This paper proposes a comprehensive optimization approach based on linear programming (LP) for the installation of multiple hybrid power plants (HPPs) in non-interconnected islands. Contrary to the current state-of-the-art solutions, the proposed approach optimizes simultaneously the size, location, and technology of each HPP in order to minimize the long-term electricity cost of the island. The optimization problem is formulated as a LP problem to ensure convergence and global optimum solution. Moreover, a series of system constraints are included in the optimization problem, e.g., power reserves, transmission constraints, etc., to ensure the secure and reliable operation of the grid; this is compatible with the actual preventive measures imposed by the network operator in real non-interconnected islands. Simulations are executed in a real Greek Island (Rhodes), confirming the applicability of the proposed method as an optimization tool for network planning studies in non-interconnected islands.

This study presents a method for extracting transformer parameters using simulations in the Finite Element Method Magnetics (FEMM) electromagnetic solver. The extracted parameters represent a full model of a linear transformer and can be used in Simulation Program with Integrated Circuit Emphasis (SPICE) simulations. A model of the two-winding transformer is presented in three variants, for which different approaches were used in the transformer simulation in the SPICE program, all yielding the same simulation results. A three-winding transformer T-model extraction is also developed in the study. A method for extracting transformer parameters from FEMM is proposed, along with an automated tool based on a scripting language built into the FEMM software.

Improvements in efficiency and flexibility in industry are leading to an increasing number of networked control systems. However, the network communication also arises new security issues. In particular, stealthy integrity attacks can cause costs and even safety issues including personal injuries. The so-called deception attacks are designed with the aim of not being detected by (classical) detection systems. Numerous methods are developed to address the detection of attacks in networked control systems. Often the transmitted signals are preprocessed with additional time variant elements (e.g. permutation matrix). Usually, the usage of an attack detection system can easily recognised, which give the attacker the opportunity to prepare a more complex attack. This work presents a combination of measures to achieve protection of confidentiality and detect all commonly considered deception attacks in two parts. The basic attack detection scheme for cyber physical systems was presented in part one. The scheme consists of an additive and multiplicative sensor coding. In the second part, two extension are realised to improve the protection and enable the scheme to detect all in the literature often considered deception attacks (linear man-in-the-middle, replay, covert and zero dynamics). The first extension add a auxiliary system, which is integrated in a novel way. Second extension is a process input feedback to improve detection of attacks which have the process input as target. The efficiency of the proposed extensions is illustrated by numerical examples of detecting the aforementioned attacks.

In this paper, we propose an energy-efficient small cell operation algorithm for ultra-dense cellular networks to improve the energy efficiency of the networks. To that end, the algorithm generates a dataset consisting of the location information of inactive small cells and static UEs. For an overload condition in a macro cell, the algorithm performs clustering with the density-based spatial clustering of applications with noise to find clusters with small cells. Therefore, based on conditions a target cluster is selected and the small cells in the cluster are switched to the active mode in order to share loads from the macro cell. For an under-load scenario in the macro cell, the algorithm finds a small cell with the lowest radio load and conditionally switches the cell to sleep mode. Through systemlevel simulations, the performance of the proposed algorithm is evaluated. The simulation results showed that the proposed approach can ensure a higher energy efficiency than previous algorithms.

I propose a new semiconductor device capable of switching between ground or resistance. I use 2 electrodes Anode and Gate respectively. The gate is used to turn the component into a resistor, and without gate voltage, it's a normal ground element. In this paper, we will look at the results of the simulation obtained with Visual TCAD. Every circuit requires a ground element and usually, it's used to complete the circuit, this component acting as a resistor can save some electrons from grounding and we will use these electrons to provide an alternate path so they can flow through a different branch of the circuit this can save from power loss and every little current can be used in some way.

An essential building block of the smart grid of today is the integration of various renewable power generators in the system. Renewable distributed generators (DG) provide environmentally friendly energy at a low cost. A distributed economic dispatch (ED) system for renewable DGs has been formulated in this work to enhance scalability and reduce single points of failure as compared to the centralized ED system. A self-triggered consensus control mechanism is devised over a wireless channel to reduce the communication cost due to communication overhead in distributed systems. Communication losses due to wireless communication between local DGs have been addressed in this work, and a regression-based estimation has been proposed to account for lost or corrupted packets. The proposed approach is compared with the existing centralized ED system and also with periodic communication. The proposed self-triggered communication approach achieves the same level of performance as periodic communication while using half the resources.

Online construction management systems are the latest development in the construction industry. These systems have been designed to help make the process of construction management easier and more efficient.

In this letter, we present a mathematical characterization of the Federal Communication Commission (FCC) F(50,50) curve for the Very High Frequency (VHF) and the Ultra High Frequency (UHF) band. The proposed empirical model is based on a novel step-wise curve fitting the FCC signal strength data. The F(50,50) model is intended for the TV broadcasting system and has been employed effectively in different literary works. Following this, we proposed a simple mathematical expression applicable to a 100 km distance with antenna height ranging from 30 to 300 m. The results showed that the proposed mathematical formulation agreed well with the F(50,50) curve data. Furthermore, the model presented is straightforward and can readily be implemented in VHF and UHF signal loss calculation requiring minimal input parameters.

‎Atmospheric sound waves with frequencies below the human hearing threshold in the range of 0.002 Hz to 20 Hz are generally referred to as infrasound‎. ‎Wind is the main noise in the above-mentioned frequency range‎. ‎The operation of receiving and detecting infrasound are often hampered by wind‎. ‎Therefore‎, ‎high quality detectors are required‎. ‎For this purpose‎, ‎sensor arrays and array signal processing techniques are utilized‎. ‎Fisher ratio-based signal detection is a widely used and powerful method in the field of infrasound‎. ‎The main drawback of this approach is its high computational time due to the repeated computation of test statistics for each element of the slowness grid‎. ‎Thus‎, ‎the researchers use a relatively low-resolution slowness grid in order to save time in processing‎. ‎On the other hand‎, ‎low resolution grid results in an error in the values of estimated parameters of infrasound wave‎. ‎In this study‎, ‎a genetic algorithm based detection method is proposed in order to overcome the fundamental problems of the Fisher method‎. ‎In the proposed method‎, ‎the slowness grid components (px‎,‎py) are defined as the chromosome for the genetic algorithm‎. ‎Despite the previous methods‎, ‎the genetic algorithm has created the advantage that searching could be conducted in a continuous slowness grid‎. ‎Therefore‎, ‎the continuity of the grid and searching only a limited number of slowness vectors reduce error rates and processing time respectively‎. ‎The error of apparent velocity and incoming orientation became 0.5923 and 0.0710 respectively‎, ‎and the processing time decreased considerably from 25835.07 seconds to 533.55 seconds on average.

This is a comprehensive survey on Cybersecurity and Quantum Theory.

Image registration is a technique of matching and superimposing of features, which can be better applied to high-level vision tasks. However, high-level vision tasks often pay more attention to analysis of target areas. In summary, we rethink the collaborative relationship between image registration and high-level vision tasks, and propose a local registration strategy for constructing a robust adaptive attention gauge fields, and construct robust adaptive attention gauge fields to register images focus more on important target areas. In order to improve the robustness and timeliness of the algorithm for feature matching of target areas in complex environments, we propose a robust adaptive probability distribution(RA), and construct robust adaptive mixed model expectation maximum attention(RAMM-EMA). In order to make robust adaptive parameters to reach the global optimum in deep learning, we designed a simulated annealing method to optimize the robust adaptive parameters during training. In order to focus on important regions in the image as much as possible, propose a feature fusion map with weak boundaries and probabilistic properties, called Robust Adaptive Attention Gauge Fields(RAA-Gauge Fields). High-level vision tasks experiments on image fusion, object delect and 3D-reconstruction, the method presented in this paper is the most helpful, and registration effect of the target areas perform best under more extreme conditions.

Extracting cortical features, which are the most relevant at characterizing structure and function for normal or abnormal brain conditions, would greatly benefit from multimodal neuroimage processing following the surface-based style. This style recognizes the natural definition space for such features due to the layered (surface-based) Cortex structural and functional organization. It may therefore be more sensitive and specific than the former volume-based style. The Human Connectome Project (HCP) multimodal pipelines render high-quality surface-based processing for some of the most consistently acquired neuroimaging modalities, with the quality too reliant on their precise acquisition requirements. Relevant international brain initiatives are espoused to develop an HCP-compatible neuroinformatic facility for the quality-ensured processing of international neuroimaging datasets, which may not follow the specific HCP acquisition requirements, also coined as legacy datasets. We appointed some initiatives to introduce multimodal pipelines in two HCP-compatible processing branches. a) Structural: forward- modeling with geometry (sources and head) and Lead Fields defined for legacy MEG, or EEG, in the HCP individual cortical space (Cifti) obtained from legacy MRI. Our pipeline (Ciftify-MEEG) leverages a more diverse neuroinformatic repository than the HCP structural or MEG pipelines. Ciftify-MEEG produces substantial processing illustrated here with EEG examples, incorporating alternative processing paths and corrections based on a quality control loop and upon qualitative and quantitative indicators. b) Functional: identifying spectral topographies and connectomes for the cortical oscillatory activity observed in the MEG or EEG frequency bands. We leverage a novel repository of Bayesian sparse inverse methods that target identifying the topographies and connectomes with actual statistical guarantees, the brain connectivity Variable Resolution Electromagnetic Tomographic Analysis (BC-VARETA). Our pipeline design for BC-VARETA, which we denominate Ciftify-bcVARETA, is integrated into the Ciftify- MEEG outputs and with Bayesian sparse priors structured upon Cifti space information. Ciftify-bcVARETA identification is less biased to forward models and more biased to the observations than the HCP pipeline illustrated here with topographies obtained for MEG and EEG legacy databases.

It is well known that CMOS Cross-coupled Differential Drive (CCDD) is the commonly used approach in the design of RF-DC converters. However, multistage designs are usually used to produce higher DC output voltage. This in turn will lead to using the twin-well CMOS process for fabrication since the NMOS source will be at higher potential in the next and consequent stages. The twin-well process technology is expensive compared to the n-well process. On the other hand, the DC output of these designs will saturate when higher voltage is required using multistage. This brief presents a new CMOS n-well process RF-DC converter. The design is hybrid in which the first stage is designed using CCDD since the NMOS source is grounded and the second and consequent stages are designed using only PMOS transistors. The functionality of the proposed design is confirmed using CADENCE in 0.18µm TSMC CMOS technology. Simulation results show that the design is working properly and achieves linear DC output voltage with the number of stages and an input power range of 33dB for PCE >20%.

Intelligent reflecting surface (IRS) is envisioned to become a key technology for the upcoming six-generation (6G) wireless system due to its potential of reaping high performance in a power-efficient and cost-efficient way. With its disruptive capability and hardware constraint, the integration of IRS imposes some fundamental particularities on the coordination of multi-user signal transmission. Consequently, the conventional orthogonal and non-orthogonal multiple-access schemes are hard to directly apply because of the joint optimization of active beamforming at the base station and passive reflection at the IRS.  Relying on an alternating optimization method, we develop novel schemes for efficient multiple access in IRS-aided multi-user multi-antenna systems in this paper. Achievable performance in terms of the sum spectral efficiency is theoretically analyzed. A comprehensive comparison of different schemes and configurations is conducted through Monte-Carlo simulations to clarify which scheme is favorable for this emerging 6G paradigm.

In this research paper, we show that the convergence theorem of Hopfield neural network hods true without the condition that the diagonal elements must be non-negative (using epsilon perturbation of diagonal elements). More general version of this result is also proved. It is also shown that the eigenvalue vector of a symmetric matrix and the diagonal element vector are related through a linear system of equations with the coefficient matrix being a doubly stochastic matrix. Also, Perturbation analysis of Hopfield neural network is discussed.

Reconfigurable intelligent surface (RIS) has recently drawn intensive attention due to its potential of simultaneously realizing high spectral and energy efficiency in a sustainable way. This paper focuses on the design of efficient transmission methods to maximize the uplink sum throughput in a RIS-aided multi-user multi-input multi-output (MU-MIMO) system. To provide an insightful basis, the channel capacity of RIS-aided MU-MIMO is theoretically analyzed. Then, the conventional transmission schemes based on orthogonal multiple access are presented as the baseline. From the information-theoretic perspective, we propose two novel schemes, i.e., \textit{joint transmission} based on the semidefinite relaxation of quadratic optimization problems and \textit{opportunistic transmission} relying on the best user selection. The superiority of the proposed schemes over the conventional ones in terms of achievable rates is justified through simulation results.

Efficiently and accurately separating infrared (IR) small targets from complex backgrounds presents a significant challenge. Numerous studies in the literature have proposed various feature fusion modules designed specifically to enhance the extraction of IR small target features. While these designs offer some incremental improvement to the accuracy of IR small target detection, they come at a steep cost of significantly increasing network parameters and FLOPs. Striving for a balance between computational efficiency and model accuracy, we decided to forgo these complex feature fusion modules. Instead, we developed a new lightweight encoding and decoding structure known as the Lightweight IR Small Target Segmentation Network (LW-IRSTNet). This structure integrates regular convolutions, depthwise separable convolutions, atrous convolutions, and asymmetric convolutions modules. In addition, we devised post-processing modules including an eight-neighborhood clustering algorithm and an online target feature adjustment strategy. Experimental results indicate that: 1) the segmentation accuracy metrics of LW-IRSTNet match the best results of 14 state-of-the-art comparative baselines; 2) the parameters and FLOPs of LW-IRSTNet, at only 0.16M and 303M respectively, are significantly smaller in comparison to these baselines; and 3) the post-processing modules enhance both user-friendliness and the robustness of algorithm deployment. Moreover, LW-IRSTNet has been successfully implemented on both embedded platforms and websites, expanding its range of applications. Utilizing the ONNX framework, NPU acceleration, and CPU multi-threaded resource allocation, we have been able to achieve high-performance inference capabilities, as well as online dynamic threshold adjustment with the LW-IRSTNet. The source codes for this project can be accessed at https://github.com/kourenke/LW-IRSTNet.

Most prior works on intelligent reflecting surface (IRS) merely consider point-to-point communications, including a single user, for ease of analysis. Nevertheless, a practical wireless system needs to accommodate multiple users simultaneously. Due to the lack of frequency-selective reflection, namely the set of phase shifts cannot be different across frequency subchannels, the integration of IRS imposes a fundamental challenge to frequency-multiplexing approaches such as frequency-division multiple access (FDMA) and the widely adopted technique called orthogonal FDMA (OFDMA). It motivates us to study (O)FDMA-based multi-user IRS communications to clarify which user scheduling and passive beamforming are favorable under this non-frequency-selective reflection environment. Theoretical analysis and numerical evaluation reveal that (O)FDMA does not need user scheduling when there are a few users. If the number of users becomes large, neither user scheduling nor IRS reflection optimization is necessary. These findings help substantially simplify the design of (O)FDMA-based IRS communications.