Miracle-dz/UniFAR-FTUs huggingface dataset:
Autonomous Non-Technical Surgical Skill Assessment and Workload Analysis in Laparoscopic Cholecystectomy Training

Despite the fact that Minimally Invasive Surgery (MIS) offers undeniable clinical benefits, such as less tissue damage, smaller scars and faster recovery, it requires extensive training from the surgeons, including technical and non-technical skills. Coping with stress and distractions, maintinaing situation awareness, prompt decision making, advanced communication, leadership and teamwork are all essential in MIS. Workload-which represents the human effort to perform a task-shows a strong correlation with non-technical skills. In this paper, a MIS training experiment is introduced, developed to autonomously assess non-technical surgical skills based on sensory data (im-age and force). For this, a surgical phantom and adequate workflow were designed to simulate a stressful laparoscopic cholecystectomy tasks, such as peritoneum dissection and cystic artery clipping. The experiment included the simulation of an abrupt situation (cystic artery bleeding). 20 training session were recorded from 7 subjects (3 non-medicals, 2 residents, 1 expert surgeon and 1 expert MIS surgeon). Analysis of the surgical workload and autonomous skill classification based on surgical tool tracking and force measurements were presented. Workload was tested for the two groups (medical and control) with the Surgical Task Load Index (SURG-TLX) workload assessment tool. Unpaired t-tests showed significant differences between the two groups in the case of mental demands, physical demands and situational stress (p <0.0001, 95 % confidence interval (CI)), and also in the case of task complexity (p <0.05). There were no significant differences in temporal demands and distraction levels. Learning curve in workload was studied with paired t-tests; only task complexity resulted significant difference between the first and the second trials. Autonomous non-technical skill classification was done based on image data with tracked instruments based on Channel and Spatial Reliability Tracker (CSRT) and force data. Time series classification was done by a Fully Convolutional Neural Network (FCN), which resulted high accuracy on temporal demands classification based on the $z$ component of the used forces (85 %) and 75 % accuracy for classifying mental demands/situational stress with the $x$ component of the used forces validated with Leave One Out Cross-Validation (LOOCV). It suggests there are non-technical skills and workload components which can be classified autonomously with objectively measured data.
Implementing ERP through the Use of Mobile Technologies: A Case Study

Ion Mobility is a small tech startup company that has not been utilizing any software for asset and inventory management prior to the introduction of ERPNext due to the small size of the company that allowed for personal communication within the company and ensure keeping track of asset movements. As the company is continuously evolving and expanding, it is facing a number of asset and inventory management difficulties. In order to keep up with the accelerated growth of Ion Mobility it has decided to introduce asset and inventory management system and has chosen to solve problem by ERPNext implementation. The case study takes a look at the advantages of the open-source ERP system and describes how the company is approaching its implementation of it. The information that describes the implementation of the system in the company was collected by one of the authors who is the software engineer at the company working on the ERP itself. It was found out, that although it offers a cost-effective solution to inventory tracking, it requires significant efforts from the company and engineers to implement and adopt the system compared to mature systems such as SAP. The implementation and deployment of a portable and mobile ERP systems can contribute to the success of a company since it allows flexible and agile reaction to the challenges of today's changing business environment.
Signal Preprocessing and Artifact Removal for Brain-Computer Interfaces

Brain-Computer Interfaces (BCIs) have become an important research field in the past decades. Interest has grown in this area given the tremendous potential of BCI systems of restoring, replacing, improving, enhancing and supplementing the activity of the central nervous system. Moving from laboratory refined circumstances to portable neuroprosthetics poses great challenges on signal processing and artifact removal. This paper discusses some methods used in signal preparation and preprocessing and also some techniques for artifact removal from electroencephalogram-recorded brain activity.
Consensusability Margin Optimization for Second-Order Multiagent Systems With Communication Uncertainties

In this article, the <inline-formula><tex-math notation="LaTeX">$\mathcal {H}_{\infty }$</tex-math></inline-formula> optimization approach is used to study the consensusability margin optimization problems for distributed second-order sampled-data multiagent systems with communication uncertainties. The considered uncertainties are frequency-dependent and bounded in <inline-formula><tex-math notation="LaTeX">$\mathcal {H}_{\infty }$</tex-math></inline-formula> norms. Specifically, for both the state-based protocols with relative damping and absolute damping, this article attempts to answer two questions: 1) how to characterize the control parameters for achieving robust consensus; and 2) what is the maximal consensusability margin and how to find, if one exists, the parameter to achieve this optimal performance. It is shown that the consensusability margin optimization problems are constraint optimization problems, which are to be specified by specific problem parameters and can be discussed by designing the network complementary sensitivity function matrices of the closed-loop multiagent systems. Moreover, it is shown that the infimums of the <inline-formula><tex-math notation="LaTeX">$\mathcal {H}_{\infty }$</tex-math></inline-formula> norms of the network complementary sensitivity function matrices under both protocols are independent of network topologies.
Measurement-Based Analysis of a Non-Coherent MIMO Radar Network for Automotive Applications

In this work, we perform a measurement-based analysis of a four-module cooperative radar network for automotive applications. The radar network is mounted on the front bumper of a car and operates as a multiple-input multiple-output system with 192 channels that exploits spatial diversity. We present an analysis of four different real-world scenarios in which the car is moving and encounters both static and moving targets. The results focus on the behaviour of the network through a non-coherent signal processing that is capable of including the bistatic paths while reducing the complexity of the setup. We include quasi-monostatic and bistatic responses as well as a combined network response that fuses the information from all the modules. This approach offers robustness against miss detections and ghost targets as well as improvements in target identification.
Weld Image Recognition based on Deep Learning

In order to improve the automatic operation level of welding robot, which is very important for weld recognition, this paper proposes a method based on deep learning to identify and locate target welds for the determination of weld types and weld positions in images. Through the idea of classification and then segmentation, the three weld types used in this paper can be accurately segmented. Firstly, MobileNetV3, a lightweight network with a special bneck structure, is used to classify the three images, and SeGAN neural network is used to segment the weld images to obtain the results. In this paper, a few sample images are used for training, and then a higher accuracy is achieved by expanding the sample. The experimental results show that the accuracy of the classification results reaches 99.39%, which is 3.74% higher than that of VGG, and the accuracy of the positioning results can reach 95%, which proves the effectiveness of the method and has important significance in industrial automation welding.
Matlab GUI-based Tool to Determine Performance Metrics of Physical Unclonable Functions

This paper presents a MATLAB Graphical User Interface (GUI) based tool that determines the performance evaluation metrics of the physically unclonable functions (PUFs). The PUFs are hardware security primitives which can be utilized in several hardware security applications like integrated circuits protection, device authentication, secret key generation, and hardware obfuscation. Like any other technology approach, PUFs evaluation requires testing different performance metrics, each of which can be determined by at least one mathematical equation. The proposed tool (PUFs Tool) reads the PUF instances’ output and then computes and generates the values of the main PUFs’ performance metrics: uniqueness, reliability, uniformity, and bit-aliasing. In addition, it generates a bar code for each PUF instance considered in the evaluation process. The PUFs Tool is designed and developed using the app designer of MATLAB software 2021b.
Waveguide components analysis and design with customized hybrid mode-matching and 2D finite element methods

This work addresses the use of structured meshes composed of quadrilateral finite elements, instead of the classic unstructured meshes made of triangular elements. These meshes are used in the modal analysis of waveguides with a certain types of cross-sections, namely Manhattan-type, defined by polygons only presenting angles of 90° or its multiples. To achieve this, the computation of TE and TM modes is firstly considered, to show how structured meshes made up of quadrilateral-element cells with fewer degrees of freedom (d.o.f.) offer a higher rate of accuracy to calculate degenerate modes with the same cutoff wavenumber, since the use of triangular-element cells results in meshes which generally do not guarantee that the computed modes will yield that exact same eigenvalue. In the next step, the accuracy in computing waveguide modes that are achieved by using quadrilateral cells will be assessed in the analysis of actual devices. In this case, the mode matching (MM) method will be used to simulate a ridge waveguide bandpass filter where modes in their different cross-sections are computed with 2D-FEM using either structured (with quadrilateral cells) and unstructured (with triangular cells) meshes.
Anomaly Detection Model in APCS Using AutoML

This paper presents an approach to anomaly detection using automatic machine learning (AutoML) algorithms. For this purpose, a dataset containing both real and synthetic records of more than 2 million records was obtained. A preliminary exploratory data analysis (EDA) was performed, and the main attributes of the dataset, which can further influence the quality of anomaly detection, were considered. A histogram of the target variable distribution was constructed and analyzed, as well as a correlation plot of the variables. Using AutoML, the basic machine learning models for solving the classification problem were constructed, the top 3 models by quality metrics were selected and subsequently combined into an ensemble, where the MaxVoting method was used for prediction. A table of distributions of errors of the first kind and errors of the second kind was formed, and a conclusion on the definition of the minimum threshold of their errors for the output of the model in production was made.
Dynamics of Magnetic Relaxation in Spheromaks

The first attempts to get energy from the controlled fusion of two light atoms nuclei date back to the beginning of the fifties of the last century. The crucial difficulty to achieve this goal is that particles need to have a large amount of thermal energy in order to have a significant chance of overcoming the Coulomb repulsion. At such high temperatures the atoms are fully ionized conforming a plasma. Such a hot plasma can not be in contact with solid walls because it will be rapidly cooled down. Two main methods have been developed to confine plasmas: the magnetic confinement and the inertial confinement. Here we are concerned with the magnetic confinement approach.
Trustworthy Protection Technology for Industrial Internet

Industrial Internet is a new model and new format of industrial control, which realizes intelligent production, networked collaboration, personalized customization and service extension; it makes production control develop from layered, closed, local to flat, open, and global; information security issues May directly lead to functional safety failure. Facing the status quo of information security protection of the Industrial Internet of Things, this paper analyzes the security risks and protection requirements from the three aspects of equipment and control systems, data, and networks in the Industrial Internet, and proposes corresponding security protection technologies to ensure the system security and data of the Industrial Internet, security and cybersecurity.
Verification of the design methodology for configurable electromechanical systems

The purpose of the study was to verify the previously created methodology for designing electromechanical systems. This methodology is based on SysML language and artificial intelligence tools such as conceptual graphs and Bayesian networks, and involves the construction of an information design model that contains knowledge about the requirements for the system being designed and a knowledge base of available technical solutions. Further, such an information model is used for automatic logical inference of technical solutions according to the requirements specified by the designer. The proposed methods and tools for design automation make it possible to trace the requirements for electromechanical systems at all stages of design, and evaluate the robustness of technical solutions to changes in these requirements. For quick and efficient implementation of the proposed methodology in the practice of industrial design, it must be verified by evaluating the degree of efficiency, completeness, consistency and feasibility of the synthesized design solutions, the convenience of using artificial intelligence tools in the course of solving typical industrial equipment design problems. For this purpose, a configurable laboratory stand was built, which is a physical model of a simple workcell of an industrial manufacturing system. The stand performs the typical functions of a workpieces sorter, has a modular design, and can include the implementation of specified functional requirements by various technical means. Thus, it is possible to obtain different configurations of the sorting device having differences in the sequence of operations and technical properties. To automate the design of these configurations using the CoGui software tool, an information model was created that compares the possible system requirements for the stand with suitable variants of technical solutions. This model is used to automatically obtain descriptions of stand configurations. The performance and other properties of the resulting configurations were then tested on the operating equipment of the stand.
Circuit Techniques for Efficient SWIPT

The increasing development of IoT technologies has allowed the design of sensors enabling the simultaneous wireless information and power transfer (SWIPT), making compactness and miniaturization important prerogatives of this new paradigm. This paper presents the design of a SWIPT circuit that exploits a single circuital implementation for both energy harvesting purposes and signal demodulation, overcoming the need for designing two different dedicated circuit boards. The simulation results show about 50% of RF-to-dc power conversion efficiency with −10 dBm of input power and demonstrate the possibility to demodulate the received signal to retrieve the data coming from the same frequency-modulated Bluetooth low energy signal.
Visual Monitoring Intelligent System for Cardboard Packaging Lines

Nowadays, corrugated packaging is designed and manufactured to provide protection to products and goods when they are shipped. Packaging machinery is being broadly used by manufacturers in cardboard-based packaging production. The results of this machinery are disassembled packaging on pallets. Monitoring these palletized packaging is also recommended to optimize the logistic distribution inside a factory. In this paper, we present a visual monitoring system to perform visual control of palletized packaging workflow. Our system, based on low-cost sensors and deep learning techniques, facilitates the storage in warehouses. Our proposal detects and recognizes different type of cardboard packaging on pallets. Besides, the system counts the pallets of each type for helping to automatically address them on the roller conveyors. We have tested its behavior on a real scenario at Smurfit Kappa facilities as well as evaluated its precision-performance, which reaches an APIoU of 0.93 at 14 FPS using low cost cameras.
Measuring accurate Angle of Arrival of weak LoRa signals for Indoor Positionning

In this paper, we propose an Autocorrelation method for measuring the angle of arrival (AoA) of a weak LoRa signal. A weak LoRa signal has a negative SNR down to −20 dB. The objective is to detect a LoRa signal that operates at low transmission power (TX). Operating at low transmission power (TX) reduces power consumption and extends the battery life of LoRa devices. Besides, the transmission of weak signals strengthens the radio communication protocol, preventing an enemy device from accessing the location coordinates. The detecting algorithm consists of finding Autocorrelation peaks of the LoRa signal. We show that Autocorrelation peaks decrease when the signal is buried in the noise. However, using a large number of Fast Fourier Transform (FFT) will increase the Autocorrelation peaks and the signal-to-noise ratio (SNR). Once the peak of the LoRa signal is detected under the noise, the algorithm will calculate the AoA. All of the proposed algorithms are implemented using a Universal Software Radio Peripheral (USRP), Software Defined Radio (SDR) receiver with the help of GNU Radio software. We, therefore, believe that our Autocorrelation method can detect the LoRa signal accurately and measure the AoA at very low SNR in real_ time being usable for indoor positionning.
A PDR Heading Estimation Method Based on Motion Mode Recognition Using Adaptive UKF

Among indoor positioning systems, Pedestrian dead reckoning (PDR) system has been widely used for less requirement for expensive infrastructure or laborious surveys. Heading estimation is one of the important parts of PDR. There are two main sources for heading estimation. The gyroscope-based method suffers from error accumulation problem, while the method using magnetometer is vulnerable to magnetic disturbances. Therefore, a novel heading estimation method using adaptive Unscented Kalman Filter (UKF) is proposed in this paper, which fuse accelerometer, magnetometer and gyroscope on the basis of motion mode recognition. ZARU (Zero angular rate update) is utilized to estimate gyro biases and correct the gyro output based on pedestrian still/walking classification. Straight feature is then applied on the basis of straight/turning classification. Magnetometer is finally used to further reduce heading error. In addition, the adaptive adjustment mechanism of filter parameters based on the quality evaluation of measurements is designed in this paper to improve the applicability of the method to different speeds and people. Experiments have been conducted by four experimenters at three sites using two smartphones. During the experiments, the phone is waist-mounted or handheld. The results show that the 3σ positioning error of the proposed method is reduced by more than 55% compared with the gyroscope-based method and magnetometer-based method.
Large-scale 3D Point Cloud Semantic Segmentation with 3D U-Net ASPP Sparse CNN

3D geometric modelling of urban areas has the potential for further development, not only for 3D urban visualization. 3D point cloud, as 3D data commonly used in 3D urban geometry modelling, is needed to extract objects from point clouds to analyze urban landscapes. An automated method to analyze objects from the 3D point cloud can be achieved by using the semantic segmentation method. Unlike other segmentation tasks in 3D point cloud data, 3D urban point cloud segmentation has the challenge of segmenting different object sizes on various types of landscape contours with imbalanced distribution of the object. Therefore, this study modified 3D U-Net Sparse CNN by adding Atrous Spatial Pyramid Pooling (ASPP) as one of the modules in this model, called 3D U-Net ASPP Sparse CNN. The use of ASPP aims to get the contextual multi-scale information of the input feature map from the encoder part of U-Net. Furthermore, 3D U-Net ASPP Sparse CNN is implemented by using weighted dice loss as the loss function. The experiment result shows 3D U-Net ASPP Sparse CNN with weighted dice loss has achieved the best evaluation score in our experiment, with OA = 96.53 and mIoU = 63.59.
Lifelong Learning Repetitive Neuro-Controller

Over the past five years, almost every application of engineering has tried to implement an artificial neural network to improve performance in one way or another. The study of real neurons in the human brain on the other hand, which paved the way for such applications, has been ongoing for well over five decades. While significant progress has been made, artificial neurons fall well short of the capabilities of real neurons in some key areas, such as the ability to perform lifelong learning. This limitation can be traced back to fundamental differences in network architecture and overall implementation. To remedy this, attention must first be directed back to the foundational science behind how the human brain acquires, stores, uses, and selectively removes specific memory or knowledge. Next, novel architectural concepts and implementation strategies to address the aforementioned limitations must be developed. Here, this will be done through careful considerations of all aspects within the repetitive regime. The human brain, by design, is very reliant on continual lifelong learning to solve problems. Control strategies used today, artificial neural networks, and even combinations of both are unable to optimally engage in this fundamental process. This paper attempts to bridge the gap and push toward enhanced lifelong learning control strategies for future use.
The Tessi Clergy Algorithm Inspired by Potential Gaming Behavior in the Hunt for Geo-data

The paper constructs a geo-knowledge/genetic algorithm model based on mathematical methods, which always links the behavior time, behavior distance, and geo-data acquisition quality of all mobile patterns with the overall geo-knowledge evolution process. For the demand of the Tessi missionary groups (geo-data carriers) to acquire geo-data and the response of the local decision-making group, we combine the quality evaluation of the map artifacts after data alignment, realize the discrimination of the dominant strategy, partial game, complete conflict game and natural game in their data game process, deduce the game strategies in the knowledge evolution process of data acquisition, data processing, data management, data protection and data competition. The model is used to describe the above behaviors algorithmically and to explain the extent of knowledge evolution behaviors at the macro level, as well as to provide a new reference solution for computational modeling of games in complex social computing using coupled object-event-process data.