Miracle-dz/UniFAR-FTUs huggingface dataset:
Fiducial marker detection via deep learning approach for electron tomography

Marker-based alignment widely used for tilt series alignment in electron tomography (ET) is crucial to high-resolution tomographic reconstruction. However, accurate alignment with markers remains a challenge because it is difficult to detect markers accurately and obtain the precise positions of fiducial markers in the tilt series. Conventional marker detection algorithms highly depending on marker template and threshold for classification lack the adaptation for different types of samples. The classification accuracy is severely affected by high contrast structures other than markers and high-density areas. In this paper, we present an automatic fiducial marker detection algorithm that applies a fine-tuned classification model to fit with the particular dataset. The classification via a convolutional neural network (CNN) for marker detection is solved as a binary classification problem distinguishing between the positive samples and negative samples. Also, we established the training data for the model to learn the patterns of the fiducial marker and background noise. The experimental results indicate that our deep learning based marker detection algorithm can identify sufficient fiducial markers with high accuracy in a fully automatic manner and shows superiority compared with previous work.
Adaptive Weighted Performance Criterion for Artificial Neural Networks

Extended Weighted Performance Criterion (EWPC) which is motivated from Weighted Information Criterion (WIC) has been shown promising results in previous study and results of the application showed that EWPC is capable of giving noticeable performance among other measures. EWPC includes MSE, RMSE, R4MS4E, MAPE, MAE, GMAE, MdAE, MdAPE, NS, MRAE, MdRAE, GMRAE, RMSPE, RMdSPE, SMAPE, SMdAPE, MSMAPE, MASE, and RMSSE with fixed coefficients. In this study, we attempt to improve the performance of EWPC by using adaptive coefficients. The application study which consists of several simulated and real-world time series data is utilized for showing the performance of the criterion. Comparisons of the results show that Adaptive Weighted Performance Criterion (AWPC) is quite preferable as a consistent measure for model selection in Artificial Neural Networks.
H-infinite control of time-varying polytopic quadratic systems with applications to a robotic manipulator

The H-infinite control of time-varying polytopic quadratic (TVPQ) systems is investigated in this paper. By the polytopic decomposition of all parameter dependent matrixes in every vertex and constructing an Lyapunov Function of parameter-dependent, the bilinear LMI-based conditions for obtaining the H-infinite control law of state feedback form are presented. Furthermore, a nonlinear semidefinite programing (SDP) solver-PENLAB is introduced to solve the corresponding bilinear LMI constraints. A practical example of two-joint planar robotic manipulator is used for illustration purposes.
A Framework for IoT Data Acquisition and Forensics Analysis

A framework for IOT data acquisition and analysis is proposed. This framework can help data collection from a variety of different IoT devices. More precisely, our objective is to construct a practical methodology to facilitate forensic collection of forensic data from a multitude of IoT devices and related sites, such as smart phone, hubs, and cloud. Then we will use timeline analysis to present all related data in digital admissibility. In this paper, we will be focusing on building a framework of data acquisitions and forensic analysis for IoT devices being connected to mobile device and cloud.
Transmitter Current Tracking Method of Improving Power-Transfer Efficiency for Two-Coil Wireless Power Transfer System

Starting from a brief overview of two-coil wireless power transfer (WPT) system, a transmitter current tracking method is realized to improve the power-transfer efficiency of the system. The fixed-load receiver is allowed to be moved to nearly any position within the system's working range, and the system still achieves a near maximum power-transfer efficiency. The simulation results show that the proposed method can be used to improve the WPT system's efficiency more rapidly and more accurately.
Combining ElasticFusion with PSPNet for RGB-D Based Indoor Semantic Mapping

Semantic segmentation has been a research focus of scene parsing and robotic manipulation. And increasing efforts are being made to empower geometry-solely robotic mapping with semantic sensing ability in complex spatial cognition tasks, the so-called semantic mapping. However, semantic mapping has not yet fully exploited the state-of-the-art of semantic segmentation and suffered limitations of application scenario, number of object category and accuracy of object detection. Towards a robust semantic mapping solution, we propose a RGB-D based fusion method that combines an improved ElasticFusion with PSPNet. PSPNet has the potential for breaking restrictions of limited object category in semantic segmentation of RGB images. As for map rendering, the improved ElasticFusion algorithm is validated for its robust performance in indoor scenes in terms of overall model accuracy and integral object shape geometry. Based on the two aforementioned modules, we fuses the semantic images and the depth images to generate 3D point cloud with semantic information. Experiments on the ICL-NUIM datasets have proven the feasibility of the proposed method.
Compliant Biped Locomotion of Hydra, an Electro-Hydrostatically Driven Humanoid

The backdrivability of joints is a critical requirement for the robots that perform tasks in uncertain environments. While series elastic actuators are intrinsically backdrivable, their control bandwidth is limited by the low resonant frequency of the elastic component. To simultaneously realize both of the backdrivability and high control bandwidth, Electro-Hydrostatic Actuator (EHA) is a solution. Based on this idea, we developed the fully electro-hydrostatically driven humanoid robot Hydra, while its evaluation was limited to the joint level one. In this paper, we present evaluations of its whole-body control performance, including locomotion. This is the first time to report a bipedal locomotion by an EHA driven humanoid. We first confirm that Hydra can realize a position feedback control with enough stiffness to realize a position control based locomotion. Secondly, we show that the joint backdrivability can suppress the effect of a disturbance applied to the distal part of the robot on the whole-body motion. As the result, we realized a torque control based locomotion with both a proper COM stabilization and nulls pace compliance.
A Biased Random-Key Genetic Algorithm for Regression Test Case Prioritization

The Test Case Prioritization Problem (TCPP) is a real-world problem that arises in regression testing. It lies in finding an ordering of the test cases of a test suite, such that test cases ordered first should be run first. The classical approach to solve this problem employing GAs is to use permutational encoding, but it requires specific operators to keep the feasibility of the solutions. The Biased Random-Key Genetic Algorithm (BRKGA) follows a different philosophy for dealing with permutations, using a string of real numbers and a decoder for computing the permutation. In this paper, we propose a BRKGA for solving the TCPP. The experimental evaluation on eleven instances of seven real-world programs shows that BRKGA is able to outperform two different permutational encoding based GAs (with order and cycle crossover operators), and that it has at least a similar performance than another permutational encoding based GA (with partially-mapped crossover) and a GA from a previous work specially conceived for tackling the TCPP.
Downsizing the Motors of a Biped Robot Using a Hydraulic Direct Drive System

Biped robots require a high power to be provided alternately on their two legs while walking, hopping, and running. However, the mounting of high-power and large electrical motors is challenging in conventional mechanical transmission systems because of space limitations. To address this issue, we employ herein a combination of hydraulic and transmission systems with an independent driving mode and a power-shared driving mode. In the independent driving mode, an actuator can be independently controlled based on flow-control, and pressure loss can be reduced. In the power-shared driving mode, actuators can also be controlled based on flow-control, and this mode allows the motor power of the left and right legs to be shared. We also employ a simulation to evaluate the proposed novel system and confirm that the motor power could be reduced by 35.6% for the hopping movement. This result shows that the rated output of the required motor can be reduced, and the selection of smaller and lighter motors is possible for installation in biped robots.
Predictive Meta-analysis of Multiple Microarray Datasets: An Application to Classification of Malignant Gliomas

In this study, we conducted a predictive meta-analysis of multiple microarrays to identify a gene signature that can be potentially used to distinguish different grades of malignant gliomas (i.e., Grade III and IV). We showed our developed classification rule achieved an average accuracy and a J index of 75% and 52.6%, respectively, as measured by a dataset cross-validation strategy. Furthermore, we showed that clustering samples on the basis of similarity of expression profiles of the gene signature divides the data across available studies mainly into the two phenotypic groups regardless of the actual study. From the standpoint of data analytics, the results of this study confirm the utility of meta-analysis in integrating raw data from multiple studies into a predictive framework. From a biological perspective, the identified gene signature can be potentially used to shed light on the molecular mechanisms underlying the formation of malignant gliomas.
Learning Light-Weight Edge-Deployable Privacy Models

Privacy becomes one of the important issues in data-driven applications. The advent of non-PC devices such as Internet-of-Things (IoT) devices for data-driven applications leads to needs for light-weight data anonymization. In this paper, we develop an anonymization framework that expedites model learning in parallel and generates deployable models for devices with low computing capability. We evaluate our framework with various settings such as different data schema and characteristics. Our results exhibit that our framework learns anonymization models up to 16 times faster than a sequential anonymization approach and that it preserves enough information in anonymized data for data-driven applications.
Research on Base Station Energy Saving Strategy Oriented Toward Multi-Service User Experience

With the rapid development of the Information &Communication Technology (ICT) and the popularity of Internet applications, wireless networks are with the tendency of dense deployment, high energy consumptions and carbon emission problems. It incurs huge impact on environment. The user associated base station switching on-off strategy can effectively reduce energy consumptions. The existing strategies mainly focus on Quality of Service for users, without fully considering the influence of user experience for specific applications. We propose a QoE based Energy Saving Scheme oriented toward MultiService (ESS). Nonlinear integer programming is utilized to model the above problem, which achieves the tradeoff between user experience and energy consumptions. By comparing with other relative work, the results show that ESS achieves better performance on user experience and energy savings.
Predicting Polarization Beyond Semantics for Wearable Robotics

Semantic perception is a key enabler in robotics, which supposes a very resourceful and efficient manner of applying vision information for upper-level navigation and manipulation tasks. Given the challenges on specular semantics such as water hazards, transparent glasses and metallic surfaces, polarization imaging has been explored to complement the RGB-based pixel-wise semantic segmentation because it reflects surface characteristics and provides additional attributes. However, polarimetric measurements generally entail prohibitively expensive cameras and highly accurate calibrations. Inspired by the representation power of Convolutional Neural Networks (CNNs), we propose to predict polarization information from monocular RGB images, precisely per-pixel polarization difference. The core of our approach is a cluster of efficient deep architectures building on factorized convolutions, hierarchical dilations and pyramid representations, aimed to produce both semantic and polarimetric estimations in real time. Comprehensive experiments demonstrate the qualified accuracy on a wearable exoskeleton humanoid robot.
Intelligent Recognition of Underwater Acoustic Target Noise on Underwater Glider Platform

The underwater acoustic target detection system based on the underwater glider platform requires the platform itself to have the ability of target automatic tracking, identification and evaluation, but the traditional methods of underwater target noise identification have strong human-computer interaction characteristics. It can not meet the needs of automatic target recognition. In order to solve this problem, the feature extraction method, automatic recognition model had been studied with the combination of the characteristics of underwater glider in this paper. An intelligent recognition model of underwater target noise based on long-short terms memory network is established. The model is tested in the laboratory, and it is verified by the underwater acoustic signal obtained on the sea. The results show that the intelligent identification model of underwater acoustic target noise can identify underwater acoustic target noise without artificial participation, and meet the requirements of underwater acoustic target detection and identification of the underwater glider platform.
A Novel Base Station Analysis Scheme Based on Telecom Big Data

Telecom big data stores valuable information to support the telecom operation, and hence, telecom big data has been pushed to the forefront in both academics and industry. In this paper, a novel telecom big data based BS analysis (TBDBSA) scheme is designed. The aim of the proposed TBDBSA scheme is to precisely analyze BS performance and seek the problematic BS. The key of TBDBSA scheme includes three steps, including BS fault and reliability analysis, wireless environment analysis, BS capacity analysis. We apply the TBDBSA scheme to a northeast city in China, and results show it can assist telecom operator to locate the problematic BS.
FOC-based Speed Control Algorithms of Induction Motor Drive with System Parameter Mismatch

The field oriented control algorithms (FOC) of induction motor (IM) drives are generally sensitive to model parameter mismatch. Especially, the proper flux orientation and flux amplitude is influenced by the rotor/stator resistance values. Moreover, the rotor and stator resistances are temperature dependent and thus time-varying variables. The real-time testing of different kinds of FOC algorithms to parameter mismatch robustness is a difficult task. The motor parameters such as resistances, inductances, inertia etc. are not commonly tunable in the laboratory experiments. Furthermore, the IM fluxes are not commonly directly measured. The hardware in the loop (HIL) IM simulators operating real-time could be the right choice to FOC algorithms robustness testing. This paper describes the conventional cascade based FOC, feedback linearization based FOC and novel adaptive FOC algorithm tested on self-developed IM HIL FPGA-based platform. The paper presents both HIL based results and experimental results.
A Hybrid Convolutional and Recurrent Deep Neural Network for Breast Cancer Pathological Image Classification

Hematoxylin and Eosin H&E stained breast tissue samples from biopsies are observed under microscopy for the gold standard diagnosis of breast cancer. However, a substantial workload increases and the complexity of the pathological images make this task time-consuming and may suffer from pathologist’s subjectivity. Facing this problem, the development of automatic and precise diagnosis methods is challenging but also essential for the field. In this paper, we propose a new hybrid convolutional and recurrent deep neural network for breast cancer pathological image classification. Our method considers the short-term as well as the long-term spatial correlations between patches through RNN which is directly incorporated on top of a CNN feature extractor. Experimental results showed that our method obtained an average accuracy of 90.5% for 4-class classification task, which outperforms the state-of-the-art method. At the same time, we release a bigger dataset with 1568 breast cancer pathological images to the scientific community, which are now publicly available from http://ear.ict. ac.cn/? page id=1576. In particular, our dataset covers as many different subclasses spanning different age groups as possible, thus alleviating the problem of relatively low classification accuracy of benign.
A Heuristic Based Pre-processing Methodology for Short Text Similarity Measures in Microblogs

Short text similarity measures have lots of applications in online social networks (OSN), as they are being integrated in machine learning algorithms. However, the data quality is a major challenge in most OSNs, particularly Twitter. The sparse, ambiguous, informal, and unstructured nature of the medium impose difficulties to capture the underlying semantics of the text. Therefore, text pre-processing is a crucial phase in similarity identification applications, such as clustering and classification. This is because selecting the appropriate data processing methods contributes to the increase in correlations of the similarity measure. This research proposes a novel heuristic-driven pre-processing methodology for enhancing the performance of similarity measures in the context of Twitter tweets. The components of the proposed pre-processing methodology are discussed and evaluated on an annotated dataset that was published as part of SemEval-2014 shared task. An experimental analysis was conducted using the cosine angle as a similarity measure to assess the effect of our method against a baseline (C-Method). Experimental results indicate that our approach outperforms the baseline in terms of correlations and error rates.
Cell Tracking Across Noisy Image Sequences Via Faster R-CNN and Dynamic Local Graph Matching

Automated tracking of cells in time-lapse live-imaging datasets of developing multicellular tissues is gaining popularity in developmental biology for understanding the cell growth dynamics. The tracking of plant cells across noisy microscopy image sequences is very challenging, because plant cells in noisy region cannot be correctly segmented and cause serious errors in subsequent cell tracking procedure. In this paper, we present to track plant cells across noisy images using a tracking method which is based on Faster R-CNN and dynamic local graph matching. Faster R-CNN is employed to detect cells in noisy images, and it is improved by cell characteristic prior bounding box design and soft non-maximum suppression strategies. Then a dynamic local graph matching model is proposed to track the detected plant cells, by exploiting the cells’ tight spatial and temporal contextual information. It tends to prevent the cell matching error accumulation by selecting the most similar cell pair in the dynamically growing neighbor set of matched cells. Compared with the existing tracking methods for plant cells, the experimental results show that the proposed method can greatly improve the tracking accuracy.
Differentially Private Semi-Supervised Learning With Known Class Priors

Machine learning algorithms benefit greatly from a large amount of data in the big data regime, however, the labeling cost for all training samples is prohibitively high. Semi-supervised learning is a designed framework to overcome the labeling challenge. In particular, in semi-supervised learning, there are only a small number of labeled samples and unlabeled samples are used as side information to leverage the overall performance. Additionally, preserving the privacy of the data is another challenge in big data analysis. This paper addresses challenges by designing a privacy-preserving method for semi-supervised learning. Assuming that class priors are available, the proposed method can achieve the optimal classifier. Moreover, the privacy and utility of the proposed method are theoretically guaranteed. Experiments on real UCI datasets illustrate the effectiveness of our proposed method.
A Hog & Graph Based Human Segmentation from Video Sequences

Human segmentation from video is a significant problem to recognize a specific person which is desired to find. There are a remarkable number of studies discussing segmentation process in videos. Almost all study is examined how well a human segmentation process could be created by considering their position, clothing and face. Unfortunately, it has been assumed that the background is static by approximately all of these works. Main challenges in this research area are; non-static background derived from dynamic structure of videos, human body and face position, shadows and clothing changing after a period of time in videos. In this script, we have proposed a multi-model (Histogram of the Gradients – HOG and Graph-based) human segmentation technique that has worked with respect to HOG features and detected low-level and mid-level features of human clothing by supposing their positions in video. The offered technique is designed to demonstrate robustness against such challenges emphasized above. In this study, a well-known video series have been used. The video scenes can reach 15~25 fps and have the size about 640×480px. To compare graph-based method robustness a well-known segmentation method Watershed is also experimented and both methods are simply compared. Eventually, we determined that the proposed technique can produce satisfactory quality segmentation mentioned and detailed in following sections.
New Cross-Step Enabled Configurations for Humanoid Robot

This paper explores two new configurations for humanoid robot balancing and locomotion. Centroidal momentum manipulability analysis has been performed to study the features of the newly proposed configurations. Numerical simulations show that they outperform the regular ones in terms of angular momentum manipulability. More than that, the new configurations allow the humanoid robot to perform cross-step motions which is usually risky or mechanically impossible for most existing robots. However, cross-step introduces non-convex feasible region which makes it difficult to be incorporated into our existing step planner. Therefore, a simple heuristic has been proposed to help choosing a sub-convex region for the step planner. To validate the cross-step movement, walking simulations have been performed.