Miracle-dz/UniFAR-FTUs huggingface dataset:
Globally Defined Dynamic Modelling and Geometric Tracking Controller Design for Aerial Manipulator

This study presents a globally defined dynamics for a conventional multirotor equipped with a single $n\mathbf{-DOF}$ manipulator using modified Lagrangian dynamics. This enables the reformulation of entire dynamics directly on $\text{SO}(3)$ without exploiting any local coordinates, and thus problems such as the singularity of Euler angles can be avoided. Since skew-symmetric property of Coriolis matrix $C$ and inertia matrix facilitates stability analysis, we propose a method to compute $C$ which guarantees the skew-symmetric property by considering $C$ as a summation of two sub-matrices. Then, a geometric tracking controller is designed based on decoupled dynamics applying passive decomposition. The proposed controller guarantees almost global region of attraction. We validate our method via consecutive aerial flipping experiments.
Review of Speech Enhancement Methods using Generative Adversarial Networks

The goal of speech enhancement is to restore the quality of noise affected speech by removing the noise. The purpose of speech enhancement is to increase the damaged speech signal’s comprehensibility and overall perceptual quality. Given the nature of the noise, which is very non-stationary, speech enhancement is a difficult challenge. In recent years, speech enhancement has been accomplished using Generative Adversarial Networks (GANs). This review includes GAN based speech enhancement algorithms, data sets and evaluation metrics. We also discuss the issues and compare the performance of the different speech enhancement methods.
Proper In deposition amount for on-demand epitaxy of InAs/GaAs single quantum dots

The test-QD in-situ annealing method could surmount the critical nucleation condition of InAs/GaAs single quantum dots (SQDs) to raise the growth repeatability. Here, through many growth tests on rotating substrates, we develop a proper In deposition amount (θ) for SQD growth, according to the measured critical θ for test QD nucleation (θ c). The proper ratio θ/θ c, with a large tolerance of the variation of the real substrate temperature (T sub), is 0.964−0.971 at the edge and > 0.989 but < 0.996 in the center of a 1/4-piece semi-insulating wafer, and around 0.9709 but < 0.9714 in the center of a 1/4-piece N+ wafer as shown in the evolution of QD size and density as θ/θ c varies. Bright SQDs with spectral lines at 905 nm–935 nm nucleate at the edge and correlate with individual 7 nm–8 nm-height QDs in atomic force microscopy, among dense 1 nm–5 nm-height small QDs with a strong spectral profile around 860 nm–880 nm. The higher T sub in the center forms diluter, taller and uniform QDs, and very dilute SQDs for a proper θ/θ c: only one 7-nm-height SQD in 25 μm2. On a 2-inch (1 inch = 2.54 cm) semi-insulating wafer, by using θ/θ c = 0.961, SQDs nucleate in a circle in 22% of the whole area. More SQDs will form in the broad high-T sub region in the center by using a proper θ/θ c.
A Dynamic Time Warping Based Method to Synchronize Spectral and Protocol Domains for Troubleshooting Wireless Communication

An increase in popularity of wireless networks, mainly in industrial automation and manufacturing, has escalated the need for reliable wireless networks. One subtle way of achieving reliability is early diagnosis of transmission failures in order to take preventive measures against them. However, this can be difficult to achieve because these failures mainly arise from challenging signal propagation conditions and interference from co-existing networks, which is hard to diagnose. This leads to Quality-of-service (QoS) degradation and faulty applications, which may result in system breakdown and financial distress. In this paper, we propose a novel Dynamic Time Warping (DTW) based method named Variable Adaptive DTW (VADTW) for synchronizing spectral and protocol domains to obtain precise and complete information about collisions and co-existing links that cause interference, which can be utilized for troubleshooting. VADTW divides the spectral and protocol sequences in adaptive time bins and calculates variable window limits for each frame to perform the synchronization, which aims to complement the cross-RF-standard interference detection of the spectral domain with the MAC-Layer information from the protocol domain. To prove the effectiveness of the proposed approach, we tested it in a Wi-Fi network as a proof-of-concept. The experiments are performed with real-time data traffic captured in different scenarios. VADTW successfully synchronizes sequences even with a frame loss ratio of 50%. We also perform comparative analysis of VADTW with other DTW based approaches on the basis of performance, computational cost, and execution time.
A novel interface architecture for differential capacitive sensors

In this work, we propose an analog fully differential interface for capacitive sensors. The working principle of the proposed solution is based on an auto-balancing bridge topology entirely formed by capacitive components. In particular, two capacitors form the sensor, while other components are voltage-controlled capacitors (VCCs) which in steady-state conditions follow sensor capacitor values through a negative feedback performing the zero-convergence of the differential output at the bridge. Theoretical curves involving VCCs control voltages have been extracted and compared with experimental values on a discrete component printed circuit board (PCB), showing very good agreement. The effect of parasitic capacitances have been also considered from evaluations on the PCB, obtaining that their contribution increases if the reference baseline capacitance of the auto-balanced bridge decreases. This study opens to further investigations about modeling and compensation of these capacitance sensors.
ARC-FSM-G: Automatic Security Rule Checking for Finite State Machine at the Netlist Abstraction

Modern system-on-chip (SoC) designs are becoming prone to numerous security threats due to their critical applications and ever-growing complexity and size. Therefore, the early stage of the design flow requires comprehensive security verification. The control flow of an SoC, generally implemented using finite state machines (FSMs), is not an exception to this requirement. Any deviations from the desired flow of FSMs can cause serious security issues. On the other hand, the control FSMs may be prone to fault-injection and denial-of-service (DoS) attacks or have inherent information leakage and access control issues at the gate-level netlist abstraction. Therefore, defining a set of security rules (guidelines) for obtaining FSM implementations free from particular security vulnerabilities after performing logic synthesis is crucial. Unfortunately, as of today, no solution exists in the state-of-the-art domain to verify the security of control FSMs. In this paper, we propose a set of such security rules for control FSM design and a verification framework called ARC-FSM-G to check for those security rule violations at pre-silicon to prevent any security vulnerabilities of FSM against fault-injection, access control, and information leakage threats. Experimental results on several benchmarks varying in size and complexity illustrate that ARC-FSM-G can effectively check for violations of all the proposed rules within a few seconds.
A comparative study on ML-based approaches for Main Entity Detection in Financial Reports

Modern AI technologies which exploit the classification and/or prediction capacities of Deep Neural Architectures demonstrate superior performance to traditional approaches in most cases. However, they come with the unavoidable shortcoming of lack of transparency in their outcomes. This attribute renders them unsuitable for big industrial sectors, such as finance, investment management, etc. Specifically, their "black-box" nature makes them unattractive in cases where human understanding in the decision making process is required and may be legally mandatory. In such cases, traditional (i.e., non-deep learning) ML approaches are still preferred, to minimize for example the presence of false positives. In this context, this paper introduces an unsupervised, trustful, bottom-up probabilistic approach for Named Entity Recognition (NER) in financial reports, while in parallel it provides a comparative study on well-known ML-approaches in terms of their performance. The proposed approach builds on the probability of appearance of representative tokens within the given reports and utilizes Kronecker’s Delta and the Total Probability Theorem to construct a probabilistic model that estimates the overall classification probability of a document.
Plant Species Identification Based on Plant Leaf using Machine Learning Techniques

In the absence of plants, we would not be able to live on this planet. A growing number of plant species are available, and they number in the hundreds. Foresters, farmers, environmentalists, and educators need knowledge of species to make informed decisions. As a result, species identification belongs to an interdisciplinary field of study. It may be challenging and tedious for nonexperts who are not familiar with typical botanical terms. In the fields of computer vision and machine learning, advances in this area can make this task easier. Although some efforts have been made, all plant species cannot be identified with one system. In this study, a similar approach was taken. In order to identify a plant, four steps have to be completed: acquisition of images, pre-processing, feature extraction, and classification. For this study, 1,125 images of 15 different species of leaves were used from the Swedish leaf dataset. Pre-processing is performed using Gaussian filtering mechanisms, and then texture and colour features are extracted.
Non-Linear Networked Systems Analysis and Synthesis using Dissipativity Theory

We consider networked systems comprised of interconnected sets of non-linear subsystems and develop linear matrix inequality (LMI) techniques for their analysis and interconnection topology synthesis using only the dissipativity properties of the involved subsystems. In particular, we consider four networked system configurations (NSCs) and show that the analysis of their stability/dissipativity can be formulated as corresponding LMI problems. Using some matrix identities and mild assumptions, we also show that the synthesis of interconnection typologies for these NSCs can also be formulated as LMI problems. This enables synthesizing the interconnection topology among subsystems to enforce/optimize specific stability/dissipativity properties over the networked system. The formulated LMI problems can be solved efficiently and scalably using standard convex optimization toolboxes. We also provide several numerical examples to illustrate our theoretical results.
Benchmarking Potential Based Rewards for Learning Humanoid Locomotion

The main challenge in developing effective reinforcement learning (RL) pipelines is often the design and tuning the reward functions. Well-designed shaping reward can lead to significantly faster learning. Naively formulated rewards, however, can conflict with the desired behavior and result in overfitting or even erratic performance if not properly tuned. In theory, the broad class of potential based reward shaping (PBRS) can help guide the learning process without affecting the optimal policy. Although several studies have explored the use of potential based reward shaping to accelerate learning convergence, most have been limited to grid-worlds and low-dimensional systems, and RL in robotics has predominantly relied on standard forms of reward shaping. In this paper, we benchmark standard forms of shaping with PBRS for a humanoid robot. We find that in this high-dimensional system, PBRS has only marginal benefits in convergence speed. However, the PBRS reward terms are significantly more robust to scaling than typical reward shaping approaches, and thus easier to tune.
Multi-Modal Learning and Relaxation of Physical Conflict for an Exoskeleton Robot with Proprioceptive Perception

Exoskeleton robots provide assistive forces to suit the human subject via physical human-robot interaction. During the closely-coupled interaction, a mismatch between the wearer and the robot may result in physical conflict, which could affect assistance efficiency or even compromise safety. Therefore, such conflicts should be accurately detected and then properly relaxed by adjusting the robot's action. This paper proposes a new learning scheme to detect physical conflicts between humans and robots. The constructed learning network receives multi-modal information from proprioceptive sensors and then outputs the anomaly score to specify the physical conflict, which score is further used to continuously adjust the robot impedance to ensure a safe and efficient interaction. Such a formulation allows the robot to explore the semantic information during the interaction (e.g., gait phases, imbalance, human fatigue) and hence react properly to the physical conflict. Experimental results and comparative studies on a lower-limb exoskeleton robot are presented to illustrate that the proposed learning scheme can deal with physical conflicts in a faster and more accurate manner.
Employing Neighborhood Component Analysis as a Feature Selection Method in the Pattern Recognition Approach for Surface Electromyography Signal Classification

Many research works regarding the design of a pattern recognition (PR) approach for surface electromyography (sEMG) signal analysis have attempted to find the most relevant features leading to the highest classification performance. While some classical works have outlined the general time-domain (TD) features related to sEMG signals, the research area still lacks data-driven feature selection (FS) methods that would generalize the effect of features on the classification performance. This paper proposes a novel approach of using a Neighborhood Component Analysis (NCA) algorithm as the FS step in the PR scheme. The NCA-based feature selection (NCA-FS) has resulted in an increase in the classification accuracy and F1 score of four machine learning (ML) models while reducing the total complexity of the PR scheme for two subjects in the NinaPro Database 2 (DB2). Moreover, the proposed method has elicited the most powerful TD features, positively affecting the overall performance, compared to some classical and state-of-the-art works regarding sEMG signal recognition.
Cloud Detection in Optical Remote Sensing Images With Deep Semi-Supervised and Active Learning

Clouds hinder the surface observation by optical remote sensing sensors. It is of great significance to detect clouds and nonclouds in remote sensing images. Compared with the traditional cloud detection methods, deep learning methods usually achieve promising detection results. Moreover, large-scale, high-quality labeled datasets can effectively improve the accuracy and generalization of deep learning models. However, this incurs a great deal of label effort and cost. In this letter, we propose a cloud detection method based on deep semi-supervised learning (SSL) and active learning (AL) in optical remote sensing images named SSAL-cloud detection (SSAL-CD). SSL part of SSAL-CD is implemented by the cross-validation between two-way neural networks, which can effectively train in the case of insufficient labeled data and reduce the cost of labeling. AL part proposes a suitable query strategy based on the uncertainty, degree of divergence, and diversity information provided by the training model. The query strategy can screen out high-value samples for SSL, which further improves the detection effect. Repeat the process of SSL and AL until the accuracy meets requirements or the labeling budget is exhausted. Extensive experimental results in Landsat-8 Biome dataset demonstrate that SSAL-CD can achieve the state-of-the-art segmentation performance with a small number of labels.
Constraining Aging Processes of Black Carbon in the Community Atmosphere Model Using Environmental Chamber Measurements

The direct radiative forcing of black carbon aerosol (BC) on the Earth system remains unsettled, largely due to the uncertainty with physical properties of BC throughout their lifecycle. Here we show that ambient chamber measurements of BC properties provide a novel constraint on the crude BC aging representation in climate models. Observational evidence for significant absorption enhancement of BC can be reproduced when the aging processes in the four‐mode version of the Modal Aerosol Module (MAM4) aerosol scheme in the Community Atmosphere Model version 5 are calibrated by the recent in situ chamber measurements. An observation‐based scaling method is developed in the aging timescale calculation to alleviate the influence of biases in the simulated model chemical composition. Model sensitivity simulations suggest that the different monolayer settings in the BC aging parameterization of MAM4 can cause as large as 26% and 24% differences in BC burden and radiative forcing, respectively. We also find that an increase in coating materials (e.g., sulfate and secondary organic aerosols) reduces BC lifetime by increasing the hygroscopicity of the mixture but enhances its absorption, resulting in a net increase in BC direct radiative forcing. Our results suggest that accurate simulations of BC aging processes as well as other aerosol species are equally important in reducing the uncertainty of BC forcing estimation.
Enhancing the Efficacy of Lower-body Assistive Devices Through the Understanding of Human Movement in the Real World

In previous studies, researchers have successfully measured walking in healthy able-bodied humans to create safe control strategies for lower body assistive devices. measurements used to establish design requirements often come from testing and evaluation that takes place in laboratory settings during steady-state tasks, where participants often select movement strategies that minimize the cost of transport. However, human walking in these conditions does not neces-sarily represent the natural behavior of an individual in the real world. In this work, we conducted a study to characterize human walking in the real world. We combined week-scale free-living measurements of gait with in-lab data collection to: 1) quantify the proportion of steady-state walking in a population of healthy able-bodied adults, and 2) evaluate whether this population favors the selection of a range of walking speeds that minimize their cost of transport in the real world. We found that the majority of walking bouts contain mostly transient walking, suggesting that researchers should complement steady-state characterization with non-steady-state tasks. We also found that the most often used steady-state walking speeds for all participants were higher than the range that minimizes cost of transport, suggesting that individuals are influenced by more than energy economy when moving in the real world. Thus, when developing control strategies for these devices, researchers should consider a variety of optimization objectives to adapt for the multifarious situations of daily life.
The Quantum Lattice Boltzmann Equation: Recent Developments †

The derivation of the quantum lattice Boltzmann model is reviewed with special emphasis on recent developments of the model, namely, the extension to a multi-dimensional formulation and the application to the computation of the ground state of the Gross-Pitaevskii equation (GPE). Numerical results for the linear and non- linear Schrodinger equation and for the ground state solution of the GPE are also presented and validated against analytical results or other classical schemes such as Crank-Nicholson.
A Computationally-Friendly Data-Driven Safety Filter for Control-Affine Discrete-Time Systems Subject to Unknown Process Noise

A supervisory safety filter is developed to minimally modify nominal control inputs to a nonlinear control-affine discrete-time system to ensure satisfaction of potentially time-varying state and input constraints, i.e., safety constraints, with high probability. The system model is known while the environment model, i.e., distribution of additive Gaussian process noise, is unknown. State measurements are used to learn the statistics of the process noise. The safety filter employs a robust optimization problem involving tightening of the safety constraints based on the learned statistics and the corresponding confidence.
Predictions of prices and volumes in the Nordic balancing markets for electricity

The electricity market is driven by complicated interactions that are hard to model analytically. This is particularly the case for the balancing market, where imbalances between supply and demand after the day-ahead market clearance are balanced. The balancing market bridges the gap between the day-ahead market and the actual power system operations. Being able to predict the necessary balancing volumes and prices some hours in advance of the operational hour will allow power producers to plan their production and trading in a more optimal way. There exist large amounts of open data that could contain predictive information about the balancing market, including day-ahead market data and climatic data. However, the literature on forecasting volume and prices in the balancing market is sparse compared to the rich literature on forecasting for the day-ahead market. Neural networks are powerful functional approximators and well-suited to model the complex relationships in the power market. It may also be used to study the predictability of the balancing volumes and prices forward in time. In this paper, we develop a model based on long short-term memory (LSTM) recurrent neural networks to predict volumes and prices in the Nordic balancing market based on public accessible data. Results show that the LSTM model performs well when compared to the two baselines selected. However, the performance is not significantly better, which indicates that the market data does not hold significant predictive information.
Task-Oriented Stiffness Setting for a Variable Stiffness Hand

The integration of variable stiffness actuators (VSA) in robotic systems endows them with intrinsic flexibility and therefore robustness to unknown disturbances. However, this characteristic presents a challenge: choosing the best intrinsic stiffness setting guaranteeing the required force ap-plication capability while keeping the system as adaptable to uncertainties as possible. This paper proposes a method to set the optimal stiffness for a multi-finger VSA hand to perform a desired manipulation task. The task is generically represented as a force (with unknown magnitude) applied along a reference direction. According to the force application's direction and the hand's kinematic state, the fingers assume a certain role to split the collective force application. We employ the endpoint stiffness ellipsoid to analyze the required finger stiffness to fulfill the task. We evaluate the optimized stiffness settings in a door opening application with an iterative adaption of the stiffness behavior to handle the unknown force requirement. The results show a successful collective behavior of the fingers, where the stiffness setting considers a task-oriented force-adaptability trade-off and effective use of independent VSA fingers.