Miracle-dz/UniFAR-FTUs huggingface dataset:
Magnetic Localization for an Intra-abdominal Wireless Laparoscope

In single-incision laparoscopic surgery, the mag-netically driven laparoscope is a promising technique to provide larger workspaces and field of view, relief space for other instruments, and require less incision. To apply closed-loop control which guarantees safety and precise motion, a reliable localization is critical during the operation. In this paper, a real-time magnetic localization method is proposed for a wireless magnetically driven laparoscope. Integrated with two pairs of magnets, the robotic laparoscope is shown to have sufficient coupling force to work in a region where the mapping from capsule pose to magnetic field measurement can be accurately expressed by two single dipole models. Combining multiple sensor feedback, the pose of the laparoscope in six degrees of freedom is obtained at 30Hz, while the position and yaw angle are solved based on Newton-Raphson iteration directly without suffering from measurement drift. The average error is 2.9mm in position detection, and below 5.5° for angular motion.
Design of Mesochronous Dual Clock Fifo Buffer with Modified Synchronizer Circuit

In today's substantially scaled technologies, Systems-on-Chip (S oC) suffer from slow wiring as well as process, voltage, temperature (PVT) fluctuations. All these difficulties making synchronous abstractions across larger chips regions more impractical, requiring a significant amount of design work in multiple author approaches to guarantee temporal closure. Because of various phase differences, the transmitter and receiver in this system, which are connected to the mesochronous interface and receive a clock signal, operate at a different frequency. Clock synchronizations are necessary in certain circumstances while sending data across units. The proposed mesochronous FIFO can be developed in a modular manner to support multicycle link delays with less changes to baseline structure. Proposed design consists of only Delay flip-flop and some basic gates to avoid the meta-stability state in FIFO. The delay flip-flop provided sufficient delay to avoid meta-stability state. These synchronizers are need for communication between Tx and Rx side to correctly transfer the data but with some delay. This type of implementation of synchronizers will reduce the hardware complexity of the design compared to the existing method. The proposed method implemented in Xilinx ISE 14.7 version tool and synthesize of the proposed design mesochronous FIFO that use the cadence genus tool with 45nm technology for delay, power and area reports. The proposed design of mesochronous dual clock FIFO buffer with modified synchronizer circuit achieves a power consumption improvement of approximate 10.32% for 64bit mesochronous FIFO.
The Effect Frequency Characteristics of the Series Active Filter with a Voltage-positive Transformer Power Circuit on Quality of Filtering

The article is devoted to study the effect frequency characteristics of the series active filter with a voltage-positive transformer power circuit on quality of filtering. Therefore, the work's purpose is to study the frequency characteristics of the power circuit and the filter-compensating device. A study of its frequency characteristics was carried out according to the scheme power circuit of the network using the state space theory. When analyzing the obtained data, a decrease in the power circuit efficiency was noted when the frequency increased. Using the four-pole method, the power active filter frequency characteristics were investigated. The obtained results analysis showed that when increasing the frequency, to maintain the active filter input current at a given level, it is necessary to increase the filter input voltage. Also, the obtained data show that when increasing the frequency, in order to maintain the filter output current, which, taking into account the magnetizing current, is related to the current at the filter input, at a given level, measures must be taken to force the voltage at the compensator output.
Mechanical Systems of Rigid Bodies Subject to Unilateral Constraints

The properties of mechanical systems of rigid bodies subject to unilateral constraints are investigated. In particular, properties of interest for the digital simulation of the motion of such systems are studied. The constraints give rise to discontinuities in the solution. Under general assumptions on the system a unique solution is constructed using the linear complementarily theory of mathematical programming. A numerical method for solution of these problems and generalizations of the constraints studied in this paper are briefly discussed.
Towards the suitability of gait wearable signal processing for long term recognition

One of the present approaches to gait recognition exploits the signals captured by wearable sensors, especially the accelerometers embedded in modern smartphones. However, the different speed, the ground slope, or simply the time lapse between captures cause variations that negatively affect long term recognition in a dramatic way. The proposed procedure aims at extracting gait characteristics that are as invariant as possible, and therefore useful for accurate long term recognition. The experiments compare the performance of the proposal with others in state-of-the-art that use the same benchmark, namely the ZJU-gaitacc dataset. This dataset includes a high number of samples per subject, captured in two time-separated sessions. This allows to assess the performance of the proposed method also in the long term, i.e., when comparing templates captured in different times. Most works using the same benchmark so far have not exploited both sessions. They use samples captured in the same time, constraining the use of this trait to continuous recognition, e.g., of the smartphone owner. The obtained results testify that, in this condition, the proposed feature-based method outperforms competitors in the current literature. The experiments also compare the results from a session-based partition with those obtained from a training that mixes-up samples from different sessions. As expected, the latter strategy can dramatically improve the measured performance. The significantly different results seem to suggest that the session-based partition, when feasible, can provide more realistic results, closer to the real-world application context when behavioural traits are involved in the medium/long term. The same results seem also to testify that there is still need to improve the accuracy of gait recognition via wearable sensors. This calls for further investigation of the problems related to the variability over time in the pattern of individual gait signals.
Air Quality Monitoring and Prediction using SVM

All countries in the world are facing the major problem of Air Pollution. All countries are coming across with various ways of finding solution to deal with air pollution and reduce the number of pollutants in air over several years. Several types of respiratory problems such as heart diseases and lung cancer are caused by air pollution. So it is the utmost need to find solution to monitor the index of air quality as it is always better to know the level of pollution at the earliest so that preventive measures can be taken. The amount of pollution present in the air is generally termed as Air Quality Index which is abbreviated as AQI. The air quality monitoring system is proposed in this paper which makes use of prediction module for forecasting AQI. The proposed system monitors the air quality over a period and predicts the AQI for upcoming 15 hours. This paper provides Support Vector Machine (SVM) model to forecast the quality of air with AQI for the upcoming 15 hours. The proposed model predicts the amount of pollutants such as PM 2.5, PM10, NO, NO2, NH3 and shows the better performance than linear prediction models when compared in terms of RMSE.
UAV Classification from Micro-Doppler Signature-based Time Frequency Images using SVM

Nowadays, UAVs are one of the most challenging flying objects to detect and classify. As their RCS is very low, traditional radar finds it very difficult to detect. To make the scenario even worse, birds are also exhibiting almost the same characteristics as UAV. Thus, this work tried to investigate the best options to identify UAV in all aspects. In such an endeavor, it delved into the area of sensor options, identifying options, distinguishing options, options to get better resolution, feature extracting options and image classifying options. This study identified that spectrogram-based micro-Doppler signature (m-DS) analysis retrieved from the radar is the most suitable option to do this task. To obtain good resolution in m-DS analysis, there is a grey area between STFT and covariance-based S- Transform. In addition to this, among several image classification options, CNN and SVM were found the most effective. Studies suggest that SVM has lower accuracy than CNN. However, as SVM has very little computation process, if the accuracy can be improved, SVM may be one of the best options in this regard. In pursuant to such aim, this paper identified the complete pathway of classifying UAV by X Band FMCW radar with the help of SVM with a few mathematical equations. This paper also checked all the viable options to use the SVM including the kernel.
Micro-Expression Recognition Based on Multi-task Learning and Resnet18

Micro-Expression recognition (MER) based on deep learning has broad research prospects. Under the background that the current mainstream micro-expression recognition (MER) algorithms mostly use single-task learning models, to achieve an effective breakthrough in model performance, we apply multi-task learning to micro-expression recognition (MER). This paper proposes an attention model based on multi-task learning, which consists of a single ResNet18 network as the shared network, and an attention module is inserted among the intermediate layers of the network to improve the ability of image feature extraction. Specifically, we take the task of facial action unit detection as an auxiliary task, and jointly train the two tasks of micro-expression recognition (MER) and facial action unit detection. We selected two datasets, CASME II and SAMM, and designed microexpression recognition (MER), facial action unit detection, and ablation experiments for performance verification. According to the experimental results, the performance of the multi-task learning model we designed is significantly improved on the two evaluation indicators of UAR (81.97%) and UF1 (81.98%), and it has both adaptability and robustness.
GUI based Thoracic Disease Detection using Segmented Deep Convolutional Neural Network

Deep learning has shown exciting results in the field of medical imaging. Thoracic problems can be serious, and if not detected and treated in a timely manner, they can result in death. Chest X-ray imaging is being used to diagnose numerous thoracic disorders. Generally, we required a knowledgeable and experienced radiologist to examine chest X-ray images and determine whether or not a person has certain thorax abnormalities. Everyone doesn't have the opportunity to consult an expert in a timely manner to treat chest disease because in certain circumstances, speedy diagnostics are required. As a consequence, we present an image classification and segmentation-based algorithm that would predict a specific thoracic condition and produce a mask, empowering doctors to make critical decisions. Deep Learning approaches have proven their worth in a range of contexts, outperforming many state-of-the-art models. We used the U-Net framework with ResNet as the backbone and got good results. In medical image processing and semantic segmentation, U-Net outperforms.
A Comprehensive Review for Classification and Segmentation of Gastro Intestine Tract

The term “gastrointestinal tract” refers to the digestive system that receives food, breaks it down, absorbs its nutrients, and then expels it as waste.The Gastrointestinal (GI) tract has a significant role in the global burden of cancer-related mortality. According to the Global Cancer Statistic 2020 figures, GI tract cancers are the main reason for cancer-related mortality and provide a substantial challenge to the rising life expectancy. Investigating and identifying GI tract anomalies need a thorough examination of the GI tract. So there is a need for a method by which these anomalies can be detected at an early stage. In this article, a comprehensive study of the research done in the area of the GI tract based on machine learning and deep learning techniques has been presented. The analysis of GI is divided into classification and segmentation. The paper covers all the techniques for classification and segmentation used in the previous years on different datasets.
A Kalman Filter for the Identification of Impulse Responses with Particular Symmetry Features

The identification of long-length impulse responses raises challenging issues in the context of many important applications. In this paper, we consider a theoretical model of the system to be identified, which consists in a combination of bilinear forms that owns particular symmetry features. Following a decomposition of the global impulse response using the Kro-necker product, the conventional system identification problem can be solved based on a combination of the estimates provided by two shorter filters. The solution is formulated in terms of a Kalman filter, with lower computational complexity and improved performance, as compared to the conventional approach. Simulation results outline the advantages of the proposed method.
Using Hybrid Deep Learning Model to Detect Anomaly Traffic Efficiently

Internet traffic has escalated throughout recent years, resulting in information security evolving into a severe issue. Therefore, utilizing Intrusion Detection System to monitor malicious attacks in achieving high network protection has become a significant research issue. The study proposes a novel IDS (Intrusion Detection System) to detect network malicious behaviors with a hybrid CNN+LSTM model. To effectively reduce the classification time, PCA is adopted for dimensional reduction from 47 attributes to 31 components, covering 99% of feature information in the UNSW-NB15 dataset. The accuracy of our proposed solution reaches 98.24% along with a much shorter detection time.
An Ensemble Deep Learning Approach for Diabetic Retinopathy Detection using Fundus Image

Detection of diseases, including diabetic retinopathy, may be greatly improved by taking a fundus picture of the back of the eye (DR). Complications in diabetics are the most common cause of vision problems, notably in younger and much more financially secure age groups. The risk of blindness in patients with DR may be reduced if they are diagnosed early enough. An ophthalmologist examined the fundus picture and used DR screening to look for lesions. However, the increase in incidence of DR is not correlated with the number of ophthalmologists who are able to interpret fundus pictures. Delay in prevention and treatment of DR may result as a result of this. Consequently, an automated diagnosis system is required to assist ophthalmologists in increasing the diagnostic process efficiency. The concatenate model is used in this study to differ fundus images into three categories: those without diabetic retinopathy, those with non-proliferative diabetic retinopathy, and those with proliferative diabetic retinopathy. We're using DenseNet121 and Inception-ResNetV2 for our models. Two models' feature extraction findings are integrated using the multilayer perceptron (MLP) classification approach. Compared to a single model, our strategy provides an increase in accuracy, precision, and recall of 91 percent and 90 percent for the F1-score. Deep-learning-based DR categorization utilizing fundus picture data was successfully shown in this experiment.
Surface Reconstruction from Unorganized Points Using Graph Cut

A new surface reconstruction algorithm is presented for unorganized 3D points in this work. By properly building a graph, the surface is shown to correspond to the minimum s-t cut of the graph. In addition, since our proposed algorithm is based on the three dimensional Delaunay triangulation, it is guaranteed the reconstructed surface is composed by the patches with fine shapes in the Delaunay tetrahedra. Besides, the reconstructed surface can interpolate the input data and can also decimate the outliers automatically through proper adjusting the parameters. The experiments indicate our algorithm is practical and easy to implement.
Artificial Intelligence (AI) in Medicine and Modern Healthcare Systems

The modern healthcare landscape involves collection, processing and analysis of large volumes of data, related to medicines, patient records, treatment paradigms, medical reports, immunization, and various other attributes. These data points need to be handled precisely and efficiently, as they lead to critical health decisions. The role of emerging technologies like AI, ML, Deep Learning, IoT and Robotics is gaining importance in handling such data using various algorithms. Each of these algorithms has a large number of applications in the healthcare domain, including decision on appropriate treatment journeys, analysis of medical reports, making informed clinical decisions, assistance in operational tasks at hospitals, tracking of vital signs, detection of diseases at an early stage and so on. Usage of such technologies introduces a level of automation in these medical procedures. Data can be accessed and analysed accurately and quickly. This also gives more time to health care professionals for patient care. However, it should be ensured that human supervision is maintained, so as to avoid any unexpected risk to patients. Also, human interaction and empathy is necessary for a patient's well-being and thus, not all medical processes should be performed by machines alone.
An Empirical Analysis of Microservices Systems Using Consumer-Driven Contract Testing

Testing has a prominent role in revealing faults in software based on microservices. One of the most important discussion points in MSAs is the granularity of services, often in different levels of abstraction. Similarly, the granularity of tests in MSAs is reflected in different test types. However, it is challenging to conceptualize how the overall testing architecture comes together when combining testing in different levels of abstraction for microservices. There is no empirical evidence on the overall testing architecture in such microservices implementations. Furthermore, there is a need to empirically understand how the current state of practice resonates with existing best practices on testing. In this study, we mine Github to find different candidate projects for an in-depth, qualitative assessment of their test artifacts. We analyze 16 repositories that use microservices and include various test artifacts. We focus on four projects that use consumer-driven-contract testing. Our results demonstrate how these projects cover different levels of testing. This study (i) drafts a testing architecture including activities and artifacts, and (ii) demonstrates how these align with best practices and guidelines. Our proposed architecture helps the categorization of system and test artifacts in empirical studies of microservices. Finally, we showcase a view of the boundaries between different levels of testing in systems using microservices.
Towards Precise Model-free Robotic Grasping with Sim-to-Real Transfer Learning

Precise robotic grasping of several novel objects is a huge challenge in manufacturing, automation, and logistics. Most of the current methods for model-free grasping are disadvantaged by the sparse data in grasping datasets and by errors in sensor data and contact models. This study combines data generation and sim - to- real transfer learning in a grasping framework that reduces the sim-to-real gap and enables precise and reliable model-free grasping. A large-scale robotic grasping dataset with dense grasp labels is generated using domain randomization methods and a novel data augmentation method for deep learning-based robotic grasping to solve data sparse problem. We present an end-to-end robotic grasping network with a grasp optimizer. The grasp policies are trained with sim-to-real transfer learning. The presented results suggest that our grasping framework reduces the uncertainties in grasping datasets, sensor data, and contact models. In physical robotic experiments, our grasping framework grasped single known objects and novel complex-shaped household objects with a success rate of 90.91%. In a complex scenario with multi-objects robotic grasping, the success rate was 85.71%. The proposed grasping framework outperformed two state-of-the-art methods in both known and unknown object robotic grasping.
Preliminary study of online real-time control system for lower extremity exoskeletons based on EEG and sEMG fusion

Motion imaging (MI) is widely used in exoskele-ton robot control direction. However, online real-time con-trol of exoskeletons based on biological signals is difficult to achieve. Because the anti-interference of biological signals in real life is poor and affected by individual differences, the control of auxiliary equipment by the realization of human-machine interface is still facing great challenges. In this paper, a human-machine interface (HMI) based on hybrid biological signals is designed and online real-time control of lower extremity exoskeletons is realized. Based on a comprehensive analysis of human walking, the HMI uses the electromyography signals (upper extremities) and EEG signals generated during real exercise as input signals. Different analysis methods are designed for different modal signals. We extracted four features of sEMG signals to distinguish between different movement processes in the human body. For EEG signals, the use of Independent Component Analysis (ICA) increases the robustness of the system. Event-related desynchronization/synchronization (ERS/ERD) and power spectral densities are reused for analysis from the perspective of the interaction of multiple information in the channel, time and frequency domains. The human-machine interface integrates EEG and EMG multimodal biological signals, and the average recognition accuracy of different subjects has reached 93.7%. In order to increase the recognition accuracy of practical applications and reduce the differences between different individuals, a window voting mechanism is designed. Healthy subjects were tested, and the recognition accuracy of the lower limb exoskeleton online real-time assistance system reached 99%, with high accuracy and reliability.