Miracle-dz/UniFAR-FTUs huggingface dataset:
Managing Video Processing and Delivery using Big Packet Protocol with SDN Controllers

This paper describes the use of the Big Packet Protocol (BPP) for carrying video from servers to clients, and how SDN controllers can effectively manage the flow-rate and QoE, based on the available bandwidth. BPP relies on meta-data being injected into packets in order to provide information for network nodes on how to process those packets. Given specific commands, the network node can drop parts of the payload, called chunks in BPP. When using BPP, the strategy is not to drop whole packets, but to reduce the packet size be eliminating specific chunks. The approach allows for reducing the load on the network, when there is a limited bandwidth, by having a flow of packets regularly arriving at the receiver, so there is continuous delivery and minimum guaranteed quality. To make video transmission over BPP effective, a video encoder and decoder that can do multiple encodings for the same region is selected–namely scalable video coding (SVC). The results show the successful implementation of a system using these combined techniques.
A Broadband Linear Ultra-Compact mm-Wave Power Amplifier With Distributed-Balun Output Network: Analysis and Design

This article presents a broadband power amplifier (PA) with a distributed-balun output network that provides the PA optimum load impedance over a wide bandwidth. The proposed output network comprises two coupled-line sections and absorbs the device output capacitance. It employs a scalable coupled-line modeling approach that captures both the magnetic (inductive) and electric (capacitive) couplings between windings with fewer parameters and supports a rapid design process. Closed-form design solutions, design space limitations, bandwidth limits, and design tradeoffs are derived and analyzed comprehensively. Its extension to differential output and common-mode response is also discussed in detail. As a proof of concept, a prototype PA is implemented for multiband fifth-generation (5G) applications in 45-nm SOI CMOS. With no biasing retuning or network reconfiguration, the PA consistently achieves >19.1 dBm <inline-formula> <tex-math notation="LaTeX">$P_{\mathrm {sat}}$ </tex-math></inline-formula>, >37.3% peak power-added efficiency (PAE), 17.8–19.6 dBm <inline-formula> <tex-math notation="LaTeX">$P_{\mathrm {1dB}}$ </tex-math></inline-formula>, and 36.6%–44.3% PAE<inline-formula> <tex-math notation="LaTeX">$_{P\mathrm {1dB}}$ </tex-math></inline-formula> over 24–40 GHz, verifying the truly wideband large-signal matching. The PA demonstrates 5G new radio (NR) frequency range 2 (FR2) modulation signals over 24–42 GHz, covering n257/n258/n260 5G bands. For 5G NR FR2 800-MHz 2-CC 64-QAM signals (11.78-dB PAPR), the PA achieves 11.3-dBm/16.6% average <inline-formula> <tex-math notation="LaTeX">$P_{\mathrm {out}}$ </tex-math></inline-formula>/PAE with −25.1-dB rms EVM at 28-GHz and 10.2-dBm/13.6% average <inline-formula> <tex-math notation="LaTeX">$P_{\mathrm {out}}$ </tex-math></inline-formula>/PAE with −25.1-dB rms EVM at 37 GHz.
The Hydraulic Performance of Waste Stabilization Ponds in Portugal

The hydraulic regime in two Portuguese facultative waste stabilization ponds, a primary facultative pond receiving raw sewage (in Portimao) and a secondary pond receiving the effluent from an anaerobic pond (in Vidigueira) was studied by means of a fluorescent dye tracer technique. The resulting dispersion numbers, respectively for winter and summer, for each pond were as follows: 0.595 and 0.371 in Portimao, and 0.574 and 0.523 in Vidigueira. These results did not agree with those obtained from the available predictive equations in the recent literature.
Application of Blockchain Technology in Used Vehicle Market: A Review

The used vehicle market is one of the most important economic sectors, which suffers from severe frauds and the absence of integrity measures since multi-variant stakeholders are involved in the vehicle trade. For example, odometer frauds have become a serious concern that cost European consumers approximately 5.6 to 9.6 billion euros per year. Therefore, a solution to track and log vehicle data is necessary to satisfy a high level of trust, data integrity, and traceability. Blockchain technology has great potential to change the way specific industries operate by eliminating middleware layers between seller and buyer. Blockchain (BC) provides a secure ledger of transactions that can be reliable even in monetary applications. The characteristics of Blockchain technology make it an excellent solution to address many issues, such as counterfeiting and trustless between parties in various applications like supply chain, healthcare, and used vehicles market. This paper surveys the status of research until 2020 related to the used vehicles market. Based on the previous literature review and analysis, there is a need for using Blockchain technology in the used vehicles market to reduce fraud by logging vehicle life-cycle events in a secure ledger. The study also shows that most of the previous work focused on collecting data from sensors and did not take into consideration the stakeholders of the application, such as insurance companies, governments, and repair shops.
BlendVLC: A Cell-free VLC Network Architecture Empowered by Beamspot Blending

In visible light communication (VLC), the quality of communication is primarily dominated by line-of-sight links. To ensure an appropriate link quality anywhere, beamsteering has been proposed where transmitters (TXs) dynamically steer their beams to create beamspots on the users. However, these highly dynamic TXs face the beam tracking problem and result in highly variable illumination. In this work, we propose BlendVLC, a cell-free network architecture to improve the mobility robustness of users by blending the beamspots from both steerable and fixed TXs. We solve the beam tracking by designing a centimeter-level visible light positioning algorithm empowered by a neural network. Relying on this location information, we formulate and solve an optimization problem on the beamspot blending, and design a fast and scalable heuristic for large networks. We build a proof-of-concept testbed as well as a simulator to evaluate BlendVLC. We show that it achieves superior performance compared to denser networks with fully fixed TXs. For example, in a large-scale VLC network of 8 m x 4 m, BlendVLC improves the average system throughput by 30%, while only requiring half the number of TXs.
Autonomous Quadrotor Navigation With Vision Based Obstacle Avoidance and Path Planning

Due to the high demands on military and commercial applications, the development of UAVs (unmanned aerial vehicles) has become increasingly important in recent years. In this paper, we present a vision guided autonomous navigation approach for quadrotor UAVs. A map-based offline path planning technique is developed to generate an initial path, followed by the waypoints of the trajectory for flight guidance. During the navigation, an onboard camera is utilized to acquire a sequence of monocular images for environment perception. A vision-based obstacle detection technique using optical flow is proposed for collision avoidance. The optical flow field constructed from the image sequence is used to provide the depth cues for the incoming obstacle detection. A single-board computer is adopted as a control platform, and the proposed algorithms are implemented for online and real-time processing. Several experiments are carried out in the outdoor environment for obstacles avoidance and visual guidance. The results have demonstrated the feasibility of our proposed method for path planning and autonomous navigation.
A Blockchain-Based Federated Forest for SDN-Enabled In-Vehicle Network Intrusion Detection System

In-vehicle communication systems are usually managed by controller area networks (CAN). By broadcasting packets to their bus, the CAN facilitates the interaction between Electronic Control Units (ECU) that coordinate, monitor and control internal vehicle components. With no authentication mechanism for identifying the legitimacy and source of packets, CAN are vulnerable to cyber-attacks. An Intrusion Detection System (IDS) can detect attacks on CAN and machine learning can be used to create the models for the IDSs to detect non-linear attack patterns. However, car manufacturers and owners might want to keep the sensitive information required for training the models confidential. Therefore, we proposed a Blockchain-based Federated Forest Software-Defined Networking (SDN)-enabled IDS (BFF-IDS) to address the problem of data sharing the sensitive CAN data. To ensure scalability, we used InterPlanetary File System (IPFS) to host the models, and the blockchain is designed to store only a hash of the model and a pointer to its location. The SDN provides the dynamic routing of packets and model exchanges. We used Federated Learning (FL) to create a random forest model. Individuals provide partially trained models, allowing them to keep the underlying data confidential. Using Fourier transform, we decomposed the CAN IDs cycle from CAN bus traffic in the frequency domain for better generalization in multiclass detection of attacks. Multiple statistical and entropy features were extracted to handle the high complexity and non-linearity in CAN bus traffic. The proposed system allows manufacturers and car owners to contribute to the training of the models, as their sensitive data is protected. By storing hashes of the models on a blockchain, the risk of adversaries poisoning the models is reduced and a single point of failure is avoided. We evaluated the proposed system by conducting experiments on a testbed. We found that the proposed system has efficient use of memory and CPU resources and that the detection rate of closely related attacks was high. We recorded the highest model attack detection rate of about 0.981.
Effects of acanthopanax senticosus saponins on platelet,blood coagulation and blood viscosity in rabbits

Objective:To study the effects of acanthopanax senticosus saponins (ASS)on platelet,blood coagulation and blood vis- cosity in rabbits.Methods:Platelet aggregation,APTT (activated partial thromboplastin time),PT (prothrombin time),TT (Thrombin time),blood viscosity,plasma viscosity and hematocrit were measured by mutifunctional intellectual blood coagulation analyzer and self erasure rotary viscometer after rabbit was fed with ASS.Results:ASS significantly inhibited platelet aggregation induced by ADP, and ASS can decrease the blood viscosity significantly.But it had no effect on APT1",PT and I3". Conclusion:ASS possessed the abili- ty of improving blood viscosity and inhibiting thrombosis.This effect was due to its action on decreasing erythrocyte aggregation and blood stickiness.
MPPT techniques application and comparison for photovoltaic panels

Photovoltaic (PV) panel modelling and control is very important in renewable energy systems. Due to it variability, PV panel generation power should be maximized for the given climate conditions. This paper considers three Maximum Power Point Tracking (MPPT) algorithms: Perturb & Observe, Incremental Conductance and Fuzzy Logic. After a clear description for each algorithm, modelling, testing and validation is conducted on real experimental data for a 250W PV panel. Over one day period, a comparison between the 3 control MPPT algorithms show that Perturb & Observe gives the best results, close to Incremental Conductance algorithm application.
Ultra-Low Power, Low-Voltage, Fully-Tunable, Bulk-Controlled Bump Circuit

This work proposes an ultra-low power (6.0nW), low voltage (0.6V), bulk-controlled, 10-transistors bump circuit architecture for Gaussian-function implementation. It can be used as a building block for analog implementation of Gaussian Mixture Model and Kernel Methods. The Gaussians curve width, height and center are independently and electronically adjustable. It consists of a modified current correlator and a bulk-controlled differential block with all transistors operating in sub-threshold. Proper operation, accuracy and robustness are confirmed via simulation and theoretical analysis. It was implemented in TSMC 90nm CMOS process and was simulated using the Cadence IC Suite.
A novel time register with process and temperature calibration

This paper presents a novel time register circuit suitable for time-based or time-domain signal processing. The proposed circuit is based on the capacitor discharging method and is compensated against technology process and chip temperature variations using a novel calibration loop based on master-slave approach. The loop contains a high-speed comparator based on simple current-starved inverter logic along with a triple-point threshold voltage stabilization loop. The circuit is designed using 28nm Samsung FD-SOI process under 1V supply voltage with 10MHz operating frequency. Simulation results present an almost constant capacitor voltage discharging slope of the time register over worst case process corners and temperature between 0°C and 100°C while consuming only 10μA.
Active control of total internal reflection and its application in electrophoretic displays

The main objective of this thesis is to analyze and quantify methods for controlling the phenomenon known as Frustrated Total Internal Reflection (FTIR). Several methods of numerical and analytical study of FTIR are presented. A direct solution of the Maxwell equations is shown to be a powerful tool in understanding FTIR. An analytic solution is derived for a specific planar case of FTIR. Results that were obtained in the form of transmission coefficients are compared with the numerical modeling, with favorable results. The numerical modeling results were obtained with a new computer code based on the FDTD algorithm that finds the solution of Maxwell equations in 3 D space with periodic boundary conditions. Several devices based on the active control of total internal reflection (ACTIR) have been proposed, with the most promising being an electrophoretic display cell, in which the frustration of TIR is done by electrostatically charged particles suspended in liquid medium. An analysis is presented from the physical and chemical points of view. An optimization of the electrophoretic display cell surface parameters has been performed by means of ray tracing modeling, and these results and meir implications to the future cell design are presented. Finally a comparative analysis of presented electrophoretic display technology is presented, in the context of alternative existing technologies.
Covid-19 and Flattening the Curve: a Feedback Control Perspective

Many of the policies that were put into place during the Covid-19 pandemic had a common goal: to flatten the curve of the number of infected people so that its peak remains under a critical threshold. This letter considers the challenge of engineering a strategy that enforces such a goal using control theory. We introduce a simple formulation of the optimal flattening problem, and provide a closed form solution. This is augmented through nonlinear closed loop tracking of the nominal solution, with the aim of ensuring close-to-optimal performance under uncertain conditions. A key contribution of this paper is to provide validation of the method with extensive and realistic simulations in a Covid-19 scenario, with particular focus on the case of Codogno - a small city in Northern Italy that has been among the most harshly hit by the pandemic.
Resilient Memory Event-Triggered Finite-Time Bounded for Networked Control Systems with Multiple Cyber-Attacks

This work is concerned with the resilient memory event-triggered finite-time bounded of networked control systems (NCSs) with actuator faults and multiple attacks. The deception and denial-of-service (DoS) attacks are modelled in a unified framework within multiple attacks. Firstly, the purpose of memory event-triggered scheme (METS) is utilized to release certain packets to establish new events contrasting with the existing event-triggered method. This approach not only enhances the system dynamics but also improves the flexibility. The attack strategy is then developed to consider the security issue of NCSs. A random variable is presented to relate multiple attacks where the malicious signals are injected by adversaries. By utilizing Lyapunov stability theory and linear matrix inequalities (LMIs), a sufficient condition is constructed to attain the exponentially mean-square finite-time bounded of NCSs subjected to multiple attacks. In the end, a satellite system is simulated to illustrate the efficacy of the proposed METS.
On the Resource Allocation of Hierarchical NOMA for Fog-RAN with Energy Harvesting

In this paper, we introduce a multiple access protocol, termed hierarchical non-orthogonal multiple access (HiNOMA), optimized for fog-radio access networks (F-RANs). Resource allocation optimization is deemed critical in order to guarantee the users’ fairness in the network, while energy efficiency can be increased through energy harvesting (EH) at the user equipment (UE) nodes. Therefore, the HiNOMA protocol with energy harvesting capabilities is examined for F-RANs, leading to the optimization of the proportional fairness metric. Finally, numerical results reveal the effectiveness of the joint design and the interesting trade-off between harvested power and achievable rate in the case of F-RAN.
Deep Learning for Analysis of Collagen Fiber Organization in Scar Tissue

The evaluation of the morphology and organization of collagen fibers is critical in understanding wound healing and tissue remodeling after a thermal injury of the skin. However, histological analysis conducted by pathologists is often labor-intensive and limited to qualitative evaluations and scoring within a narrow field of view. In this study, we propose a convolutional neural network (CNN) model to classify Masson’s trichrome (MT)-stained histology images of burn-induced scar tissue and to characterize the microstructures of normal tissue and scar tissue in a quantitative manner. The scar tissue is created on in vivo rodent models and prepared for MT-stained histology slides after wound healing. A CNN model is developed, trained, and tested with various sizes of the histology images for classification and characterization. The proposed model classifies both normal tissue (i.e., without burn, as the control) and scar tissue at various scales with over 97% accuracy. The features acquired from the proposed CNN model visually characterizes the density and directional variance of the collagen fibers distributed in the dermal layers from whole histology images. The proposed deep learning technique can provide an objective and reliable method to rapidly assess and quantify wound repair and remodeling.
Improving Quality Control with Industrial AIoT at HP Factories: Experiences and Learned Lessons

Enabled by the increasingly available embedded hardware accelerators, the capability of executing advanced machine learning models at the edge of the Internet of Things (IoT) triggers wide interest of applying the resulting Artificial Intelligence of Things (AIoT) systems in industrial applications. The in situ inference and decision made based on the sensor data containing patterns with certain sophistication allow the industrial system to address a variety of heterogeneous, local-area non-trivial problems in the last hop of the IoT networks, avoiding the wireless bandwidth bottleneck and unreliability issues and also the cumbersome cloud. However, the literature still lacks presentations of industrial AIoT system developments that provide insights into the challenges and offer important lessons for the relevant research and engineering communities, no matter the development is successful or not. In light of this, we present the design, deployment, and evaluation of an industrial AIoT system for improving the quality control of Hewlett-Packard’s ink cartridge manufacturing lines. While our development has obtained promising results, we also discuss the lessons learned from the whole course of the effort, which could be useful to the developments of other industrial AIoT systems.
SDN Intent-based conformance checking: application to security policies

With the popularity of software defined networking architectures, the growing complexity of its use cases dictates the need for better auditability especially for security. In this paper, we aim at facilitating high-level management-plane policy configuration conformance auditing and their reflection in the data plane, to detect missing or spurious flow rules with respect to security policies. To this end, we propose an efficient conformance checking approach based on an intentional northbound interface as well as traces of management, control and data plane. Leveraging a proof-of-concept implementation of our approach, we compare its conformance-checking runtime and precision against a direct method on virtual topologies and find that it significantly improves scalability. We conclude by proposing directions for further enhancements extending the techniques presented herein.
Intrusion Detection using Network Traffic Profiling and Machine Learning for IoT

The rapid increase in the use of IoT devices brings many benefits to the digital society, ranging from improved efficiency to higher productivity. However, the limited resources and the open nature of these devices make them vulnerable to various cyber threats. A single compromised device can have an impact on the whole network and lead to major security and physical damages. This paper explores the potential of using network profiling and machine learning to secure IoT against cyber attacks. The proposed anomaly-based intrusion detection solution dynamically and actively profiles and monitors all networked devices for the detection of IoT device tampering attempts as well as suspicious network transactions. Any deviation from the defined profile is considered to be an attack and is subject to further analysis. Raw traffic is also passed on to the machine learning classifier for examination and identification of potential attacks. Performance assessment of the proposed methodology is conducted on the Cyber-Trust testbed using normal and malicious network traffic. The experimental results show that the proposed anomaly detection system delivers promising results with an overall accuracy of 98.35% and 0.98% of false-positive alarms.
Push the Limit of Device-Free Acoustic Sensing on Commercial Mobile Devices

Device-free acoustic sensing has obsessed with renovating human-computer interaction techniques for all-sized mobile devices in various applications. Recent advances have explored sound signals in different methods to achieve highly accurate and efficient tracking and recognition. However, accuracies of most approaches remain bottlenecked by the limited sampling rate and narrow bandwidth, leading to restrictions and inconvenience in applications. To bridge over the aforementioned daunting barriers, we propose PDF, a novel ultrasound-based device-free tracking scheme that can distinctly improve the resolution of fine-grained sensing to submillimetre level. In its heart lies an original Phase Difference based approach to derive time delay of the reflected Frequency-Modulated Continuous Wave (FMCW), thus precisely inferring absolute distance, catering to interaction needs of tinier perception with lower delay. The distance resolution of PDF is only related to the speed of actions and chirp duration. We implement a prototype with effective denoising methods all in the time domain on smartphones. The evaluation results show that PDF achieves accuracies of 2.5 mm, 3.6 mm, and 2.1 mm in distance change, absolute distance change, and trajectory tracking error respectively. PDF is also valid in recognizing 2 mm or even tinier micro-movements, which paves the way for more delicate sensing work.