volt battery and directly from the power socket. The cost of this prototype is only about $200 which will further diminish if the units are produced on an industrial scale.
Revisiting the Feld’s Friendship Paradox in Online Social Networks

Feld’s friendship paradox is a widely accepted observation that states that the friends of any particular individual tend to have more friends on average than the individual has. Due to its implications for information transmission, the friendship paradox has become a generalized paradigm in many disciplines. However, how many of an individual’s friends have more friends than the individual does is still unknown, yet interesting. In this paper, we revisited the Feld’s friendship paradox and we found that only a limited number of a person’s friends have more friends than the person herself has. This conclusion was reached from empirical studies using real-world networks and is true regardless of the number of one’s neighbours, which contradicts the intuitive deduction from the friendship paradox. For one thing, if a person is unpopular, the number of her friends that have more friends than she does grows with the number of friends that she makes. This observation crystallizes the tenable margin of the friendship paradox. In another case, if a person is popular, as she acquires more friends, fewer of her friends have more friends than she does. This finding suggests an observation bias in the friendship paradox, which makes individuals feel less popular than their friends. Besides enriching our knowledge of the friendship paradox in psychological science, the findings reported here are also beneficial for technical areas such as large-scale triangle discovery. Although the friendship paradox was proposed from the perspective of the number of neighbours, the findings reported here can shed light on theories and applications from different disciplines like information, happiness, obesity, and extroversion, to name a few.
Learning-Based Fingertip Force Estimation for Soft Wearable Hand Robot With Tendon-Sheath Mechanism

Soft wearable hand robots with tendon-sheath mechanisms are being actively developed to assist people with lost hand mobility. For these robots, accurately estimating fingertip forces leads to successful object grasping. An approach can utilize information from actuators assuming quasi-static environments. However, non-linearity and hysteresis with regards to the dynamic changes of the tendon-sheath mechanism hinder accurate fingertip force estimation. This paper proposes a learning-based method to estimate fingertip forces by integrating dynamic information of motor encoders, wire tension, and sheath bending angles. The model is modified from Long Short-Term Memory by incorporating a residual term that governs the dynamic changes in sheath bending angles. Using a tendon-driven soft wearable hand robot, the proposed model obtained RMSE less than 0.44 N. It was further evaluated under criteria ranging from different object sizes, bending angle ranges, and forces. Finally, a repeatability test (0.46 N in RMSE), real-time applicability (125 Hz), and force control (12.7% in MAPE) were performed to verify the feasibility of the proposed method.
Pedestrian Motion Trajectory Prediction With Stereo-Based 3D Deep Pose Estimation and Trajectory Learning

Existing methods for pedestrian motion trajectory prediction are learning and predicting the trajectories in the 2D image space. In this work, we observe that it is much more efficient to learn and predict pedestrian trajectories in the 3D space since the human motion occurs in the 3D physical world and and their behavior patterns are better represented in the 3D space. To this end, we use a stereo camera system to detect and track the human pose with deep neural networks. During pose estimation, these twin deep neural networks satisfy the stereo consistence constraint. We adapt the existing SocialGAN method to perform pedestrian motion trajectory prediction from the 2D to the 3D space. Our extensive experimental results demonstrate that our proposed method significantly improves the pedestrian trajectory prediction performance, outperforming existing state-of-the-art methods.
Deep Inductive Matrix Completion for Biomedical Interaction Prediction

In many real tasks, side information in addition to the observed entries is available in the matrix completion problem. To make good use of this information, an inductive approach to matrix completion was proposed where the matrix entries are modeled as a bilinear function of real-valued vectors associated with the rows and the columns. However, it is not effective in handling data of nonlinear structures. In this paper, we propose a novel model called Deep Inductive Matrix Completion (DIMC) for nonlinear inductive matrix completion, which consists of two deep-structure neural networks to extract latent features from high-dimensional known side vectors, and then to predict their relationships using the latent features. In DIMC, the parameters of the neural networks are alternatively optimized to minimize the reconstruction error. Then the missing entries can be readily recovered with the side vectors of rows and columns. We compare DIMC with state-of-the-art methods of linear and nonlinear matrix completion in the tasks of drug repositioning, gene-disease and miRNA-disease association prediction. The experimental results verified that DIMC is capable to provide higher accuracy than existing methods and is applicable to predict inductively on new row-column interactions with auxiliary side information. In addition, we discuss the effects of alternating training frequency on the performance of DIMC and how we can utilize such property to implement a GPU-based parallel computing algorithm that significantly shortens the training time.
Real-Time Human Detection and Tracking Using Two Sequential Frames for Advanced Driver Assistance System

Real-time human detecting and tracking is an important task in Advanced Driver Assistance System (ADAS) especialy in providing an information about situation in front of vehicle. Deep Convolutional Neural Networks (CNN) is one algorithm that is widely applied to classify and detect objects. CNN has shown an impressive performance. However, the high computation of Deep CNN makes the algorithm difficult to be applied to the real ADAS system. Since 2014, the One-stage Detector approach such as SSD and YOLO began to be applied on devices with low computation. In this experiment, we present a real-time system for the detection and the tracking of humans (pedestrians, cyclists, and riders) for the ADAS system implemented in Raspberry Pi 3 Model B Plus. The object detection approach in this study applies the SSD framework, and the tracking human movements approach is done by calculating the movement of midpoint coordinates from bounding box objects from two sequenced frames. The result shows the realtime human detection and tracking on Raspberry Pi 3 B devices with input frame with a height 300 and a width 300 runs at 0.8 FPS with 77.6 percent processor consumption and 70.3 percent memory. Therefore, the use of Raspberry Pi 3 B Plus for human detection and tracking in ADAS systems is not suitable for the vehicle speeds above 50 Km per hour when runs at 0.8 FPS. Then the tracking system based on the coordinate movement of the midpoint bounding box has a problem when there is a bounding box overlapping or slicing each other
Predicting Immune Cell Composition Using Linear Programming

A large data set is used to infer the fraction of immune cells using gene expression data. A model should be developed to infer the fraction of immune cells from a small set of genes used in cell surface marker experiments. We present a model created by linear programming (LP) by simplex method. To evaluate the accuracy of the algorithm, we created a simulated data set and evaluated its performance against the digital cell quantization (DCQ) algorithm. Finally, we applied our LP method to the systemic lupus erythematosus (SLE) patient dataset and examined the differences from healthy controls.
A Module-Level Pipeline Implementation Based on Inter-Board Heterogeneous

With the internet of things, autonomous driving, and smart security rapid development, data growth puts higher and more urgent requirements on processor computing performance. A single chip is hard to meet the real-time computing requirements in many scenarios, so it is necessary to expand multiple chips. This paper proposes a module-level pipeline FPGA cluster structure based on inter-board heterogeneous. The method divides a single enormous task into multiple modules and running each module on different FPGA as the pipeline structure between boards. In addition, considering the impact of communication delay between boards, it is proposed to use the delay between development boards as a separate stage of the pipeline. In this way, we can minimize a stage’s runtime of module level pipeline. In order to evaluate the calculate capacity of per hardware resource, the concept of resource conversion efficiency (RCE) is proposed. Experimental results of LeNet -5 neural network with the module-level pipeline structure design demonstrates that proposed method increased processing capacity and resource utilization efficiency. Compared with the method of sequential execution, the execution speed and RCE of the proposed method are increased by 46.1% and 84.7% respectively.
Comparison of Decision Tree, Naïve Bayes and K-Nearest Neighbors for Predicting Thesis Graduation

Thesis is one of the evaluations of learning for students. In Universitas Budi Luhur (UBL), especially in the Informatics Department, the thesis is one of the requirements for graduating students to obtain a Bachelor of Computer degree. In each semester, the number of Informatics Department students who take thesis is around 200–300 students. The problem that is still faced is that student graduation in the thesis is not optimal. Student failures in the thesis are allegedly related to several technical and nontechnical factors. In this study, an analysis using data mining algorithms was carried out to determine the factors that influence student graduation in the thesis. The dataset obtained from the Informatics Department students who took a thesis in the 2016/2017, and 2017/2018. In order to obtain the right classification method, this research was tested with three classification methods, namely Decision Tree, Naïve Bayes, and k-Nearest Neighbors (kNN). The results of the comparison of the values of accuracy, precision, and recall indicate that the kNN algorithm has advantages, so this method is chosen to predict graduation. In this study also developed an application for predicting graduation of students' thesis by applying the kNN classification method. The test results showed an accuracy of 78.20%, precision of 80.32%, and recall of 96.49%. This research is expected to be useful for improving the service quality of student thesis.
Visual-inertial SLAM aided estimation of anchor poses and sensor error model parameters of UWB radio modules

Local positioning technologies based on ultrawideband (UWB) ranging have become broadly available and accurate enough for various robotic applications. In an infrastructure setup with static anchor radio modules one common problem is to determine their global positions within the world coordinate frame. Furthermore, issues like the complex radiofrequency wave propagation properties make it difficult to design a consistent sensor error model which generalizes well across different anchor setups and environments. Combining radio based local positioning systems with a visual-inertial navigation system (VINS) can provide very accurate pose estimates for calibration of the radio based localization modules and at the same time alleviate the inherent drift in visual-inertial navigation. We propose an approach to utilize a visual-inertial SLAM system using fish-eye stereo cameras and an IMU to estimate the anchor 6D poses as well as the parameters of an UWB module sensor error model on a micro-aerial-vehicle (MAV). Fiducial markers on all anchor radio modules are used as artificial landmarks within the SLAM system to get accurate anchor module pose estimates. Index Terms-MAVs, mobile robots, SLAM, UWB, radio localization, sensor calibration
A Smart Irrigation System to Automate Irrigation Process Using IOT and Artificial Neural Network

In the today's world we are growing towards the automation of day to day daily life task in which we are focused on creation of smart cities, smart home etc. which includes a lot of work which deals with automation of task using Artificial Intelligence, Internet of Things and machine Learning. These technologies are widely used to creates a system for automation of different tasks. In this technology driven society there is a need of creation of a system to automate the process of irrigation of crops to help farmers. The currently used system includes a lot of human efforts, money and time which will not be required after the development of an automated irrigation system. In this there is a need of creating a system which can collect data then analyze that date and generate results in real time. also, the overall cost should be low enough so that a farmer can afford it. In this paper we are focusing on creating a low-cost smart device which can be used to collect the data of moisture in soil at different places, pH and temperature of soil. We focus of creating a device with Arduino along with some sensors and modules to collect these data at different places in farm. After collecting this data this device will send this data to server with the help of a Wi-Fi module attached to the device. At the same time, we are using an Artificial Neural Network model for the analysis of this data for generating high accuracy results. Finally based on the results the device triggers a valve to automatically start and stop the flow of water in the farm.
SDN/NFV-based M-CORD for Achieving Scalability in Deploying NB-IoT Gateways

The deployment of IoT (Internet of Things) gateways opens new challenges for 5G networks to meet the rapid increase in IoT devices, services, and protocols at a high scalability level. To deal with those challenges, recently, NB- Narrow Band IoT (NB-IoT) gateway and M-CORD (Central Office Re-architected as a Datacenter for mobile networks) have been considered as key technologies for developing 5G architectures. In this study, we first propose a comprehensive architecture integrated NB-IoT gateways and M-CORD to meet diversified requirements of 5G and IoT services. Next, Software Defined Network (SDN) and Network Function Virtualization (NFV) are applied to build various virtual network functions (VNFs) on M-CORD Mobile Edges. And then, based on these functions, we perform offloading functions of an NB-IoT gateway to an M-CORD Mobile Edge. After that, a network QoS control application is deployed to perform on demand QoS control for multi-tenancy IoT services with different requirements. Finally, several experiments are implemented on a real NB-IoT gateway and our testbed environment, and the results are also presented.
Performance Analysis of Various FINFET Threshold Logic based Full Adder Design

Full adder is the basic building block of all computing systems. Various Full adder circuits have been presented here, which are uniquely designed using FINFET based Threshold Logic gates. These Full Adders consist of Ganged/ output wired FINFET based Inverter, Capacitive output wired logic (COWL) and Beta-driven threshold logic circuits. The designed circuits are compared on several important parameters e.g., time delay, power consumed by an unit cell and the number of transistor used in a cell. Here FINFET based simulations have been carried using LTSPICE for 45nm and 32nm technology. Some interesting properties have been observed in the simulations. Capacitive output wired logic based full adder cell shows the best performance with respect to transistor count, delay, average power and power delay product.
Research on Network Node Interaction Protocol in Ubiquitous Power Internet of Things

The rise of the ubiquitous power internet of things has promoted the vigorous development of new power services, diversified power communication equipment, and embedded devices have also been widely used throughout the entire power communication network. Reasonable network interaction and verification analysis techniques are improved. An important way to apply reliability and work efficiency. This paper first analyzes the definition, development process and characteristics of ubiquitous power internet of things, and summarizes the necessity of communication network communication in all aspects of power system under the circumstance of power internet of things; based on this, from the two directions of model interaction and data interaction The interactive content, interaction format and interaction form of the power communication network are studied. Then, the modeling requirements of the component interaction protocol of the embedded software are analyzed, and the interactive protocol model supported by the database is proposed. Finally, based on the interaction protocol model, the business is studied. The node interaction protocol controls the formal conversion of the flow, thereby improving the efficiency and reliability of the interaction of the power communication network, realizing the wide application of the embedded device, and providing a theoretical basis for the development of the power communication network under the ubiquitous power internet of things environment.
Maintenance of DC Link Voltage using Synchronous Reference Frame Theory

This paper implements a control strategy based on d-q axis theory or synchronous reference frame theory (SRFT) for the control of power electronic converter connected to a grid. The control objectives of this paper are to develop a Simulink model using SRFT as the controller and to maintain a constant dc link voltage for any variation in load and for any type of load. A constant DC link voltage is required for the synchronization of input side (generating side) and output side (grid side) and this is done using the SRFT controller as presented in the paper. The control technique SRFT as presented is preferred due to ease of implementation and provision of instantaneous compensation of the reactive power.
Cross Attention Densely Connected Networks for Multiple Sclerosis Lesion Segmentation

Automatic lesion segmentation on conventional magnetic resonance imaging is an essential component in disease diagnosis, assessment, and follow-up. Recently, extensive deep neural networks have been designed for automatic lesion segmentation. However, these approaches are not easy to further optimize owing to the poor interpretability. In this paper, we present a novel cross attention densely-connected network(CA-DCN) for multiple sclerosis lesion segmentation, which integrates attention mechanism into the encoder-decoder architecture. Aiming for further improving the performance of the model, we propose a comprehensive cross attention mechanism module by combining the characteristics of spatial and channel domains. Our method is evaluated on the public International Symposium on Biomedical Imaging (ISBI) 2015 multiple sclerosis segmentation challenge. At the time of submission, our method was amongst the top performing solution.
Analysis of Factors Affecting the Loyalty of Using Online Health Services: Case Study of Alodokter

This study aims to analyze the factors affecting the continuity intention and loyalty in using Alodokter services. This research uses quantitative approach with a total of 375 respondents who have used Alodokter at least once. The data is processed and analyzed using the Partial Least Square-Structural Equation Modelling (PLS-SEM) method and SmartPLS version 3 tools. The result of this study indicates that perceived usefulness, perceived ease of use, satisfaction, and trust in application affect continuance intention of using Alodokter services, as well as satisfaction and trust in application affect loyalty of using Alodokter services.