Miracle-dz/UniFAR-FTUs huggingface dataset:
LoRa Mapper System for Coverage Mapping of LoRaWAN Networks

The paper presents implementation of LoRa Mapper system developed for the purpose of determining network coverage of LoRaWAN (and other) networks by using LoRa measurement modules. This system combines device for measuring of achieved coverage, and complete software (server and user application) for processing of received packets, i.e. for the visualization of the obtained measurement data. The main goal was to enable a simpler coverage mapping for LoRaWAN networks, although it is possible to simultaneously measure coverage of other wireless networks, and to achieve this with the satisfactory quality at the lowest possible cost. The paper explains the concept of LoRa Mapper operation, the method of implementation, and gives first results of testing this system in real-life conditions.
Multichannel Blind Source Separation Based on Evanescent-Region-Aware Non-Negative Tensor Factorization in Spherical Harmonic Domain

There is growing interest in new audio formats in the context of virtual reality (VR), and higher-order ambisonics (HOA) is preferred for VR systems to transmit recorded scenes owing to its transmission efficiency and its flexibility to work with different loudspeaker setups. However, the conversion between another well-known format, i.e., object format, and the HOA format is not fully addressed in the literature. To address this issue, blind source separation in a spherical harmonic (SH) domain can be considered as the best way to extract objects in terms of efficiency, i.e., decoding HOA signals for separation can be omitted. A few authors attempted to extract objects from encoded HOA signals directly by using multichannel non-negative matrix factorization (MNMF), but these approaches either assume only far-field sources or do not take array characteristics into account, which make these methods difficult to use for VR in practical situations where singers or speakers often perform close to microphones. Furthermore, MNMF generally requires a huge computational cost, although dimensional reduction to the SH domain is performed. In this work, we also model near-field sources by estimating the model parameters of non-negative tensor factorization (NTF) in the SH domain assuming that microphone signals can be obtained with a rigid spherical array. We propose a masking scheme to exclude noisy evanescent regions in the SH domain from the NTF cost function. Evaluations show that our method outperforms existing methods devised for the HOA format and that our masking approach is effective in improving the separation quality.
An Efficient Weakly-Supervised Learning Method for Optic Disc Segmentation

Accurate optic disc segmentation plays an essential role in the early diagnosis of glaucoma, which has been a major cause of irreversible blindness for the past decade. Recently, U-shape Convolutional Neural Network (CNN) models have achieved favourable performance in optic disc segmentation. However, it is worth noting that these models require a large number of pixel-level annotations while these annotations are difficult to obtain in clinical practice. As a solution, weakly-supervised training methods are commonly implemented, but it will provoke U-shape CNN generating inaccurate, diluted, and grid-like segmentation results. In this paper, we propose a novel Hybrid Network (HyNet) to solve the issue above. HyNet consists of a U-shape backbone hybridized with a cross-scale connection structure, which makes better use of multi-scale visual semantics. Nevertheless, the generalization ability of HyNet is affected by the domain shift among different datasets. Therefore, we innovatively combine weakly- and fully-supervised training methods, namely Hybrid Process (HyProcess), to solve the domain shift problem. Experimental results on ONHSD, DRIONS-DB, and DRISHTI-GS datasets show that our model outperforms the state-of-the-art, reaching Dice of 82.39(%), 93.72(%), and 95.34(%) respectively. Additionally, our ablation study validates the effectiveness of HyNet along with HyProcess, and further analysis reflects their value in clinical practice.
Joint Synthesis of Robust Dynamic State Feedback and Dynamic Disturbance Feed-Forward for Uncertain Systems

Joint synthesis of dynamic state feedback is considered together with dynamic disturbance feedforward. First a novel parameter transformation is introduced to derive a new LMI condition for synthesis in the case of LTI systems. This condition forms the basis of a new synthesis method, which can easily be specialized to combination of static or dynamic sate feedback with static or dynamic disturbance feedforward. Moreover, it can also be used to synthesize LTI or scheduled controllers for systems that depend on uncertain time-varying parameters, some of which are not measurable online.
An Approach for Determining Frequency Deviation Coefficient Considering Different Operating Conditions in AGC System

In automatic generation control (AGC), the determination of frequency deviation coefficient under the mode of tie line bias control (TBC) plays an important role in the control system stability and frequency recovery. Based on the analysis of the actual operation of AGC, this paper presents a method to determine the frequency deviation coefficient. Firstly, considering the limitation of current automation level, a frequency deviation coefficient model for all-day multiperiod under different operating modes is established according to the frequency response characteristic of power system. Then, in order to meet the requirements for frequency recovery in the actual power grid, a strategy to modify the frequency deviation coefficient according to the frequency deviation is proposed. Finally, due to the continuous improvement of the penetration rate of new energy in the power system, this paper also considered the influence of wind power in the load frequency control system (LFC) model. The effectiveness of the method is verified by the simulation results of two areas interconnected power system.
A Novel Wrapper–Based Feature Selection for Early Diabetes Prediction Enhanced With a Metaheuristic

Diabetes leads to health problems for hundreds of millions of people globally every year. Available medical records of patients quantify symptoms, body features, and clinical laboratory test values, which can be used to perform biostatistics analysis aimed at finding patterns or features undetectable by current practice. In this work, we proposed a machine learning model to predict the early onset of diabetes patients. It is a novel wrapper-based feature selection utilizing Grey Wolf Optimization (GWO) and an Adaptive Particle Swam Optimization (APSO) to optimize the Multilayer Perceptron (MLP) to reduce the number of required input attributes. Moreover, we also compared the results achieved using this method and several conventional machine learning algorithms approaches such as Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighbor (KNN), Naïve Bayesian Classifier (NBC), Random Forest Classifier (RFC), Logistic Regression (LR). Computational results of our proposed method show not only that much fewer features are needed, but also higher prediction accuracy can be achieved (96% for GWO - MLP and 97% for APGWO - MLP). This work has the potential to be applicable to clinical practice and become a supporting tool for doctors/physicians.
Heart Rate Variability of 30-minute ECG Measurements and Correlation to Glucose Levels

This paper shows the correlation between Heart Rate Variability parameters and glucose levels, which might potentially lead to a novel non-invasive glucose monitoring methodology. The study is conducted using a dataset with 143 patients from the Gluco project. The dataset consists of plain ECG data. Heart rate variability (HRV) parameters are then extracted from the electrocardiogram (ECG) data. Outlier handling, statistical measures and HRV distribution charts are covered in this paper. Our previous results show that a 24h ECG measurement will produce a more relevant result for the HRV parameters. In this paper, we investigate the hypothesis is: “if 30-minutes ECG measurements are relevant as an estimation of a body regulation ability instead of 24h measurements”.
Open-source application for real-time gait analysis using inertial sensors

In this paper an open-source application for real-time gait analysis using Xsens inertial measurement units is presented. The application enables real-time data acquisition, visualization, gait segmentation, spatio-temporal parameter estimation and recording of raw and processed data. The developed algorithms are validated with an optical camera system on 8 healthy subjects. A segmentation error of 0.44±24.06 ms and a stride length root mean square error of 3.33 cm were achieved. Validated algorithms, a simple interface and an open-source implementation enable easy use of the application regardless of user's technical skills and the possibility for further research and integration with other sensor systems.
Innovative RFID Sensors for Internet of Things Applications

Radio-Frequency IDentification (RFID) devices and sensors are among the main innovations of the last years, with an enormous impact on the Internet of Things (IoT) physical communication layer as well as on logistics and robotics. The aim of the present paper is to review the main technologies available for RFID sensors, and to identify the corresponding state-of-the-art when these technologies are applied to realistic IoT scenarios. Firstly, the concepts of radio bascattering and harmonic bascattering are analyzed, highlighting the pros and cons of each approach. Then, state-of-the-art solutions are reported, and the performance of each of them are discussed, to provide an overview of the potential of RFID-based sensing in different scenarios.
Emerging Trends in Techniques and Technology as Applied to Filter Design

In the last decade, the filter community has innovated both design techniques and the technology used for practical implementation. In design, the philosophy has become “if you can't avoid it, use it”, a very practical engineering approach. Modes previously deemed spurious are intentionally used to create in-line networks incorporating real or imaginary transmission zeros and also reduce the number of components and thus further miniaturize; spurious responses are re-routed to increase the passband width or stopband width, frequency variation in couplings is used to create complex transfer functions, with all of these developments using what was previously avoided. Clever implementations of baluns into passive and active networks is resulting in a new generation of noise-immune filters for 5G and beyond. Finally, the use of a diakoptic approach to synthesis has appeared an evolving approach in which small blocks (“singlets”, “doublets”, etc.) are cascaded to implement larger networks, (reducing the need for very complex synthesis), with this new approach promising a large impact on the implementation of practical structures. Filter technology has migrated towards “observe it and then adapt it”, pragmatically repurposing tools not specifically originally intended for the applications. Combinations of surface wave and bulk wave resonators with L-C networks are improving the loss characteristics of filters in the region below 2 GHz. Lightweight alloys and other materials designed for spacecraft are being used in filters intended for space, to provide temperature stability without the use of heavy alloys such as Invar. Fully-enclosed waveguide is being replaced in some cases by planar and quasiplanar structures propagating quasi-waveguide modes. This is generically referred to as SIW (Substrate Integrated Waveguide). Active filters trade noise figure for insertion loss but perhaps will offer advantage in terms of size and chip-level implementation. Finally, the era of reconfiguration might be approaching, as the basic networks are evolving, perhaps lacking only the appearance of lower-loss, higher-IP solid-state tuning elements.
Unstructured Road Detection and Steering Assist Based on HSV Color Space Segmentation for Autonomous Car

One of the important things in a self-driving car (SDC), also known as an autonomous vehicle (AV) is detecting the road so that it remains in the right lane. Therefore this paper aims to be able to detect roads, especially unstructured roads based on the results of the HSV color space segmentation on the road, then produce car position information from the center of the lane (center offset) which is a parameter in making the decision to move the car's steering wheel to return to the center of the lane. In marking the edge of the roadside, the method used is Hough transform based on the resulting edge line using an edge detector, then the coordinates of the left and right curb lines which represent the width of the road. The results of this paper indicate that the system's ability to distinguish between road and non-road areas in several sections with an average percentage of 99.59% for accuracy, 99.49% for precision, and 98.84% for recall and the system's ability to mark the left and right edge of the roadside is very good with an average percentage reaches 99.27% and the percentage error and accuracy obtained in providing information on the position of the car from the center (center offset) based on the actual value and prediction results are 16.05% and 84.14%.
Mut-Detecter: An EGFR activating mutation type classification method with a deep convolutional neural network

Epidermal growth factor receptor (EGFR) plays an essential role in tumor cell proliferation, angiogenesis and apoptosis inhibition; it is a crucial factor leading to cancer occurrence. For example, EGFR tyrosine kinase inhibitors in treating lung cancer patients have an excellent therapeutic effect. Targeted therapy based on EGFR gene mutation is one of the mainstream lung cancer treatment methods. Recent studies have shown that pulmonary nodules’ characteristics are associated with the mutant status of EGFR, which provides the possibility of using CT images of patients with pulmonary nodules to predict the mutant status of EGFR. This study used the deep learning algorithm to establish the EGFR mutation type prediction model based on CT image recognition. The data sets used for model training and testing included 121 labeled CT images from hospital patients with pulmonary nodules. The research results showed that the model could be used for the non-invasive EGFR mutation type based on CT images.
An Improved Paradigm for Robust Optimal Filtering Over Lossy Networks

This paper focuses on the state estimation over unreliable communication channels, where the packet dropouts occur from the sensor side to the filter, for discrete-time systems with both bounded power disturbances and white Gaussian noises. A cascaded estimation scheme with recovered robustness is proposed, driven by the residual signal related to the modeling mismatch. Through the adjoint operator, the estimation gains are explicitly characterized by two modified algebraic Riccati equations, together with a complete and rigorous stability analysis in the mean square sense. An example is included to validate the presented design method.
A Wearable Anthropomorphically-Driven Prosthesis with a Built-In Haptic Feedback System

Recent developments in experimental anthropomorphically-driven prostheses have shown their potential as highly dexterous prosthetic devices. However, these prostheses are both unwearable and lack haptic feedback regarding antagonistic tensions. Here, we present a wearable, anthropomorphically-driven prosthesis with a built-in haptic feedback system. Two distinct control schemes were proposed and compared in a user study with N=6 able-bodied participants performing the Box and Blocks test. The first control scheme was designed to provide a more intuitive, human like actuation and relaxation of the hand, while the simpler controller was designed to reduce fatigue from sustaining EMG signals. Participants performed significantly better with lower fatigue levels while using the controller designed to be intuitive as opposed to the simpler controller. In addition, task performance with both controllers was better than reported performance with standard myoelectric prostheses. These findings suggest that there is potential utility in wearable anthropomorphically-driven prostheses, and provide support for future studies aimed at exploring the utility of haptic feedback in anthropomorphically-driven prostheses.
A State and Output Sensitivity Controllability Approach for Structural Identifiability of Linear State Space Models

In this paper structural identifiability of state space models, possibly nonlinear in parameters, is assessed by analyzing the controllability of the output sensitivities. Sensitivity analysis provides a mathematical setting to analyze parameter identifiability from a physically intuitive perspective. Both SISO and MIMO cases are treated; in the former case the output controllability matrix rank directly allows to draw conclusions on the model structural identifiability. In the latter case, the analysis requires special attention due to the ordering induced by the vector derivative. The approach is illustrated on a linear compartmental model
Addressing Weak Links in Automotive Reliability Semiconductor Contamination Control, Inspection and Test

As we move into a more electrified and automated reality, the sustainability of functional safe and secure electronic systems is a major concern of automobile manufacturers. The complexity of high performance systems is not possible without the application of the latest semiconductor technology nodes. However, the effort to compensate for reliability and robustness gaps in those technologies via resilient design strategies, that are also compliant to cyber security IP and that address defect related attack vectors, creates challenges with respect to performance, size, power, security and cost (PSPSC). Technology generation dependent resilient design measures always relate to the physics-of-failure (PoF), making it evident that defectivity management in the leading edge domain is no longer simply a manufacturing concern, but rather an interdisciplinary task for design and process engineering teams, including process logistics. A process monitoring approach that effectively helps to reduce a) the number of critical defects and b) the number of defect types automatically reduces the resilient design effort for detection and compensation (circuit adaption). In this paper we will illustrate anonymized case studies and exemplary fab benchmarks to bridge the link between design and process engineering in order to infer best practices for automotive related semiconductor manufacturing. Following a systems engineering approach, the effort/cost for effective defectivity management including contamination control is weighted against effort/cost for resilient design and test strategies. It is therefore possible to create a framework that systematically identifies the optimization potential in the below defect detection threshold domain.
Comprehensive-GWAS: a pipeline for genome-wide association studies utilizing cross-validation to assess the predictivity of genetic variations

Genome-wide association studies is an important approach to associate genetic variations among individuals with a particular trait. Despite many GWAS programs have been developed based on different statistical models, their results could vary to a large extent. To obtain a more comprehensive and accurate set of associated SNPs with a trait, we present comprehensive-GWAS, a novel automated pipeline that allows a two-step wrapper model for seamless GWAS analyses between various programs involved in performing traditional GWAS analyses and machine learning methods with additional population structure analysis. It first performs population structure analysis, then executes multiple GWAS software and combines their results into a single SNP subset. After that, it selects relevant SNPs with high individual and/or joint effects from that SNP subset and assess the predictivity of the model using cross-validation by LASSO. The combined and validated “true” significant SNPs are output as Manhattan plot, QQ plot and statistical results for each trait. To demonstrate the utility of the comprehensive-GWAS pipeline, it was applied to 199 wheat varieties that were genotyped with 90K infinium SNP array and phenotyped for traits related to fusarium head blight (FHB) disease in greenhouse condition in the year 2019 with three replications. It pinpoints genome regions that are more likely to be responsible for FHB resistance. The results will contribute to characterizing the genetic architecture of wheat lines with the highest FHB resistance. The pipeline is publicly available at https://github.com/notTrivial/Comprehensive-GWAS.
Impact of Switching Variability of 65nm CMOS Integrated Hafnium Dioxide-based ReRAM Devices on Distinct Level Operations

Limitations related to the von Neumann bottleneck have resulted in novel circuits and architectures, including designs that utilize Resistive Random Access Memory (ReRAM) as nonvolatile memory (NVM) devices. ReRAM implemented with hafnium oxide (HfO2) is a strong candidate for such applications. The non-volatility of these devices and their amenability to compute in memory functionality makes them ideal for neuromorphic applications, deep learning, and mathematical accelerator circuits (e.g. Vector Matrix Multiplication - VMM). However, these devices suffer from stochastic switching variability that currently limits their usage and performance. To realize the full potential of these devices, reliability analysis is required. In this work, a reliability study was performed using previously developed a 65 nm CMOS/Memristor process on a 300 mm wafer platform. To address the influence of switching compliance current on the variability of Low Resistance State (LRS) and High Resistance State (HRS), a total of 23 different compliance current values were implemented. The effects of temperature on device performance was also measured.
Looking Back on the Actor–Critic Architecture

This retrospective describes the overall research project that gave rise to the authors’ paper “Neuronlike adaptive elements that can solve difficult learning control problems” that was published in the 1983 Neural and Sensory Information Processing special issue of the IEEE Transactions on Systems, Man, and Cybernetics. This look back explains how this project came about, presents the ideas and previous publications that influenced it, and describes our most closely related subsequent research. It concludes by pointing out some noteworthy aspects of this article that have been eclipsed by its main contributions, followed by commenting on some of the directions and cautions that should inform future research.