Miracle-dz/UniFAR-FTUs huggingface dataset:
Implementation and Evaluation of 5G MEC-enabled Smart Factory

The 5G network can provide more comprehensive bandwidth connectivity for the industry 4.0 environment, which requires faster and tremendous data transmission. 5G network engages with Multi-access Edge Computing providing the computing functions dedicated to the users on edge nodes. The MEC network architecture presents the significant facilities, schematic of the network, and routers of data transmission. This work demonstrates the 5G network performance evaluation with MEC and without MEC and compares it with various wired and wireless networks in a machining workshop. The field test performs high-definition streaming video and heavy-traffic load testing to evaluate the performance based on different protocols by comparing throughput, latency, jitter, and packet loss rate. Streaming video performance shows that the 5G with MEC achieved 16.47 ms latency on average. In summary, the 5G network implementation and data transmission performance provide a paradigm for those interested. MEC can help data transmission efficiency, but it requires knowledge of configuration on optimization. Mobile network operators should offer authentication for accessing partial MEC functionality for industrial users.
Transient Thermal Analysis of System-in-Package Technology by the Finite Element Method (FEM)

Since accurate thermal analysis is essential for the design and thermal management of System-in-Package (SiP). This paper proposes a transient thermal analysis of the System-in-Package technology based on four chips, in order to investigate the effect of the underlying layers on the thermal model. The finite element method (FEM) using the COMSOL Multiphysics® tool is proposed to achieve this objective. The temperature variation in the numerical simulation shows that the underlying layers are very important in decreasing the maximum temperature value of the SiP. Moreover, in terms of thermal conduction performance, and thermal management of the packaging system, this transient thermal analysis achieves high reliability and resolution when the maximum temperature in the chips is reduced by 16 °C.
Deep Reinforcement Learning with Omnidirectional Images: application to UAV Navigation in Forests

Deep Reinforcement Learning (DRL) is highly efficient for solving complex tasks such as drone obstacle avoidance using cameras. However, these methods are often limited by the camera perception capabilities. In this paper, we demonstrate that point-goal navigation performances can be improved by using cameras with a wider Field-Of-View (FOV). To this end, we present a DRL solution based on equirectangular images and demonstrates its relevance, especially compared to its perspective version. Several visual modalities are compared: ground truth depth, RGB, and depth directly estimated from these $360^{\circ}$ RGB images using Deep Learning methods. Next, we propose a spherical adaptation to take into account the spherical distortions of omnidirectional images in the convolutional neural networks (CNNs) used in the actor-critic network and show a significant improvement in navigation performance. Finally, we modify the perspective depth estimation network using this spherical adaptation and demonstrate a further performance improvement.
Multivariable Representation and Analysis of Controllers Used for Individual Pitch Control of Large Wind Energy Converters

Large wind energy systems use pitch control not only to regulate the power in the case of overrated wind speed but also for load alleviation. The reduction of blade loads is based on the so-called individual pitch control (IPC), which is a complicated configuration that includes either a Coleman or a Clarke transformation. As a result, the IPC provides good performance for its design objective, namely load reduction, but its dynamic properties are poorly known and difficult to study. The consequence is that sometimes the control system presents unexpected behaviors and difficulties in getting the controller tuned. The present contribution reformulates the IPC approaches as transfer matrices such that they are better understood from a systemic point of view, several unknown properties are derived, and some theoretic limitations are established. In particular, proportional-resonant controllers, which are normally hidden, play an important role in the dynamics. These outcomes can help to simplify the controller design as well as the parameter setting.
Determination of Angular Status and Dimensional Properties of Objects for Grasping with Robot Arm

In any task, robot arms can work more effectively without human control. With components such as imaging devices, it is possible to program robots to control autonomously. In this study, the problem of grasping moving objects with the robot arm is realized fully automatically. Deep learning-based You Only Look Once(YOLO) recognizes the objects moving at unknown speeds on a conveyor belt. The velocities of the detected objects are calculated by image processing methods with 2D camera frames. An Artificial Neural Network (ANN) was trained to output the angles required for the robot arm to grasp. According to the got values, the robot arm waits for the object to arrive and then realizes the grip. In the trials, the robot arm achieved successful gripping of over 93% without knowing the sizes, speeds, and positions of the objects.
Key Generation with Ambient Audio

Digital Contact Tracing (DCT) has been proposed to limit the spread of COVID-19, allowing for targeted quarantine of close contacts. The protocol is designed to be lightweight, broad-casting limited-time tokens over Bluetooth Low Energy (BLE) beacons, allowing receivers to record contacts pseudonymously. However, currently proposed protocols have vulnerabilities that permit an adversary to perform massive surveillance or cause significant numbers of false-positive alerts. In this paper, we present AcousticMask, which encrypts broadcast messages using a key derived from the audio signal present at each device with sufficient security levels. Our results show that a receiver sharing the same social space as a sender will hear all of the sender's ephemeral IDs (EphIDs) with Hamming distance at most 3, which can be decrypted at the rate of 10 Hz on a Raspberry Pi 4, while achieving a security factor of over 2108against attackers in our testing set, showing AcousticMask is lightweight for DCT and provides sufficient security levels to protect user's privacy.
Design of a Feature Extracted CMOS Image Sensor with a Novel Integrator and a Configurable ADC

Design of a feature extracted CMOS image sensor (CIS) with a new integrator and configurable analog-to-digital converter (ADC) is described. Since a feature extraction is normally executed at the digital block of image signal processor (ISP), an ISP must be connected to the end of CIS. Namely, the system of feature extraction has been normally based on two-chip solution with CIS and ISP. In case of simple feature extraction, however, the block of ISP can be eliminated, if a special working block could be inserted in the middle of CIS. In order to implement a feature extracted CIS, a new integrator to detect the codes of neighboring pixels is proposed. Further, a configurable 8-bit and 1-bit single-slope ADC is discussed. To verify the performance, a prototype of CIS has been fabricated with an 180nm CMOS technology. The number of pixel array is 1920 x 1380, and the frame rate is 60. The measured power consumption is only 9mW, and it is drastically reduced compared to other conventional ones.
meQ: Selecting MEC Resources by Considering Service Communication Requirements

The Multi-Access Edge Computing (MEC) paradigm enables services that have strict network requirements like a maximum tolerable latency or a minimum bandwidth requirement. This consequently leads to a tight coupling of networks and computing, since the service can only function correctly if its communication requirements are met. Numerous existing strategies for MEC service deployment or resource selection put the characteristics of computing demands correlated with the free capacity of nodes or inter-service dependencies in their focus. In contrast, our work is a complementary attempt to enable especially the consideration of the strict communication needs of MEC services. It shows how edge features, i.e., knowledge of edge network conditions and service requirements, can be used to support resource selection and orchestration strategies. We consider and discuss how our model meQ can be applied in real communication systems as well as in heterogeneous networks, where clients may have multiple uplink connections. To enable communication-optimized selection strategies, meQ consists of two major components. A sharable, graph-based network representation and online searches to determine resources. The model benefits from existing work and limits the search space with the use of edge features. A performed simulation study demonstrates the benefits of meQ. The results show that it is possible to significantly reduce the search space, leading to lower search efforts also in large network graphs. An additional system-level simulation proves integration and applicability under realistic conditions.
Requirements of Settlement-Free Peering Policies

Peering between two networks may be either settlement-free or paid. In order to qualify for settlement-free peering, large Internet Service Providers (ISPs) require that peers meet certain requirements. It is widely perceived that these requirements represent the conditions under which the two peering networks perceive a roughly equal exchange of value. However, the academic literature has not yet shown the relationship between these settlement-free peering requirements and the value to each interconnecting network. We analyze the value to each network from the most common and important requirements. Large ISPs often require potential settlement-free peers to interconnect at a minimum of 6–8 locations from a predetermined list. We find that there is a substantial benefit from this requirement to the ISP, but little incremental benefit from a larger number of interconnection points. Large ISPs often require that the ratio of incoming traffic to outgoing traffic remain below approximately 2:1. We find that this requirement ensures a roughly equal exchange of value.
Exponential stability of discrete-time switched systems based on a switched time-varying Lyapunov function

This article investigates some new exponential stability criteria of discrete-time switched systems with both disturbance and impulse. By introducing a novel multiple switched time-varying matrix function, sufficient conditions are presented to ensure the discrete-time switched system to be exponentially stable and achieve $H_{\infty}$ performance under the mode-dependent interval dwell-time switching. Eventually, two numerical examples with unstable subsystems are presented which verify the validity of our conclusions.
Adjusting for Unmeasured Confounding Variables in Dynamic Networks

This letter presents a technique to identify a certain transfer function in a dynamic network when the input and the output of the transfer function are influenced by an unmeasured confounding variable. It is assumed that in an observational framework, only a subset of the variables of the network are measured and the topology of the interconnections between the variables is partially known. The focus of this letter is the challenging scenario where it is not possible to measure any variables on the directed paths from the confounding variable to either the input or the output of the transfer function of interest. Sufficient conditions are derived to determine a set of instrumental variables and a set of auxiliary variables that guarantee consistent identification of the transfer function using an algorithm based on prediction error method for the class of acyclic networks. It is also shown that similar ideas could be applied to cyclic networks. In particular, we show how consistent estimates of some transfer functions in a network with feedback loops could be used to identify some other transfer functions whose inputs and outputs are influenced by unmeasured confounding variables.
Design of CRLH-TL UWB Filter Using Interdigital Capacitor and Short Stub by Genetic Algorithm

This paper describes design of an ultra-wideband (UWB) filter using a composite right/left-handed transmission line (CRLH-TL) by a genetic algorithm (GA). A UWB filter is required wide a passband characteristic in the frequency range of 3.1 GHz to 10.6 GHz. In addition, a GA is an optimization algorithm that can handle many problems. The filter designed in this study consists of a two-cell CRLH-TL and achieves a wide passband characteristic by selecting each elements value of the CRLH-TL using a GA. As for the bandwidth, it can be controlled arbitrarily by varying the cutoff frequency which is input to a GA. As one way of modeling the filter, we employ interdigital capacitors and short stubs of the microstrip lines based on the obtained CRLH-TL. Subsequently, the layout of the UWB filter is adjusted and analyzed by electromagnetic field simulation. As a result, it is confirmed that the UWB filter with a bandwidth of 3.1 GHz to 10.6 GHz is obtained.
The Efficiency Evaluation on Different-shape Coils for Loosely Coupled Transformer of Wireless Power Transmission System

In this research, we uses low-frequency electromagnetic field software (Ansys Maxwell 16) to simulate two coil models with different shapes - rectangular and circular in wireless power transmission (WPT). Next, the equivalent model of the series-series (S-S) compensation circuit implemented for leakage inductance, simulated by software (Powersim, PSIM) to analyze the overall efficiency (η). In an inductive and loosely coupled transformer, the influences of coupling coefficient (K) and mutual inductance (M) characteristics, which caused by different coaxial heights (h) and misalignment distances (d) of the coils are discussed. As results for circular shape at coaxial height (h=10 mm), the K=0.800 and η= 98.91%, which are optimal values for all shapes. These excellent simulation performances indicate proposed transformer is a candidate for a low-cost chip for vehicle WPT applications.
Dual-band Transmitter Linearization using Spiking Neural Networks

In this paper, a novel way for dual-band power amplifiers (PA) linearization is proposed based on spiking neuron networks (SNN). A dual-input dual-output SNN is interacted with classical memory polynomial model, which can largely reduce the computational complexity. The experimental results on a real PA show that the proposed method can reach similar linearization performance compared with traditional methods but with low energy consumption. This is the first time that the SNN is deployed for multi-band PA linearization. Future work is to develop a trainable SNN model for real-time PA linearization.
60 GHz mmWave Full-Duplex Transceiver Study and Over-the-Air Link Verification

Full-duplex (FD) radios, which are expected to increase the spectral efficiency and reduce the latency, can help meet the ever-increasing capacity demands, in combination with the millimeter-wave (mmWave) band. In order to utilize the benefits of the FD technology, it is important to understand the characteristics of the self-interference (SI) in the mmWave band. In this paper, we study the impact of SI in the 60 GHz band and design self-interference cancellation (SIC) techniques. Using a 60 GHz hardware platform, we investigate passive SI isolation with beam steering. We also propose a nonlinear interference cancellation (NLIC) technique of feasible complexity with a limited number of kernels. Over-the-air link performance confirms that the proposed FD transceiver design provides sufficient SIC performances.
Performance Analysis for Secure Communication Based on CMAC

The most vehicle manufacturers have been using the CAN (Controller Area Network) in order to communicate among ECUs in vehicle systems for a long time. However, CAN has a disadvantage in that it cannot send and receive large amounts of data quickly due to its limited speed and short data transmission size. The CAN FD (Controller Area Network with Flexible Data rate) was developed to compensate for this disadvantage, and it has the advantage of being able to communicate at more fast speed and eight times the data transmission size compared to CAN. These CAN and CAN FD are vulnerable to message replay attacks because they do not provide data authentication. In this paper, secure communication is implemented by applying CMAC (Cipher based Message Authentication Code) based on CAN and CAN FD. Also, the performance of the two secure communications is compared and analyzed. To implement CMAC, HW-based AES-128 (Advanced Encryption Standard) CBC (Cipher Block Chaining) algorithm is used. This paper compares the performance of Secure CAN and CAN FD communication based on CMAC and can help to check communication traffic of ECU (Electronic Control Unit) in advance when applying the function in the real vehicle system.
A Review of Control Strategies for Operation of Distributed Resources Under Grid Faults

India is aiming for 100% electrification of its territories. In the process, many remote locations need to be connected to nearby electricity supply lines. Interconnection of a remote village with the central grid is a very extensive step, which increases the necessity of converter-based generators as many of them are of renewable based sources. These systems show different characteristics from traditional alternator under faults and affect the operation of the protection relay and system stability. Hence it is necessary to have diverse and robust control strategies to ensure the system stability during and post transient faults. In this connection, this paper aims to review the existing control strategies for operation of distributed generators under line to ground fault, the most common fault. The performance of different control strategies are studied and are compared with the help of numerical simulations.
Min-capacity of a multiple-antenna wireless channel in a static Rician fading environment

We calculate the optimal guaranteed performance for a multiple-antenna wireless link with M antennas at the transmitter and N antennas at the receiver on a Rician fading channel with a static specular component. The channel is modeled with a Rayleigh component and a rank-one deterministic specular component. The Rayleigh component remains constant over a block of T symbol periods, with independent realizations over each block. We analyze the channel under the assumption that the transmitter has no knowledge about the fading coefficients and the receiver has no knowledge about the Rayleigh component but, has complete knowledge about the specular component. Under this scenario to guarantee service it is required to maximize the worst case capacity (min-capacity). Although, it is not necessary for the receiver to have knowledge of the specular component we assume it to show that min-capacity formulation is not pessimistic by showing that min-capacity is equal to avg-capacity when the specular component is constant over time but random with isotropic distribution. This way we show that avg-capacity, which in general has no practical meaning for non-ergodic scenarios, has a coding theorem associated with it in this particular case on account of it being equal to the min-capacity.
Smartphone based Indoor Localization Technology using 1D CNN -BLSTM

The study of indoor localization technology using smart phone has been continuously studied. Fingerprinting is a representative indoor positioning technology. This technology estimates the location by comparing Radio Signal Strength (RSS) information received in one-shot at a specific location with the previously constructed Radio Map. Since the RSS received in one-shot is used, the ability to discriminate signals according to space is low. To solve this problem, the use of RSS spatial patterns based on Pedestrian Dead Reckoning (PDR) improves signal discrimination according to space and increases accuracy. However, since PDR is used, there is a problem that it is difficult to use a spatial pattern if PDR distortion occurs due to a heading drift error and a change motion. We propose an indoor positioning technology using 1D Convolutional Neural Network (CNN) and Bi-directional Long Short Term Memory (BLSTM). We estimated the position by learning the 1D RSS pattern. In order to generate a large amount of data, we used the pre-built Radio Map. We use a model that combines 1D CNN and BLSTM. 1D CNN is used to extract RSS patterns, and BLSTM is used to learn the relationship of sequential data in both directions. Through this, it is possible to estimate the position using only the RSS. To verify the proposed technology, we compared it with the previous technology. As a result, the previous technology showed 2.19m error and the proposed technology showed 4.663m error. However, the calculation speed is 30 times faster than the proposed technology. It was confirmed that indoor positioning technology using deep learning technology can provide position information with only 1D RSS pattern.
Facial Expression Emotion Recognition Based on Transfer Learning and Generative Model

Facial expression emotion recognition has been a popular research topic, which played an important role in assisting the natural human-machine conversation. The conventional method for emotion estimation from facial expressions is to learn a CNN-based image classification model from scratch, However, learning such model requires a large number of labeled facial expression images, which is still a limited resource until now. To solve this problem, we propose a data augmentation method based on StyleGAN2 to generate artificial expression images with respect to seven emotions and use them as the additional training data. We further train an expression emotion recognition model based on a VGG16 network through transfer learning. In this research, we proposed a method using transfer learning and augmented images of facial expressions using trained VGG16 and StyleGAN2 and conducted experiments to achieve higher recognition accuracy for racial expression emotion recognition. Our experiment based on the CFEE dataset suggested that an emotion recognition accuracy of 75.10% could be obtained through transfer learning and the accuracy could further improved to 82.04% with the augmented expression images.
Multi-agent systems with memories under DoS attacks

The paper is devoted to multi-agent systems with memories under denial-of-service (DoS) attacks. The memory is represented by the fractional derivative that appears in the dynamics of the considered systems. The problem that we aim to address is, under which conditions the system continues to be stable or is in consensus mode, despite DoS attacks. Solutions to these problems are proposed in the presented theorems. We strengthen the theoretical considerations with numerical simulations.