Miracle-dz/UniFAR-FTUs huggingface dataset:
Observer-Based Control of Bilateral Teleoperation with Time Delay

This paper is concerned with the stability analysis problem of a bilateral teleoperation system. An observer-based control scheme is proposed to deal with the communication delays, which are time varying and asymmetric. The proposed control method is applied to the situation that the velocities of the master and slave are not available. Two observers are designed to estimate the velocities of each robot. Some sufficient criteria are derived to ensure the considered system to be stable by using the Lyapunov functional method. The proposed criteria are given in terms of linear matrix inequalities (LMIs) which is easy to be checked. The effectiveness of the obtained results are illustrated by a numerical example.
Dual Parallel Multielectrode Traveling Wave Mach–Zehnder Modulator for 200 Gb/s Intra-datacenter Optical Interconnects

We present a silicon photonic dual parallel multielectrode Mach–Zehnder modulator (MEMZM) based transmitter targeting 200 Gb/s four-level pulse amplitude modulation (PAM4) short reach transceivers. The MEMZMs have an average <inline-formula><tex-math notation="LaTeX">$V_\pi$</tex-math></inline-formula> and electro-optic (EO) bandwidth of 5 V and 38 GHz, respectively. The transmitter is characterized versus receiver equalizer taps, received signal power, driving voltage swing, crosstalk voltage swing, bitrate, and reach. Results reveal that using only a three-tap equalizer at the receiver, 100 Gb/s PAM4 net rate per lane can be achieved at a bit error rate (BER) below the KP4 forward error correction (KP4-FEC) threshold of <inline-formula><tex-math notation="LaTeX">$2.4 \times 10^{-4}$</tex-math></inline-formula>. Moreover, up to 128 Gb/s can be received at a BER below the KP4-FEC threshold using only 2 Vpp and 1 Vpp driving the MEMZM segments. Then, both MEMZMs are driven simultaneously to assess the crosstalk impact on the BER performance at parallel operation. Driven by four binary signals, we demonstrate 200 Gb/s PAM4 transmission over up to 10 km of single mode fiber at a BER below the KP4-FEC threshold.
Algebraic List-Decoding in Projective Space: Decoding With Multiplicities and Rank-Metric Codes

The problem of list decoding algebraic subspace codes and rank-metric codes is considered. We develop two separate methods, via two different approaches, for list decoding subspace codes and rank-metric codes. These methods provide, for certain code parameters, improved tradeoffs between rate and error-correction capability than that of the Koetter-Kschischang codes, in the domain of subspace codes, and than that of the Gabidulin codes, in the domain of rank-metric codes, and several other extensions thereof. In the first approach, we introduce the notion of root multiplicities for a certain sub-ring of the ring of linearized polynomials. In the list-decoding algorithm, multiple roots are enforced for the interpolation polynomial in order to achieve an improved error-correction radius for an extended family of Koetter–Kschischang subspace codes. The normalized error-correction radius for this approach is <disp-formula> <tex-math notation="LaTeX">\begin{equation*}\tau _{A} = \frac {2(L+1)}{({r+1})} -1- \frac {L(L+1)(L+n)}{r(r+1)}R,\end{equation*} </tex-math></disp-formula> where <inline-formula> <tex-math notation="LaTeX">$L$ </tex-math></inline-formula> is the maximum list size, <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> is the subspace code dimension, <inline-formula> <tex-math notation="LaTeX">$R$ </tex-math></inline-formula> is the rate of the code, and <inline-formula> <tex-math notation="LaTeX">$r$ </tex-math></inline-formula> is the multiplicity parameter. In the second approach, we construct a folded version of Koetter-Kschischang codes. A linear-algebraic list-decoding algorithm is proposed for these codes that achieves the error-correction radius <disp-formula> <tex-math notation="LaTeX">\begin{equation*}\tau _{B} = s(1-sR),\end{equation*} </tex-math></disp-formula> where <inline-formula> <tex-math notation="LaTeX">$s$ </tex-math></inline-formula> is the folding parameter. As opposed to the first approach, the size of output list in the second approach depends on the underlying field size and is at most <inline-formula> <tex-math notation="LaTeX">$q^{m(s-1)}$ </tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">$q^{m}$ </tex-math></inline-formula> is the size of the field that message symbols are chosen from. It is also shown that the output list size is 1, with high probability, in a probabilistic setting. We utilize the techniques of the second approach in the domain of rank-metric codes to construct folded Gabidulin codes to enable a linear-algebraic list-decoding algorithm for such codes. Our algorithm makes it possible to recover the message provided that the normalized rank of error is at most <inline-formula> <tex-math notation="LaTeX">$1-R-\epsilon $ </tex-math></inline-formula>, for any <inline-formula> <tex-math notation="LaTeX">$\epsilon > 0$ </tex-math></inline-formula>. Notably, this achieves the Singleton bound on the error-correction radius of a rank-metric code.
Dual-Band Conformal Antenna for Wireless Capsule Endoscopy Applications

In this paper, a dual-band slot antenna is proposed for wireless capsule endoscopy (WCE) applications, operating at 403 MHz Medical Device Radiocommunications Service (MedRadio) band and 915 MHz Industrial, Scientific, and Medical (ISM) band. To occupy capsule space as little as possible, the antenna is attached on the wall of a capsule in conformal form. Meandered feeding strip shorted at its end is adopted to feed an open-end slot in ground, forming a miniaturized dual-band antenna. Simulation results show that a wide bandwidth of 16.1% and 47% can be achieved at MedRadio band and ISM band, respectively. Finally, the robustness of the antenna performance is evaluated and specific absorption rate (SAR) is considered, showing the antenna a good candidate for WCE application.
Orbital Angular Momentum Beam MIMO Arrays

We critically assess the possibilities for utilization of orbital angular momentum (OAM) of electromagnetic (EM) waves, alongside and in combination with other existing data transfer methods, for further capacity improvement of 5G systems. Special attention is devoted to inherent limitations imposed by the OAM beams. Trade-offs between the desired function and unavoidable limitations are discussed.
Assessment of Predictive Models for Coffee Production in the Philippines

This is a research-in-progress of developing a coffee eco-market with online bidding for different coffee stakeholders in selected provinces in the Philippines. The objective of this paper is to compare three different forecasting models using a five-year coffee production data. The three models explore and assess exponential smoothing, moving average, and regression. Different components such as seasonal, trend and irregular components are present in the data. Thus, the original data is adjusted by removing the seasonal component, trend component, and irregular component. For the computation of the forecasted values, the MS Excel data analysis tool is used. The standards used to measure the accuracy of each three model for comparison are the MAE, the MSE, and the MAPE. Among the three model, the moving average model rank first with a 9% error accuracy percentage, the next is the exponential smoothing with 12% error accuracy percentage, then the last is the regression with 14% error accuracy percentage.
Quantitative Correlation and Causality Analysis on the Cause of Haze in Henan Province

With the frequent occurrence of fog and haze in China, more and more studies on the causes of haze and the impact on people's lives were carried out. In this paper, the causes of haze are analyzed in Henan province which is the Central Plains of China. Based on the official historical data, two methods, principal component analysis (PCA) and convergent cross mapping (CCM) are used to quantitative analysis the correlation and causality of the AQI, the concentration of pollutants and the meteorological factors. First, the correlation of air quality is analyzed in the cities of Henan Province, and then Zhengzhou was taken as the main research area in the following study. According to the principal component analysis of the pollutants concentration, the former two elements PM2.5 and CO with the meteorological factors are combined to carry out CCM causality analysis.
Hierarchical Control Architecture Regulating Competition between Model-Based and Context-Dependent Model-Free Reinforcement Learning Strategies

Recent evidence in neuroscience and psychology suggests that a single reinforcement learning (RL) algorithm only accounts for less than 60% of the variance of human choice behavior in an uncertain and dynamic environment, where the amount of uncertainty in state-action-state transitions drift over time. The prediction performance further decreases when the size of the state space increases. We proposed a hierarchical context-dependent RL control framework that dynamically exerted control weights on model-based (MB) and multiple model-free (MF) RL strategies associated with different task goals. To properly assess the validity of the proposed method, we considered a two-stage Markov decision task (MDT) in which the three different types of context changed over time. We trained 57 different RL control models on a Caltech MDT data set; then, we assessed their prediction performance using a Bayesian model comparison. This large-scale computer simulation analysis revealed that the model providing the most accurate prediction was the version that implemented the competition between the MB and multiple goal-dependent MF RL strategies. The present study demonstrates the applicability of the goal-driven RL control to a variety of real-world human-robot interaction scenarios.
Entangled Polynomial Coding in Limited-Sharing Multi-Party Computation

In a secure multiparty computation (MPC) system, there are some sources, where each one has access to a private input. The sources want to offload the computation of a polynomial function of the inputs to some processing nodes or workers. The processors are unreliable, i.e., a limited number of them may collude to gain information about the inputs. The objective is to minimize the number of required workers to calculate the polynomial, while the colluding workers gain no information about inputs. In this paper, we assume that the inputs are massive matrices, while the workers have the limited computation and storage at each worker. As proxy for that, we assume the link between each source and each worker admits a limited communication load. We propose a scheme for private data sharing, called entangled polynomial sharing, and show that it admits basic operations such as addition, multiplication, and transposing, respecting the constraint of the problem. Thus, it allows computing arbitrary polynomial of the input matrices, while it reduces the number of servers needed significantly compared to the conventional scheme. It also generalizes the recently proposed scheme of polynomial sharing.
Non-metric Constraints in the Graph-Based Optimization for Personal Indoor Localization

Graph-based optimization is a framework that makes it possible to obtain the most probable estimates of the system state based on the observed constraints. This approach is considered the state-of-the-art in the localization of autonomous agents and SLAM. Most commonly, metric (quantitative) measurements are added to the graph, as non-metric (qualitative) measurements are ignored due to the troubles in representing that information as valid graph constraints. We propose methods to include non-metric information in the agent localization process employing graph-based optimization. The proposed method is experimentally verified for the case of personal indoor localization using a mobile device. The non-metric information in this application is particularly important, due to the limitations of the sensors in a typical smartphone or tablet that do not allow the agent to gather enough quantitative information to constraint its pose. Hence, constraints that stem from the qualitative information can be used to improve the reliability and accuracy of localization. These constraints come from the processing the state of the mobile device, from the known environment 2-D map (blueprints), and from appearance-based place recognition. The proposed methods of representing non-metric measurements as graph constraints are verified in real-life experiments and are a guide how different types of non-metric constraints could be included in the graph-based optimization.
ROI Detection in Mammogram Images Using Wavelet-Based Haralick and HOG Features

Digital mammography is a widespread medical imaging tech-nique that is used for early detection and diagnosis of breast cancer. Detecting the region of interest (ROI) helps to locate the abnormal areas, which may be analyzed further by a ra-diologist or a CAD system. In this paper, a new classification method is proposed for ROI detection in mammography im-ages. Features are extracted using Wavelet transform, Haralick and HOG descriptors. To reduce the number of di-mensions and eliminate irrelevant features, a wrapper-based feature selection method is implemented. Several feature ex-traction methods and machine learning classifiers are com-pared by performing a leave-one-image-out cross-validation experiment on a difficult dataset. The proposed feature ex-traction method provides the best accuracy of 87.5% and the second-best area under curve (AUC) score of 84% when em-ployed in a random forest classifier.
Lexicon and Heuristics Based Approach for Identification of Emotion in Text

Recognition of emotion from text is emerging as new field of research which can add another dimension to the analysis of textual data. People directly or indirectly express their emotion through facial expressions, speech or written content. People are putting a lot of textual content on social media and microblogging platforms. This data can be very useful in discovering different aspects including emotions. Recognition of emotion is a very complex task. We propose a technique which uses word lexicons, emoticons, negations and intensity modifiers. Our approach follows Ekman's emotion model. Word lexicons are generated using WordNet from a set of seed words collected manually. Twitter is used to collect emoticons. Negations and intensity modifiers are collected manually from literature. Several heuristic rules are designed to support our keyword spotting technique. Experiments on data obtained from Twitter shows high precision and recall for all emotional classes.
Estimation of the K-Factor for Temporal Fading From Single-Snapshot Wideband Measurements

The Ricean K-factor is the ratio of the power of the deterministic multipath component (MPC) and the power of all other stochastic MPCs. The classical moment-based method has been successfully used to estimate the K-factor from time series measurements. In this paper, we apply this method to estimate the K-factor for narrowband temporal selectivity, based upon the analysis of frequency-selectivity in single-snapshot <inline-formula><tex-math notation="LaTeX">$\text {wideband}$</tex-math></inline-formula> measurements. We also derive the theoretical bias of this <inline-formula><tex-math notation="LaTeX">$\text {estimator}$</tex-math></inline-formula> and find it depends jointly on the <inline-formula><tex-math notation="LaTeX">$\text {number}$</tex-math></inline-formula> of <inline-formula><tex-math notation="LaTeX">$\text {channel}$</tex-math></inline-formula> transfer function envelope samples across the <inline-formula><tex-math notation="LaTeX">$\text {measurement}$</tex-math></inline-formula> bandwidth, the correlation among such samples, and the K-factor values. Qualitative analysis indicates that the bias increases nearly linearly with the K-factor (<inline-formula><tex-math notation="LaTeX">$K\geq 1$</tex-math></inline-formula> on the linear scale) and is affected by correlation amongst the samples. Furthermore, the bias is inversely proportional to the number of samples. Simulations confirm the validity of the derivations. Moreover, a measurement campaign is designed at 28 GHz with a system bandwidth of 400 MHz in an urban micro-cell scenario. The proposed estimator is used to extract the statistics of the K-factor in line of sight and non-line of sight scenarios. The relationship between the K-factor and distance is investigated and a linear model is used to characterize it.
Bone Age Assessment Based on Two-Stage Deep Neural Networks

Skeletal bone age assessment is a clinical practice to diagnose the maturity of children. To accurately assess the bone age, we proposed an automatic bone age assessment method in this paper based on deep convolution network. This method includes two stages: mask generation network and age assessment network. A U-Net convolution network with pretrained VGG16 as the encoder is used to extract the mask of bones. For the assessment module, the original images are fused together with the generated mask image to obtain segmented normalized hand bone images. We then built a multiple output convolution network for accurate age assessment. Finally, the bone age regression problem is transformed into the K-1 binary classification sub-problems. Our model was tested on RSNA2017 Pediatric Bone Age dataset. We were able to achieve the mean absolute error (MAE) of 5.98 months, which outperforms other common methods for bone age assessment. The proposed method could be used for developing fully automatic bone age assessment with better accuracy.
Stiffness Control of Deformable Robots Using Finite Element Modeling

Due to the complexity of modeling deformable materials and infinite degrees of freedom, the rich background of rigid robot control has not been transferred to soft robots. Thus, most model-based control techniques developed for soft robots and soft haptic interfaces are specific to the particular device. In this letter, we develop a general method for stiffness control of soft robots suitable for arbitrary robot geometry and many types of actuation. Extending previous work that uses finite element modeling for position control, we determine the relationship between end-effector and actuator compliance, including the inherent device compliance, and use this to determine the appropriate controlled actuator stiffness for a desired stiffness of the end-effector. Such stiffness control, as the first component of impedance control, can be used to compensate for the natural stiffness of the deformable device and to control the robot's interaction with the environment or a user. We validate the stiffness projection on a deformable robot and include this stiffness projection in a haptic control loop to render a virtual fixture.
A New Set of Bluetooth-Based Fingerprinting Algorithms for Indoor Location Services

Indoor Location Based Services (LBSs) are likely to make significant contributions to society and the economy in the near future. Unlike outdoor LBSs, the technologies and methodologies used are still very much under development, and there are a number of challenges which must be addressed, namely: accuracy, precision, and time complexity. For the accuracy metric, the Euclidean distance error is calculated based on the difference between the known location (points of interests) and the localized position. Regarding the precision metric, the distribution of the distance errors is computed. In this paper, new fingerprinting-based algorithms, namely, the Nearest Neighbor Version 2 (NNV2), Nearest Neighbor Version 3 (NNV3) and Nearest Neighbor Version 4 (NNV4), are proposed, and tested to determine the most effective and efficient one with respect to those challenges. Our analysis reveals that: (1) the time complexity for each of the Nearest Neighbour (NN) and KNN algorithms (i.e., K is constant) is $(1\ast \mathbf{n}\ast \mathbf{m}+1\ast \mathbf{m})$ -comparison which is more than that for NNV2 and NNV4 (i.e., n is the number of centroids between any two rows, m refers to the Received Signal Strength Indicators (RSSIs) acquired at the offline stage, and 1 is the number of rows that holds some of the grid points), (2) NNV4 outperforms the NN, KNN and Path-loss based Fingerprint Localization algorithms (PFL) in terms of accuracy by approximately 29%, 13%, 22%; respectively, (3) NNV4 outperforms the NN, KNN and PFL in terms of precision by approximately 53%, 28%, 52%; respectively, and (4) NNV4 has a lower probability of positional error compared to those for the existing indoor localization algorithms.
A Low-Power Arithmetic Element for Multi-Base Logarithmic Computation on Deep Neural Networks

Computational complexity and memory intensity are crucial in deep convolutional neural network algorithms for deployment to embedded systems. Recent advances in logarithmic quantization has manifested great potential in reducing the inference cost of neural network models. However, current base-2 logarithmic quantization suffers from performance upper limit and there is few work that studies hardware implementation of other bases. This paper presents a multi-base logarithmic scheme for Deep Neural Networks (DNNs). The performance of Alexnet is studied with respects to different quantization resolutions. Base $-\sqrt{2}$ logarithmic quantization is able to raise the ceiling of top-5 classifying accuracy from 69.3% to 75.5% at 5-bit resolution. A segmented logarithmic quantization method that combines both base-2 and base $-\sqrt{2}$ is then proposed to improve the network top-5 accuracy to 72.3% in 4-bit resolution. The corresponding arithmetic element hardware has been designed, which supports base $-\sqrt{2}$ logarithmic quantization and segmented logarithmic quantization respectively. Evaluated in UMC 65nm process, the proposed arithmetic element operating at 500MHz and 1.2V consumes as low as 120 $\mu \mathbf{W}$. Compared with 16-bit fixed point multiplier, our design achieves 58.03% smaller in area, with 73.74% energy reduction.
Control System Design, Analysis & Implementation of Two Wheeled Self Balancing Robot (TWSBR)

Self-balancing robot has the ability of locomotion with balancing capabilities. The two wheeled self-balancing robot can rotate to swiftly change its direction of motion and precisely navigate tight gaps as compared to a three or four-wheeled robot. The paper aims to present modeling, analysis and control design of Two Wheeled Self Balancing Robot (TWSBR) using simulation tools and hardware. Mathematical and state space modelling of TWSBR are performed using Lagrange equations. Based on the model, Linear Quadratic Regulator (LQR), Proportional Integral Derivative (PID) and pole placement control methods are anticipated for stabilization and balancing control of the system. A comparative assessment of the controllers is presented after analyzing performance of each controller individually. Based on results and simplicity of controller, PID controller is implemented in hardware. The implemented robust controller design is able to self balance the robot as expected on tuning PID parameters found from simulations. Hardware implementation of TWSBR shows that it has ability of motion with balancing aptitudes and can be used to handle plethora of tasks proficiently as humans do.