Miracle-dz/UniFAR-FTUs huggingface dataset:
Indoor Object Distance Measurement for Robots Based on YOLO and Depth Foreground Prediction

Object detection and distance measurement are core technologies for indoor robots. RGB-D cameras enable robots to accurately calculate the distance between objects. However, the irregular surfaces of small indoor objects, including curved surfaces and protrusions, can significantly affect distance measurement accuracy. To improve the estimation of distance, an effective method for obtaining accurate object sampling region is necessary. In this paper, we propose a unified architecture to obtain accurate depth values for objects in the scene and achieve high-precision distance measurement. Firstly, the YOLO-based object detection algorithm is utilized to identify objects in the scene and obtain bounding boxes for corresponding depth images. Then, a depth foreground prediction method is proposed to predict the foreground mask assigned to the target object. Finally, a sampling method based on the depth gradient is used to select a smoother region in the foreground depth value, and accurate distance values are calculated based on this region. Experimental results demonstrate the high accuracy and practicality of the proposed method.
Hyperspectral imaging for detection of scab in wheat

Scab (Fusarium head blight) is a disease that causes wheat kernels to be shriveled, underweight, and difficult to mill. Scab is also a health concern because of the possible concomitant production of the mycotoxin deoxynivalenol. Current official inspection procedures entail manual human inspection. A study was undertaken to explore the possibility of detecting scab-damaged wheat kernels by machine vision. A custom-made hyperspectral imaging system, possessing a wavelength range of 425 to 860 nm with neighboring bands 3.7 nm apart, a spatial resolution of 0.022 mm2/pixel, and 16-bit per pixel dynamic range, gathered images of non-touching kernels from three wheat varieties. Each variety was represented by 32 normal and 32 scab-damaged kernels. From a search of wavelengths that could be used to separate the two classes (normal vs. scab), a linear discriminant function was constructed from the best R((lambda) 1)/R((lambda) 2), based on the assumption of a multivariate normal distribution for each class and the pooling of the covariance error that averaged between 2 and 17%, dependent on wheat variety. With expansion to the testing of more varieties, a two-to-four wavelength machine vision system appears to be a feasible alternative to manual inspection.
Motion Planning to Cartesian Targets Leveraging Large-Scale Dynamic Roadmaps

In this paper, we achieve reliable, sub-second motion planning to Cartesian end-effector targets in changing real-world environments for a high degree-of-freedom $(\mathbf{12} + \mathbf{7}$ DoF), redundant robot. Several recent works have tackled a subset of these challenges, leveraging innovations in optimization, deep learning, and/or intelligent sampling. However, deployment of robots in real-world environments demands reliably tackling all three challenges: changing environments, fast planning, and high DoF robots. In this work, we leverage (1) large-scale Dynamic Roadmaps (DRM) enabled through GPU-accelerated collision-checking, combined with (2) an optimization-based local inverse kinematics (IK) solver. The high-level principle behind our approach is to offload as much computation either offline or onto the GPU as possible in order to simplify and speed up online planning. Even in changing environments, this enables fast planning in configuration space to a neighborhood of a target pose(s) specified in Cartesian space, whereby the final connection to the target pose is made via a local IK solver. We ran several experiments in an unmodified real-world grocery store with this motion planner on a $\mathbf{12} + \mathbf{7}$ DoF mobile manipulation robot executing a grocery fulfillment task, achieving ≈0.3s average planning times with 100% success rate across 950 motion plans.
Efficient Phase Equilibria Calculations in a Reduced Flash Context

Compositional models for hydrocarbon reservoirs become, every year, a better and better instrument for the evaluation of reservoir production. The phase equilibria calcuation represents one of the keys of the compositional simulator performance. Irrespective to the algorithm used to ensure the convergence toward the solution, all clasical models of two-phase flash calculation iterate on n independent variables (molar fractions, number of moles and logarithms of K-values), n being the number of components in the mixture
An Educational Game to Promote the Development of Computational Thinking in Children Both Neurotypical and with Intellectual Disabilities

Computational Thinking (CT) is a way to structure thinking to solve problems, which develops cognitive skills. Educational Digital Games (EDG) are a potential tool to work with CT, stimulating the student's interest and learning. Still, to make these games playable by children with Intellectual disabilities (ID), it is necessary to have adaptations suitable to their educational needs. Therefore, this paper presents the development and evaluation of the EDG “Pensar e Vestir” (PeV). This game addresses a daily life activity, the process of choosing clothes and the act of wearing them, and aims to promote the development of CT in children both neurotypical and with ID. The development process was supported by ID, Education, and Specialized Education Treatment specialists based on accessibility guidelines. In the end, there were two evaluations, firstly with the specialists and then with Computation students, to validate the functional and technical requirements. Currently, the game is in evaluation by its target audience.
DC Microgrid Applications and Their Control Techniques

Direct current (DC) power systems are gaining interest in the last decade due to increased utilization of DC outputted power sources, DC based energy storage (ES) elements and DC inputted loads. Microgrids are also becoming widely researched as the main foundation of smart grid. It is therefore logical to try to utilize DC microgrid (DCMG) concepts in organization of power systems in wide range of applications. DC microgrids have several important advantages compared to alternating current (AC) microgrids. The control system is essential in order to keep DCMG operating properly, reliable and efficient. Their control structures, with special interest in hierarchical control are explored and compared in this paper in terms of architecture and techniques. This paper presents real world applications using DCMG concept. Future research propositions given in the final chapter can be used as a foundation for researchers exploring the area.
Detection of Leaf Diseases in Agricultural Plants Using Machine Learning

Identifying plant leaves early on is key to preventing catastrophic outbreaks. An important study area is automatic disease detection in plants. Fungi, bacteria, and viruses are the main culprits in most plant illnesses. Choosing a classification method is always challenging because the quality of the results can differ depending on the input data. A few examples of several classification techniques include K-Nearest Neighbour Classifier (KNN), Probabilistic Neural Network (PNN), Genetic Algorithm, Support Vector Machine (SVM) and Principal Component Analysis, Artificial Neural Network (ANN), and Fuzzy Logic. Classifications of plant leaf diseases have many uses in various industries, including agriculture and biological research. Pre-symptomatic diagnosis and crop health information can aid in the ability to manage pathogens through proper management approaches. Convolutional neural networks (CNNs) are the most widely used DL models for computer vision issues since they have proven very effective in tasks like picture categorization, object detection, image segmentation, etc. The experimental findings demonstrate the proposed model’s superior performance to pre-trained models such as VGG16 and InceptionV3. The range of categorization accuracy is 76% to 100%, based on.
Comparison Analysis of Intrusion Detection using Deep Learning in IoT Networks

This study focuses on network security for Internet of Things (IoT) devices, which are particularly vulnerable to attack due to limited security measures. To protect IoT networks from attackers, the research implemented intrusion detection on the networks. The study compared several Deep Learning algorithms, including DNN, CNN, LSTM, and AE, to identify the most effective algorithm for solving network security problems using intrusion detection. The research used the UNSW-NB15 dataset for testing and employed binary classification for evaluation. The results showed that the DNN algorithm achieved an accuracy value of 99.76% and a loss value of 0.006%, outperforming the other algorithms. This study highlights the importance of implementing intrusion detection in protecting IoT devices and networks and demonstrates the efficacy of the DNN algorithm in detecting and preventing network security breaches.
Fault-Tolerant and System-Wide Communication for Metamorphic Robots

One of the future directions the robotic community deals with is the vision of reconfigurable and self-assembling robots. Such robots can assemble themselves into more complex configurations to take the shape that suits their mission the most. An exciting concept is a metamorphic robotic system in which all the modules serving as building blocks are uniform and small-scale. In such a system, the modules must be able to connect and disconnect from other modules dynamically using a connector that provides the modules with data and power connection on top of the physical binding. The limited physical size and computation resources prevent the usage of the standard existing network and power interfaces. While some solutions to connectors of metamorphic robots have been introduced at the hardware level, the software solutions are still quite incomplete. In this paper, we address a piece of the software part. We present an efficient and system-wide solution to inter-module communication. Moreover, we demonstrate and evaluate our solution on an existing metamorphic robotic platform.
Long-Distance Avian Identification Approach Based on High-Frame-Rate Video

Monitoring avian activities is an essential procedure for bird behavior research and pest damage management in agriculture. Existing bird detection and identification methods work well at near distances; however, they are ineffective at long distances. Herein, we propose an avian identification approach based on high-frame-rate video that can detect flying birds at a distance above 50 m. First, we detect birds' contours using an improved three-frame-difference method. Thereafter, we calculate the center point position of their contours and measure the displacement of their body. We then transform the body displacement values into a signal format and proceed with the empirical mode decomposition method and discrete wavelet transformation. Finally, the filtered signal is analyzed using Fast Fourier Transform for further frequency examination. We conducted an experiment to verify our proposed method by analyzing displacement signals recorded from three different species of birds. The result proves the feasibility of the proposed method.
Understanding the Stress of Online Learning Among University Students

As the pandemic Covid-19 erupted worldwide in 2020, the entire world rapidly adopted online education as a means of sustaining the education system in universities and schools. However, the majority of students were unfamiliar with these methods, as they were accustomed to traditional classrooms. Some students may prefer online education over traditional education, but online education stress remains a persistent issue. This study investigated the stress encountered by students during online learning sessions. 220 university students participated in this quantitative correlational study. On the basis of 11 items developed and tested using Confirmatory Factor Analysis, a questionnaire consisting of three constructs of online learning stress, financial capacity, and positive perception of online learning was developed and administered. The data were subsequently evaluated using the Structural Model in order to comprehend the relationships between the variables. The model was discovered to have a high level of fitness and validity. Positive perceptions of online learning were discovered to predict student stress negatively. The financial capacity of students to purchase the necessary devices for online learning sessions had no effect on their stress levels. However, financial capacity is the antecedent of how positively students viewed online learning for themselves. This suggests that for online education to be successful, it is crucial to consider the students' perceptions by evaluating their access to all the devices and technologies associated with online learning.
Aspects of Enhancing Loop Band Width for FVF based LDO Regulators : An Intuitive Analysis

High band width (BW) and fast slewing are key to the faster time domain performance of the low drop-out (LDO) voltage regulators. Voltage buffers act as an essential stage in improving bandwidth (BW) and stability by moving the pole appearing at the gate of bulky pass transistor away from the operating band of interest. This method is traded off with the quiescent current requirement of an LDO in low power IoT applications, with a potential threat over the desired regulation owing to the low loop gain. In this context, the approaches of extending the BW with voltage buffers are explored, and analysed in comparison with the emerging technique such as: a voltage combiner. The theoretical intuitive analysis regarding the BW is formulated and a relative comparison of based on the simulation of output impedance is performed. Different topology of LDO designs are simulated in the 45nm CMOS technology for the BW investigations and additional design freedom provided by the combiner block is explained.
Research on Generating Xi'an Drum Music Based on Generative Adversarial Network

Currently most AI music generator platforms mainly focus on creating modern music, without focusing on the creation of folk music. And most music generation neural network models use recursive neural networks and their variants, resulting in excessively repetitive music information, greatly reducing the fun of music. When using only generative adversarial networks (GANs) to generate music, it is easy to experience unstable training, vanishing gradients, and mode collapse. Failure to consider time dependence can reduce the authenticity of generated music. In order to solve the problem that most of the conventional deep learning GAN music generation network models do not consider music theory rules in music generation and Xi'an drum music faces the scarcity of tracks and difficulties in composition, we proposed a GAN network music generative model based on music theory rules and characteristics of Chinese Folk Songs. When generating music, the generator CNN and the regulator CNN train together, and generate music melodies under the constraints of the voice column CNN module. The generated results are sent to the discriminator CNN, which submits feedback to the generator CNN for competition, to compensate for the shortcomings of previous technology and ultimately generate better melodies, thereby improving the authenticity, sweetness, and rhythm of the generated music.
Static Friction Torque Estimation for Robot Manipulators Using a Data-Driven Approach

In analyzing dynamic multi-joint robots, the accuracy of model-based robot dynamics is significantly affected by various forces that are not adequately modeled in the joints. Among these factors, joint friction torque plays a crucial role. Numerous studies have focused on modeling joint friction, and this paper proposes joint friction models for multi-joint robots based on a data-driven approach. The key ideas are summarized into two aspects. First, comprehensive data acquisition is essential to utilize the data-driven model. This study covers a method to obtain data that includes comprehensive information which is insufficiently addressed in previous research. Second, previous studies using neural networks have only considered partial joint information. This paper proposes a model that utilizes joint velocity, torque, and temperature information together to develop a comprehensive friction model. The proposed friction torque models are verified using a real-world 6-axis collaborative robot.
Lidar-retrieved cloud and precipitation parameters

A multiple-scattering lidar technique is used to retrieve simultaneously the extinction coefficient and the effective particle diameter of clouds and precipitation. In addition, the linear depolarization ratio is measured to determine the liquid or solid phase of the particles. The reported measurements were made with a ground-based multiple-field-of-view (MFOV) lidar pointed at zenith. The lidar was fired in 10-s bursts, every minute for periods ranging from 30 min to 3 hours. The vertical resolution was selectable from 1.5 to 6 m but typically set at 3 m. The retrieved profiles are collected in the form of time-height maps of 1 min x 3 m resolution of the extinction coefficient, effective particle diameter, and depolarization ratio. Here, we analyze only the cloud layer and identify, from the statistics of the retrieved extinction coefficient and effective droplet diameter, important physical behaviors of water clouds. The results not only demonstrate the validity of the lidar retrievals but show that systematic lidar probings can yield significant information on cloud physics.
An Application of Data Prediction Model Based on CNN-BiLSTM Method

This article states that using convolutional neural network and bidirectional long short-term memory (CNN-BiLSTM ) data prediction model to analyse and predict the movement of some data in PABS DataSet. CNN-BiLSTM used convolutional neural network (CNN) to extracted spatial structure features of data, and then extract the input time series features by bidirectional long short-term memory (BiLSTM), finally, predict the data movement during a short time period. Experimental results show that comparing with convolutional neural network and long short term memory (CNN-LSTM), the CNN-BiLSTM data prediction model can predict more exact data movement due to lowest RMSE=0.1245 and highest R2 =0.9452. Therefore, CNN-BiLSTM data prediction model works well and can provide some references for other fields.
Research on Telecom Customer Churn Prediction Model Based on BA-BP Neural Network

Customer churn prediction is very important for telecom operators to achieve higher customer retention. This paper takes telecom customer data as the research object, and the research process is as follows: Firstly, the attributes of the cleaned data are filtered by Relief feature filtering method, and the individual BA bat is initialized by Hammersley sequence, and the dynamic mutation mechanism is introduced into the location update. Adaptive learning rate and flight speed are designed for BP neural network and bat algorithm respectively to improve the iterative speed and prediction accuracy of the combined algorithm. In the actual experiment, the Naive Bayes algorithm, BP neural network, bat-neural network (BA-BP) combination algorithm were used to train, and the results were analyzed by landscape comparison. After comparison, the accuracy of telecom customer churn model based on BA-BP combination algorithm can be improved to 97.43%. The precision (75.98%) and recall (80.45%) can meet the application requirements. Compared with the initial BP neural network, the iteration times of the combined algorithm is significantly reduced, which can provide reference for telecom enterprises to maintain the steady development of customer resources.
Enhancement in IoT through Custom Instruction Set Architectures and TinyML: Review

The Internet of Things or IoT means to the confluence of the network of interconnected devices, the technologies that enable communication among these devices, and between these devices and the cloud. IoT contains embedded CPUs for performing a variety of functions. The processors for enhancing IoT applications form an active area of research in the domain of Computer Architecture. Embedded processors are resource and power-constrained challenging the execution of deep learning and machine learning algorithms. One proposed solution in literature is TinyML (Tiny Machine Learning) to overcome this limitation. TinyML architecture for IoT aims to deliver low latency, and effective bandwidth usage, reinforce data security, enhance privacy, and save costs. TinyML enables IoT devices to perform ML computations to be performed in the edge and avoid frequent access to the cloud which increases the performance. This paper presents a review of processor designs that are designed for TinyML architecture. For addressing other important issues in IoT a review of custom ISAs (Instruction Set Architectures) developed is also presented in this review.
Exploring the Technical Advances and Limits of Autonomous UAVs for Precise Agriculture in Constrained Environments

In the field of precise agriculture with autonomous unmanned aerial vehicles (UAVs), the utilization of drones holds significant potential to transform crop monitoring, management, and harvesting techniques. However, despite the numerous benefits of UAVs in smart farming, there are still several technical challenges that need to be addressed in order to render their widespread adoption possible, especially in constrained environments. This paper provides a study of the technical aspect and limitations of autonomous UAVs in precise agriculture applications for constrained environments.
Adaptive Neural Network Tracking for a Magnetic Microrobot in Presence of Model Uncertainty and Unknown Disturbance

Magnetic microrobots have potential applications in many medical diagnosis and treatment applications due to the unique characteristics in biological fine manipulation, such as precise drug delivery and being able to be used in complex biological environments. Precise and robust trajectory tracking control of magnetic microrobots under complex disturbances and uncertain dynamic model is still one of the most important and challenging steps in achieving ideal applications. In this paper, an adaptive neural network control algorithm is proposed to address the uncertain dynamic parameters and complex external disturbances. The developed state feedback control strategy based on a radial basis function neural network (RBFNN) can effectively estimate the uncertainties online from the state and desired trajectories, rather than the model-based control. The controller require as little knowledge of system parameters as possible. Subsequently, the closed-loop stable system is guaranteed based on Lyapunov theory. Finally, the two experimental researches are conducted on our developed electromagnetic manipulation system to validate the effectiveness of the proposed controller.