Miracle-dz/UniFAR-FTUs huggingface dataset:
Delay Performance of Distributed Physical Layer Authentication Under Sybil Attacks

Physical layer authentication (PLA) has recently been discussed in the context of URLLC due to its low complexity and low overhead. Nevertheless, these schemes also introduce additional sources of error through missed detections and false alarms. The trade-offs of these characteristics are strongly dependent on the deployment scenario as well as the processing architecture. Thus, considering a feature-based PLA scheme utilizing channel-state information at multiple distributed radio-heads, we study these trade-offs analytically. We model and analyze different scenarios of centralized and decentralized decision-making and decoding, as well as the impacts of a single-antenna attacker launching a Sybil attack. Based on stochastic network calculus, we provide worst-case performance bounds on the system-level delay for the considered distributed scenarios under a Sybil attack. Results show that the arrival-rate capacity for a given latency deadline is increased for the distributed scenarios. For a clustered sensor deployment, we find that the distributed approach provides 23% higher capacity when compared to the centralized scenario.
On Coded Broadcasting for Wireless Recommendation Systems

This paper considers benefits of coding techniques in recommendation systems operating over wireless channels with erasures. We identify scenarios where coded broadcasting can increase the overall user satisfaction at a fixed channel utilization level. Such opportunities arise both when user preferences are unknown, and must be explored, and when they are known and must be exploited to recommend optimally. We determine the magnitude of the potential gains and show that coding is most beneficial if users have heterogeneous preferences. Finally, we provide inequalities that can be evaluated to determine whether coding would be beneficial for a certain reward structure.
Discrete-State Event-Driven Numerical Prototyping of Megawatt Solid-State Transformers and AC/DC Hybrid Microgrids

Solid-state transformers (SSTs) enable electric energy distribution with higher flexibility, sustainability and efficiency, and the AC/DC hybrid microgrid (MG) is one of the representative applications. However, the design and analysis of SSTs and MGs were limited by lack of fast simulation tools that can accurately and efficiently handle systems with massive number of switching devices. This article demonstrates how the discrete-state event-driven (DSED) approach allows an accurate and faster simulation of the SSTs and MGs than the state of the art and benefit the practical design. The numerical prototyping of a hybrid MG composed of a megawatt SST together with multiple converters (e.g. PVs, EVs and batteries) is presented in this article. The performance is compared with existing simulation approaches. Various scenarios are investigated, including the grid-connected mode, the islanded mode, and the mode transition considering the communication delay. A time cost of several hours is decreased to tens of seconds with same accuracy using DSED, enabling faster simulations for complicated tasks. It is discussed how the speed and accuracy benefit the practical SST and MG design.
A System Based on Piezoelectric Micromachined Ultrasound Transducer for Social Distancing and Human-Machine Interaction

A Lead Zirconate Titanate (PZT) based piezoelectric Micromachined Ultrasound Transducer (pMUT) array designed, fabricated and used for real-time proximity detection is presented in this paper. One-dimensional pMUT array containing 192 elements (24 column, each column contains 8 elements) was fabricated in silicon substrate. The size of the pMUT array is 2 × 0.8mm2 and the resonant frequency of the pMUT elements is 7.54MHz. In order to get appropriate signal for data processing, back-end circuit is designed for the pMUT array in speciality. In the air, the distance of 15cm can be detected precisely and displayed on computer in real time and send an alarm at setting distance. This system suggests that pMUTs have sufficient performance for application in social distancing and human-machine interaction to avoid collision.
Task Scheduling Game Optimization for Mobile Edge Computing

Task scheduling on edge computing servers is an important issue that affects user experience. Existing scheduling methods require centralized control to achieve the best overall performance. However, it is impractical to force all users to act according to centralized control. We propose a distributed edge computing server task scheduling model based on game theory. Our method comprehensively considers the link quality from the mobile device to the server and the server's computing resource allocation when selecting edge computing servers, and achieves a balance between link quality and computing resources. Once the Nash equilibrium is reached, our model can provide different QoS for users of different priorities. Acceleration methods are proposed to achieve the Nash equilibrium faster. The simulation results show that the proposed model can provide differentiated services while optimizing the scheduling of computing resources, and ensure that the algorithm achieves an approximate Nash equilibrium in polynomial time.
Reconfiguration in Maritime Networks Integrated with Dynamic High Altitude Balloons

Nowadays, maritime communication has attracted more and more attention. To provide high-speed and low- cost communication in maritime networks, an efficient architecture and flexible routing are expected. In this paper, we propose a novel high altitude balloon-enabled maritime network (HABMN) architecture. In the proposed architecture, considering that high altitude platforms (HAPs) are difficult to maintain station-keeping in practice, we integrate dynamic balloon networks to guarantee maritime communication coverage. Then, we formulate an integer programming to maximize the total traffic accepted by whole networks over time. Considering the unpredictable link disruption caused by sea surface movement and wave occlusions, we update the network configurations to get optimal performance. However, frequent flow update will increase the burden on control channels and lower down system stability. Then, we propose a lazy policy (LP) to wisely determine whether to apply the optimal network configurations immediately or not. At last, extensive simulation experiments demonstrate the effectiveness of the proposed policy.
An Ultra-Broadband Contact-Capacitive RF MEMS Switch for 15-110GHZ Applications

An ultra-broadband RF MEMS (radio frequency micro-electro-mechanical system) switch for 15-110GHz applications is presented in this paper. A contact-capacitive structure is used to realize the low insertion loss and stable isolation in the broad band. Owing to the contact-capacitive topology, the ON-state and OFF-state can be designed separately. An RLC (resistor-inductor-capacitor) model is established to design the switch. The elements in the RLC model are designed carefully to obtain the ultra-broadband performance. The device is fabricated by using surface manufacturing process. The S-parameters of the switch are tested. The insertion loss and return loss at ON-state are better than -0.5dB and -15dB, respectively, from 15GHz to 110GHz. The isolation at OFF-state is better than -15dB.
Possibilistic aggregation of inhomogeneous streams of data

Streams of data collected from sensors are usually large and inhomogeneous in time. In this paper, we consider the case when data consist of subsegments of different lengths governed by possibly different probability distributions. The data describing consecutive subsegments are presented in the form of histograms. Next, these subsegments are grouped in larger segments whose characteristics, such as measures of location or variability, are used in further analysis. We present a possibilistic method for the aggregation of subsegment data represented by histograms into segment data represented by possibilistic distributions. The performance of the proposed method is illustrated in monitoring of bipolar disorder patients using their voice data collected from smartphones.
Keyed Polar Coding for Physical-Layer Security without Channel State Information

Polar codes have been shown to provide an effective mechanism for achieving physical-layer security over various wiretap channels. A majority of these schemes require channel state information (CSI) at the encoder for both intended receivers and eavesdroppers. In this paper, we consider a polar coding scheme for secrecy over a Gaussian wiretap channel when no CSI is available. We show that the availability of a shared keystream between friendly parties allows polar codes to be used for both secure and reliable communications, even when the eavesdropper knows a large fraction of the keystream. The scheme relies on a predetermined strategy for partitioning the bits to be encoded into a set of frozen bits and a set of information bits. The frozen bits are filled with bits from the keystream, and we evaluate the security gap when the cyclic redundancy check-aided successive cancellation list decoder is used at both receivers in the wiretap channel model.
An AI-based Traffic Matrix Prediction Solution for Software-Defined Network

Traffic Matrix (TM) clearly describes the volume and the distribution of traffic flows inside a network. TM plays an important role in many network management fields, such as traffic accounting, short-time traffic scheduling or re-routing, network design, anomaly detection, etc. Hence, an accurate TM prediction strategy is essential to handle those tasks effectively. Fortunately, Artificial Intelligence (AI) has been developing very strongly, thanks to computer technology developments such as GPU and TPU. That offers an opportunity to apply AI to TM prediction methods. However, applying Machine Learning techniques in traditional networks encounters some issues due to the distributed control and restricted local view of network nodes. For this purpose, a centralized control architecture, e.g., Software-defined Network (SDN), is a promising candidate. In this paper, we apply Long Short-Term Memory (LSTM) and its two variants, Bidirectional LSTM (BiLSTM) and Gate Recurrent Unit (GRU), for TM prediction mechanisms of an SDN architecture network. The prediction models have been evaluated using two datasets: the popular GÉANT backbone network traffic data and our dataset generated through a testbed. The experimental results show that our approach yielded promising traffic prediction accuracy.
Case-study for HW Accelerated FEA Model for Electrical Machine Control Prototyping

The challenging market in the e-mobility domain requires higher speed of development. To accelerate the time to market of the products, this phenomena is also addressing the tools and methods are used in the procedure of development. System simulations are widely used to control prototyping with validation purposes. Within a frame of a case study this paper presents the most common branches of the e-machine simulation methods. Highlights the main benefits and downsides of the simulation schemes. Draws results and sources from studies, are done in the field of finite element based e-machine modeling and simulation by the engineering community. These applications are evaluated and giving overview about the complexity of model structures, details and the computational sources from the perspective of required value of run-time. Explains the common approaches of the e-machine simulation methods, with an investigation in the field of hardware accelerated finite element analysis. Invests within the available studies and experiences from different non e-machine applications of the finite element method. Consequently notes a form of application and adaption of these techs of the finite element analysis that keep the tempo of development at a high-rate beside to offer a more detailed simulation than traditionally used tools for control prototyping purposes.
Application of the Fuzzy Logic to Evaluation and Selection of Attribute Ranges in Machine Learning

In the paper, we show how the importance of individual ranges of values of attributes describing cases can be determined using the attribute fuzzification process. The importance is determined on the basis of classification capabilities. The described approach is based mainly on fuzzy set theory and the rough set based discretization method. Moreover, an experimental study of the computer-aided classification task is presented.
Comparative assessment of aggregated classification algorithms with the use to mining a cyber-attack dataset

Currently, we observe an enormous growth in the frequency of using the Internet, which is also causing an increase in attacks on computer nets. These phenomena significantly raise the importance of the use of Intrusion Detection Systems (IDS). Classification systems are an essential part of a cyber-attack detection task by classifying the attacks based on certain criteria. The purpose of this research is to assess the relative performance of five extensions of well-known classification methods using the distributivity law. The results of this investigation can help in the design of classification systems that use several classification methods, namely k-Nearest Neighbor, Naive Bayes, Support Vector Machine, Random Forests, and Multilayer Perceptron Network can be employed to increase the accuracy of the classification. This method requires the use of some adequate aggregation operators (e.g. average functions and triangular norms/conorms) for which the distributivity law occurs. The work contains principally the results of experiments carried out on the KDD'Cup 99 dataset using WEKA (Waikato Environment for Knowledge Analysis) tool.
Energy Efficient Multi-Objectives Optimized Routing for Opportunistic Networks

This paper proposes a novel routing protocol for Opportunistic networks called Energy Efficient MultiObjectives Optimized Routing (E2MOOR), which uses a multi-objectives weight function for efficient routing. The proposed protocol is energy efficient and predicts the next optimal forwarder based on four objectives, which consists of hop encounter, distance between the source/intermediate and destination, delivery probability, and node’s energy consumption, as context information. The pareto optimal solutions set is extracted through the Naive and Slow algorithm. The nodes in the set are further used for forwarding the data packets to the destination node. The proposed protocol is evaluated considering the double, triple, and quadruple objective functions, when the predefined threshold values are varied. Simulations results show that the proposed E2MOOR scheme outperforms the E-Epidemic, E-PRoPHET, and E-EDR chosen as benchmarks routing protocols.
Adaptive and Collaborative Edge Inference in Task Stream with Latency Constraint

With the rapid development of the Internet of Things (IoT), more and more smart devices are connected to the Internet, implementing Deep Neural Network (DNN) models on edges for collaborative inference via device-edge synergy has become a feasible method for improving application performance in many scenarios. However, when faced with the task stream scenario with latency guarantee such as video surveillance and industrial production line, we need adaptive edge intelligence to make adjustments in real-time according to the changes of the task stream. There are many adaptive edge intelligence technologies in the existing works, such as early-exit mechanism and model selection, but they don’t take the requirements of the task stream scenario into consideration. In this paper, we propose a device-edge collaborative inference system based on the early-exit mechanism to solve the problem of adaptive edge intelligence in the task stream scenario. Then, we design an offline dynamic programming (DP) algorithm and an online deep reinforcement learning (DRL) algorithm to dynamically select the exit point and partition point of the branchy model in the task stream, which aims to balance the number of tasks accomplished and task inference accuracy in the system. Experimental results show that the DRL algorithm can achieve performance close to that of the DP algorithm in the task stream scenario.
Efficient Packet Classification with Learned Bloom Filter in Software-Defined Networking

The development of emerging network technologies represented by Software-Defined Networking (SDN) has made traditional routing technologies that are based on a single IP address domain unable to meet the increasing demand for network services and security. Packet classification is a technique to differentiate multi-domain network traffic in a fine-grained manner using packet header fields. Packet classification requires to operate efficiently to avoid it becoming a bottleneck in the packet routing process. In this paper, we propose a learned Bloom filter (LBF)-based packet classification algorithm that combines the RNN learned model with a support Bloom filter (SBF) to improve the classification accuracy. Specifically, the learned model is trained with the positive and negative sets, which is used as a pre-filter to identify the two sets. For the filter outcomes with a negative result from the learned model, SBF is constructed to perform the second filtration. To ensure efficiency of RNN and SBF, we carefully select key features to be used in RNN and SBF, which can also maintain efficient search in the final stage of rule matching. We perform simulation with various datasets showing the performance advantages of our proposed algorithm over existing solutions in terms of memory usage and classification accuracy.
Self-Heating CMOS Flow Sensor

This paper reports a new design of polysilicon flow sensor fabricated by UMC $0.18\ \mu \mathrm{m}$ CMOS MEMS foundry. Our self-heating design is different from the conventional one which has floating plate with resistive temperature detectors (RTDs) heated by another heater aside, and our self-heating RTD half-bridge is only $300\times 250\ \mu \mathrm{m}^{2}$ in chip size. This new design outputs a normalized sensitivity of $138\ \mu \mathrm{V}/\mathrm{V}/(\mathrm{m}/\mathrm{s})$ within the speed range of 0-15 m/s. This sensitivity is 89% of Y. -K. Lee's work in IEEE MEMS 2020. The dummy specimen and the orientation-free wind tunnel tests also confirmed the sensor performance.
Performance Optimization of Distributed Primal-Dual Algorithms over Wireless Networks

In this paper, the implementation of a distributed primal-dual algorithm over realistic wireless networks is investigated. In the considered model, the users and one base station (BS) cooperatively perform a distributed primal-dual algorithm for controlling and optimizing wireless networks. In particular, each user must locally update the primal and dual variables and send the updated primal variables to the BS. The BS aggregates the received primal variables and broadcasts the aggregated variables to all users. Since all of the primal and dual variables as well as aggregated variables are transmitted over wireless links, the imperfect wireless links will affect the solution achieved by the distributed primal-dual algorithm. Therefore, it is necessary to study how wireless factors such as transmission errors affect the implementation of the distributed primal-dual algorithm and how to optimize wireless network performance to improve the solution achieved by the distributed primal-dual algorithm. To address these challenges, the convergence rate of the primal-dual algorithm is first derived in a closed form while considering the impact of wireless factors such as data transmission errors. Based on the derived convergence rate, the optimal transmit power and resource block allocation schemes are designed to minimize the gap between the target solution and the solution achieved by the distributed primal-dual algorithm. Simulation results show that the proposed distributed primal-dual algorithm can reduce the gap between the target and obtained solution by up to 52% compared to the distributed primal-dual algorithm without considering imperfect wireless transmission.
Design and Modelling of a Compliant Constant-Force Surgical Tool for Objective Assessment of Ossicular Chain Mobility

Surgeons generally assess the mobility of the ossicle chain by manual palpation using rigid elongated hooks inserted through the ear canal. As the applied forces are in the range of a few grams equivalent force, the measurement remains subjective and its reliability highly dependent on the level of experience and skills of the surgeon. This paper presents the design, modelling and experimental testing of an innovative surgical device based on a novel compliant constant-force mechanism used to apply a predefined force of 1 gf (9.81 mN) to the ossicles. Using this handheld constant-force tool along with an operating microscope for visual feedback of ossicle displacement, surgeons can objectively quantify ossicular mobility.
Design of a low cost EEG sensor for the measurement of stress-related brain activity during driving

In this paper we present the design, development, characterization and testing of a six channels EEG wearable sensor for the measurement of stress-related brain activity during driving. The sensor is battery operated and transmits data to a laptop using WiFi. The metrological characterization showed non-linearity in the order of 0.8%, the resolution is 50 nV using oversampling technique and the bandwidth is in the range [0.8, 45] Hz. The sensor has been tested on a driving simulator where the aim was comparing manual driving with a smooth ADAS algorithm and with an aggressive ADAS algorithm. The results show that the measured power of beta waves (related to stress) is much higher in aggressive ADAS with respect to smooth ADAS and manual driving.
Optimal Versus CSI-Based SIC Ordering in Downlink Multi-Cell NOMA Systems

The key idea of non-orthogonal multiple access (NOMA) is to achieve the channel capacity of degraded broad-cast channels by a linear superposition coding combined with successive interference cancellation (SIC). In this line, SIC decoding order among users plays an important role in downlink NOMA systems. The SIC decoding order based on the users’ channel gains within the cell normalized by noise (channel state information (CSI)-based decoding order) is known to be optimal in downlink single-antenna single-cell NOMA. However, this strategy is not optimal in single-antenna multi-cell NOMA, because of the existing inter-cell interference (ICI) which depends on the power consumption of neighboring cells. In this work, we address the problem of finding globally optimal joint SIC ordering and power allocation strategy for the sum-rate maximization problem in downlink single-antenna multi-cell NOMA systems. We propose a globally optimal solution based on the exploration of base stations power consumption and distributed power allocation. We show that this algorithm has a reduced computational complexity compared to other existing optimal solutions. Numerical results show that the optimal decoding order results in significant performance gains in terms of outage probability and users total spectral efficiency compared to the CSI-based decoding order.