Miracle-dz/UniFAR-FTUs huggingface dataset:
Experimental Demonstration of Dynamic Optical Beamforming for Beyond 5G Spatially Multiplexed Fronthaul Networks

This paper presents a beyond 5G fronthaul network with dynamic beamforming and -steering. The proposed fronthaul solution deploys optical beamforming (OBF) by combining space division multiplexing (SDM), analogue radio-over-fiber (ARoF), and the novel optical beam forming network (OBFN) technologies. From the service management and orchestration (MANO) point of view, the proposed fronthaul solution also deploys an advanced software defined networking (SDN) and Network Function Virtualization (NFV) control and orchestration architecture developed with the goal to optimally manage and reconfigure the physical layer resources (i.e., optical and radio) at the central office and cell sites (i.e., pool of baseband units (BBUs), remote radio heads (RRHs), ARoF transceivers and OBFNs). The proposed beyond 5G fronthaul architecture is primarily oriented to deploy massive machine-type communication (mMTC) services with high-bandwidth requirements, such as for industry 4.0. In this paper we experimentally validate the novel OBFN system, and the dynamic SDN/NFV MANO of the transport connectivity and network services for optical beamforming. The obtained experimental results show that the overall delay for the provisioning and removal of an OBF service, considering the contribution of the involved optical and radio systems and the SDN/NFV MANO layer, is 134s and 18s respectively. The reconfiguration of the OBF service to add or remove a beam can be performed in the range of 65–87s.
Short-term PM2.5 Forecasting with a Hybrid Model Based on Ensemble GRU Neural Network

PM2.5 (particular matter with a diameter of 2.5µm or less) is one of the most important indicators of air pollution. In the field of environmental science, how to forecast PM2.5 is an important topic. We construct a previous 24-hour indicator before the predicted point to construct an enhanced dataset for PM2.5 concentration prediction. However, with a large scale of features, the performances of fundamental neural networks are not stable or accurate enough. As a result, an ensemble GRU (Gate Recurrent Unit) neural network is proposed for short-term PM2.5 prediction. This approach can improve accuracy while maintaining stability by combining the outputs after varying training. In this study, a dataset, which recording 6 indicators (PM2.5, PM10, CO, NO2, O3, SO2) for more than 20,000 hours in Shenzhen, is adopted to evaluate the proposed approach. Experimental results indicate that the proposed ensemble GRU model provides the lowest scores in MSE, RMSE criteria, and the best average-results in R2, MSE, RMSE scores.
Water Pressure Monitoring Using a Temperature-Compensated WP-SAW Pressure Sensor

This paper presents a method on improving water pressure monitoring using wireless passive surface acoustic wave (WP-SAW) sensor. An experimental framework acting as water pipe simulator is built. Signal processing methods such as down-conversion, quadrature sampling, average, and multi-iterative two-point simple moving average (MI-2P-SMA) are utilized for improving sensing accuracy. The results show the relative errors of temperature at all testing points are less than 1% and the relative errors of pressure at all testing points are less than 2%.
SEMSDNet: A Multiscale Dense Network With Attention for Remote Sensing Scene Classification

Remote sensing image scene classification plays an important role in remote sensing image interpretation. Deep learning brings prosperity to the research in this field, and numerous deep learning models are proposed in order to improve the performance of scene classification. However, images of different remote sensing scenes vary a lot, showing similar or diverse textures and simple or complex contents. Using a fixed convolutional neural network framework to classify scene images is performance-limited and not practice-flexible. To address this issue, in this article, we propose the SEMSDNet (multiscale dense networks with squeeze and excitation attention). The framework multiscale dense convolutional network (MSDNet) with multiple classifiers and dense connections can automatically transform between a small network and a deep network according to the complexity of test samples and the limitation of computational resources. Moreover, in order to extract more effective features, the squeeze-and-excitation (SE) attention mechanism is introduced into the framework to process the features of various scenes self-adaptively. In addition, considering the limited computing resources, we impose two settings with computational constraints at the test time: budgeted batch classification, which is a fixed computational budget setting for sample classification, and anytime prediction, which forces the network to output a prediction at any given point-in-time. Experimental results on several public datasets show that the proposed SEMSDNet method is superior to the state-of-the-art methods on both performance and efficiency. Experiments also reveal its capability to treat samples of different classification difficulties with uneven resource allocation and flexible network architecture, showing its potentials in practical applications.
Single Event Analysis on SRAM 180nm CMOS Memory with Super Voter Protection Technique

This work presents excellent results from measuring Single Event Effect (SEE) on SRAM memory devices fabricated on 180nm CMOS technology with a Super Voter Protection Technique. The Super Voter consists of concepts from conventional Hamming encoding and Triple Modular Redundancy (TMR). Robustness was increased using data frame separation between Most Significant Bit (MSB) and Least Significant Bit (LSB) on some error correction stages and the final output restored joining again the two parts, keeping their error flags apart, to increase performance. This novel design provides an error signaling logic called DOUBLE ERROR LSB and DOUBLE ERROR MSB to detect double errors. In Space, there are many natural phenomena such as SEE, a radiation-induced disturbance that causes unwanted behavior in the logic of Integrated Circuits (IC). Therefore, the use of techniques that mitigate these effects are widely studied in space applications. We ran tests to compare three architectures: a) without protection, b) using only hamming code and c) using Super Voter. The results indicate that the Super Voter can be heavily adopted in aerospace applications.
AI and IoT-Enabled Smart Exoskeleton System for Rehabilitation of Paralyzed People in Connected Communities

In recent years, the number of cases of spinal cord injuries, stroke and other nervous impairments have led to an increase in the number of paralyzed patients worldwide. Rehabilitation that can aid and enhance the lives of such patients is the need of the hour. Exoskeletons have been found as one of the popular means of rehabilitation. The existing exoskeletons use techniques that impose limitations on adaptability, instant response and continuous control. Also most of them are expensive, bulky, and requires high level of training. To overcome all the above limitations, this paper introduces an Artificial Intelligence (AI) powered Smart and light weight Exoskeleton System (AI-IoT-SES) which receives data from various sensors, classifies them intelligently and generates the desired commands via Internet of Things (IoT) for rendering rehabilitation and support with the help of caretakers for paralyzed patients in smart and connected communities. In the proposed system, the signals collected from the exoskeleton sensors are processed using AI-assisted navigation module, and helps the caretakers in guiding, communicating and controlling the movements of the exoskeleton integrated to the patients. The navigation module uses AI and IoT enabled Simultaneous Localization and Mapping (SLAM). The casualties of a paralyzed person are reduced by commissioning the IoT platform to exchange data from the intelligent sensors with the remote location of the caretaker to monitor the real time movement and navigation of the exoskeleton. The automated exoskeleton detects and take decisions on navigation thereby improving the life conditions of such patients. The experimental results simulated using MATLAB shows that the proposed system is the ideal method for rendering rehabilitation and support for paralyzed patients in smart communities.
A Queueing Model for Threshold-Based Scaling of UPF Instances in 5G Core

The fifth-generation networks User Plane Function (UPF) provides necessary procedures to connect end-devices and data networks. A Protocol Data Unit (PDU) session between specific user equipment (UE) and the UPF is established before data transmission in 5G networks. The UPF software can be packed into either a virtual machine (VM) or a container image and instantiated in service providers’ cloud infrastructure. Each UPF instance may handle several concurrent PDU sessions from various users. Operators should control the number of UPF instances to save resource consumption in the provision of Quality of Service (QoS). This paper presents a queueing model for a scenario where a threshold-based algorithm controls the number of UPF instances depending on users’ traffic. We derive a method to compute the steady-state probabilities and performance measures efficiently. We investigate the performance of the UPF scaling algorithm in various scenarios with the assumption of specific hardware. Numerical results show that scaling UPF instances can lead to the good utilization of the system resource.
Diester Derivatives from Chemically Modified Waste Cooking Oil as Substitute for Petroleum Based Lubricating Oils

In order to provide a new way for waste cooking oil (WCO) resource utilization, several diester derivatives were obtained from WCO through a three-step chemical modifications, viz.: transesterification, epoxidation and oxirane ring opening with carboxylic acids. The effects of the chain length of side chain groups on the viscosity, acid value, low temper- ature fluidity, thermo-oxidative stability, tribological properties and surface tension of diester derivatives were investigated. The results showed that increasing the chain length of side chain groups had a positive influence on the viscosity, viscosity index, acid value, pour point, friction coefficient and wear scar diameter along with a negative influence on the oxidation onset temperature, volatile loss, insoluble deposit, maximum non-seizure load and surface tension. These diester derivatives exhibited improved physicochemical and tribological properties that make themselves promising environmentally friendly biolubricant basestocks.
A Spam Transformer Model for SMS Spam Detection

In this paper, we aim to explore the possibility of the Transformer model in detecting the spam Short Message Service (SMS) messages by proposing a modified Transformer model that is designed for detecting SMS spam messages. The evaluation of our proposed spam Transformer is performed on SMS Spam Collection v.1 dataset and UtkMl’s Twitter Spam Detection Competition dataset, with the benchmark of multiple established machine learning classifiers and state-of-the-art SMS spam detection approaches. In comparison to all other candidates, our experiments on SMS spam detection show that the proposed modified spam Transformer has the optimal results on the accuracy, recall, and F1-Score with the values of 98.92%, 0.9451, and 0.9613, respectively. Besides, the proposed model also achieves good performance on the UtkMl’s Twitter dataset, which indicates a promising possibility of adapting the model to other similar problems.
EKF-SLAM with Autonomous Exploration using a Low Cost Robot

Autonomous exploration and SLAM (Simultaneous Localization and Mapping) are two of the main problems in robotics due to their relevance for many military and commercial real world applications. This article proposes a solution, tested using a low cost robot (less than US$ 500), based on EKF-SLAM (Extended Kalman Filter) to autonomously explore an static and unknown 2D environment. This environment is assumed to be composed of walls and cylinders which are modeled respectively as line segments and circles. The proposed solution applies border detection techniques to occupancy grid maps and performs path planning using the $\mathrm{A}^{*}$ search algorithm to generate safe and feasible paths for the robot autonomous exploration. A differential drive robot with a Raspberry Pi 3 Model B+ single board computer, a laser scanner, a electronic compass and wheel encoders was designed and built to test the proposed solution. This robot, via a Wi-Fi network, communicates with a remote personal computer that runs the autonomous exploration and EKF-SLAM algorithms. Simulation and real world experiments are shown for an indoor closed environment with approximately 41 square meters, 6 thin cylinders and 10 walls. In the real world experiment the proposed solution achieved an average error of 8.11 cm for the walls’ characteristic points and 4.33 cm for the cylinders’ centroids.
Two-Aggregator Topology Optimization Using Single Paths in Data Center Networks

This paper focuses on the data aggregation problem to two aggregators in a data center network under the constraint that each source rack must use a single path to each aggregator. We derive bounds on the approximation ratios of two classes of aggregation algorithms– Restricted 1-Round (R1R) and Restricted 2-Round (R2R). We achieve tighter bounds for the problem with additional constraints on the inputs. We propose another strategy using the 2Chain topology for k = 2, where k denotes the maximum degree of each top-of-rack(ToR) switch (number of uplinks in a ToR switch) in the data center and show that the optimal 2Chain cannot have an aggregation time greater than that of the optimal R1R and R2R topologies. For k <inline-formula><tex-math notation="LaTeX">$\geq$</tex-math><alternatives> <mml:math> <mml:mo>≥</mml:mo> </mml:math> <inline-graphic xlink:href="das-ieq1-2885053.gif"/></alternatives></inline-formula> 4, we propose a 1-round aggregation algorithm (1R) that uses trees for aggregation. Experimental results illustrate that 1R, R2R and R1R reduce the aggregation time by up to 85, 67 and 67 percent respectively for k = 4 relative to the two-round aggregation algorithm proposed by Wang et al. Moreover, for k = 2, the 2Chain can reduce the aggregation time up to 42 and 24 percent respectively relative to R1R and R2R.
Cooling-Aware Optimization of Edge Server Configuration and Edge Computation Offloading for Wirelessly Powered Devices

Edge computing (EC) provides an effective means to cope with explosive computation demands of the Internet-of-Things (IoT). This paper presents a new cooling-aware joint optimization of the CPU configuration of the edge servers, and the schedules of wireless power transfer (WPT), offloading and computing for WPT-powered devices, so that the resource-restrained devices can have tasks accomplished in a timely and energy-efficient manner. Alternating optimization is applied to minimize the total energy consumption of WPT, EC, and cooling, while satisfying the computation deadlines of the devices. A key aspect is that semi-closed-form solutions are derived for the WPT power, offloading duration, and CPU frequency by applying the Lagrange duality method. With the solutions, the alternating optimization converges quickly and indistinguishably closely to the lower bound of the energy consumption. The semi-closed-form solutions also reveal the structure underlying the optimal solution to the problem, and can validate the result of the alternating optimization. Extensive simulations show that the proposed algorithm can save up to 90.4% the energy of existing benchmarks in our considered cases.
Secrecy performance analysis of UAV wireless powered communications against full-duplex proactive eavesdropping

We investigate the physical layer security of energy-constrained Unmanned Aerial Vehicle (UAV) wireless powered communications in the presence of an eavesdropper. We consider a full-duplex proactive eavesdropper who performs both passive eavesdropping and active jamming simultaneously. To ensure the reliability and improve the security of UAV communications, we propose a transmission outage constrained scheme to limit the transmission outage probability to a maximum tolerable threshold and the optimal coding rate of the base station is obtained. We derive the analytical closed-form expression of UAV's secrecy outage probability to characterize the secrecy performance. Through analytical and numerical results, we examine the effect of different system parameters on the security of UAV wireless powered communications. Simulations verify that there is a tradeoff between transmission outage probability and secrecy outage probability.
Autonomous Raman amplifiers in multi-band software-defined optical transport networks

A controller architecture for managing multi-band Raman amplification within software-defined optical transport networks is presented. In the perspective of optical fiber network softwarization and disaggregation, this work provides an operative description of a system capable of autonomously handling Raman amplification on a single fiber span, achieving the working point requested by the control plane in terms of mean gain and tilt. The developed architecture is composed of two software modules: the Raman design unit (RDU) and the Raman controller unit (RCU). First, the RDU defines the nominal optimal working point designing the power level configuration of the Raman card pumps according to the required amplification constraints without considering any channel spectral load. Then, due to the telemetry feedback, the RCU performs a tracking operation of the mean gain towards the target one, linearizing the problem at the nominal optimal working point. The fiber physical parameters needed by the controller for correct operation of the system are extracted through a conceived probing procedure. The proposal shows a high degree of adaptability of the defined Raman amplifier to the particular in-field scenario in which it is deployed, also counteracting spectral load modifications. The behavior of the described system is validated by means of an experimental session, confirming the effectiveness of the controller architecture design and the high accuracy achieved in terms of performance.
A Three-Dimensional Discontinuous Galerkin Time-Domain Finite Element Method for Electromagnetic Modeling of Wireless Power Transfer Coils

Inductive coupling based wireless power transfer (WPT) is a popular short-range power delivery mechanism for many industrial, biomedical, and home electronic appliances applications. A numerical methodology is needed for the analysis of the electromagnetic propagation, radiation, scattering and coupling of highly efficient WPT systems. This study is based on the discontinuous Galerkin time-domain (DGTD) finite element method. A brief survey of the DGTD method is given, which is a finite element method using discontinuous piecewise polynomials as basis functions and numerical fluxes based on up-winding for stability. DGTD method is characterized by the fact that no continuity is enforced between the elements, then it is easy to parallelize and results in fast solvers. Even though the finite element method is used by a few researchers to study WPT problems, we found no study using the DGTD method to study WPT problems, which is surprising given that this discretization technique seems particularly well suited for these problems. A design of two coils at the frequency of 3 MHz is introduced, and the effects of the distance and misalignment between two coils on the mutual coupling are studied. The numerical results are validated by experimental and analytical results.
Adversarial multi-domain adaptation for machine fault diagnosis with variable working conditions

Due to the complexity of industrial intelligent diagnosis, transfer learning-based fault diagnosis has become an evolving focus of the research field. Transfer learning uses knowledge of the source domain to identify faults in the target domain, which is a powerful tool to solve the problem of fault signal domain shift. However, existing methods have a limitation on multiple target domains. In other words, for different domains, respective transfer tasks are necessary. To seek a breakthrough, a adversarial multi-domain adaptation (AMDA) fault diagnosis method is proposed, realizing the fault diagnosis of multiple target domains by using the knowledge of a single source domain. AMDA is divided into three parts, namely, feature extractor, fault classifier and domain classifier. Through multi-domain adversarial learning, feature extractor and domain classifier mine the knowledge shared by multiple domains, and fault classifier can identify fault features distributed in different domains. The proposed AMDA method can surpass some traditional transfer learning fault diagnosis methods. Furthermore, as feature visualization result revealed, AMDA has significant advantages in multi-domain and broad research prospects.
Multi-source Unsupervised Domain Adaptation for Machinery Fault Diagnosis under Different Working Conditions

Owing to distribution discrepancy between source training and target testing data, the performance of fault diagnosis by traditional supervised learning models will degenerate. Though domain adaptation methods for diagnosis have been actively investigated recently, most of them are devoted to learning from a single source. However, in reality, the supervised samples can be collected from different sources such as various working conditions. These sources are not only different from target but also from each other. The way of effectively fusing these sources to contribute the prediction of target remains a challenge. In this work, a new framework of multi-source domain adaptation is proposed for cross-domain fault diagnosis under different working conditions. Specially, this framework is realized by two alignment stages. At the first stage, multiple specific feature spaces are obtained, then the distributions of each pair of source and target domain are aligned since it is difficult to extract the common domain-invariant features for all domains. At the second stage, by considering the domain specific decision boundaries, the probabilistic outputs of classifiers are also aligned. Various experimental analysis on four different bearing working conditions is conducted to show the effectiveness of the proposed method. The performance of the proposed method is superior to state-the-art cross-domain fault diagnosis methods.