Generation Scheme Research Articles

For Security and Higher Spectrum Efficiency: A Variable Packing Ratio Transmission System Based on Faster-Than-Nyquist and Deep Learning

With the rapid development of various services in wireless communications, spectrum resource has become increasingly valuable. Faster than Nyquist (FTN) signaling, proposed in the 1970s, is a promising paradigm for improving spectrum utilization. This paper proposes the variable-packing-ratio (VPR)-based transmissions for high spectrum efficiency (SE) and security, respectively. Aided by deep learning (DL)-based estimation, the proposed scheme for high SE can achieve a higher capacity than the conventional Nyquist-criterion transmission with negligible modification to existing communication paradigms (e.g., spectrum allocation or frame structure). More importantly, for VPR-based secure transmission, a dynamic generation scheme is proposed to produce randomly distributed positions to switch the packing ratio, which can effectively avoid detections and attacks. In addition, we propose a simplified DL-based packing ratio estimation for both of these two scenarios so that the receiver can estimate the packing ratio without any in-band or out-band control messages. Simulation results show that the proposed simplified estimation achieves nearly the same accuracy and convergence speed as the original multi-branch fully-connected structure with a complexity reduction of 20 folds. Finally, we derive the closed-form SE of the proposed VPR transmission under different channels. The numerical results validate the correctness of the derivation and demonstrate the SE gains of the VPR scheme beyond conventional Nyquist transmission.

Extended belief rule base with ensemble imbalanced learning for lymph node metastasis diagnosis in endometrial carcinoma

Lymph node metastasis (LNM) constitutes one of the main prognostic factors for long-term survival in endometrial carcinoma (EC). However, the previous studies on LNM diagnosis failed to consider both model interpretability and class imbalance. In this study, the extended belief rule base (EBRB) expert system is introduced to develop a novel EBRB-based LNM diagnosis model. First, the interpretability of the EBRB expert system is investigated to demonstrate the feasibility on LNM diagnosis; Second, imbalanced learning is introduced to improve rule generation scheme for constructing base EBRBs; Third, by considering the trust of base EBRBs and base diagnoses, ensemble learning is introduced to improve rule inference scheme for diagnosing final LNM. In the case study, real EC patient data collected from Fujian Provincial Maternity and Children's Hospital are used to verify the effectiveness of the proposed EBRB-based model by comparing with the variants of rule generation schemes and rule inference schemes, as well as some machine learning-based LNM diagnosis models. The comparative results showed that the proposed EBRB-based model has better sensitivity, specificity, and geometric mean in diagnosing LNM for EC patients.

Gamma-flash generation in multi-petawatt laser–matter interactions

The progressive development of high power lasers over the last several decades enables the study of γ-photon generation when an intense laser beam interacts with matter, mainly via inverse Compton scattering at the high intensity limit. γ-ray flashes are a phenomenon of broad interest, drawing the attention of researchers working in topics ranging from cosmological scales to elementary particle scales. Over the last few years, a plethora of studies predict extremely high laser energy to γ-photon energy conversion using various target and/or laser field configurations. The aim of this article is to discuss several recently proposed γ-ray flash generation schemes, as a guide for upcoming γ-photon related experiments and for further evolution of the presently available theoretical schemes.

Implementation of Secret Key Generation on Mobile Crowdsensing Application to Secure Tracking Location of Motorcyclists

Mobile crowdsensing is a method for collecting many data from sensors on smartphone. In this research, mobile crowdsensing application will be developed to display location of motorcyclits who connected in an Ad-Hoc network along with a security system using the Secret Key Generation (SKG) scheme to generate a secret key that will be used to encrypt and decrypt the data. From the results it can be concluded that the highest measurement correlation is 0.0398 and the lowest is 0.0018 but after randomness extraction proccess, the highest correlation is 0.996 and the lowest is 0.978. After encryption, information of Alice and Bob is stored as random character in database and decrypted as plaintext as shown as in application. In the attacking result, the data after encrypted just shown random character in traffic monitor. When the eavesdroppers manipulate its IP address like Alice's, they can’t connect to Bob.

Millimeter-wave noise generation based on a monolithically integrated dual-mode chaotic laser.

A millimeter-wave noise generation scheme is proposed in this paper. The scheme is based on a monolithically integrated dual-mode chaotic laser, which consists of a distributed Bragg feedback (DFB) section, a phase section, and an optical amplification section. The output spectrum state of the dual-mode laser can be controlled by adjusting the injection current in the three regions. The monolithically integrated dual-mode chaotic laser has stable chaotic output and can be used as a light source for integrated millimeter-wave noise source. As a feasibility demonstration, a dual-mode chaotic laser with a mode interval of 2.05 nm was generated in the experiment, the optical mixing on a photodetector produced millimeter-wave noise with a center frequency of 259 GHz and a bandwidth of 44 GHz (237-281 GHz), achieving a typical value of excess noise ratio of 47 dB. It has the advantages of high noise source utilization, small noise source volume, and high integration.

Beam-Plasma Generator of the THz Radiation Based on an Induction Accelerator (LIA-PET Project)

BINP SB RAS together with RFNC-VNIITF carry out a research in the field of creating new sources of electromagnetic radiation in the THz range. Within the framework of this article, a project of the THz beam-plasma generator based on an electron beam generated by a linear induction accelerator is presented. The article provides generator scheme and describes the main elements of the electron beam formation system. In addition, the results of modeling the beam transport and its cross-section compression are presented. These calculations were performed under the current up to 1 kA and energy up to 1 MeV for the subsequent injection of the beam into the plasma section with plasma density up to 1015 –1016 cm–3. The article also contains the analysis of previous experimental studies results which are connected with the beam-plasma interaction for various beam and plasma parameters. Based on this analysis, a requirement for the ratio of the beam and plasma electron densities was formulated. This requirement should be satisfied for creation of the beam-plasma generator of EM radiation for the range of 0.1–1 THz with a pulse power of several MW.

Robust Predictive Current Control of PMSG Wind Turbines with Sensor Noise Suppression

Model predictive control (MPC) is an efficient and multi-functional control scheme for synchronous permanent magnet generators (PMSGs). However, the effective management of traditional MPC depends on precise system models. Multiple uncertainties of permanent magnet flux, motor inductance, filter inductance and parameter measurement noise will limit MPC’s performance. The conventional linear extended state observer (ESO) can perform robust predictive control of the ultralocal model of the PMSG system to cope with parameter mismatches. However, the ESO is limited in balancing disturbance rejection with measurement noise attenuation. Since the amplification of high-frequency noise pollution can lead to both poor control performance and system instability, this challenge is of significant importance. To solve the problem, a new hybrid parallel cascaded ESO (PCESO) model-free predictive control framework is proposed using the three-level neutral-point-clamped (NPC) power electronic converter, on both the machine side and grid side. Analytical discussions of the time and frequency domain characteristics of the PCESO demonstrate its superior characteristics over the ESO. The proposed method can effectively balance parameter mismatch, disturbance rejection and high-frequency noise suppression. Finally, the effectiveness of the proposed method, under uncertainties of parameter mismatches, measurement noise and permanent magnet flux, is verified through real-time hardware-in-the-loop tests on a back-to-back grid-tied PMSG interfaced with an NPC power converter.

Generation of multipartite entangled states based on a double-longitudinal-mode cavity optomechanical system.

An optomechanical system is a promising platform to connect different "notes" of quantum networks. Therefore, entanglements generated from it is also of great importance. In this paper, the parameter dependence of optomechanical and optical-optical entanglements generated from the double-longitudinal-mode cavity optomechanical system are discussed and two quadrapartite entanglement generation schemes based on such a system are proposed. Furthermore, 2N and 4N-partite entangled states of optical modes can be obtained by coupling N cavities that used in the above two schemes with N-1 beamsplitters, respectively. Certain ladder or linear entanglement structures are included in the finally obtained entangled state, which are important for its application in one-way quantum computing.

Multiband LFM waveform generation and band-selection using stimulated Brillouinscattering.

Modern radar systems are designed to simultaneously serve multiple applications such as ranging, surveillance, imaging, or warfare, which necessitates operation at multiple carrier frequencies. Linear frequency modulated (LFM) signals are inherently capable of pulse compression leading to enhanced range resolution and good signal-to-noise ratios; therefore, they are widely employed in various radar applications. In this paper, a photonic-based generation scheme for carrier frequency multiplication of LFM waveforms up to a factor of four through a single dual-drive Mach-Zehnder modulator is proposed and experimentally demonstrated. The technique is employed to produce multiband LFM having wide-bandwidth chirps (500MHz, 1GHz) as well as narrow bandwidth chirps (10, 20MHz) that are compatible with the intrinsic linewidth of stimulated Brillouin scattering (SBS). The frequency bands of the narrow bandwidth chirps are further selected through a frequency-agile Brillouin RF filter. The generated tupled chirped waveforms are at continuous multiples of the RF carrier frequency at 2, 4, 6, and 8GHz, respectively, with the first three multiples having 10MHz and the fourth multiple having 20MHz chirp bandwidth. This scheme is also experimentally verified for generating different tupled products and respective filtering through SBS at multiples of 4GHz up to 16GHz, thereby verifying the system's agility and flexibility.

Identification of photovoltaic module parameters by implementing a novel teaching learning based optimization with unique exemplar generation scheme (TLBO-UEGS)

The performance evaluation of a Photovoltaic (PV) system heavily relies on accurately estimating the parameters based on its current—voltage relationships. However, due to the PV model’s inherent complexity, obtaining these parameters with precision and efficiency is a challenging task. In this study, a new variant known as teaching learning-based optimization with unique exemplar generation schemes (TLBO-UEGS) is proposed to address PV module parameter estimation problems with robustness and effectiveness. To enhance the performance of TLBO-UEGS, a modified initialization scheme that leverages the strengths of chaotic maps and dynamic oppositional based learning is introduced. This scheme ensures the generation of an initial population with improved solution quality. Furthermore, both the modified teacher phase and modified learner phase are integrated within the TLBO-UEGS optimization framework. This integration allows for different learning strategies to be employed based on the fitness values of each learner, effectively updating their search trajectories. Within the modified teacher phase, two unique exemplar generation schemes are designed to facilitate more effective guidance for learners in the first half of the population while maintaining population diversity. Meanwhile, the modified learner phase emulates a realistic knowledge acquisition process by enabling learners in the second half of the population to engage in collaborative learning with multiple peer learners or retain valuable knowledge from previous learning processes. Extensive simulations demonstrate that TLBO-UEGS achieves superior results, with the minimum root mean square error (RMSE) values of 3.5644 × 10−04 ± 0.0014, 1.3237 × 10−04± 0.0043, and 6.6016 × 10−06± 0.00011 obtained for Photowatt-PWP201, Leibold Solar (LSM 20), and Leybold Solar (STE 4/100) PV modules, respectively.

NIR to MIR ultra-broadband supercontinuum laser source based on all-silica fibers.

We demonstrated an ultra-broadband supercontinuum (SC) laser source with a wavelength range spanning the near-infrared (NIR) to mid-infrared (MIR) region. The SC spectrum was generated in a very short piece of highly nonlinear silica fiber (HNLF) which has a zero-dispersion wavelength (ZDW) of 1.55 µm. The pump source used has a spectral coverage of 1.5∼2.4 µm which covers the ZDW of HNLF, resulting in a dramatic blue and red shift of the spectrum through strong non-linear effects. As the pump laser pulse launched into HNLF, a SC spectrum with broadband range of 0.92∼2.92 µm and maximum average power of 5.09 W was achieved, which sets record coverage of HNLF-based watts magnitude SC laser sources for now, to the best of the authors' knowledge. The setup consists of silica fiber that can be considered easy-to-implement and with a cost-effectiveness scheme for ultra-broadband SC generation that could be easily applied to optical fiber sensing and spectral imaging technology.

Physical Layer Key Generation Scheme for MIMO System Based on Feature Fusion Autoencoder

Recently, the use of wireless channel state information (CSI) to generate encryption keys in the physical layer has gained significant attention from researchers. Unlike classical cryptography, this approach relies on the variability of the wireless channel, channel reciprocity, and spatial decorrelation to ensure security, making it more lightweight and providing strong randomness. This paper proposes a physical layer key generation scheme for wireless LAN MIMO systems based on feature fusion autoencoder (FFAEncoder) to address the issue of a high key disagreement rate (KDR). Our approach involves extracting amplitude and phase features separately, fusing them through multiplication operator in a neural network, and using an autoencoder to extract common features. The proposed scheme was evaluated on multiple datasets in different real-world scenarios, and it was found that the transmitter and receiver codeword’s mean squared error (MSE) and mean absolute error (MAE) were smaller than those of the current models, indicating better key generation performance. Additionally, the proposed scheme’s KDR was smaller, with a decay rate faster than that of the other two models in the same environment, and the primary key bits were 1/2 and 1/3 that of the other models, respectively, as the signal-to-noise ratio (SNR) increased.

Generative schemes for drug design with shape captioning

Generative schemes for drug design with shape captioning

Coordinated Power Management of a Laboratory Scale Wind Energy Assisted LVDC Microgrid With Hybrid Energy Storage System

The present work proposes a coordinated power management scheme (CPMS) of wind energy-fed self-excited induction generator (SEIG) based low-voltage direct current (LVDC) autonomous microgrid. The battery and supercapacitor (SC) based hybrid energy storage system (HESS) is interfaced with the common 48 V LVDC bus using a parallel-active configuration through two bidirectional converters. The central idea of hybridization is to mitigate the instantaneous surge current demand and alleviate the charge/discharge stress during transients, thus ultimately enhancing the cycle life of the battery. The experimental validation of the autonomous LVDC microgrid equipped with the CPMS is conducted for various operating modes. The dSPACE DS1103 digital controller is used for the real-time implementation of the complete system. The effectiveness of the proposed CPMS is supported by the analysis of comparative experimental results demonstrates its ability to incorporate seamless mode transition. The developed 48 V LVDC microgrid can be a promising solution for powering consumer electronics in homes/office spaces or other small-scale applications.

Random Beam Switching: A Physical Layer Key Generation Approach to Safeguard mmWave Electronic Devices

Extracting secret keys from millimeter wave (mmWave) wireless channels fast and efficiently for securing communications between mmWave electronic devices is a challenge due to the limitations of static environments. In this paper, we propose a random beam switching (RBS) based physical layer key generation (PLKG) scheme in the mmWave systems. Utilizing the sparsity characteristics of mmWave channels between electronic devices, we apply virtual angles of effective-beams and activated-beams as the random source. Through channel detection, we establish an RBS space between the transmitter and receiver. Then they randomly select beams from the RBS space and transmit reference signals to generate consistent keys. Furthermore, by using information theory, we analyze the effectiveness, reliability and security performance of RBS scheme theoretically. Specifically, we derive the theoretical expression of key generation rate (KGR) and then obtain its upper bound. We also prove that the key mismatch ratio (KMR) approaches zero in the high signal-to-noise ratio (SNR) region. Then we demonstrate that the eavesdropper cannot obtain any information about the activated-beams. Numerical results are provided to verify our theoretical analysis. We demonstrate that the RBS scheme can provide sufficient secret keys even in static environments while achieving good secrecy performance to safeguard electronic devices.

Consumer society and consumer behavior: The evolution of concepts and modern understanding.

The article presents the results of a theoretical analysis of significant sociological, economic, psychological and related concepts of consumer society and consumer behavior. It is shown that the evolution of these concepts has a pronounced division into stages corresponding to the historical trends of the emergence and development of consumer society as a socio-economic phenomenon. Organically interconnected with the formation of consumer society was the formation of typed consumer behavior practices, which today is characterized by a complex structure, and its specific forms depend on a significant number of factors of the most diverse nature. These forms need to be comprehensively studied in order to use the acquired knowledge in the activities of both commercial enterprises and public administration. Consumption within the framework of this stage was considered most often as a derivative of class or estate, and its most prominent representatives were K.Marx (consumption as belonging to a class), M.Weber, T.Geiger, T.Veblen (theory of prestigious consumption), G.Simmel (theory of fashion), V. Sombart (theory of luxury). Further studies of consumer behavior began to be conducted mainly within the framework of economic theory, behavioral economics and marketing at the beginning of the XX century. The Marxist idea of "commodity fetishism" was continued in the writings of J.Baudrillard (criticism of consumer society), and the concept of M. Weber – in the so-called "class factions" of P. Bourdieu, and the ideas and J.Baudrillard and P. Bourdieu (habitus as an "acquired system of generative schemes") are quite popular today in sociological, socio-cultural and socio-philosophical concepts of human behavior in general and consumer behavior in particular. The "new" theory of consumption was proposed by G. Becker, K. Lancaster and J.Stigler, and M. Friedman and J.Dusenberry formulated the concepts of permanent income and sustainable consumer practices, respectively, to explain "advanced consumption".

Dynamic Neural Network for Bicriteria Weighted Control of Robot Manipulators.

In recent years, bicriteria optimization schemes for manipulator control have become preferred by researchers, given their satisfactory performance. In this article, a bicriteria weighted (BCW) scheme to remedy joint drift and minimize the infinity norm of joint velocity is proposed. The scheme adopts a novel repetitive motion index that can theoretically decouple the joint error and the position error, which many conventional cyclic motion generation schemes cannot achieve. Subsequently, through transformation, the BCW scheme is converted into a time-varying quadratic programming (QP) problem. Then, a dynamic neural network (DNN) system with a new Fisher-Burmeister function is proposed to address the resulting QP problem. It is proven that the proposed DNN system is free of residual errors, which means that the actual solution is able to converge to the theoretical solution. Another essential feature of the DNN system is that it has a suppression effect on noise. To demonstrate the convergence and robustness of the proposed DNN system, comparative simulations are carried out in nominal and noisy environments. Finally, experiments on Franka Emika Panda are conducted to elucidate the availability of the BCW scheme addressed by the DNN system.

A Content-Assisted Dynamic PUF Key Generation Scheme Using Compressive Autoencoder for Internet-of-Things

A physical unclonable function (PUF) is broadly investigated as a secret key generator for internet-of-things (IoT) devices because of its uniqueness and randomness. Security vulnerability may occur in conventional PUF-based schemes if an attacker eavesdrops on the challenge and response pair (CRP) associated with PUF. To mitigate the impact of such key compromise, this study proposes a novel dynamic PUF key generation scheme, where the sensing data-based dynamic features are integrated to a static PUF-based key. The compressive autoencoders (AEs) typically employed for compressing the time-series data can be customized to extract data-based features. The dynamic features are quantized to generate the data-based key, and then combined with the PUF-based key for the dynamic key generation. An attacker finds it difficult to extract the PUF-based key from the synthesized ones; hence, CRPs cannot be estimated, even if the attacker can obtain the dynamic key through eavesdropping. The event signal decomposition algorithm, which disjoins the dominant event signal in the dataset, is proposed to enhance the data-based key diversity. For numerical evaluation, the static random access memory-PUF and the public energy dataset are used for the dynamic key generation. Numerical results show that the AE model can generate a data-based key that prohibits key leakage to the attacker while improving the reconstruction performance. The dynamic key is evaluated for diverse attack models (e.g., replay and modeling attacks), and the attacker cannot estimate the CRP tables.

Use of Synchronous Generators for Gas Turbine Power Plants

The combination of gas turbine engines and low-speed generators that are part of power plants is a large-mass unit with low specific indicators of the mass-to-unit power ratio. (Research purpose) The research purpose is developing a new kinematic scheme of a gas turbine engine with a high-speed synchronous electric generator, higher reliability and long service life, reduced weight and size characteristics of the power plant. (Materials and methods) Established that small gas turbine power and general-purpose power plants are not produced in Russia, research work was not systematic. It was determined that synchronous generators and gas turbine engines are used on two-shaft kinematic systems that reduce the rotation frequency of the generator rotor, where a large diameter of the free turbine entails a higher level of vibration, and due to the high level of sliding leads to a lower torque of the power plant at low rpm. (Results and discussion) It has been shown that the use of single-channel kinematic schemes contributes to simpler control, this opens up a wide prospect for their use in power plants for the purpose of supplying electricity to consumers and mobile agricultural vehicles. A new kinematic scheme of a high-speed generator has been developed, in which the rotor and stator interact with each other according to the type of sliding bearing. (Conclusions) The advantages of using a single-shaft kinematic scheme for the joint operation of a gas turbine engine with a high-speed synchronous generator have been revealed, which are expressed in a smaller moment of straining of the rotor and the absence of a sliding coefficient between the shafts. It was stated that as a result of using the proposed design of the power module, the reliability of the generator increases, the operating speed increases due to the use in the design of the polished outer surface of the rotor and the polished inner surface of the stator, working according to the type of sliding bearing, with forced oil supply at a pressure of 2-5 kilograms per square centimeter, at the same time, heat removal from the stator winding improves.

Adaptive differential evolution with fitness-based crossover rate for global numerical optimization

Differential evolution (DE) is one of the most efficient evolution algorithms (ES) for dealing with nonlinear, complicated and difficult global optimization problems. The main contribution of this paper can be summarized in three directions: Firstly, a novel crossover rate (CR) generation scheme based on the zscore value of fitness, named fcr, is introduced. For a minimization problem, the proposed CR generation strategy always assigns a smaller CR value to individual with smaller fitness value. Therefore, the parameters of individuals with better fitness are inherited by their offspring with high probability. In the second direction, the control parameters are adjusted by unused bimodal settings in which each parameter setting is selected according to the evolution status of individual. The third direction of our work is introducing the L1 norm distance as the weights for updating the mean value of crossover rate and scale factor. Theoretically, compared with L2 norm, L1-norm is more efficient to suppress outliers in the difference vector. These modifications are first integrated with the mutation strategy of JADE, then a modified version, named JADEfcr, is proposed. In addition, to improve the optimization ability further, another variant LJADEfcr by using a linear population reduction mechanism is considered. So as to confirm and examine the performance of JADEfcr and LJADEfcr, numerical experiments are conducted on 29 optimization problems defined by CEC2017 benchmark. For JADEfcr, its experimental results are made a comparison with twelve state-of-the-art algorithms. The comparative study demonstrates that in terms of robustness, stability and solution quality, JADEfcr are better and highly competitive with these well-known algorithms. For LJADEfcr, its results are compared with JADEfcr and other nine powerful algorithms including four recent algorithms and five top algorithms on CEC2017 competition. Experimental results indicate that LJADEfcr is superior and statistically competitive with these excellent algorithms in terms of robustness, stability and the quality of the obtained solutions.