Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Coupling Structure for Inline Realization of Cross-Coupled Rectangular Waveguide Filters

This paper presents a new cross-coupling structure for introducing transmission zeros in waveguide filters with inline configuration. The cross-coupling is realized by an internal bypass metallic loop structure (wire), short-circuited to the filter top plate and passing through the waveguide cavities. Positive and negative cross-couplings are realized by only varying the length of the structure suitably. Full-wave electromagnetic (EM) simulations of the coupling structure are performed for extracting the coupling coefficient sign and value ( ${k}$ ). The relationship of the values of ${k}$ with the coupling structure dimensions is presented graphically. The loading effect of coupling structure on the resonance frequency of the waveguide cavities is also discussed, as well as the possibility of post-manufacturing tunability. The feasibility of the proposed coupling structure is demonstrated by the design of three test filters. Two filters verify the in-line design of a triplet configuration using the proposed coupling wire to introduce a transmission zero below or above the passband. These filters have been also manufactured and measured for an experimental validation of the new structure. The third filter demonstrates the use of the wire loop to create a negative coupling in a cross-coupled configuration. Power handling capability of this configuration has been also thoroughly analyzed by full-wave EM simulation.
Detection of non-standard situation in smart water metering

In this paper the algorithm for detection of non-standard situation in smart water metering based on machine learning is designed. The main categories for non-standard situation or anomaly detection and two common methods for anomaly detection are analyzed. Proposed solution needs to fit the requirements for correct, efficient and real-time detection of non-standard situation in actual water consumption with minimal required consumer intervention to its operation. The final implemented and tested solution evaluates anomalies in water consumption for a given time in specific day and month using machine learning with semi-supervised approach.
A Preliminary View on Automotive Cyber Security Management Systems

Cyber security is increasingly recognized as an essential topic for automotive systems, especially in the area of connected and automated driving. Upcoming regulation defines requirements for cyber security on multiple levels in the automotive domain in order to achieve type approval. On the organizational level, a cyber security management system (CSMS) which covers the whole vehicle lifecycle and ecosystem is required. In addition, an argumentation for the cyber security of each vehicle type for which type approval is requested has to be given. Due to the novel nature of these requirements compared to existing type approval requirements, a test phase is ongoing. The components and scope of a CSMS are an open issue. We give an overview of the requirements for a CSMS, identify approaches and gaps, and give an outlook towards a potential framework which can address the requirements.
Aggregated Time Series based Vehicular Traffic Path Recommendation

Periodic data related to vehicular traffic information have been flare-up and entered the era of big data. Vehicular traffic network is monitored continuously by motion detectors and video cameras. Advanced information about a travelling path is being used as an extraneous intervention tool to positively influence recommendation system performance. This situation directs us to think vehicular traffic path recommendation problem based on time series analysis. In this paper, a graph processing based vehicular traffic path recommendation method is proposed, which considers the spatial and temporal attributes. We cast a problem as an optimal path selection problem for the fixed origin and destination based on various data points acquired at a different time interval. Rigorous experimental evaluation on publicly available dataset shows the efficacy of the proposed method.
Novel Four-Layered Software Defined 5G Architecture for AI-based Load Balancing and QoS Provisioning

Software defined 5G network (SD-5G) is an evolving networking technology. The integration of SDN and 5G brings scalability, and efficiency. However, Quality of Service (QoS) provision is still challenging in SD-5G due to improper load balancing, traffic unawareness and so on. To overwhelm these issues this paper designs a novel load balancing scheme using Artificial Intelligence (AI) techniques. Firstly, novel four-layered SD-5G network is designed with user plane, smart data plane, load balancing plane, and distributed control plane. In the context to 5G, the data transmission rate must satisfy the QoS constraints based on the traffic type such as text, audio, video etc. Thus, the data from the user plane is classified by Smart Traffic Analyzer in the data plane. For traffic analysis, Enriched Neuro-Fuzzy (ENF) classifier is proposed. In the load balancing plane, Primary Load balancer and Secondary Load Balancer are deployed. This plane is responsible for balancing the load among controllers. For controller load balancing, switch migration is presented. Overloaded controller is predicted by Entropy function. Then decision for migration is made by Fitness-based Reinforcement Learning (F-RL) algorithm. Finally, the four-layered SD-5G network is modeled in the NS-3.26. The observations shows that the proposed work improves the SD-5G network in terms of Loss Rate, Packet Delivery Rate, Delay, and round trip time.
Joint Estimation of Phase Offset and Frequency Skew for Saving Energy in Wireless Sensor Networks

Due to the inherent instability of quartz crystal oscillators and difference in starting time, there are always phase offsets and frequency skews between sensor nodes in wireless sensor networks (WSNs). Time synchronization is a procedure to provide a common time notion across a WSN. Since a limited energy supply is the main constraint condition, energy-saving time synchronization design is a complex problem in WSNs. At present, time synchronization mainly relies on the statistical signal processing method. This increases the number of timing messages to improve the accuracy, which will lead to an energy overhead. In this paper, we introduce an effective energy-saving method to jointly estimate the phase offset and frequency skew. In testing, we identify boundary properties of the clock drift and analyze them in detail. Based on these properties, the proposed time synchronization method only requires two data samples and obtains a suitable estimator by using linear programming. Therefore, this approach attains a lower energy consumption than the conventional likelihood estimation method. In addition, it does not need to consider the probability distribution of the network delay and attains a notable robustness.
Testing the relative effectiveness of traditional and non-traditional antifouling substrates on barnacle and macroalgae settlement

Abstract Due to economic impacts, there is considerable interest in determining effective methods for limiting the attachment of marine invertebrates to submerged materials. We tested the effectiveness of traditional and non-traditional coatings on materials used for boat construction to limit the settlement of barnacles and macroalgae. Substrates included fibreglass, fibreglass coated with wax, aluminium, aluminium coated with antifouling paint, aluminium coated with Vaseline®, and aluminium coated with Vaseline® mixed with cayenne pepper. Tiles of each substrate were attached to frames, placed at two sites in the Intracoastal Waterway near Jacksonville, Florida, and collected after one or two successive months in the field. Barnacles as well as macroalgae showed significantly greater settlement on fibreglass than aluminium. Each type of coating tested reduced settlement relative to controls, with the lowest overall settlement of barnacles being observed on aluminium coated with Vaseline®, both with and without the addition of cayenne pepper.
Optimal power scheduling in a grid-connected prosumer-only DC microgrid

With the growth of renewable energy adoption by home owners, prosumer-only microgrids could be the future of power grids, hence it is worthwhile to investigate them. Although their advantages abound, sophisticated energy management schemes may be required to ensure proper and optimal operation. In this paper, a mathematical model for the energy management of a prosumer-only microgrid (MG) is proposed. In the model, the excess/deficit energy is sent/sourced to/from an external utility, as well as down/up reserves which cater for uncertainty in predictions of prosumer power imports or exports. The model, which takes the form of an optimization problem, is solved using Gekko - a Python optimization package. Results show the advantages of a DC prosumer-only microgrid with prosumers having varying power import/export profiles over one with prosumer having similar profiles.
LORD: LOw Rate DDoS Attack Detection and Mitigation Using Lightweight Distributed Packet Inspection Agent in IoT Ecosystem

Now a Days, Internet of Things (IoT) is an emerging field of Wireless Networks. It will have a ubiquitous presence in the future, with millions of devices connected to the Internet and aiding day to day applications like smart home, hospitals, etc. However, all these applications come with huge security and privacy threats in IoT. A Low Rate Distributed Denial of Service (LRDDoS) attack is one of the most dangerous threats to resource-constrained devices and services. The DDoS attack is strenuous to detect because of its small-scale signal properties. In this paper, we innovatively propose lightweight distributed packet inspection agent which detects and alleviates LRDDoS attack in the IoT network. The approach is implemented in the Contiki OS, and the experimental outcomes exhibit that the proposed information metrics, and packet flow behavior graph can efficiently identify LRDDoS attacks with the minimum response time (1-3.47 seconds). The overall False Negative Rate, False Positive Rate on the benchmark and generated data set measured by the proposed approach are 5.15%, 3.82%, 5.41%, and 5.12% respectively.
Towards a formal description and automatic execution of production processes

Industry 4.0 enforces digital revolution in the manufacturing industry by introducing smart devices and knowledge bases to form intelligent manufacturing information systems. One of the goals of the digital revolution is to support an automatic production from product specification to allow faster reaction on the product changes and a lot size one production. In order to make this possible, a formal language to describe production processes is needed, together with a code generator for its models and an engine to execute the code on smart devices. In this paper we propose a research on Industry 4.0 manufacturing using a Model-Driven Software Development (MDSD) approach to model production processes, use the models to generate instructions to smart devices and human workers, and gather a feedback from them during the process execution.
Compact Model of Read Disturbance by Hot Carrier Injection in 3D NAND Flash Memory

In this paper, we analyzed and modeled read disturbance phenomenon by hot carrier injection in 3D NAND Flash Memory using lucky electron model. The maximum electric field between selected cell and unselected cell, band to band tunneling, and impact ionization generation rate were represented with simple equations and applied to proposed hot carrier injection current model. Also, our models take into account the change of electric potential due to hole accumulation during read operation. Proposed models show good agreement with TCAD simulation results.
Molecular identification of bacteria isolated from dairy herds with mastitis.

Summary The purpose of the present study was to identify environmental and contagious aerobic pathogen agents causing bovine clinical and subclinical mastitis using sequencing. A total of 244 cows were studied for the presence of mastitis using California Mastitis Test (CMT), clinical observations and microbiological isolations. Milk samples were obtained from 226 quarters of 123 cows which were diagnosed having clinical (11.4%) and subclinical (88.6%) mastitis. From these milk samples, 38 (16.8%) had no bacterial growth and from remaining 188 samples (83.2%) microorganisms were isolated. A total of 42 species were identified by sequencing amplified 16S rRNA fragments. The most common species were Staphylococcus aureus (22.9%) followed by Escherichia coli (10.1%), S. chromogenes (8.5%), S. haemoliticus (7.5%) and Hafnia alvei (6.4%). Of 188 isolates 43 were found contagious (22.9%), and 145 (77.1%) environmental agents. Use of sequencing offer identification of aetiological agent species level useful and practical that can help to chose a suitable therapeutic agent.
Solving Constraint Satisfaction Problems Using the Loihi Spiking Neuromorphic Processor

In many cases, low power autonomous systems need to make decisions extremely efficiently. However, as a potential solution space becomes more complex, finding a solution quickly becomes nearly impossible using traditional computing methods. Thus, in this work we present a constraint satisfaction algorithm based on the principles of spiking neural networks. To demonstrate the validity of this algorithm, we have shown successful execution of the Boolean satisfiability problem (SAT) on the Intel Loihi spiking neuromorphic research processor. Power consumption in this spiking processor is due primarily to the propagation of spikes, which are the key drivers of data movement and processing. Thus, this system is inherently efficient for many types of problems. However, algorithms must be redesigned in a spiking neural network format to achieve the greatest efficiency gains. To the best of our knowledge, the work in this paper exhibits the first implementation of constraint satisfaction on a low power embedded neuromorphic processor. With this result, we aim to show that embedded spiking neuromorphic hardware is capable of executing general problem solving algorithms with great areal and computational efficiency.
An Efficient Recognition Method for Watermelon using Faster R-CNN with Post-Processing

This study proposes a deep learning based recognition approach from aerial photographs for estimating the yield of watermelons. The precise quantity estimation of the yield is favorable for farmers to negotiate prices at marketing and distribution channel. First of all, obtain high-resolution aerial photograph of watermelon field, Faster R-CNN is used as the deep learning architecture, then label samples for training to get the trained model. Next, detect watermelons on the aerial photograph of watermelon field. But the result is not as satisfactory that there are some watermelons didn't be recognized. Therefore, adding two post-processing to improve the results. With cross scanning, the missing rate reduced from 30% to 18%. After filter by HSV color space, the error rate reduced from 6.1% to 1.4%. Testing different amount of samples, the two approaches can provide higher decrease rate in smaller quantities. This study can get well recognition and yield estimation of watermelon from aerial photographs, and saving considerable time than manually label but also has highly accurate.
Energy-aware Placement for SRAM-NVM Hybrid FPGAs

Field-programmable gate arrays (FPGAs) have been widely used in many applications due to their reconfigurability. Notably, the short development time makes the FPGAs one of the promising reconfigurable architectures for emerging applications, such as deep learning. As CMOS technology advances, however, conventional SRAM-based FPGAs have reached their limitations. To overcome these obstacles, NVM-based FPGAs have been introduced. Although NVM-based FPGAs have the features of high area density, low static power consumption, and non-volatility, they are struggling to reduce energy consumption. Their challenge is mainly caused by the access speed of NVM, which is relatively slower than SRAM. In this paper, for compensating this limitation, we suggest SRAM-NVM hybrid FPGA architecture with SRAM- and NVM-based CLBs. In addition, we propose an energy-aware placement for utilizing the SRAM-NVM hybrid FPGAs. As a result of our experiments, we were able to reduce the average energy consumption of SRAM-NVM hybrid FPGA by 22.23% and 21.94% compared to SRAM-based FPGA on the MCNC and VTR benchmarks, respectively.
Task Allocation Into a Foraging Task With a Series of Subtasks in Swarm Robotic System

In swarm robotic systems, task allocation is a challenging problem aiming to decompose complex tasks into a series of subtasks. We propose a self-organizing method to allocate a swarm of robots to perform a foraging task consisting of sequentially dependent subtasks. The method regulates the proportion of robots to meet the task demands for given tasks. Our proposed method is based on the response threshold model, mapping the intensity of task demands to the probability of responding to candidate tasks depending on the response threshold. Each robot is suitable for all tasks but some robots have higher probability of taking certain tasks and lower probability of taking others. In our task allocation method, each robot updates its response threshold depending on the associated task demand as well as the number of neighbouring robots performing the task. It relies neither on a centralized mechanism nor on information exchange amongst robots. Repetitive and continuous task allocations lead to the desired task distribution at a swarm level. We also analyzed the mathematical convergence of the task distribution among a swarm of robots. We demonstrate that the method is effective and robust for a foraging task under various conditions on the number of robots, the number of tasks and the size of the arena. Our simulation results may support the hypothesis that social insects use a task allocation method to handle the foraging task required for a colony’s survival.
Driving Circuit Design of Vibrating Wire Sensor Based on Frequency Sweep Mode

In order to meet the needs of monitoring the pressure and tension of the construction site, a high-precision low-cost driving circuit of vibrating wire sensor is designed. In order to simplify the driving circuit, a single power supply is used to solve the problem of negative power supply that we do not want to face. The STM32 microcontroller is used as the main control chip to realize the overall control of the system. The control mode of frequency sweep stimulation is adopted to realize the fast excitation of the vibrating wire sensor, and the equal precision frequency measurement method is used to read the resonant frequency of the vibrating wire sensor accurately.
A Simple Low-Profile Coaxially-Fed Magneto-Electric Dipole Antenna Without Slot-Cavity

A simple coaxially-fed magneto-electric dipole (ME dipole) antenna is designed and experimentally evaluated. The proposed antenna does not require the conventional quarter-wavelength slot cavity for generating the magnetic dipole mode, and only consists of two simple rectangular horizontal patches, a vertical semi-rigid coaxial cable and a square ground plane. It makes the fabrication easier and can reduce the production cost. Also, as the quarter-wavelength slot cavity is removed in the proposed design, the thickness of the antenna can be reduced to 21 mm, i.e., 16.4% of the free space wavelength at the center frequency. The low-profile antenna shows comparable wide impedance bandwidth of 41.03% ( $\text{S}_{11} \leq -10 dB$ ), and a more stable and higher realized gain from 7.90 - 9.74 dBi (± 0.92 dB variation) over the operating frequency band from 1.86 GHz to 2.82 GHz (centered at 2.34 GHz). The maximum gain has increased around 9.4% when compare with that of the highest reported. While the gain variation in the passband of the proposed antenna is about 58% lower than that of those ME dipole antennas reported in the literature. The radiation mechanism and the effects of the critical parameters of the antenna are also explained with the assistance of the parametric study presented.
Analysing protein dynamics using machine learning based generative models

The ability to understand and model proteins’ dynamics is of great relevance in biology and medicine. A good comprehension of the way the proteins change their structure is important as these transitions give the function of the protein within the organism. In this paper we are introducing an unsupervised learning based approach using variational autoencoders, for uncovering protein motions and conformational transitions. The main goal of the research is to offer an interpretable method for proteins’ trajectories visualisation by learning a low dimensional space that accurately represents the input data, as empirically confirmed through the performed experiments. An additional aim is to comparatively evaluate the impact of two protein representations on the learning process.
Kinetics of the Inhibitory Effects of Ethanolic Ext ract of Vernonia amydgalina on the Corrosion of Aluminium in 1.0 M HCl

The inhibition efficiency of ethanolic extract of Vernonia amygdalina (V. amygdalina) extract on aluminium in hydrochloric acid (HCl) solutions has been evaluated by weigh t loss technique. Values of inhibition efficiency o btained were dependent upon the concentration of inhibitor and temperat ure. Inhibition increased as inhibitor concentratio n, half-life, activation energy was increased but decrease with temperature and first-order rate constant at the temperatures s tudied. Physical adsorption mechanism has been proposed for the inhibiti on and Langmuir adsorption isotherm was obeyed.
Design, modeling and control of an owl-inspired robot in a standing posture

This paper deals with an original study based on anatomical observation of owl’s motions in a standing posture. Owls are specific birds typified by upright stance, sharp talons and adapted feathers for silent flight. Owls can rotate their heads and necks as much as a complete turn without any difficulty. A new kind of robot is proposed here inspired from owls in two phases: standing posture and flight. The first complicated case presents the goal of this work. To model different possible motions of owl’s head and neck, a large observation of the natural behavior can allow us to propose a close design to reality. A dynamic model is then generated to verify miscellaneous rotations and movements. Finally, a fuzzy control system is developed to reach desired values of head and neck’s angles. The paper is ended with a conclusion and future work suggestions.