Miracle-dz/UniFAR-FTUs huggingface dataset:
Energy-Efficient Flow Routing and Scheduling in Hybrid Data Center Networks

Constructing energy-efficient data center networks (DCNs) is becoming increasingly significant. In the hybrid DCNs with both wired and wireless links, reconfigurable wireless links can effectively reduce the routing path length and the usage of the switch, thereby greatly reduce the energy consumption DCNs. In this paper, we propose an energy-efficient hybrid flow routing and antenna scheduling scheme for tree-based hybrid DCNs. Firstly, the original problem of hybrid routing and scheduling is decomposed into two subproblems by taking advantage of the wireless energy-saving features. Then, a novel weight-relaxing-rounding algorithm is developed to solve the first subproblem. Specifically, the topology characteristics of the tree-based DCNs is used to perform weight transformation and link remapping, and then to find energy-efficient wired subnet. After that, the relax-and-rounding technology is adopted to obtain energy-efficient wireless links scheduling solution. Finally, the numerical results show that the proposed scheme can achieve a near optimal solution when the network scale is small. As the network scale increases, the proposed scheme is still able to save more energy than the existing algorithm.
A Reinforcement Learning Approach for D2D-Assisted Cache-Enabled HetNets

Mobile edge caching (MEC) and device to device (D2D) communications are two potential technologies to resolve traffic overload in heterogeneous networks. Prior works usually investigate them separately with MEC for traffic offloading and D2D for information transmission. In this paper, a composite framework consists of MEC and cache-enabled D2D communications is proposed to minimize the energy cost of systematic traffic transmission, where file popularity and user preference are the critical criteria for small base stations (SBSs) and users respectively. Under this framework, we propose a novel caching strategy where Markov decision process (MDP) is applied to model the requesting behaviors of users. A new algorithm based on reinforcement learning (RL) is proposed to reveal the popularity of files as well as users' preference. In particular, Q-learning (QL) algorithm and deep Q- network (DQN) algorithm are respectively applied to users and SBS due to different complexity of status. To save the energy cost of systematic traffic transmission, users acquire partial traffic through D2D communications based on the cached contents. Taking the memory limits, D2D available files and status changing into consideration, the proposed RL algorithm enables users' devices and SBS to prefetch the optimal files while learning, and hence reducing the energy cost significantly. Simulation results demonstrate the superior energy saving performance of the proposed RL-based algorithm over other existing methods under various conditions.
Multi-Cell MIMO Transceiver Design for Mission-Critical Communication

Business and Mission critical communication (MCC) is a major communication paradigm that is used by public agencies, e.g., during emergency situations, or critical infrastructure companies, e.g., airports, transportation, etc. MCC has very stringent requirements in terms of reliability, coverage and should offer group communications. Coordinated Multimedia Multicast/Broadcast single frequency network (MBSFN) is considered as a potential technology for MCC as it benefits from increased coverage and inter-cell interference mitigation. In this paper, we propose multi-input-multi-output (MIMO) multimedia MBSFN system design wherein each base station (BS) of a coordinated cluster multicasts a common message to all the users in a group. We use a greedy algorithm to dynamically form the cluster of synchronized BSs for optimal utilization of resources within an MBSFN. We assume the availability of perfect channel state information (CSI) knowledge and jointly obtain the optimal precoder and receive filters by minimizing the overall sum-mean- square-error (sum-MSE) constrained over the total transmit power. We further extend the proposed design to a robust case by considering the imperfections in available channel knowledge and obtain the transceiver matrices that are resilient to channel errors. We also present both the joint and robust system design for Single-Cell point-to-multipoint (SC-PTM) which is an alternative solution to MBSFN in MCC. Numerical results show the effectiveness of the proposed network architecture for future mission critical communication. Furthermore, the comparison results show that the proposed robust design demonstrate better performance and is resilient to the presence of CSI errors.
Efficient and Fast Optimization Algorithms for Quantum State Filtering and Estimation

In this paper, based on Alternating Direction Multiplier Method (ADMM) and Compressed Sensing (CS), we develop three types of novel convex optimization algorithms for the quantum state estimation and filtering. Considering sparse state disturbance and measurement noise simultaneously, we propose a quantum state filtering algorithm. At the same time, the quantum state estimation algorithms for either sparse state disturbance or measurement noise are proposed, respectively. Contrast with other algorithms in literature, simulation experiments verify that all three algorithms have low computational complexity, fast convergence speed and high estimation accuracy at lower measurement rates.
GRAB: Joint Adaptive Grouping and Beamforming for Multi-Group Multicast with Massive MIMO

We consider the problem of downlink multicast transmission of user data in massive MIMO systems. Due to the nature of multicast transmission, the common data rate in a multicast group is constrained by that of the user with the worst Signal-to-Noise-Ratio (SNR). As a consequence, serving a large number of users in a single multicast group might degrade the system performance. To overcome this drawback, utilizing spatial degrees of freedom offered by a large number of transmit antennas, we propose to dynamically divide the set of serving users into multiple multicast groups and jointly design the user grouping pattern and co-channel beamforming vectors of these groups. Given the NP-hardness of the considered problem, we decompose it into a multi-group multicast beamforming subproblem and a user grouping subproblem. We proposed several low-complexity methods to iteratively solve these subproblems in order to obtain a suboptimal solution to the original problem. Simulation results show that our proposed GRouping And Beamforming (GRAB) scheme achieve significantly higher average sum-rate performance compared to that of the existing multicast schemes.
Design of Data Reduction Approach for AIoT on Embedded Edge Node

In recent years, the Internet-of-Things and AI technologies have promoted the rapid development of intelligent applications. However, due to the large increase in the number of devices connected to the Internet, the amount of data transmitted at the same time is very large for data collection. Also, the database and storage requirements are also important challenges. In order to mitigate the above problems, the paper proposes a data reduction mechanism for transmitting sensor data under the condition of less data transmission and low storage requirement for further deep-learning applications. We deploy the proposed architecture in a cooperated precision machinery factory in Taichung to collect sensor data. Experimental results show that the amount of collected data can be reduced significantly as well as keep the same features for further training of AI applications.
Detection of Swallowing Times Using a Commercial RGB-D Camera

For assisting early detection and treatment for dysphagic patients, an assessment system for swallowing disorders is necessary. Detecting the swallowing time is one of the key components for developing such an assessment system. Hence, we propose a swallowing detection algorithm using a commercial depth camera. The proposed method considers movements in the front of the neck, before and during swallowing, to determine the swallowing time. We then experimentally demonstrate the performance of the algorithm. The results show that the proposed algorithm achieves a recall and precision of 0.958 and 0.813, respectively. Thus, this algorithm can assist in the early detection of dysphagia.
Burst Erasure Correction by 2D LDPC Codes

Two dimensional magnetic recording systems are investigated extensively to achieve higher recording density. Since the recording density is very high, a small physical defect may cause many two dimensional errors. Hence many two dimensional signal processing of magnetic recording systems are also proposed and investigated. Matcha et al. have proposed a two dimensional LDPC code and investigated the error correcting performance of the code. In this paper we propose a code which is a variant of their code but we can show that the theoretical lower bound of the burst erasure correction capability of our code is better than that of Matcha's code.
Gain-Cell Embedded DRAMs: Modeling and Design Space

Among the different types of dynamic random-access memories (DRAMs), gain-cell embedded DRAM (GC-eDRAM) is a compact, low-power, and CMOS-compatible alternative to conventional static random-access memory (SRAM). GC-eDRAM achieves high memory density, as it relies on a storage cell that can be implemented with as few as two transistors and that can be fabricated without additional process steps. However, since the performance of GC-eDRAMs relies on many interdependent variables, the optimization of the performance of these memories for the integration into their hosting system, as well as the design investigation of future GC-eDRAMs, proves to be highly complex tasks. In this context, modeling tools of memories are key enablers for the exploration of this large design space in a short amount of time. In this article, we present GC-eDRAM modeling tool (GEMTOO), the first modeling tool that estimates timing, memory availability, bandwidth, and area of GC-eDRAMs. The tool considers parameters related to technology, circuits, and memory architecture, and it enables the evaluation of architectural transformations as well as advanced transistor-level effects, such as the increase in the access delay due to the deterioration of the stored data. The timing is estimated with a maximum deviation of 15% from postlayout simulations in a 28-nm FD-SOI technology for different memory sizes and architectures. Moreover, the measured random cycle frequency of a GC-eDRAM fabricated in a 28-nm CMOS bulk process is estimated with a 9% deviation when considering 6-sigma random process variations of the bitcells. The proposed GEMTOO modeling tool is used to show the intricacies in design optimization of GC-eDRAMs, and based on the results, optimal design practices are derived.
Schmidt-Kalman Filter with Polynomial Chaos Expansion for State Estimation

Errors due to uncertain parameters of dynamical systems can result in deterioration of state estimation performance or even filter divergence sometimes using a conventional Kalman filter algorithm. Even worse, these parameters cannot be measured accurately or are unobservable for many applications. Hence, estimating parameters along with state variables would not achieve satisfactory performance. To handle this problem, the Schmidt-Kalman filter (SKF) was introduced to compensate for these errors by considering parameters' covariance, with an assumption of only Gaussian distributions. This paper introduces a new SKF algorithm with polynomial chaos expansion (PCE-SKF). Within the framework of PCE, the dynamical system is predicted forward with an ability to quantify non-Gaussian parametric uncertainties as well. More specifically, the a priori covariance of both the state and parameters can be propagated using PCE, followed by the update step of SKF formulation. Two examples are given to validate the efficacy of the PCE-SKF. The state estimation performance by PCE-SKF is compared with the extended Kalman filter, SKF, unscented Kalman filter and unscented Schmidt-Kalman filter. It is implied that the covariance propagation using PCE leads to more accurate state estimation solutions in comparison with those based on linear propagation or unscented transformation.
Software Defined Network Security Framework for IoT based Smart Home and City Applications

As a popular application of Internet of Things (IoT), Smart City Frameworks aim to provide real time tracking, intelligent control and surveillance across the city. Thus the improvement of resource utilization is a big concern in the management, how to administer such a massive network to meet the requirement of different services? Software Defined Network (SDN) is an ideal solution in customizing networks; however the security feature is the common challenge in both SDN and IoT. In this paper, we propose a framework that uses smart techniques for improving the security features of SDN for smart city applications and diminishing the risk of network invasion. Our SDN Security Framework (SDN-SF) combines two techniques: i) it restrains the unnecessary path between IoT nodes, and ii) it classifies devices into three levels from a combination of MAC address and HTTP request. Additionally, thresholds derived from historical behavior are used for anomaly detection in order to enhance network adaptation. Our result collected from real SDN-based IoT testbed demonstrates that our SDN-SF for Smart City scenarios is able to detect and mitigate malicious traffic with 99.9% of detection rate and 0.5-1 second of detection time in both the control and data plane, respectively.
Context-Similarity Collaborative Filtering Recommendation

This article proposes a new method to overcome the sparse data problem of the collaborative filtering models (CF models) by considering the homologous relationship between users or items calculated on contextual attributes when we build the CF models. In the traditional CF models, the results are built only based on data from the user’s ratings for items. The results of the proposed models are calculated on two factors: (1) the similar factors based on rating values; (2) the similar factors based on contextual attributes. The findings from the experimentation on two datasets DePaulMovie and InCarMusic, show that the proposed models have higher accuracy than the traditional CF models.
Validation du manuel-photos utilisé pour l'enquête alimentaire de l'étude SU.VI.MAX

Nous avons cherche a valider un document iconographique prepare pour mesurer les consommations alimentaires individuelles de 15 000 volontaires participant a l'etude SU.VI. MAX. Trois phases de validation ont ete necessaires pour valider, sur 687 volontaires, l'ensemble des 237 photos presentees. Sur les 237 items testes, les coefficients de concordance des photographies de 148 ont ete consideres comme satisfaisants d'emblee (coefficient Kappa de Cohen > 0,60). Pour 73 aliments, la validation a ete repetee au cours d'une 2 e phase, apres avoir repete les photographies suite a la modification des quantites presentees ou des presentations differentes. Pour 16 aliments, cette validation a du etre repetee 3 fois. Le pourcentage d'aliments pour lesquels la concordance entre photographies et quantites reelles a ete jugee satisfaisante etait d'environ 60 % a l'issue de la premiere phase de validation, de 80 % a l'issue de la seconde phase et de 87 % a la troisieme. Ces resultats confirment l'absolue necessite de valider tout document iconographique ayant la vocation d'aider l'estimation des tailles de portion alimentaires consommees par des individus.
An Efficient Service Function Chain Placement Algorithm in a MEC-NFV Environment

Mobile Edge Computing (MEC) is a promising network architecture that pushes network control and mobile computing to the network edge. Recent studies propose to deploy MEC applications in the Network Function Virtualization (NFV) environment. The mobile network service in NFV is deployed as a Service Function Chains (SFC). In this paper, we solve the SFC placement problem in a MEC-NFV environment. We formulate the SFC placement problem as a weighted graph matching problem, including two sub-problems: a graph matching problem and a SFC mapping problem. To efficiently solve the graph matching problem, we propose a linear programming-based approach to calculate the similarity between physical nodes and VNFs. Based on the similarity, we design a Hungarian-based placement algorithm to solve the SFC mapping problem. Evaluation results show that our proposed solutions outperform the greedy algorithm in terms of execution time and resource utilization.
Control and Operation of an AC Micro-grid Based on Real-time Simulation

In this paper, an AC micro-grid containing renewable energy sources, energy storage system and different types of loads is proposed. The mathematical model of distributed energy sources is presented and the inverter controller including internal loops and external loops are designed. Control strategy for energy storage system and load is also addressed. The real-time simulations verify the effectiveness and robustness in grid connected mode and island mode, as well as in the transition between these two modes.
Delay-Bounded Virtual Emotion Recognition Using IoT Barriers in Advanced Smart Environment

Human emotion recognition has attracted much interest of researchers since it can be utilized for various applications and communication software services. For emotion recognition, there are several approaches including facial expression, human motion. In particular, the emotion derivation through wireless signal and its reflection has been developed recently. Also, a concept of virtual emotion barrier has been introduced, which the emotion can be detected by the built virtual emotion barrier. Because human emotion can be changed frequently, the emotion detection delay through virtual emotion barrier should be minimized for possible accurate emotion recognition. In this paper, we introduce delay bounded virtual emotion barriers in IoT-enabled smart cities, which deliberate on the minimum virtual emotion detection latency among the constructed virtual emotion barriers for next generation software services. Then, we formally define a problem whose goal is to create delay-bounded virtual emotion barriers in IoT-enabled area such that the virtual emotion detection maximum delay among virtual emotion barriers is minimized. To solve the problem, we devise a novel scheme which admits virtual emotion recognition with minimum delay. Furthermore, the performance of the proposed approach is evaluated through extensive experiments.
Improved Chord Recognition using Synthetic Data Generation by Robot

In this paper, we propose a new framework for generating big sized dataset using synthetic data generation by robotics. In learning-based recognition, for example, using convolutional neural networks (CNNs), it is critical for the performance, to collect high-quality and large amounts of training data. Previously, to increase the training data set, a data augmentation technique based on digital signal processing were applied to the original sound data. However, the data augmentation based on digital signal processing data is a limited method, because it depends on some previous knowledge of the data and cannot perform for all domains. On the other hand, we propose a new dataset collection technique using a robot that automatically plays instruments, by which it becomes possible to add high-quality data to training samples. Experimental results for guitar chord recognition show that the proposed method using CNNs and a guitar robot can outperform the CNN systems with the traditional data augmentation.
Consensus Algorithms for Bidirectional Teleoperation of Aerial Manipulator Robots in an Environment with Obstacles

This paper presents a consensus algorithm for formation of aerial manipulators in congested environments. The formation of aerial manipulators is made up of virtual leaders and follower robots, the distribution of the follower robots is determined by a Laplacian matrix, which is built from the reference positions of the follower robots within the convex hull composed of virtual leaders. The references of the of virtual leaders are given by the force of the operator, which is modeled by mechanical impedance and is entered through the haptic device. Obstacle avoidance is based on a repulsion zone, this zone generates a repulsion force, which is fed back to the haptic device so that the operator can feel the force and evade the obstacles. For the validation of the algorithm, simulation tests are carried out for formation of aerial manipulators, teleoperation and force feedback is implemented by means of a haptic device for the generation of references. Finally, the performance of the control algorithms is analyzed and the stability tests are presented.
Bidirectional Dual-active DC-DC Converter Based on Traction Auxiliary Power Supply System

The auxiliary power supply system is used to supply the power to the electric equipments, like the lighting and the heating equipments, etc and plays an important role in the normal operation of the Electric Multiple Units (EMU). As to the generalized auxiliary converters, it is difficult to achieve their lightweight design due to their properties of large capacity and volume. To address this issues, an improved auxiliary converter is proposed in this paper, which has the following advantages: 1) The proposed topology directly takes power from the DC high-voltage bus and effectively reduces the conversion order of the entire converter. 2) The charger adopts bidirectional isolated DC-DC converter to realize bidirectional energy transmission, which can overcome the disadvantage that the traditional structure cannot maintain the power supply of the AC bus when the pantograph loses power. In addition, for the DC-DC converter and inverter in this structure, the corresponding mathematical models are established and the related control strategies are applied to ensure the whole system normal operation and finally verified by simulation and experimental results.
An Experimental Study Towards Attacker Identification in Automotive Networks

The auxiliary functions of modern vehicles have increased the number of attack surfaces. As the most important information exchange channel for vehicles, Controller Area Network (CAN) attracts more and more attention. Due to no encryption and authentication, CAN bus is vulnerable to attack. Thus, the attacker can manipulate the message with serious consequences. Several methods have been proposed to improve the security of vehicles. However, most of them have their own deficiencies. In this paper, we propose an LOF-based attack detection scheme which utilizes the voltage physical characteristics of the CAN frame to judge whether a message is transmitted by a legitimate Electronic Control Unit (ECU). As validated by extensive experiments on two vehicles, our scheme can identify the attacker with an average detection rate of 98.9\%, and the false detection rate is less than 0.5\%. In addition, our scheme can accurately identify attacks from external devices.