Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Design of Approximate Sobel Filter

Approximate computing (AC) is an emerging computing paradigm for energy efficiency. Typically, AC is implemented at the primary arithmetic level, e.g., addition, multiplication, and division, and its performance is evaluated by integration within an application. However, the achieved design efficiency may not be satisfactory. Therefore, for a specific approximate application, we need to study the most suitable settings of its basic approximate component. In this paper, we investigate several approximate designs of the Sobel filter, which is used for image edge detection. We consider different target designs, e.g., for 25% area reduction, we determine the various types of the used full adders and the number of components for each type. For an approximate Sobel filter with 15% to 55% area and power reduction compared to the exact design, we determine the settings for each target design. The obtained Sobel designs are evaluated for different benchmark images, i.e., Cameraman, Lena, and Bikesgray, and show a highly acceptable quality for edge detection. The average multiscale structural similarity (MSSSIM) index for all evaluated designs on the three benchmark images was 0.73.
Value-function estimation and uncertainty propagation in Reinforcement Learning: a Koopman operator approach

In Reinforcement Learning, model-free and model-based methods are known to have both strengths and weaknesses. The most recent literature on these topics focuses on trying to combine them in a way that preserves their advantages: sample-efficiency and good asymptotic behaviour respectively. This paper presents a new approach for directly estimating value-functions from data, which however relies on a particular model of the environment, therefore lying in between the two perspectives. The estimation problem indeed is targeting only the rewards of the transitions. This is achieved through the Koopman operator framework, which allows to describe a generic dynamical system in terms of its action on functions rather than on states, lifting the evolution in a Reproducing Kernel Hilbert Space. By leveraging a Bayesian perspective the latter approach enables also the propagation of estimation error and the definition of confidence intervals.
Differential Equation Model for the Population-Level Dynamics of a Toggle Switch with Growth-Feedback

Bistable genetic circuits that can be toggled between two states have been engineered in bacterial cells for a variety of applications. These circuits often impose state-dependent resource loads on the cell, creating growth feedback. In the context of a population of cells, each with a copy of the genetic circuit, cells in either circuit state grow at different rates, thereby affecting the emergent population-level dynamics. It is generally difficult to predict how this growth heterogeneity will affect the composition of the population over time. In this work, we consider an ODE population model and evaluate its ability to predict the transient dynamics of the fraction of cells in either state. These dynamics are driven by two processes. The first is due to the difference in growth rate between the cells in the two states, while the second process arises from the probability that the circuit switches state. For the latter, we compute switching rates for the toggle switch using a Markov chain two-dimensional model and exploit the system’s structure for efficient computation. We demonstrate via simulations that the ODE model well approximates the dynamics of the system obtained by a published population simulation algorithm for sufficiently large molecular counts and population sizes. The ability to approximate via ODEs the population-level dynamics of cells engineered with multi-stable circuits will be especially relevant to forward engineer such circuits for desired population dynamics.
Adaptive single-stage control for uncertain nonholonomic Euler-Lagrange systems

This work introduces a new single-stage adaptive controller for Euler-Lagrange systems with nonholonomic constraints. The proposed mechanism provides a simpler design philosophy compared to double-stage mechanisms (that address kinematics and dynamics in two steps), while achieving analogous stability properties, i.e. stability of both original and internal states. Meanwhile, we do not require direct access to the internal states as required in state-of-the-art single-stage mechanisms. The proposed approach is studied via Lyapunov analysis, validated numerically on wheeled mobile robot dynamics and compared to a standard double-stage approach.
Deep Learning-Based Beam Pair Angle Prediction for Beyond 5G Millimeter-Wave Vehicular communication

The communication at millimeter wave (mmWave) offers a very promising future for vehicular network in beyond fifth generation (B5G), where the high-throughput data exchange is required. However, due to the high mobility and channel attenuation in vehicular environment, it becomes more challenging to establish an efficient mmWave Vehicle-To-Everything (V2X) communication. To overcome the beam misalignment problem, it is necessary to employ fast and reliable beamforming methods at the transmitter and receiver sides to align the beams reliably and continuously. In this paper, we propose a new deep learning approach based on beam alignment that suggests a bidirectional long short term memory (BiLSTM) model followed by a fully connected layer, to predict the suitable beam pair in real time, i.e., the Angle of Departure (AoD) at the mmWave base station side and Angle of Arrival (AoA) at vehicle side (AoD/AoA) for which the received signal power is maximized. The BiLSTM-BPP is performed to identify automatically the strongest beam pair that connects the mmBS to the vehicle for each vehicle position. After a set of simulations, the obtained results showed that BiLSTM-BPP achieves the lowest beam prediction error between the predicted and the real AoD/AoA compared to existing traditional machine learning algorithms, including linear regression, Support Vector Regression (SVR), K-Nearest Neighbors (KNN), decision tree, random forest. We evaluated the performance of our proposal in terms of Mean Squared Error (MSE), Mean Absolute Error (MAE), Median Absolute Error (MedAE) and Root Mean-Square-Error (RMSE).
On the Performance of Autoencoder-Based Space Optical Communications

In this paper, we propose a deep learning autoencoder (AE) to model and design space optical communications (SOC) systems from end-to-end performance. The proposed AE is based on multiple-decoders and a new layered structure for constructing both encoders and decoders. The use of multiple-decoders can increase the receiver diversity, which allows the gradient descent to minimize the cost function compared to state-of-the-art models and therefore improve the bit error rate (BER) performance. In addition, we utilize system tool kit (STK) simulator for a realistic SOC channel modeling in Log-normal fading and additive white Gaussian channel. Numerical results reveal that the traditional convolution codes-based SOC systems are outperformed by the proposed learning-based AE for various code rates. Moreover, the proposed AE outperforms the benchmark AE learning-based models in terms of BER performance metric.
Broadband SPST Switches in 250-nm GaN HEMT Process

Two SPST broadband switches are reported in this paper. The proposed switches are designed using the travelling-wave topology and implemented in the 250-nm GaN HEMT process. The L- to C-band switch covers a 1-dB bandwidth from 0.9-4.2 GHz with a minimum 0.52-dB insertion loss and a minimum 20.2-dB isolation at all measured frequencies, and the S-to Q-band switch covers a 2.7-36.3 GHz 1-dB bandwidth with a 0.64-dB minimum insertion loss and minimum 20-dB insertion loss above 12 GHz. The 0.1-dB compression points of the two $\mathbf{sw}$ itches are higher than 17.3 dBm and 7.2 dBm, respectively.
Robust Capacity of Wireless Networks Under Cascading Failures

In this paper, we study the impact of node cascading failures and network structure robustness on the capacity of wireless networks. Especially, in order to quantify how much information can be conveyed by wireless networks under node cascading failures, robust capacity is defined to capture the influence of the intensity of the initial failure nodes $m$ and the connectivity parameter $k$ on capacity. Note that increasing $k$ could provide $k$ disjoint data paths for any two nodes, thereby combating the cascading failure. Denoting the intensity of the nodes as $n$ and $m=n^{\frac{1}{\beta}}$, it is shown that robust capacity $O\left(\sqrt{\frac{n}{k\log n}}\right)$ can be achieved when the initial failure exponent $\beta > 2$. In contrast, robust capacity would converge to zero with increasing $n$ when $1 < \beta\leq 2$ since the data paths of most source-destination pairs are interrupted due to the cascading failures. Moreover, increasing the connectivity parameter $k$, although capable of enhancing the network structure robustness, is shown to degrade cascading failures and robust capacity.
Design of a Multi-bending Flexible Manipulator for Gastrointestinal Surgery

Unlike the existing rigid surgical robots, flexible endoscopic robotic platforms have been actively researched owing to the advantages of high lesion accessibility and scar-free. However, there still exists difficult-to-access areas such as cardia and fundus in the gastrointestinal tract due to the limited Degrees of Freedom (DoFs) of the manipulator. In this study, we propose a multi-bending endoscopic manipulator to improve accessibility to the gastric region in any position and orientation. We perform mechanism design, kinematics, and cable-control analysis of the proposed manipulator. Finally, we verify the multi-bending motion in the simulated flexible pathway.
Construction of Worst-Case Input Signals for Discrete-Time Linear Time-Varying Systems

This paper provides two algorithms for constructing worst-case input signals that have finite support and approximately achieve the induced gain of a stable, discrete-time, linear time-varying system. The focus of this work is on eventually periodic systems, which include finite horizon and periodic systems, and so the results can be immediately specialized for these systems. Novel results on the Riccati Difference Equations for eventually periodic systems are provided and utilized in one of the algorithms. The utility and effectiveness of the proposed algorithms are demonstrated in an illustrative example.
Towards Articulating Failures and Fault Trees and Verification for Building Resilience in Robot Swarms

Robot Swarms are starting to enter real-life scenarios. These scenarios pose more complex events than the swarms face in lab settings. Chances of failure are higher in practical situations due to the increased number of potential failure cases. However, current swarm design methodologies do not explicitly consider fault analysis during the design phase of robot swarms. In this paper, we approach the problem of fault analysis in robot swarm mission design from a systems engineering perspective and provide a framework to identify a swarm’s potential failure modes while designing the robot swarms.
Metric Learning-based Few-Shot Malicious Node Detection for IoT Backhaul/Fronthaul Networks

The development of backhaul/fronthaul networks can enable low latency and high reliability, but nodes in future networks like Internet of Things (IoT) can conduct malicious activities like flooding attack and DDoS attack, which can decrease QoS of smart backhaul/fronthaul network. Timely detection of malicious nodes in future networks is significant for low-latency backhaul/fronthaul networks. However, conventional supervised learning-based detection models require abundant malicious training samples, while capturing adequate malicious samples can not meet the requirement of timely detection. In this paper, we propose a novel few-shot malicious node detection system for improving QoS of IoT backhaul/fronthaul network, which can detect malicious nodes with unknown malicious activities through a limited number of network traffic samples. In our proposed system, we first design a fresh IoT traffic sample processing approach, which integrates normal activity samples and known malicious activity samples to generate training pairs. Then, we design a metric learning-based malicious node detection model training method, which employs a contrastive loss over distance metric to distinguish between similar and dissimilar pairs of samples. Besides, the trained model can detect nodes with unknown malicious activities by comparing real-time samples with few-shot samples of malicious nodes. Finally, the proposed system is evaluated on a real-world IoT network dataset named N-BaIoT. The exhaustive experiment results show that our model can achieve an average accuracy around 97.67 % when detecting malicious nodes with unknown malicious activities, which is comparable to state-of-the-art supervised learning models while our model only needs 5-shot samples of malicious node.
A Deep Reinforcement Learning Scheme for SCMA-Based Edge Computing in IoT Networks

The application of sparse code multiple access (SCMA) to multi-access edge computing (MEC) networks can provide massive connections as well as timely and efficient computation services for resource-constrained Internet of Things (IoT) devices. This paper investigates the maximization of computation rate in SCMA-MEC networks under a dynamic environment. We first formulate an initial optimization problem to maximize the long-term computation rate of IoT devices under task delay constraints. Then, a joint computation offloading and SCMA resource allocation algorithm based on long short-term memory (LSTM) network and dueling deep Q network (DQN) is proposed. Specifically, each IoT device acts as an agent in the algorithm. Since each device can only observe part of the environment state, the LSTM network is used to predict the states of other devices. The computation rate of devices is taken as a reward to conduct action exploration in dueling DQN, and then the near-optimal computation offloading decision, SCMA codebook allocation, and power distribution of IoT users are obtained after training. Numerical simulation results demonstrate that the proposed algorithm can achieve higher computation rate compared with other baseline schemes.
PV-Grid Interface Hybrid Energy Management System for EV Charging Station without PLL

Electric vehicles are gaining popularity as an environmentally benign, long-term, and cost-effective alternative to internal combustion engines based automobiles. It deals with depleting fuel, climate crisis, and pollution. The proposed work focuses on designing a PV grid interface hybrid energy management system for EV-charging stations without using a PLL synchronizing unit. The above methodology ensures that the system functions to optimize grid energy utilisation. The power from PV is extracted via Maximum Power Point Tracking (MPPT). Various operating modes are observed depending on the amount of power available at the PV and the power demand at the charging station. The power may flow from PV to charging station and utility or from PV and grid to charging station depending upon the power availability and demand scenario. The power injection or absorption capability of the converter is significantly affected in presence of a weak grid. The paper provides the details regarding the power electronics-based converter model and their respective controller design that are used in the system.
Generalized DCM models for pre-filtering compensation

Estimation of brain effective connectivity (EC) faces several issues, among which are computational complexity and estimation accuracy. Here, we focus on increasing estimation accuracy, in particular addressing the bias introduced by the pre-processing pipeline employed on resting-state (rs) fMRI signals, which includes temporal band-pass prefiltering.In this paper, we propose an alternative model (so-called "Filtered rs-DCM") that adapts a stochastic linear rs-DCM model to the filtered input. Filtered rs-DCM is built by augmenting the neuronal rs-DCM with the filter model used in the pre-processing step. The parameter estimation is performed with EM-algorithm with a sparsity inducing prior. The simulation experiments run on band-pass filtered synthetic datasets demonstrate that this approach outperforms classical DCM models in terms of EC estimation accuracy.
Optimal Capacity-Constrained COVID-19 Vaccination for Heterogeneous Populations

COVID-19 and the ensuing vaccine capacity constraints have emphasized the importance of proper prioritization during vaccine rollout. This problem is complicated by heterogeneity in risk levels, contact rates, and network topology which can dramatically and unintuitively change the efficacy of vaccination and must be taken into account when allocating resources. This paper proposes a general model to capture a wide array of network heterogeneity while maintaining computational tractability and formulates vaccine prioritization as an optimal control problem. Pontryagin’s Maximum Principle is used to derive properties of optimal, potentially highly dynamic, allocation policies, providing significant reductions in the set of candidate policies. Extensive numerical simulations of COVID-19 vaccination are used to corroborate these findings and further illicit optimal policy characteristics and the effects of various system, disease, and population parameters.
Adaptive RKHS-based functional estimation of structurally perturbed second order infinite dimensional systems

This paper proposes a new approach for the adaptive functional estimation of second order infinite dimensional systems with structured perturbations. First, the proposed observer is formulated in the natural second order setting thus ensuring the time derivative of the estimated position is the estimated velocity, and therefore called natural adaptive observer. Assuming that the system does not yield a positive real system when placed in first order form, then the next step in deriving parameter adaptive laws is to assume a form of input-output collocation. Finally, to estimate structured perturbations taking the form of functions of the position and/or velocity outputs, the parameter space is not identified by a finite dimensional Euclidean space but instead is considered in a Reproducing Kernel Hilbert Space. Such a setting allows one not to be restricted by a priori assumptions on the dimension of the parameter spaces. Convergence of the position and velocity errors in their respective norms is established via the use of a parameter-dependent Lyapunov function, specifically formulated for second order infinite dimensional systems that include appropriately defined norms of the functional errors in the reproducing kernel Hilbert spaces. Boundedness of the functional estimates immediately follow and via an appropriate definition of a persistence of excitation condition for functional estimation, a functional convergence follows. When the system is governed by vector second order dynamics, all abstract spaces for the state evolution collapse to a Euclidean space and the natural adaptive observer results simplify. Numerical results of a second order PDE and a multi-degree of freedom finite dimensional mechanical system are presented.
On Discretization Methods for Indirect Adaptive Sliding Mode Control

In this paper, discrete-time versions of the first-order indirect adaptive sliding mode control (SMC) algorithm are developed and analyzed. In particular, a scalar linear system with parametric uncertainty linear in the state and with bounded input disturbance is considered. The discretization of continuous-time first-order indirect adaptive SMC algorithm may not preserve continuous-time properties like asymptotic stability of the origin. In order to ensure boundedness, knowledge about where the actual parameter may be located is used in this work. Then, for each proposed discretization scheme, conditions on the sampling time are derived that guarantee convergence into a bounded set. The theoretical results are illustrated with simulation examples and the proposed discretization schemes are compared with regard to the condition on sampling time and precision.
An Inter-Stage Filter Network for Distortion Reduction in Concurrent Dual-Band Power Amplifier Operation

This work presents a novel approach for reducing the out-of-band distortion generated in a concurrent dual-band power amplifier (PA) without penalty to output power or efficiency using a filter between a driver amplifier and final-stage PA that manipulates the driver amplifier out-of-band distortion such that the overall distortion of the cascade is minimized. The cascaded PA operates at 2.4-GHz and 3.5-GHz with peak output power and drain efficiency of 41.6/40.4 and 65.2/55.1 respectively. The filter reduces the out-of-band distortion of the cascade when excited by dual 10-MHz LTE-like signals by 10 dB while improving average drain efficiency by 5 percentage points.
The physical optics method in electromagnetic scattering

The purpose of this work is to analyze the physical optics method as applied to electromagnetic scattering theory and to point out its physical and mathematical drawbacks. The main conclusions are (1) that the boundary values assumed by physical optics lead to electromagnetic fields that do not satisfy the finiteness of energy condition and, as a consequence, that integral representations of these fields cannot be obtained via the divergence theorem; (2) that the commonly accepted representations are not solutions of the physical optics problem because they fail to reproduce the assumed discontinuities of the fields on the scatterer. Despite the above conclusions, the present work should not be construed as an attempt to discredit the method but rather as an effort toward a better understanding of it. As it is well known, there have been a number of occasions in which physical optics has yielded quite satisfactory results.
A New Converse Lyapunov Theorem for Global Exponential Stability and Applications to Stochastic Approximation

In this paper, we give a simple and direct proof of the convergence of the stochastic approximation algorithm under suitable conditions. The main result here can be compared to that in Borkar and Meyn (2000), which is based on the ODE method, that is, showing that the sample paths of the algorithm converge towards the deterministic trajectories of an associated ODE. In contrast, the present proof is based on martingale theory, first proposed in Gladyshev (1965). Consequently, there are fewer assumptions here compared to previous papers. An important part of the proof is a new converse Lyapunov theorem for global exponential stability. Aside from its application to stochastic approximation theory, this new converse Lyapunov theorem would be useful for researchers in stability theory.
Relay-based Network Architectures for Collaborative Virtual Reality Applications

Currently deployed NAT devices are designed primarily around the client/server paradigm, in which relatively anonymous client machines inside a private network initiate connections to public servers with stable IP addresses and DNS names. Thus, the asymmetric addressing and connectivity regimes established by NAT devices have created unique problems for Peer-to-Peer (P2P) applications and protocols. Multiple NAT-traversal techniques have been developed to overcome these shortcomings, each offering a different set of pros and cons. In the context of a P2P collaborative virtual reality (CVR) system, the difficulty of selecting a convenient and effective NAT-traversal technique increases exponentially because of the added constraints related to CVR. In this view, this article discusses the trade-offs of different NAT-traversal techniques and the CVR challenges that need to be taken into account when choosing a NAT-traversal technique. Finally, it presents a relay-based approach that leverages container migration to mitigate the drawbacks that come with this solution and accentuate its advantages.