Miracle-dz/UniFAR-FTUs huggingface dataset:
Location Aware Multi-cell MIMO Communications Assisted by Optical Positioning

This paper proposes a high-energy-efficiency location aware multi-cell multiple input multiple output (MIMO) communication system assisted by optical positioning. The location information acquisition method in the optical positioning subsystem has been designed, which can perform real-time angle of arrival and distance estimation for users and other scatterers and identify the users through the feedback signal. Then based on the location information, we estimate the channel coefficient matrix and derive the energy-efficient optimization algorithm of the beamforming matrix to increase the system's achievable ergodic rate. Simulation results are presented to verify the effectiveness of the algorithm.
Transient gas turbine performance diagnostics through nonlinear adaptation of compressor and turbine maps

Gas turbines are faced with new challenges of increasing flexibility in their operation while reducing their life cycle costs, leading to new research priorities and challenges. One of these challenges involves the establishment of high fidelity, accurate, and computationally efficient engine performance simulation, diagnosis, and prognosis schemes, which will be able to handle and address the gas turbine's ever-growing flexible and dynamic operational characteristics. Predicting accurately the performance of gas turbines depends on detailed understanding of the engine components behavior that is captured by component performance maps. The limited availability of these maps due to their proprietary nature has been commonly managed by adapting default generic maps in order to match the targeted off-design or engine degraded measurements. Although these approaches might be suitable in small range of operating conditions, further investigation is required to assess the capabilities of such methods for use in gas turbine diagnosis under dynamic transient conditions. The diversification of energy portfolio and introduction of distributed generation in electrical energy production have created need for such studies. The reason is not only the fluctuation in energy demand but also more importantly the fact that renewable energy sources, which work with conventional fossil fuel based sources, supply the grid with varying power that depend, for example, on solar irradiation. In this paper, modeling methods for the compressor and turbine maps are presented for improving the accuracy and fidelity of the engine performance prediction and diagnosis. The proposed component map fitting methods simultaneously determine the best set of equations for matching the compressor and the turbine map data. The coefficients that determine the shape of the component map curves have been analyzed and tuned through a nonlinear multi-objective optimization scheme in order to meet the targeted set of engine measurements. The proposed component map modeling methods are developed in the object oriented MATLAB/SIMULINK environment and integrated with a dynamic gas turbine engine model. The accuracy of the methods is evaluated for predicting multiple component degradations of an engine at transient operating conditions. The proposed adaptive diagnostics method has the capability to generalize current gas turbine performance prediction approaches and to improve performance-based diagnostic techniques. Copyright Â© 2015 by ASME.
Cassava Leaf Disease Classification using Deep Neural Networks

In recent years, deep learning has gained much popularity over traditional machine learning techniques in terms of accuracy and precision when trained on substantial amount of data. In this work, a state-of-the-art deep learning technique has been employed for classification and prediction of cassava leaf diseases. Being the second largest producer of carbohydrates in the world, cassava plant has become an important source of calories for people in tropical regions, but it is highly susceptible to viral, bacterial, and fungal attacks resulting in stunted plant growth and hence the yield. So, the aim of the research is to help the farmers quickly identify diseased leaves before they cause any severe damage. The dataset that is used in this work is taken from Kaggle competition 2020 containing 21,397 images of cassava plant leaves belonging to 5 classes: Cassava Bacterial Blight, Cassava Brown Streak Disease, Cassava Green Mottle, Cassava Mosaic Disease and Healthy leaves. In this work, EfficientNet model B4 was trained using transfer learning approach. Further, to remove background noise, Segmentation was performed using U-Net to extract only the leaves from images. Our system provided reasonable performance when validation data was provided to trained model yielding 81.43% and 89.09% accuracy on original and segmented datasets, respectively.
MPC-based Steady-state Drift Control under Extreme Condition

This paper proposes a steady-state drift control strategy based on model predictive control(MPC). A three-degree-of-freedom vehicle model is established to calculate the equilibrium state of steady-state drift. The equation of deviation vehicle dynamics is used to track the equilibrium state. Carsim/Simulink joint simulation is carried out, and the results show that the controller is able to control the steady-state drift of the vehicle accurately. The influence of variation of road adhesion coefficient on vehicle steady-state drift is also analyzed.
Modeling and optimization of 5G network design

Every second, a large amount of digital data is transported through infinite types of devices via cellular networks worldwide, and expectations are at a greatly accelerated growth with increasingly large requests. In few years, these networks could thereby reach their maximum capacities in terms of data transmission. To face these challenges, Network Slicing has been presented as a novel virtualized infrastructure for the new generation cellular network system. This technology now not only covers application-level abstraction but also physical and switching layers virtualization, with different radio access and link communication technologies. Hence, each service provider is to be able to deploy its communication services on top of logical networks, named Network Slices, specifically tailored to its technical requirements. The Device-to-Device communication mode is another approach presented as a promising alternative to traditional communication in cellular networks. This technology allows to reuse radio resources and to decrease the end-to-end latency of local communications. Consequently, the optimization of physical resources in cellular networks becomes crucial to better deploy virtual networks. The overall objective of our research is therefore to define and study the concept of device-to-device communication and network slice design in 5G systems, and propose mathematical models and innovative algorithms to solve the underlying optimization problems.
A robust decentralized decision-making approach for mobile supply chains under uncertainty

The mobile supply chain (MSC) is a new concept that allows companies more adaptability and ﬂexibility. In MSCs, a product family can be produced, distributed, and delivered by a mobile factory, carried by trucks, and shared among different customers. In this paper, to optimize production scheduling and the mobile factory routing problem under uncertainty, a robust decentralized decision-making approach (RDDMA) based on the Analytical Target Cascading (ATC) approach is developed. The RDDMA is a bi-level hierarchical optimization method that divides an all-in-one model into sub-problems and aims to address each agent’s target. It is a 4-phase procedure, including time window determination, robust mobile factory routing, actual production scheduling, and adjustment. In real-world applications, the service time at each site is uncertain. Therefore, a scenario-based robust optimization approach is utilized to manage the uncertainties of the problem. Finally, the RDDMA performance is evaluated using several instances. The results suggest the proposed approach can provide robust solutions for such a multi-agent problem.
VARIATIONS OF THE POSTERIOR CONDYLAR CANALS

BACKGROUND: To assess prevalence, normal anatomic patterns and variations of the condylar canal as the variations in course of vessels and in foramina of skull have importance for their correct identification as normal or abnormal. METERIALS AND METHODS: 64 skulls were examined by direct observation from the Anatomy deparments of the various medical colleges of Gujarat for the unilateral or bilateral presence or absence of the canal and for the presence of multiple canals or any other variations. RESULTS: The condylar canal was found bilaterally in 45 skulls (70.31%); unilaterally in 13 skulls (20.31%) and was absent in 6 skulls (9.38%). Double condylar canals were found unilaterally in 5 skulls (7.81%), 2 skulls (3.12%) showed septation in the posterior condylar canal. A higher prevalence of Intrasinus canal type was found (71.88%). Retrosinus canal type was found 8.59%. CONCLUSIONS: The posterior condylar canal in the occipital bone showed some differences, which may accompany variations in the posterior condylar veins. Knowledge of the anatomical relationships and variations of these veins is necessary not only for radiological diagnosis, but also when considering surgical or endovascular treatment of skull base diseases.
Rigorous nonlinearity analysis of multicarrier wireless systems

Based on a rigorous analysis of the nonlinearity of amplifiers, tradeoffs among power back‐off factor, 1‐dB compression point, 3rd‐order interception point, composite triple beat, carrier number, receiver dynamic range, and bit error rate are presented for multicarrier wireless systems. Testing data of a LMDS system validates the analytical results. © 2004 Wiley Periodicals, Inc. Microwave Opt Technol Lett 44: 176–180, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.20580
A Data-mining Method to Assess Automatic Generation Control Performance of Power Generation Units

Automatic generation control (AGC) of power generation units aims at providing a satisfactory response of generated active power to desired active power dispatched from a power grid center. This paper proposes a data-mining method to estimate three metrics assessing the AGC performance of power generation units in terms of response latency, rapidity and accuracy. The proposed method is composed by two parts. The first part is to select data segments via a matrix profile technique from long-term data samples of the desired and generated active powers. The second part is to estimate performance metrics from a dynamic model between the desired and generated active powers, where the model is built by a system identification technique from the selected data segments. The proposed method resolves a major challenge that the performance metrics are defined for step responses, but the desired active power in practice changes in various forms, many of which are not suitable for performance assessments. An industrial example is provided to support the proposed method.
Comparison of Deep Learning Models in Pothole Avoidance for Self-Driving Car

Self-driving car is one of the automotive innovation technologies that uses a computerized system to control a car without human assistance. Big manufactures have been developing this innovation into the fifth level autonomous technology. This study contributes to create a new system as innovation for this self-driving car to avoid road hazards and potholes. This paper reports the result of the conducted experiment on how the self-driving model is able to avoid potholes using end-to-end approach with Convolutional Neural Network (CNN) as a driving simulator called AirSim. Three different CNN models were tested to compare their performance. The result indicates that all the models were able to evade the road hazards.
Optimal Control for Markov Jump Linear System with Multiplicative Noise and Input Delay

This paper investigates the finite-horizon quadratic optimal control problem of discrete-time Markovian jump linear systems subject to multiplicative noises and input delays. A new stochastic Markovian jump maximum principle is developed for dealing this control problem. Applying this principle to the case of stochastic jumping parameter systems with one-step input delay, a necessary and sufficient condition for the problem admitting a unique solution is given. Under this condition, an explicit analytical optimal controller and the corresponding optimal cost are presented in terms of two generalized jumping coupled difference equations with the same dimension as the original state. The key technique is to establish relations between the optimal costate and the state.
Radar target identification using the bispectrum: a comparative study

Radar target identification is performed using time-domain bispectral features. The classification performance is compared with the performance of other classifiers that use either the impulse response or frequency domain response of the unknown target. The classification algorithms developed here are based on the spectral or the bispectral energy of the received backscatter signal. Classification results are obtained using simulated radar returns derived from measured scattering data from real radar targets. The performance of classifiers in the presence of additive Gaussian (colored or white), exponential noise, and Weibull noise are considered, along with cases where the azimuth position of the target is unknown. Finally, the effect on classification performance of responses horn extraneous point scatterers is investigated. >
Deep Neural Networks-based Air Data Sensors Fault Detection for Aircraft

A deep neural networks (DNN) based fault detection (FD) scheme for aircraft air data sensors (ADS) is proposed. We first investigate kinematic relations of the aircraft air data. Measurements of inertial reference unit (IRU) are modeled as equivalent inputs to the relations. We then model the FD task as a mapping process. Whilst ADS fault cases are exported directly in the process, the inputs to the mapping involve the ADS outputs, and other measurable states including accelerations/angular speeds along different axes of the aircraft body. We adopt both convolutional neural network and long-short time memory blocks to construct the mapping. We detail the database that is established in training the DNN. Training history and testing results of the DNN are also illustrated. Testing performances of the proposed DNN -based FD scheme present itself promising with the high FD accuracy and robustness to different aircraft/flight conditions.
Broadband Circularly Polarized Antenna Based AMC Reflector

In this paper, a broadband circularly polarized square slot antenna improved gain with AMC reflector is designed. The proposed antenna is fed by L-shaped patch. The operating frequency band covers the C band of 3.7-4.2 GHz. Two small square patches are embedded in the diagonal of the square slot, then two orthogonal surface currents are generated and right-handed circularly polarized radiation is realized. A stub is introduced between the L-shaped patch and the feeding line to improve the impedance matching of the antenna, then the wide impedance bandwidth and axial ratio bandwidth are obtained. In addition, a 4×4 rectangular artificial magnetic conductor reflector is loaded under the proposed antenna to improve the gain of the antenna, then the unidirectional radiation of the antenna is realized. The simulated and measured results show that the impedance bandwidth of the antenna is 3.58-4.16 GHz, the axial ratio bandwidth is 3.5-4.05 GHz, the relative bandwidth is 14.6%, and the peak gain is 6.0 dBi.
Roomsemble: Progressive web application for intuitive property search

A successful real estate search process involves locating a property that meets a user’s search criteria subject to an allocated budget and time constraints. Many studies have investigated modeling housing prices over time. However, little is known about how a user’s tastes influence their real estate search and purchase decisions. It is unknown what house a user would choose taking into account an individual’s personal tastes, behaviors, and constraints, and, therefore, creating an algorithm that finds the perfect match. In this paper, we investigate the first step in understanding a user’s tastes by building a system to capture personal preferences. We concentrated our research on real estate photos, being inspired by house aesthetics, which often motivates prospective buyers into considering a property as a candidate for purchase. We designed a system that takes a user-provided photo representing that person’s personal taste and recommends properties similar to the photo available on the market. The user can additionally filter the recommendations by budget and location when conducting a property search. The paper describes the application’s overall layout including frontend design and backend processes for locating a desired property. The proposed model, which serves as the application’s core, was tested with 25 users, and the study’s findings, as well as some key conclusions, are detailed in this paper.
Experimental Investigation of Conversion Loss of Phase Conjugate FSS

Recently, the investigation of scattering properties of a two-dimensional double periodic array of wires that loaded with nonlinear lumped elements was reported [1]. The numerical investigation has demonstrated the evidence of parametric amplification of specular wave and the production of phase conjugated, retro directed wave. The ability to produce such signals as mentioned latter is useful in a variety of applications at microwave and millimeter wave frequencies, such as subwavelength imaging [2] and self-tracking wireless communications [3]-[4]. As is well-known the straightforward extension of optical phase conjugation techniques into the microwave frequency region is still a problem. This is due to the small nonlinear susceptibility of naturally occurring nonlinear materials at microwave frequencies. Hence, the sampling method of incoming wavefront using an antenna array has been proposed [5]. This array together with active microwave circuitry is applied to produce mixing of the wavefront samples with a local oscillator operated at the second harmonic of the signal to be phase-conjugated. In this paper, a different approach wherein we assess the means for experimental characterization of a 2D nonlinear electromagnetic medium with phase conjugation properties is adopted. By using parallel plate waveguide (PPW) characterization techniques, a conversion loss of a doubly periodic diode loaded frequency selective surface phase conjugating structures is experimentally investigated.
Comparative Study on Autonomous Robot Trajectory Determination in an Unknown Indoor Environment

In this paper is presented a comparative study between using extended Kalman filter and particle filter applied on SLAM algorithm for an autonomous mobile robot. The robot navigates through an unknown indoor environment in which are placed 80 landmarks and it creates the map of the environment. Because the sensors placed on the robots produce measurement errors it is necessary to use Bayesian filters as the Kalman filter or the particle filter. An application was implemented that shows the estimated measurement errors produced while using both filters in order to create the estimated map of the closed environment in which the autonomous mobile robot is navigating.
Recent advances in the performance of InP Gunn devices and GaAs TUNNETT diodes for the 100-300-GHz frequency range and above

Improved heat dissipation in InP Gunn devices resulted in RF power levels exceeding 200, 130, 80, and 25 mW at oscillation frequencies of around 103, 132, 152, and 162 GHz, respectively. Corresponding dc-to-RF conversion efficiencies exceeded 2.3% from 102 to 132 GHz. Power combining increased the available RF power levels to over 300 mW at 106 GHz, around 130 mW at 136 GHz, and more than 125 mW at 152 GHz with corresponding combining efficiencies from 80% to over 100%. Operation in a second harmonic mode yielded RF power levels of more than 3.5 mW at 214 GHz, over 2 mW around 220 GHz as well as over 1 mW around 280, 300, and 315 GHz. RF power levels exceeding 10 mW at 202 GHz, 9 mW around 210 GHz, and 4 mW around 235 GHz were obtained from GaAs TUNNETT diodes in a second harmonic mode as well. Corresponding dc-to-RF conversion efficiencies were around 1% at 202 and 210 GHz.
Please Scroll down for Article International Journal of Electronics Partially Reconfigurable Point-to-point Fpga Interconnects Partially Reconfigurable Point-to-point Fpga Interconnects

This article may be used for research, teaching and private study purposes. Any substantial or systematic reproduction, redistribution , reselling , loan or sub-licensing, systematic supply or distribution in any form to anyone is expressly forbidden. The publisher does not give any warranty express or implied or make any representation that the contents will be complete or accurate or up to date. The accuracy of any instructions, formulae and drug doses should be independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims, proceedings, demand or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or arising out of the use of this material. We present a novel use of wiring flexibility in modern FPGA technology in order to implement an on-demand network topology. Conventional rigid router-based networks on chip incur certain overheads due to huge logic resources occupation and topology embedding. In this work, we implement partially reconfigurable point-to-point (-P2P) interconnects to alleviate such overheads. In our implementation, arbitrary topologies can be realised by updating a partial bitstream for the-P2P interconnects. We consider parallel merge sort, Cannon's matrix multiplication, and wavelet applications to generate network traffic. Furthermore, we implement a packet switched network to serve as a reference. The experiments show that the utilisation of our P2P interconnects performs 2 times better and occupies 70% less area when compared to the reference network. Furthermore, the topology reconfiguration latency is significantly reduced using the Xilinx module-based partial reconfiguration technique. Finally, our experiments suggest that higher performance gains can be achieved as the problem size increases. 1. Introduction In modern on-chip multi-core systems, the communication latency of the network interconnects is increasingly becoming a significant factor hampering performance. Consequently, network-on-chips (NoCs) as a design paradigm has been introduced to deal with such latencies and related issues. At the same time, NoCs provide improved scalability and an increased modularity (Dally and Brian 2001). However, these multi-core systems still incorporate rigid interconnection networks, i.e., mostly utilising a 2D-mesh as the underlying physical network topology combined with packet routers. More specifically , it is necessary for the designer to either (i) modify algorithms to suit the underlying fixed topology or (ii) embed the logical topology (intended by the algorithm) onto the physical topology, as depicted in Figure 1(a). In both cases, reduced performance is the result. The topology embedding techniques are well-studied (Leighton …