Miracle-dz/UniFAR-FTUs huggingface dataset:
User-Wise Asymmetric Beamforming for Millimeter-Wave MIMO Systems

In this paper, a beamforming scheme that aims to support user-wise asymmetric uplink-downlink (UL-DL) traffic in a more energy-efficient manner is proposed for time-division duplex (TDD) millimeter-wave massive multiple-input-multiple-output (MIMO) systems. Assuming that proper data links have been established during the initial access stage for DL or UL traffic, we consider the beamforming problem for UL or DL whose payload traffic is much lighter than the other link. Such asymmetric traffic allows part of the massive MIMO array to be deactivated in order to achieve a higher energy efficiency while still meeting the spectral efficiency (SE) requirements for UL or DL. To deal with such a problem, we propose the corresponding phase shifter (PS) deactivation strategies based on different SE constraints for each mobile stations or user independently, which select the PSs to be deactivated accordingly with low computational complexity. Simulation results demonstrated the superiority of the proposed energy efficient PS deactivation approach and the corresponding codebook design.
Distributed Routing Controller for Large-scale Live Video Streams in Real-Time Networks

Overlay routing control for live video delivery has become an important yet challenging task for Real-Time Networks (RTNs), but existing approaches designed for traditional Content Delivery Networks (CDNs) fall short of meeting the challenge. In this paper, we develop a distributed overlay routing controller for RTNs to deliver massive high-quality live video streams in low latencies. We first formulate a joint optimization that offers rich control flexibility and can yield low-latency and cost-effective routing solutions. To obtain the appealing potential value of the optimal solution in the context of large-scale live videos, we develop a distributed control framework that can find near-optimal solutions at fine-grained timescales. Evaluations on real-world live video traces show that our distributed controller derives high-quality (in terms of both performance and cost) overlay routing solutions while reducing the decision latency by 38%-89% compared to the state-of-the-art centralized controller.
A Secure BlockChain Framework for IoT Healthcare

The medical industry is growing significantly along with emerging technologies like IoT. , The fundamental idea behind integrating IoT into healthcare facilities is to enable it remotely accessible. The contact between patient and his doc tor is easy as well detection of disease. The purpose of this research is to provide a secure healthcare framework. This framework used blockchain technique to encrypt the patients' data on cloud servers. The blockchain is adopted in the healthcare industry since data is vulnerable to attacks and patients' record is having sensitive information that should be secure from attacks. For the last few years, different blockchain models have been put forth in the health system. However, these models' decentralized nature-a hallmark of blockchain technology-has led to the emergence of an issue known as illness overlaps as the chains expands. This problem is addressed using a blockchain, which has the ability to add a new block of data in the chain whenever there is an updating in patients' record. Additionally, multilayer blockchain approach is considered to secure the sensitive patient's data. This strategy protects data as well as maintain trust for the user. In this framework, AES 128-bit key is generated which is then provides support to SHA-256. Data is arranged into different blocks and then each block is encrypted to prevent unauthorized access. A system is created to demonstrate how the suggested approach is applied in the real healthcare sector. The efficiency of the proposed framework is compared with the RSA and DSA that are the two widely used encryption methods. Such algorithms, which are essential to blockchain technology and are used in the proposed work, are later evaluated by comparing their performances to one another. According on research findings, the framework has the shortest encryption and decryption time, making its efficiency more reliable and effective. Additionally, it is more durable and effective.
ScaleG: A Distributed Disk-Based System for Vertex-Centric Graph Processing

Designing distributed graph systems has drawn a lot of research interests due to the strong expressiveness of the graph model and rapidly increasing graph volume. Most of them require the graph data and all intermediate messages to reside in main memory, which may sacrifice the scalability. Even though several disk-based systems have been studied to remedy such issue, several challenges still exist in achieving both high computational efficiency and low network communication under the limitation of memory usage. In this paper, we design a novel disk-based distributed graph system, called ScaleG. The system provides a series of user-friendly programming interfaces. Unlike previous systems, the programmer in ScaleG does not need to concern any logic regarding the communication between vertices like sending messages and combining messages. In addition, we propose several techniques to reduce both disk I/Os in each machine and message I/Os via the network. We manage all messages in memory and bound all messages by the number of vertices. We also carefully design the data structure to support partial computation and automatic vertex activation. We conduct extensive experiments on six big graphs to show the high efficiency of our system.
Emergency Steering Collision Avoidance and Path Tracking System for City Bus Considering Yaw Response

As the autonomous driving expands to complex road environment, the importance of collision avoidance is emerging across industries and academia, and its application for passenger cars has been actively studied. Because the dynamic characteristics of low-floor buses are different from those of passenger cars, it is necessary to develop a corresponding collision avoidance and path tracking algorithms. In this paper, a novel Pure-Pursuit controller and collision avoidance trajectory planner considering vehicle stability for buses are proposed. First, in order to compensate for the shortcomings of the geometry-based Pure-Pursuit controller, yaw rate gain and yaw rate response analyzed from the actual vehicle data are considered to improve the stability of the bus. In addition, the Conditional Integration-Proportion Integral (CI-PI) controller is designed to reduce the effect of disturbances. Secondly, the trajectory planner is developed taking into account not only the surrounding object information, but also the dynamic limitation of the bus. A group of trajectory candidates including dynamic relation of the lateral motion is generated that satisfies path stability with pole-zero analysis. Then, an optimal trajectory is selected through the cost function which reflects the dynamic constraints. Finally, stability and performance of the proposed controller are verified through simulations and field tests.
Structural analyses of a parsimonious watermarking policy for data deception attack detection in networked control systems

In this paper, we perform structural analyses of a parsimonious watermarking policy, which minimizes the average detection delay (ADD) to detect data deception attacks on networked control systems (NCS) for a fixed upper bound on the false alarm rate (FAR). The addition of physical watermarking to the control input of a NCS increases the probability of attack detections with an increase in the control cost. Therefore, we formulate the problem of data deception attack detection for NCS with the facility to add physical watermarking as a stochastic optimal control problem. Then we solve the problem by applying dynamic programming value iterations and find a parsimonious watermarking policy that decides to add watermarking and detects attacks based on the estimated posterior probability of attack. We analyze the optimal policy structure and find that it can be a one, two or three threshold policy depending on a few parameter values. Simulation studies show that the optimal policy for a practical range of parameter values is a two-threshold policy on the posterior probability of attack. Derivation of a threshold-based policy from the structural analysis of the value iteration method reduces the computational complexity during the runtime implementation and offers better structural insights. Furthermore, such an analysis provides a guideline for selecting the parameter values to meet the design requirements.
Robust MPC approaches for monotone systems*

This paper exploits the efficient computation of reachable sets for monotone systems to formulate model predictive control approaches with guaranteed constraint satisfaction and recursive feasibility in the presence of uncertainty. To include all possible realizations of the uncertainties, the reachable sets are approximated as hyperrectangles in the problem formulation. The presented approach is extended to include recourse, i.e., the knowledge about the presence of further measurement information in the prediction horizon. By dividing the reachable sets, multiple regions are obtained, for which the future inputs can be chosen separately. The applicability of the proposed approaches are shown by the means of a temperature control example.
Deep Learning-based Multi-PLC Anomaly Detection in Industrial Control Systems

Industrial control systems (ICSs) have become more complex due to their increasing connectivity, heterogeneity and, autonomy. As a result, cyber-threats against such systems have been significantly increased as well. Since a compromised industrial system can easily lead to hazardous safety and security consequences, it is crucial to develop security countermeasures to protect coexisting IT systems and industrial physical processes being involved in modern ICSs. Accordingly, in this study, we propose a deep learning-based semantic anomaly detection framework to model the complex behavior of ICSs. In contrast to the related work assuming only simpler security threats targeting individual controllers in an ICS, we address multi-PLC attacks that are harder to detect as requiring to observe the overall system state alongside single-PLC attacks. Using industrial simulation and emulation frameworks, we create a realistic setup representing both the production and networking aspects of industrial systems and conduct some potential attacks. Our experimental results indicate that our model can detect single-PLC attacks with ~95% accuracy and multi-PLC attacks with 80% accuracy and nearly 1% false positive rate.
Joint Optimization of Battery Energy Storage and Gas Turbine for Ancillary Service Provision

Ancillary services play a crucial role in maintaining the reliability of electrical power grids with integrated intermittent renewable energy generation. They can also provide additional revenue streams for systems with distributed energy resources (DERs), e.g., battery energy storage systems (BESSs) and gas turbines (GTs). This paper evaluates the benefits of the joint optimization and coordinated control of the BESS and GT for energy arbitrage and ancillary services provision. Specifically, we mathematically model a system with BESS and GT and formulate an optimization problem to maximize the profit through participation in various markets. As a case study, we consider participation in Singapore's wholesale electricity market for energy arbitrage, regulation, and contingency reserve. Our analysis based on Singapore's actual market prices shows that the complimentary nature of BESS and GT can be leveraged for additional revenue through participation in both fast-response regulation market and a relatively slower-response but longer-term contingency reserve market.
Multiuser Massive MIMO-OFDM for Visible Light Communication Systems

Visible light communication (VLC) has emerged as a viable supplement to existing radio frequency (RF) communication systems. The limited modulation bandwidth of sources is one of the significant challenges in the multiuser (MU) VLC systems, which prohibits transmission at high data rates for each user. Since an array of LEDs is used to illuminate a room, a MU VLC system utilizing massive multiple-input multiple-output (mMIMO) orthogonal frequency-division multiplexing (OFDM) is proposed for achieving high data rates in this paper. Since the distances of the multiple transmitter-receiver links are different, their temporal delays are also different, resulting in complex channel gain and phase differences when transformed to the frequency domain in the OFDM technique. Complex channel vectors associated with different users are mutually spatially orthogonal as the number of transmitters increases. Therefore, inter-user interference (IUI) can be eliminated with simple linear signal processing, and more users can simultaneously communicate in the same time-frequency resource. In this paper, three linear precoding methods, including the maximum ratio transmission (MRT), the minimum mean square error (MMSE), and the zero-forcing (ZF), are investigated for VLC systems based on proposed MU-mMIMO-OFDM in intensity-modulation and direct-detection link. We evaluate and compare the performance of the aforementioned precoding methods in terms of the achievable spectral efficiency and total downlink optical power for different scenarios. Moreover, we derive a closed-form expression for the lower bound on the average achievable sum-rate, which we confirm the accuracy of the derived expression with the numerical simulation. The results demonstrate that the performance of the proposed method in this paper for all precoder techniques, i.e., ZF, MMSE, and MRT, is better than the previous works since the complex channel in the frequency domain is used in this paper. Furthermore, the ZF and MMSE methods are better than MRT when the ratio of the number of LEDs to the number of users is large. At the same time, when this ratio is relatively small, the MRT precoding technique outperforms the ZF and MMSE techniques.
GNSS-Augmented LiDAR SLAM for Accurate Vehicle Localization in Large Scale Urban Environments

Although accurate and reliable localization is a prerequisite for autonomous driving, in urban environments neither the Global Navigation Satellite System (GNSS) nor the Simultaneous Localization and Mapping (SLAM) ensure satisfying results in terms of both local accuracy and global consistency. Hence, we contribute in this paper a method to augment the existing LiDAR-based SLAM systems with GNSS measurements, applying the factor graph formulation of the problem. We contribute a tightly coupled GNSS/LiDAR SLAM considering constraints from LiDAR and GNSS measurements, and propose a filtering procedure to cope with GNSS measurements that introduce non-Gaussian noise. We evaluate our approach on the challenging UrbanNav dataset, considering different LiDAR SLAM algorithms and different GNSS receivers, and showing that our solution outperforms previous approaches to GNSS/LiDAR integration.
CaMP-INC: Components-aware Microservices Placement for In-Network Computing Cloud-Edge Continuum

Microservices are a promising technology for future networks, and many research efforts have been devoted to optimally placing microservices in cloud data centers. However, microservices deployment in edge and in-network devices is more expensive than the cloud. Additionally, several works do not consider the main requirements of microservice architecture, such as service registry, failure detection, and each microservice's specific database. This paper investigates the problem of placing components (i.e. microservices and their corresponding databases) while considering physical nodes' failure and the distance to service registries. We propose a Components-aware Microservices Placement for In-Network Computing Cloud-Edge Continuum (CaMP-INC). We formulate an Integer Linear Programming (ILP) problem with the objective of cost minimization. Due to the problem's $\mathcal{NP}$-hardness, we propose a heuristic solution. Numerical results demonstrate that our proposed solution CaMP-INC reduces the total cost by 15.8% on average and has a superior performance in terms of latency minimization compared to benchmarks.
Designing a Broadband MIMO Antenna for Next Generation Wireless Communication Systems

Within the last few years, the multiple-input multiple-output (MIMO) technology has been developing to cope with the stringent 5G and beyond requirements, in terms of high throughput, energy efficiency, and coverage. With the rapid development and miniaturization of Internet-of-Things (IoT) devices, there is an urgent need to design compact-size, low-cost and efficient MIMO antennas for IoT. In this context, we propose here the design of a novel four-element wideband MIMO antenna, supplied using the coplanar waveguide (CPW) technology. The design covers a wide spectrum band between 1.5 and 5.5 GHz, with a total size of 80 × 80 × 1.6 mm3, which is suitable for small IoT devices. The designed MIMO antenna achieves a diversity gain above 9.9 and an envelope correlation coefficient lower than 0.016. Its mutual coupling among any pair of elements is lower than -15 dB, while the group delay demonstrates stability for the targeted band of operation. Hence, the proposed MIMO antenna has a great potential for future wireless communication systems, especially for the sub-6 GHz portable and IoT devices.
On Data Reutilization for Historian Based Predictive Control

This paper presents a robust finite-horizon control scheme based on data that produces feasible control sequences. The scheme makes use of a database that includes information from prior experiences of the same and others controllers handling similar systems. By the convex combination of feasible histories plus an auxiliary control law that deals with uncertainties, this scheme can be used as a robust historian-based predictive controller. Further application could include a cooperative learning-based strategy in which multiple controllers share their previous executions to gain collective benefits in terms of performance. The validity of the proposed controller is tested in a simulated case study.
Transfer Learning approach for smart agriculture: Application to Blight Bacteria

The presence of plant diseases causes considerable losses in global agricultural productivity. Most of these crop losses are due to diseases and weeds account for over 30% of global productivity. To avoid losses due to this problem, farmers around the world hire professionals to diagnose their crops. The traditional method of identifying these diseases by visually inspecting certain characteristics such as leaf color, shape, and texture is not always effective. However, this method is time-consuming and expensive. In this paper, we present an approach based on a transfer learning model to predict a plant’s disease by the symptoms of late blight, which include sudden and severe yellowing, browning, spotting, wilting, or death of leaves, flowers, fruits, stems or the whole plant. The plants used in this approach are potato, tomato, and rice. The models used in this study are ResNet50, VGG16, Inception V3, and MobileNet V2, of which MobileNet V2 has a better accuracy of 97.8%.
A Proprietary ZVS-Based Approach Delivers High Efficiencies at Light Load With Half the Components of Conventional Designs

The ac–dc power supply units providing isolated, regulated outputs operating above 100 W typically require some form of input active power factor correction (PFC) [1]. To achieve this, a popular solution is to combine a boost PFC input stage with an “LLC” converter which isolates and controls the dc output voltage. However, a new, innovative single-stage technique for delivering regulated ac–dc power with a high input power factor offers the promise of high efficiency from no-load to full-load, while halving the number of active devices (MOSFETs) when compared to the traditional approach.
Time Sensitive Data Access for Massive Users: A Mean-Field Approximation Approach

The emerging deterministic networking (DetNet) has attracted considerable recent attention due to its potential in supporting services with ultra-low latency, low delay variation, and extremely low loss. In the development of the DetN et, hard delay constraints and extremely low loss are highly desired to be guaranteed. However, the bursty data traffic demands and random channel qualities bring uncertainties for transmission, incurring bit dropping due to the possible delay violation. How to support reliable transmission services by reducing bit dropping rates becomes a critical issue in the envisioned sixth-generation (6G) network. To efficiently reduce the bit dropping rate, a joint channel and buffer aware scheduling is highly desired, whose complexity increases exponentially with the number of users. In this paper, we adopt the mean-field approximation to simplify the design and analysis of the joint channel and buffer aware scheduling under a huge number of users. A threshold-based scheduling policy is proposed, which has low computational complexity. Numerical results will demonstrate the effectiveness of the mean-field approximation based scheduling.
Deep Learning-Based Authentication Framework for Secure Terrestrial Communications in Next Generation Heterogeneous Networks

Smart Grid (SG) network is growing rapidly to improve the efficiency and reliability of the power supply. It will connect billions of devices to the internet in the near future. This increase in the number of internet-connected devices has led to an exponential increase in the market size of smart meters. These smart meters and devices will generate lakhs of gigabytes of data in upcoming years. To handle this massive amount of data flow, the Information and Communication Technology (ICT) of SG will experience huge advancements in terms of 5G and 6G wireless networks. These networks will provide higher data rates by utilizing the huge bandwidth spectrum of heterogeneous networks (HetNet). As the massive amount of data will be transmitted to various components of the smart grid using the internet, an open channel. Therefore, in addition to the need for high bandwidth and low latency, HetNet security has also become a prime concern. This paper presents an efficient approach to authenticate the smart devices in SG environment by using the channel state information (CSI) of the wireless links. Firstly, Low-Density Parity-Check (LDPC) codes are used to maintain the confidentiality and reliability of data transmission in the Advanced Metering Infrastructure (AMI) of SG. Then, a Convolution Neural Network (CNN)-based authentication framework is proposed to enhance the physical layer security (PLS) of terrestrial communications in next generation SG Hetnets. Further, the proposed framework is evaluated using received Channel State Information (CSI) values from smart meters placed at different locations, while also taking the scenario of active eavesdroppers into account. Experimental analysis reveals that the proposed CNN based authentication achieves 30.76 percent better authentication rate than existing DNN based authentication scheme. Further, the LDPC coded data transmission scheme reduces the bit error rate (BER) by 20 percent as compared to uncoded data transmission.