Miracle-dz/UniFAR-FTUs huggingface dataset:
The Lobster-inspired Antagonistic Actuation Mechanism Towards a Bending Module

This paper describes a new type of bending module inspired, in part, by the musculoskeletal structure of the lobster leg joint. The bending module proposed combines enhanced torque output, reconfigurability in assembling, safe compliant actuation, and accurate control on its mechanical performance. In this module, antagonistic soft chambers are enveloped by exoskeleton shells, and the bending angle and the stiffness can be independently adjusted by controlling the input pressure in the two chambers. Theoretical models are developed to characterize the relationships between the input pressure, bending angle, and stiffness, and a controller for angle control and stiffness tuning is constructed with experimental validation. The fabricated module can reach the maximum torque output of 109.7 N•mm under 40 kPa and the stiffness range from 40 to 220 N•mm / rad, demonstrating its capacity to fulfill both safe interactions and forceful tasks.
A New Estimation Algorithm for Frequency and Amplitude in Harmonic Signal Processing

Low-rank matrix recovery is a large-scale data analysis and processing technology; its related theory has been widely used in image restoration, image denoising, video background modeling, signal recovery and other fields. This paper proposes an improved inexact augmented Lagrange multiplier (IALM) method to solve the harmonic recovery (HR) problem. After that, the performance of the original IALM and the improved IALM are compared when the sparse matrix is a diagonal sparse matrix satisfying certain conditions, and the results show that the improved IALM algorithm is more stable than the original algorithm. Then the improved strategy of the algorithm is extended to two kinds of occasions in which the positions of non-zero elements in sparse matrix are fixed or random, which provides a way to improve the algorithm in different application scenarios. Finally, the original IALM algorithm and the improved IALM algorithm are used to solve the HR problem, and the experimental results show that the improved IALM algorithm has better solution performance.
Source Camera Identification Using WLBP Descriptor

In this paper we introduce a camera identification method using WLBP texture descriptor. This descriptor has previously been used for texture and face classifiers. In the proposed method, we proposed to use WLBP operator in camera classification application to identify the imaging camera. In our method, the two-dimensional histogram of Weber’s features and LBP for camera identification are investigated. For this purpose, experiments were conducted on Dresden database. The proposed method has reached the accuracy of 99.52% on nine digital cameras of different models. In compressed JPEG images with the compression quality factor of 70% the method reached the accuracy of 89.04%. The results indicate that the proposed method has a high degree of accuracy in comparison to other proposed method and exhibits relatively good robustness to compression.
Optimizing Human Computer Interaction for Byzantine music learning: Comparing HMMs with RDFs

The current paper presents the results of an optimization study conducted on a self – training system of Byzantine chanting. The main aim of this work is the enhancement of the monophonic vocal music recognition step. Sound recognition technologies as implemented in practice, rely on classification algorithms. Various machine learning algorithms have already been applied for this purpose. In order to examine and select the most efficient one, we proceeded with a comparative evaluation of Hidden Markov Models and Random Decision Forests algorithms. Vocal performances of an expert-performer and an amateur-performer were captured to create a dataset for the training of the aforementioned algorithmic programs. Ensuing the training of the algorithms, Jackknife statistical method was applied to cross-validate the evaluation results of each algorithmic rule. The outcome of the evaluation highlights that Hidden Markov Models algorithm is more effective that Random Decision Forests.
TrajData: On Vehicle Trajectory Collection With Commodity Plug-and-Play OBU Devices

For years, vehicle trajectory data have increasingly been important for a wide range of applications, from driver behavior investigation/classification, travel time/distance estimation, and routing in vehicular networks, to vehicle energy/emission evaluation. This article presents TrajData, the first systematic solution to reliable vehicle trajectory data collection, with only reliance on commercial-off-the-shelf (COTS) onboard unit (OBU) devices that utilize lightweight GPS modules and low-cost onboard diagnostics (OBD) readers. In the practical use of trajectory collection, GPS outages inevitably occur in urban environments thereby leading to large trajectory errors as well as missing vehicle location data. To resolve this, we propose a novel data-fusion-enabled deep learning approach with the purpose of achieving reliable vehicle trajectory collection in various urban road conditions. Specifically, we leverage motion information retrieved from OBD readers in TrajData to help reconstruct the trajectory data during GPS outages. By investigating the changes of direction angle from the OBD readings, we can identify different types of road sections. Furthermore, we integrate the neural arithmetic logic units (NALUs) into our trajectory reconstruction model to tame the challenges when GPS outages take place in various road sections. Experimental results from realistic data have demonstrated the effectiveness and reliability of the proposed method. In the road test, TrajData achieves an average position error below 15-m around a 60-s GPS outage, even in complex road sections, i.e., continuous turns and driving with accelerations/decelerations resulting in frequent changes of direction and speed.
Theoretical and Experiment of Microspheres On-demand Printing

As one of the most advanced technologies in 3D printing technology, the development of 3d bio-printing has provided the possibility to solve many medical problems. However, many limiting factors should be solve to improve the regeneration quality of tissue and organ, such as the precision of microsphere distribution, microsphere controllability et.al. In this paper, we firstly have developed a novel nozzle module to improve the precision of on-demand printing, which has been controlled by Voice Coil Motor. Then we have analyzed the work mechanism of nozzle module and explored formation process and necessary condition of microsphere. We finally have designed the experiment to achieve the microspheres of on-demand printing and verify the theoretical analysis, which has provided a new approach to promote the application of 3D printing in the field of organ and tissue regeneration.
A New Complex Fuzzy Inference System With Fuzzy Knowledge Graph and Extensions in Decision Making

Context and Background:Complex fuzzy theory has a strong practical implication in many real-world applications. Complex Fuzzy Inference System (CFIS) is a powerful technique to overcome the challenges of uncertain, periodic data. However, a question is raised for CFIS: How can we deduce and predict the result in case there is little knowledge about data information and rule base? This is significance because many real applications do not have enough knowledge of rule base for inference so that the performance of systems may be low. Thus, it is necessary to have an approximate reasoning method to represent and derive final results. Motivation: Recently, the Mamdani Complex Fuzzy Inference System (M-CFIS) has been proposed with a specific inference mechanism according to the Mamdani type. A new improvement so-called the Mamdani Complex Fuzzy Inference System with Rule Reduction (M-CFIS-R) has been designed to utilize granular computing with complex similarity measures to reduce the rule base so as to gain better performance in decision-making problems. However in M-CFIS-R, testing data are checked by matching with each rule in the rule base, which leads to a high cost of computational time. Besides, if the testing data contain records that are not inferred by the rule base, the output cannot be generated. This happens in real commerce systems in which the rule base is small at the time of creation and needs to feed with new rules. Methodology: In order to handle those issues, this article first time proposes the Fuzzy Knowledge Graph to represent the rule base in terms of linguistic labels and their relationships according to the rule set. An adjacent matrix of Fuzzy Knowledge Graph is generated for inference. When a record in the Testing dataset is given, it would be fuzzified and labelled. Each component in the record is checked with the Fuzzy Knowledge Graph by the inference mechanism in approximate reasoning called Fast Inference Search Algorithm. Then, we derive the label of the new record by the Max-Min operator. Besides, we also propose four extensions of Mamdani Complex Fuzzy Inference System Rule Reduction including Sugeno Complex Fuzzy Inference Systems, Tsukamoto Complex Fuzzy Inference Systems, Complex Fuzzy Measures and Complex Fuzzy Integrals in M-CFIS-R. Results: The experiments on the UCI Machine Learning datasets show that the proposed method classifies samples as correctly as M-CFIS-R with very lower run time (6.45 times on average). The experiments are performed through many tests via 2 main scenarios. Conclusion: The proposed system has good performance in reducing the computational time of inference with acceptable accuracy. It has ability to work with systems having limited knowledge and rule base.
Combination Treatment with Arsenic Trioxide and Sulindac Induces Apoptosis of NCI-H157 Human Lung Carcinoma Cells via ROS Generation with Mitochondrial Dysfunction.

Background : Arsenic trioxide () has been used to treat acute promyelocytic leukemia, and it induces apoptosis in a variety of solid tumor cell lines including non-small cell lung cancer cells. However, nonsteroidal antiinflammatory drugs (NSAID) can enhance tumor response to chemotherapeutic drugs or radiation. It was previously demonstrated that a combination treatment with and sulindac induces the apoptosis of NCI-H157 human lung carcinoma cells by activating the caspase cascade. This study aimed to determine if a combination treatment augmented its apoptotic potential through other pathways except for the activation of the caspase cascade. Material and Methods : The NCI-H157 cells were treated with , sulindac and antioxidants such as glutathione (GSH) and N-acetylcysteine (NAC). The cell viability was measured by a MTT assay, and the level of intracellular hydrogen peroxide () generation was monitored fluorimetrically using a scopoletin-horse radish peroxidase (HRP) assay. Western blotting and mitochondrial membrane potential transition analysis were performed in order to define the mechanical basis of apoptosis. Results : The viability of the cells was decreased by a combination treatment of and sulindac, and the cells were protected using antioxidants in a dose-dependent manner. The increased generation by the combination treatment was inhibited by antioxidants. The combination treatment induced changes in the mitochondrial transmembrane potential as well as the expression of the Bcl-2 family proteins, and increased cytochrome c release into the cytosol. However, the antioxidants inhibited the effects of the combination treatment. Conclusion : Combination treatment with and sulindac induces apoptosis in NCI-H157 human lung carcinoma cells via ROS generation with a mitochondrial dysfunction.
Random SBT Precoding for Angle Estimation of mmWave Massive MIMO Systems Using Sparse Arrays Spacing

In this article, we consider the estimation of angle of arrival (AoA) and angle of departure (AoD) for mmWave MIMO channels. For mmWave systems, there is typically a large number of antennas but limited radio frequency (RF) chains. The RF chain limitation indirectly restricts the effective number of antennas or the effective array size. In radar applications, it is known that a larger array size leads to more accurate AoA estimation or more resolvable paths. It is also known that with sparse arrays, which places antennas in a nonuniform sparse manner, an enlarged virtual array can be constructed when the sources are uncorrelated. We show that the idea of sparse arrays can also be used in mmWave systems to have augmented array sizes. The main difficulty in applying sparse arrays to mmWave MIMO channels lies in the fact the signals of different paths can be correlated in mmWave system, but uncorrelatedness is necessary for applying sparse array results. We show that the paths can be decorrelated by employing random precoders and combiners that are submatrices of banded Toeplitz matrices (SBT). When the precoder and combiner are SBT that are designed according to the antenna spacing of a sparse array, we can construct enlarged virtual transmit and receive arrays. With enlarged virtual arrays, accurate estimates can be obtained, as will be demonstrated by simulation examples.
Inspection of Concrete Structures by a Computer Vision Technique and an Unmanned Aerial Vehicle

We have proposed a visual inspection technique for concrete structures using deep learning and a hardware ecosystem, an Unmanned Aerial Vehicle (UAV). The UAV is a quadcopter that can fly to unreachable sections of a site which consists of a camera that captures images of the concrete surfaces via a mobile device and feed the real time images in the CNN model. The images taken from such remote locations may contain different types of surfaces, shadowed regions and surfaces with holes. The cracks are properly detected by the CNN ‘AlexNet’ algorithm and masking with sliding window technique in such conditions due to variation in the image data set. The experimental results were simulated on a standard online data set of 40,000 images of Mendeley Data which is freely available and 3000 images have been chosen from the entire data set for this method. The classes have been divided into 2 categories of ‘crack’ and ‘no crack’ for the proposed method’s data set. There are 1050 training images and 450 testing images for each category. Experimental results were achieved on Google Colab cloud service using Python Tensorflow API (Application Programming Interface). The proposed ‘AlexNet’ CNN algorithm achieves 98.4 % accuracy and the model is deployed to a masking technique with sliding window to detect cracks in a 3008x2000 pixel resolution image by breaking the image into 227x227 pixel resolution image patches. The experimental results have proved that the proposed method handles noisy background such as cracks with shadows and stains, cracks on rusty and rough surfaces and minor dimension cracks with good efficiency.
Reliable frame-to-frame motion estimation for vehicle-mounted surround-view camera systems

Modern vehicles are often equipped with a surround-view multi-camera system. The current interest in autonomous driving invites the investigation of how to use such systems for a reliable estimation of relative vehicle displacement. Existing camera pose algorithms either work for a single camera, make overly simplified assumptions, are computationally expensive, or simply become degenerate under non-holonomic vehicle motion. In this paper, we introduce a new, reliable solution able to handle all kinds of relative displacements in the plane despite the possibly non-holonomic characteristics. We furthermore introduce a novel two-view optimization scheme which minimizes a geometrically relevant error without relying on 3D point related optimization variables. Our method leads to highly reliable and accurate frame-to-frame visual odometry with a full-size, vehicle-mounted surround-view camera system.
Machine Vision Inspection of Electrical Connectors Based on Improved Yolo v3

Aiming at the problems of electrical connector defect detection, such as low automation, low detection accuracy, slow detection speed, and poor robustness, an improved Yolo v3 algorithm was proposed in this paper to detect electrical connector defects. First, the K-means clustering algorithm is used to perform cluster analysis on the data set of this paper to obtain three kinds of candidate frames with aspect ratios, aiming at improving the detection accuracy for the defective objects in this paper; the 8-fold downsampled feature map outputted by the third residual block of the backbone network is upsampled 4 times, and the obtained feature map is merged with the 2-fold downsampled feature map outputted by the second residual block to obtain the fusion feature detection layer; the 6 DBL units passed by the target detection layer are changed to 2 DBL unit plus 2 residual units to improve feature reuse and acquisition; In addition, single-scale feature maps are chose for target detection in this paper instead of multi-scale prediction of the original network, which not only saves the calculation amount, but also avoids false detection to a certain extent. ; A new detection method is proposed for relative rotation defects between the inner ring area and the outer ring area of the electrical connector. The qualitative and quantitative experimental results show that the improved Yolo v3 algorithm in this paper has better performance and speed for defect detection of various types of electrical connectors, with an accuracy rate of 93.5%, which is more accurate than Faster R-CNN. The original Yolo v3 is faster and basically meets the requirements of the industrial field for electrical connector testing.
Implementing Tactile and Proximity Sensing for Crack Detection

Remote characterisation of the environment during physical robot-environment interaction is an important task commonly accomplished in telerobotics. This paper demonstrates how tactile and proximity sensing can be efficiently used to perform automatic crack detection. A custom-designed integrated tactile and proximity sensor is implemented. It measures the deformation of its body when interacting with the physical environment and distance to the environment’s objects with the help of fibre optics. This sensor was used to slide across different surfaces and the data recorded during the experiments was used to detect and classify cracks, bumps and undulations. The proposed method uses machine learning techniques (mean absolute value as feature and random forest as classifier) to detect cracks and determine their width. An average crack detection accuracy of 86.46% and width classification accuracy of 57.30% is achieved. Kruskal-Wallis results (p<0.001) indicate statistically significant differences among results obtained when analysing only force data, only proximity data and both force and proximity data. In contrast to previous techniques, which mainly rely on visual modality, the proposed approach based on optical fibres is suitable for operation in extreme environments, such as nuclear facilities in which nuclear radiation may damage the electronic components of video cameras.
Optimizing performance in automation through modular robots

Flexible manufacturing and automation require robots that can be adapted to changing tasks. We propose to use modular robots that are customized from given modules for a specific task. This work presents an algorithm for proposing a module composition that is optimal with respect to performance metrics such as cycle time and energy efficiency, while considering kinematic, dynamic, and obstacle constraints. Tasks are defined as trajectories in Cartesian space, as a list of poses for the robot to reach as fast as possible, or as dexterity in a desired workspace. In a simulated comparison with commercially available industrial robots, we demonstrate the superiority of our approach in randomly generated tasks with respect to the chosen performance metrics. We use our modular robot proModular.1 for the comparison.
A Lightweight CNN Architecture for Land Classification on Satellite Images

Land cover classification using satellite images is an important tool in the study of terrestrial resources. Satellite based information is presently available as huge sets of high resolution images from a large number of satellites like Sentinel, Landsat-8, etc. Land cover classification from these images is a difficult task because of very large sized data and high variation types. Deep Neural Networks can play a vital role in this regard and can perform classification on these large sized data. Related works in this field have used lighter models and included a large number of handcrafted parameters which requires domain knowledge on the subject. It is realised that most models are too shallow for such a complicated image. In this paper, a deeper Convolutional Neural Network (CNN) model without any satellite image specific parameters is proposed. On SAT4 and SAT6 images, our 13-layered network has achieved better accuracy upto 99.84% and 99.47% which is state-of-the-art. It is still called lightweight model because most models in Artificial Intelligence(AI)-CNN are much deeper and larger than ours.
E-nose Prototype to Monitoring the Growth and Maturation of Peaches in the Orchard

Indication of peach maturity and inspection of fruit quality in an orchard are generally analyzed based on the farmer’s experience, which may be subject to failure and result in financial losses due to negligence or late harvest. Volatile organic compounds (VOCs) vary in quantity and type, depending on the different phases of fruit growth. Thus, the electronic noses are an alternative, since they allow the online monitoring of the VOCs generated by the culture. The correlate works found in the literature focus on the detection of post-harvest peach maturation, not the monitoring of fruits in the orchard, to detect the stage of growth and maturation, and that, when measuring these properties, methods are generally used destructive instruments. Analyzing this context, we developed a prototype for the identification of the maturation of the Eragil peach in the pre-harvest growth cycle. The prototype was built containing thirteen metal oxide semiconductor gas sensors. The pre-processing method for feature selection was applied, Pearson’s Chi-square test, which provided the reduction for six sensors. Based on the Random Forest method with a Linear Discriminant Analysis, we reduced the dataset for six sensors and obtained a 1.92% error rate in the sample test step. It shows that the device could be optimized to this application and confirm that it is promising for local monitoring of fruit ground-based on the emission of VOCs and that several devices of this in-network can provide information for an optimized harvest.
Linear RGB-D SLAM for Atlanta World

We present a new linear method for RGB-D based simultaneous localization and mapping (SLAM). Compared to existing techniques relying on the Manhattan world assumption defined by three orthogonal directions, our approach is designed for the more general scenario of the Atlanta world. It consists of a vertical direction and a set of horizontal directions orthogonal to the vertical direction and thus can represent a wider range of scenes. Our approach leverages the structural regularity of the Atlanta world to decouple the non-linearity of camera pose estimations. This allows us separately to estimate the camera rotation and then the translation, which bypasses the inherent non-linearity of traditional SLAM techniques. To this end, we introduce a novel tracking-by-detection scheme to estimate the underlying scene structure by Atlanta representation. Thereby, we propose an Atlanta frame-aware linear SLAM framework which jointly estimates the camera motion and a planar map supporting the Atlanta structure through a linear Kalman filter. Evaluations on both synthetic and real datasets demonstrate that our approach provides favorable performance compared to existing state-of-the-art methods while extending their working range to the Atlanta world.