Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive Image-Based Visual Servoing of 6 DOF Robots Using Switch Approach∗

In this paper, an adaptive image-based visual servoing (IBVS) controller using switch approach is proposed for an eye-in-hand 6 DOF robot. The proposed controller is decomposed to three distinctive stages with its own gains. By decoupling the translational and rotational motions, this method can deal with the nonlinearity of the image Jacobian matrix created by the depth parameter. Adaptive laws are developed to estimate the camera parameters. In order to meet the needs of robotic industrial applications, a switch adaptive IBVS is designed to provide a fast response system based on the estimated camera intrinsic parameters. In terms of the response time, simulation results show the significant improvement of the control performance compared to the other IBVS methods. Besides the designed controller proves the capability to overcome one of the inherent shortcomings of IBVS where the tasks dictate a 180◦ camera rotation around its center.
Wavelet-Based Real-time Monitoring of Multi-Physiological Parameters on a Portable Embedded Platform

To help prevent cardiovascular diseases, a portable multi-physiological parameters monitoring device based on an embedded platform is able to meet people’s needs to monitor physiological conditions in daily lives. Accuracy and real-time processing play important roles in the design of the whole monitoring system. In this paper, we implemented wavelet-based parameters computing on a portable embedded device, taking into account limited embedded resources. Because of the advantages of WT in denoising and highlighting singularity, the portable multi-physiological parameters monitoring device had satisfied behavior on accuracy and real-time of test in motionless and motion status compared with the Mindray BeneView T5 patient monitor whose correlation coefficients of the parameters were up to 0.9993 and down to 0.9060. It’s better to help users know their own physical conditions without interfering with daily lives.
A 20 ~ 43 GHz VGA with 21.5 dB Gain Tuning Range and Low Phase Variation for 5G Communications in 65-nm CMOS

This paper presents a broadband variable gain amplifier (VGA) with low phase variation in 65 nm CMOS technology. The mechanism of phase variation in CMOS VGA is analyzed. According to the analysis, the feedforward paths formed by parasitic capacitive and inductive couplings are one of the main factors that result in phase variation in CMOS VGA. In order to achieve low phase variation, a parasitic capacitor elimination technique is proposed to remove the feedforward path formed by gate-drain parasitic capacitor. Besides, an isolation enhancement layout technique is proposed to minimize the inductive coupling and parasitic capacitors in the layout of the variable gain stage. As results, the measured phase variation is 0.2° ~ 2° in 18 GHz ~ 37 GHz and 0.2° ~ 5.4° in 18 GHz ~ 45 GHz when the gain variation range is 21.5 dB. The measured peak gain is 14.5 dB with 3-dB bandwidth of 20 GHz ~ 43 GHz. The gain ripple in 3-dB bandwidth is < 2.5 dB. The noise figure and input-referred P1dB are 5.5 dB and -16.5 dBm in the maximum gain state, respectively. The chip consumes 30.8 mW from 1.1 V voltage supply and the core area is 370 um × 930 um.
Voice Based Emotion Recognition with Convolutional Neural Networks for Companion Robots

In order to obtain emotional-related response from robots, computers and other intelligent machines, the first and decisive step is accurate emotion recognition. This paper presents the implementation of this function with the deep learning model of Convolutional Neural Networks (CNN). The architecture wis an adaptation of an image processing CNN, programmed in Python using Keras model-level library and TensorFlow backend. The theoretical background that lays the foundation of the classification of emotions based on voice parameters is briefly presented. According to the obtained results, the model achieves the mean accuracy of 71.33% for six emotions (happiness, fear, sadness, disgust, anger, surprise), which is comparable with performances reported in scientific literature. The original contributions of the paper are: the adaptation of the deep learning model for processing the audio files, the training of the CNN with a set of recordings in Romanian language and an experimental software environment for generating test files. Key-words: Voice Recognition, Emotion Recognition Convolutional Neural Networks, Companion Robots, pet robots.
Simple and Accurate Iris Center Localization Method based on Geometric Relationship of Eyeball Model

Precise iris localization plays a significant role on accurate gaze estimation. In this paper, a novel iris center localization method is proposed to deal with the iris contour change caused by the rotation of eyeball using the geometric relationship among iris center, eyeball center and iris contour. First, a face alignment method is employed to detect the face feature points that are used to initialize the iris center and eyeball locations. The three-dimensional rotation information of the eyeball is then described by the positional relationship between the centers of the eyeball and iris at the image level. The new obtained iris contour after the rotation is used to update the location of the iris center iteratively. To reduce the effect of illumination on image binarization, the cumulative histogram is introduced to adaptively get the threshold for binarization. Finally, experiments conducted on the BioID Face dataset and GI4E dataset as well as eight subjects demonstrate the superior performance of the proposed method.
Crystallization and Preliminary X-ray Crystallographic Analysis of Peptide Deformylase from Staphylococcus aureus

The peptide deformylase from the pathogenic bacterium Staphylococcus aureus has been overexpressed in Escherichia coli and crystallized in the presence of the inhibitor actinonin at 297 K using polyethylene glycol 20000 as a precipitant. X-ray diffraction data have been collected to 2.2 Å resolution. The crystal is trigonal, belonging to the space group P3121 (or its enantiomorph P3221), with unit cell parameters of a = b = 62.70 Å, c = 108.23 Å, α = β = 90, and γ = 120. An asymmetric unit contains a monomer of the recombinant enzyme, giving a VM of 2.84 Å 3 Da and a solvent content of 56.7%.
Research on color image segmentation algorithm based on improved Grab-cut

Because the Grab-cut algorithm mainly relies on the region information and lacks the edge information, the edge of the extraction in the segmentation result is not smooth and accurate enough. Aiming at this disadvantage, an improved method is proposed in this paper. A certain proportion of the edge information which is acquired after the edge detection of the image by using the Sobel edge operator is added to the smoothness term, in order to improve the reliability of smoothing constraint. Experimental results show that the improved algorithm reduces the error rate of the segmentation result, improves the edge smoothness and eliminates certain of the noise boundary.
Maintaining Trajectory of CoM for Stable Locomotion of Humanoid Robot Using Kalman Filter and Fuzzy Logic Controller

The ability of a humanoid robot, one of which is walking. However, a robot is often faced with a crucial stability problem when walking, for example of an uneven surface area. This paper presents a study on a humanoid robot walk on an inclined plane by forcing the trajectory of Center of Mass (CoM) to rest in a stability zone. To achieve this aim, the Kalman filter (KF) is used for state estimating and filtering error measurement influenced by the sensitiveness of the sensor. Then, the error of CoM trajectory measured by Inertial Measured Unit (IMU) serves as the input for Fuzzy Logic Controller (FLC) producing a control signal to correct the angle of some robot joints dynamically. This research, Bioloid Premium successfully to walk stably without falling in 7.5 degrees inclination area using KF and FLC.
Multi-feature Joint Sparse Representation for RGB-D Object Recognition*

It’s still a challenge to recognize object with RGB-D information. HMP is a classical method based on sparse coding, which can adapt to learn feature from RGB-D. HMP method ignore gradient information. And SIFT based sparse coding could capture gradient information well, while cannot adaptively extract other feature from RGB-D. So we propose multi-feature joint sparse representation (MJSR) algorithms, which combine sparse coding based on SIFT and HMP. At first, we extract dense-SIFT from image. Then dictionary is captured with K-SVD algorithm. Sparse coding can be obtained by using Matching Pursuit (MP) on dictionary and sift features. Spatial pyramid pooling is applied on sparse coding based on SIFT and the features consist of patch feature and associated sparse coding as HMP to capture image feature. In the end, we conduct experiment on Washington RGB-D object dataset.
Formation-containment Control of Second-order Multi-agent Systems with Sampled Data and Intermittent Communication

This paper investigates the formation-containment control of multi-agent systems with agents’ dynamics modelled by double integrators. For the agents communicating with each other intermittently, distributed coordination control algorithms are constructed for the leaders and following agents respectively. In our proposed algorithms, each agent’s input is driven by continuous-time data, sampled data and zero are proposed. By means of the theories about matrices and algebraic graphs, sufficient conditions are analyzed respectively for the leaders converging to the expected formation asymptotically and the followers reaching the containment region of the leaders asymptotically. Furthermore, the validity of our theoretical results are shown by simulations.
An Intelligent Controller for Torque Ripple Minimization of Permanent Magnet Brushless DC Motor*

Permanent magnet brushless DC motor(PMBLDC) has a series of advantages, such as small size, high efficiency and excellent performance of speed control. However, there are still some negative effects, such as torque ripple, which obviously affect its output performance. In this paper, in order to make the torque ripple minimiazation in high precision applications, iterative robust sliding mode control (IRSMC) is proposed based on a double level structure of biological intelligent control. Based on MATLAB/SIMULINK platform, we builds a PMBLDC control system model. The simulation results show that the IRSMC controller have a greatly convergence and through the on-line learning to compensate control voltage. It reduces the torque ripple improves the system dynamic performance and control precision obviously.
The Design of Tool Status Intelligent Monitoring System for CNC Machine

Based on the background of more and more repeated machining of single workpieces, this paper proposes an intelligent tool condition monitoring system. The vibration acceleration and current signals of the main shaft are collected, and the signal data is analyzed by wavelet packet decomposition and reconstruction and time domain and frequency domain to obtain characteristic values related to the tool state. The feature values extracted from the signal are then processed using normalization and PCA to obtain low-dimensional samples for representing the overall feature information for subsequent determination. Finally, C-SVM pattern recognition is used to implement tool condition monitoring.
ENGLISH SUMMARIZATION OF ICELANDIC TEXTS

The field of text summarization has been evolving along with advances in Natural Language Processing (NLP) and Computer Science but until now no known attempts have been made to create a summarizer for summarizing Icelandic texts. This project looks into the field of text summarization, giving an overview of different types and approaches towards automatically generating summaries. Additionally, a clarification towards the requirements of language resources and scope towards implementing a summarizer for Icelandic texts is given. Two known methods for text summarization (TextRank and TFxIDF) were selected for a prototype implementation, both methods rely on repetitions within the text to calculate a sentence score for identifying extract worthy sentences to include in a summary. The methods are both unsupervised and proved to be a good for a base summarizer to tailor for Icelandic texts. The methods were tested on a set of news articles. The quality of the summaries generated by the summarizer were evaluated by comparing them to human created summaries, the evaluation showed that the summarizer performs consistently and produces readable summaries. Íslenskt heiti Samantekt á íslenskum texta
Combination of Reinforcement Learning and Neural Networks

Reinforcement Learning has been already successfully applied to the problem of a target oriented robot navigation. However one of its drawbacks is the usually observed low convergence rate of the learning progress. To weaken this disadvantage the present work suggests to use a control function which is represented by a neural network to prevent the agent of collisions with objects if a dangerous situation has been recognized. This leads to a better performance of the learning process and to a reduced number of collisions. Since an evolutionary algorithm is used to develop the neural network, little effort is necessary to train the network.
Enhancement of Transient Stability in Power Systems with High Penetration Level of Wind Power Plants

Due to the power electronic converter based interface and maximum power control strategy, wind generators cannot directly respond to power system transients. This brings new challenges on power system transient stability. Taking wind turbine type 4 (WT4) as one example, this paper analyses its influence on transient stability with respect to locations, low voltage ride through parameters, wind power plant installation capacity and penetration levels. Based on the sensitivity analysis carried out for the influence of WT4, a supplementary transient stability control is proposed. The results on a 3-area system show that this supplementary control can improve transient stability of power systems with high penetration of wind power.
Passivity-Based Control for a PV/Battery/Fuel Cell/Electrolyser Hybrid Power System

In this work, a passivity-based controller is proposed for a hybrid system including photovoltaic panels, a fuel cell, a battery and an electrolyser. This short-term controller is designed by the Interconnection and Damping Assignment Passivity Based Control (IDA-PBC) method to solve the converters coordination problem. Simulation results prove that this controller achieves an optimal exploitation of the components while preserving the stability of the whole closed-loop system.
Low Temperature Cu Interconnect with Chip to Wafer Hybrid Bonding

Current DRAM advanced chip stack packages such as the high bandwidth memory (HBM) use throughsilicon-via (TSV) and thermal compression bonding (TCB) of solder capped micro bumps for the inter-layer connection. The bonding process has low throughput and cannot overcome the challenge of scaling below 40 µm pitch. These are compelling reasons to seek an alternative approach such as hybrid bonding. The pursuit of fine pitch die stacking with TSV interconnect using hybrid bonding is pervasive in the packaging industry today due to the promise of improved performance. Specifically, the Cu interconnect provides improved thermal and electrical performance and the all inorganic interface of the complete die stack offers enhanced thermal-mechanical performance and reliability in the final chip stack. Direct Bond Interconnect technology, also known as low temperature hybrid bonding, forms a spontaneous dielectric-to-dielectric bond at room temperature and then establishes metal-to-metal connection (usually Cu-to-Cu bond) by a low temperature batch annealing process (150 – 300°C). The direct bond process eliminates the need for solder and underfill and associated problems. While the hybrid bonding exists today in wafer-towafer (W2W) format in high volume manufacturing, chip to wafer (C2W) bonding developed for future product lines is making significant process in the past three years. A bonding process with high throughput has been demonstrated with electrical test yield above 90% with a daisy chain structure that covers 50mm^2 of bonding area. The bonded parts showed superior reliability performance in temperature cycling, high temperature storage and autoclave testing. This paper presents the latest development in C2W hybrid bonding and demonstrates the low temperature annealing capability and integration with TSV.
New Developments of Copper Plating Technology for Embedded Power Chip Packages Challenges

Copper plating has been extensively employed in the fabrication of embedded packaging to reach high-density, high-speed, high performance electronic products. With through holes (TH) as well as blind via aspect ratios increase, development of a reliable plating technology is very important. When the depth of through hole was over 200um, it is difficult to fill without void by using direct current (DC) electroplating. In order to overcome this problem, organic additives were applied to cause faster copper deposition at the TH center rather than at the opening. Besides of that, the x-shape through hole was also developed due to its particular geometry form, which was beneficial for the copper bridge at the TH center. In this paper, the x-shape through hole with depth of 350 um was fully filled by DC electroplating within 2hrs. This filling ability development enable the embedded chip package design more flexibility.
Moving Direction Prediction of the Autonomous Vehicle Based on Multi-cue Fusion

In this paper, we propose an approach to fuse various available cues, such as vanishing point of the road, moving direction of the moving target, road edges, road textures, and cues with vertical edges, which extracted only from images of the road scene, to predict the moving direction of the autonomous vehicle. Firstly, the Gaussian models of the moving direction of the autonomous vehicle is constructed by using above cues respectively. Then, the prediction results of different cues are fused under the Bayesian framework to estimate the most reasonable moving direction of autonomous vehicle. We test the algorithm in our campus road scene. Compared with the prediction result of single cue, our fusion algorithm effectively improves the robustness of the prediction, and It has certain reference significance for local navigation and path planning for the autonomous vehicle.
Towards a Unified Visualization Platform for Ubiquitous Analytics

© 2018 Copyright is held by the owner/author(s). Abstract We propose the vision of a unified visualization platform that supports the full range of the classic “anytime” and “anywhere” motto for data analysis: from mobile and onthe-go usage, through office settings, to collaborative conference rooms and smart environments. Furthermore, such a platform, in order to be truly unified and universal, would have to scaffold a complete set of use-cases for visualization and analytics, including exploration, sensemaking, development, and, eventually presentation and dissemination. We discuss the design space of this unified platform and each of the required components in detail. Finally, we present several existing technologies and research efforts that may be leveraged to realize this vision.
Dynamic equivalent of an active distribution network taking into account model uncertainties

A reduced-order model of a distribution network hosting inverter-based generators is derived. This model aims to be used in dynamic simulations of large disturbances in the transmission system. Uncertainties affecting the parameters of the component models are addressed using Monte-Carlo simulations. For a given disturbance, the average and the standard deviation of the randomized system evolutions are extracted. The dynamic equivalent is of the “grey-box” type. Its parameters are adjusted to match as closely as possible the average system response. This involves minimizing a weighted least-square objective function, in which the weights reflect the dispersion of the randomized Monte-Carlo responses. Multiple disturbances are considered to avoid over-fitting, and a recursive procedure is used to select a small subset of them from which the parameters of the ADN equivalent are identified. Simulations results are reported on the CIGRE MV distribution network benchmark. They show a good accuracy of the equivalent.
Experimental Investigation of the Correlation between a Load-Based Metric and Solder Joint Reliability of BGA Assemblies on System Level

A fast and accurate method based on experimentally obtainable parameters for the solder joint reliability prediction under thermo-mechanical load can add significant value in the product development process. On system level, for products with over thousand components, the evaluation of every individual solder joint is not always feasible. This is first and foremost true for products with many large BGA components that have very high ball counts. In addition, especially for these components, board level lifetime test results cannot be easily transferred to the product due to system level effects like stress triaxiality. The CTE mismatch between PCB and housing leads to PCB warpage and complex solder joint loading conditions for bending sensitive components under temperature cycling. The work presented in this paper advances the state of the art in solder joint reliability prediction by using only experimentally obtainable parameters such as PCB strain, PCB curvature and component warpage as metric for the reliability prediction. With this metric, a fast pre-qualification for BGA assemblies on system level with easier accessible parameters is possible. In order to verify the method, two fine pitch BGA packages with SAC solder joints, identical footprint and lateral package dimensions but different warpage behavior over temperature are investigated. The number of cycles to failure of the solder joints are experimentally determined in passive temperature cycling test under varying PCB strains and curvatures by superimposing a 3P-bending load. In this bending test, well defined PCB strain and curvature can be applied on the components in order to investigate the influence of these loads on the solder joint reliability. The correlation of component warpage, PCB strain and curvature with the solder joint reliability of these BGA packages is assessed.
The Fusion of Satellite and Unmanned Aerial Vehicle (UAV) Imagery for Improving Classification Performance

In recent years, Unmanned Aerial Vehicle (UAV) technology has made rapid progress, and it is becoming an important way to obtain regional vegetation types due to its characteristics of being flexible, comprehensive and dynamic. UAV-based data recognition technology can make full use of geometry and texture information in images, which is benefit to interpret the attribute characteristics of ground objects. Compared to satellite data, spectral information in UAV data is less detailed. This paper aims to increase the spectral information of UAV data, which helps to improve the accuracy of classification and recognition. For such purpose, data fusion of UAV images and satellite multispectral images is performed. In order to verify the effectiveness of the fused data, three classification algorithms are tested for comparison, which include Support Vector Machine (SVM), Artificial Neural Network (ANN) and Maximum Likelihood (ML). The accuracy of the classification result is evaluated by real ground sampling data. The result shows that, compared to the original UAV data, the classification accuracy of the fused data is significantly improved.