Generation Scheme Research Articles

Optical frequency comb assisted reconfigurable broadband spread spectrum signal generation.

A photonic-assisted scheme for spread spectrum communication signals generation is proposed and demonstrated in this article. The spreading sequence and the baseband data codes are modulated on the photonic link by electro-optic modulators, and the spread spectrum process is completed through stream processing on the analog microwave photonic link. By combining optical frequency comb and injection locking technologies, the carrier frequency of the communication signals can be tuned over an ultra-broadband range of 3-39 GHz. In the proof-of-concept experiments, spread spectrum signals at 3 GHz and 6 GHz are obtained with a spread factor of 31. The analysis results indicate that the generated signals possess excellent reconfiguration, anti-interference, and anti-interception properties. Overall, our proposed scheme offers a flexible photonic architecture with significant potential in the application of ultra-broadband covert communication systems.

A DMP-Based Motion Generation Scheme for Robotic Mirror Therapy

Hemiparesis has become prevalent in recent years, and robot-assisted rehabilitation provides great therapeutic potential. Robotic mirror therapy is a promising approach for hemiplegic patients by transmitting the motion of the healthy limb (HL) to the impaired limb (IL), where the wearable robot assists the IL to mimic the HL's action to stimulate the active participation of the injured muscles. However, complete replication of the HL's movement trajectory without considering the IL's muscle strength is not satisfactory for rehabilitation. In this article, a motion generation scheme is proposed for robotic mirror therapy. The robot's movement trajectory is modeled with dynamic movement primitive (DMP), and the physical human–robot interaction is formulated as an impedance model and coupled with the DMP model. The subject's muscle strength is evaluated with skin surface electromyography signals and transferred to the robot's joint stiffness. In order to adapt the human–robot coupled DMP model parameters to varying subject performance, a reinforcement learning algorithm is designed to optimize the robot's movement trajectory and stiffness profile, where the training safety and rehabilitation improvement are both guaranteed. The proposed method is validated using a lower extremity rehabilitation robot, and experimental results demonstrate its feasibility and superiority.

Multiple mask and boundary scoring R-CNN with cGAN data augmentation for bladder tumor segmentation in WLC videos

Automatic diagnosis systems capable of handling multiple pathologies are essential in clinical practice. This study focuses on enhancing precise lesion localization, classification and delineation in transurethral resection of bladder tumor (TURBT) to reduce cancer recurrence. Despite deep learning models success, medical applications face challenges like small and limited datasets and poor image characterization, including the absence lack of color/texture modeling. To address these issues, three solutions are proposed: (1) an improved texture-constrained version of the pix2pixHD cGAN for data augmentation, addressing the tradeoff of generating high-quality images with enough stochasticity using the Fréchet Inception Distance (FID) measure. (2) Introducing the Multiple Mask and Boundary Scoring R-CNN (MM&BS R-CNN), a new mask sub-net scheme where multiple masks are generated from the different levels of the mask sub-net pipeline, improving segmentation accuracy by including a new scoring module to refine object boundaries. (3) A novel accelerated training strategy based on the SGD optimizer with the second momentum. Experimental results show significant mAP improvements: the data generation scheme improves by more than 12 %; MM&BS R-CNN proposed architecture is responsible for an improvement of about 1.25 %, and the training algorithm based on the second-order momentum increases mAP by 2–3 %. The simultaneous use of all three proposals improved the state-of-the-art mAP by 17.44 %.

Cherenkov-Type Terahertz Generation by Long-Wavelength Ultrafast Laser Excitation of a GaP Crystal

We explore, both theoretically and experimentally, the potential of semiconductor materials for the Cherenkov scheme of terahertz generation on the example of GaP crystal pumped by femtosecond laser pulses of 1.54 μm wavelength. We use a convenient scheme with a focused-to-a-line laser beam which is introduced into the crystal by oblique incidence at the Brewster angle on the crystal front face and propagates in the crystal at the Cherenkov angle to its normal. A half of the generated Cherenkov wedge impinges normally the rear face of the crystal and generates an output terahertz beam with a plane wavefront. The whole generation process is simulated by FDTD method. In experiments, the laser pulses of 140 fs duration and 10 μJ energy were converted to wideband (~2.5 THz bandwidth) terahertz radiation with the efficiency of ~3 × 10−5, which exceeds the efficiency of the standard collinear scheme by at least an order of magnitude.

A novel day-ahead scheduling model to unlock hydropower flexibility limited by vibration zones in hydropower-variable renewable energy hybrid system

Giving full play to the flexibility of hydropower and integrating more variable renewable energy (VRE) is of great significance for accelerating the transformation of China's power energy system. For middle- to large-sized hydropower units, its irregular vibration zone (VZ) is a major factor affecting its flexibility, because VZs limits the power output and adjustment range. In the existing studies on hydropower unit commitment and flexibility scheduling, VZs only limits the output of hydropower units, while the impact on the flexibility adjustment ability of units is ignored. The hydropower flexibility in day-ahead scheme is overestimated, which increases the risk of system flexibility shortage causing power curtailment and load loss. In this study, a novel day-ahead scheduling model considering the flexibility limited by the VZs and the probability of flexibility shortage is constructed for the day-ahead generation scheme of hydropower-VRE hybrid generation system (HVHGS) to optimize the target load, VRE output and hydropower unit commitments. The impaction of the VZs on hydropower flexibility is firstly modeled by chance constraints and stochastic programming method. Moreover, a data-driven model based on machine learning and an efficient solving approach based on successive linear programming is carry out to describe the uncertainty of VRE output more realistically and ensure the timeliness of optimization scheme, respectively. The proposed model is applied to a real hydropower station in the Hongshui River Basin in China. In 16 representative scenarios, the proposed model can complete the optimization in an acceptable time, with a maximum of 444.57 s. Compared with the traditional interval optimization model, the proposed model effectively improves the flexibility supply capacity of hydropower in the day-ahead scheme. The maximum reduction value of flexibility shortage probability and expectation reach 98.77% and 442.66 MW, respectively. In particular, the flexibility of the model is most obvious under heavy load demand in flood season, and it is almost not at the cost of daily target load adjustment, which provides practical reference for decision makers.

Statistical Validation of a Physical Prime Random Number Generator Based on Quantum Noise

Random prime numbers are an essential resource for many asymmetric cryptographic protocols. However, despite the emerging popularity of quantum random number generators (QRNGs) as sources of secure randomness, physical prime number generators have not yet been explored. In this work, we experimentally implement and characterize a vacuum-based probabilistic prime number generation scheme with an error probability of 3.5×10−15. By removing the quantum source (QS), an additional scheme based on electronic noise is derived, and a comparative analysis for increasing prime lengths is made. We observed that the QS significantly outperforms the classical scheme for small prime generation, where increases up to 585.0% in the diversity of unique primes obtained are seen. Moreover, we propose a length-agnostic statistical test for prime number sequences and apply it to the output of the uniformized randomness source, which was successful in revealing underlying biases in the output prime distributions. The resultant sequences were subsequently submitted to the NIST statistical test suite, where the quantum and classical sources passed, respectively, 86.96% and 45.34% of the total test set applied.

A Novel Efficient Hybrid Encryption Algorithm Based on Twofish and Key Generation Using Optimization for Ensuring Data Security in Cloud

Cloud computing is a rapidly advancing paradigm that enables users to access various services and resources anytime, anywhere. With this advancement, security has become a major concern for business organisations and individuals, and hence, it is essential to ensure that the services are provided with high data security. Numerous researches have focused on devising effective techniques to enhance data security. However, with the increasing connectivity, security still remains to be a major challenge. This paper devises a novel data protection scheme in the cloud by using the Twofish encryption algorithm and a key generation scheme with the Bald Eagle Pelican Optimization (BEPO) Algorithm. The proposed Twofish+BEPO_KeyGen is implemented in various phases, like initialization, registration, key generation, data encryption, authentication, validation and data sharing, and data decryption. Here, the Twofish algorithm is used to encrypt the data that has to be outsourced to the cloud, and for encryption, the security key required is generated by the BEPO algorithm. The efficacy of the Twofish+BEPO_KeyGen approach is examined by considering metrics, like memory usage, validation time, normalized variance, and conditional privacy, and is detected to have achieved values of 76.3 MB, 37.278 s, 1.665, and 0.926, correspondingly.

Parametric generative schemes with geometric constraints for encoding and synthesizing airfoils

The modern aerodynamic optimization has a strong demand for parametric methods with high levels of intuitiveness, flexibility, and representative accuracy, which cannot be fully achieved through traditional airfoil parametric techniques. In this paper, two deep learning-based generative schemes are proposed to effectively capture the complexity of the design space while satisfying specific constraints. 1. Soft-constrained scheme: a Conditional Variational Autoencoder-based model that directly incorporates geometric constraints as part of the network. 2. Hard-constrained scheme: a Variational Autoencoder-based model for generating diverse airfoils, with projection onto given constraints using Free Form Deformation-based techniques. According to the statistical results, the reconstructed airfoils are both accurate and smooth, without any need for additional filters. The soft-constrained scheme generates airfoils that exhibit slight deviations from the expected geometric constraints, yet still converge to the reference airfoil in both geometry space and objective space with some degree of distribution bias. In contrast, the hard-constrained scheme produces airfoils with a wider range of geometric diversity while strictly adhering to the geometric constraints. The corresponding distribution in the objective space is also more diverse, with isotropic uniformity around the reference point and no significant bias. These proposed airfoil parametric methods can break through the boundaries of training data in the objective space, providing higher quality samples for random sampling and improving the efficiency of optimization design.

Design of an Intelligent Algorithm for Rural Antique Ceramic Bottom Pattern

The style of ancient ceramics has always been one of the most important factors affecting the sales of antique ceramic products. Although it has certain rules, due to the complexity of current product design and the limitations of designers in the design process, intelligent algorithms are needed to assist in design. In response to the diversity of intelligent design solutions for antique ceramic products, this paper proposes a research method for antique ceramic product design based on AGCGAN. Combining with the universal links in the process of intelligent product design, a generative adversarial network is used to learn the rules of excellent product design samples, and the generator generation scheme is obtained. Then, through the constructed design scheme filter, the generation scheme is filtered according to the requirements, and a design scheme generation system with certain reference value is constructed.

Instance attack: an explanation-based vulnerability analysis framework against DNNs for malware detection

Deep neural networks (DNNs) are increasingly being used in malware detection and their robustness has been widely discussed. Conventionally, the development of an adversarial example generation scheme for DNNs involves either detailed knowledge concerning the model (i.e., gradient-based methods) or a substantial quantity of data for training a surrogate model. However, under many real-world circumstances, neither of these resources is necessarily available. Our work introduces the concept of the instance-based attack, which is both interpretable and suitable for deployment in a black-box environment. In our approach, a specific binary instance and a malware classifier are utilized as input. By incorporating data augmentation strategies, sufficient data are generated to train a relatively simple and interpretable model. Our methodology involves providing explanations for the detection model, which entails displaying the weights assigned to different components of the specific binary. Through the analysis of these explanations, we discover that the data subsections have a significant impact on the identification of malware. In this study, a novel function preserving transformation algorithm designed specifically for data subsections is introduced. Our approach involves leveraging binary diversification techniques to neutralize the effects of the most heavily-weighted section, thus generating effective adversarial examples. Our algorithm can fool the DNNs in certain cases with a success rate of almost 100%. Instance attack exhibits superior performance compared to the state-of-the-art approach. Notably, our technique can be implemented in a black-box environment and the results can be verified utilizing domain knowledge. The model can help to improve the robustness of malware detectors.

Enhanced DC-link voltage control of PMSG-based wind turbine generators by machine-side converter during asymmetrical grid faults

Under asymmetrical grid fault conditions, oscillating active power can occur on the grid-side converter (GSC) of permanent magnet synchronous generator (PMSG)-based wind turbine generators. The oscillating active power flows into the DC-link, causing voltage oscillations that can lead to a shortened lifetime of DC-link capacitors and additional harmonics injected into the grid. While conventional methods can mitigate the oscillating active power through negative sequence current injection by the GSC, this hinders the grid support of the GSC regarding negative sequence reactive current during grid fault. To address this issue, a novel control scheme is proposed that uses the machine-side converter (MSC) to transfer the oscillating power to the motor. Firstly, the active power capacity of the MSC is analyzed and compared with that of GSC, and the results show that MSC has enough capacity to accommodate the oscillating active power. On this basis, a novel MSC control scheme is proposed to transfer the oscillating active power on the DC-link to the motor, which has a much larger inertia to buffer the power oscillation. Compared with conventional methods, the proposed control scheme relieves the GSC from mitigating the power oscillation to support the grid. Simulation studies using DIgSILENT PowerFactory are presented to verify the effectiveness of the proposed control scheme for PMSG-based wind turbine generators.

Tunable microwave frequency comb generation via periodic relaxation oscillation in directly modulated laser

In this paper, a novel and simple generation scheme based on a directly modulated laser (DML) is proposed for tunable microwave frequency combs (MFCs). When the input modulated RF power is intentionally controlled to be sufficiently high to drive the DML into its nonlinear operating region periodically, relaxation oscillations will be excited in the same period. The modulated light from the DML is subsequently detected by a photodetector, and a special kind of microwave signal source can be optically produced and shows up in the form of MFC in the frequency domain. In the proof-of-concept experiment, tunable MFCs are demonstrated with the bandwidth of more than 25 GHz, the comb spacing of 0.5 ∼ 3 GHz, and the flatness of less than ± 2 dB with the frequency from 3 GHz to 18 GHz. The bandwidth of the generated MFC is in accordance with the relaxation oscillation frequency of the DML. The comb spacing is strictly equal to the frequency of the modulated RF signal and thus flexibly adjustable. The carrier-to-noise ratio can be up to 48 dB, and the single-sideband phase noise is measured to be lower than − 90 dBc/Hz@1 kHz for MFCs with comb spacings of 0.7 ∼ 3 GHz.

GEO based Adaptive Dynamic Key Generation Scheme for Speck-R in IoT Applications

By integrating lightweight properties with lightweight cryptography in Intenet of Things (IoT) applications, information confidentiality and dependability can be guaranteed. In order to cut down on the number of rounds and speed up execution, a dynamic key-substitution layer was included to the original Speck's structure to create the ultra-lightweight encryption algorithm Speck-R.In this paper, we propose to design Golden Eagle Optimizer (GEO) based adaptive dynamic key generation scheme for Speck-R. In this algorithm, the block size and key size are adaptively chosen based on the IoT application and data size. Then the dynamic key (DK) is generated using GEO algorithm in which a fitness function is derived in terms of the metrics LPF, DPF and BIC. Then the key with optimum fitness is chosen as the dynamic key for further encryption /decryption process of Speck-R. By simulation results, we show that the proposed technique enhances the security.

Generation of jet-forming plasma bunch with gigagauss axial magnetic field from impact of linearly polarized laser on microtube targets

Generation of a thin plasma jet with embedded gigagauss axial magnetic fields from the frontal impact of a short linearly polarized laser pulse on an overdense microtube target is considered. It is a new scheme of axial magnetic field generation without initial laser angular momentum. Three-dimensional particle-in-cell simulations show that the space-charge field of the laser expelled tube-front electrons will pull out carbon ions to form at the tube entrance a long-living low-density plasma bunch with gigagauss magnetic fields. The front center of the plasma bunch then stretches forward to form a thin gigagauss-magnetized plasma jet, which survives for sub-picosecond after the core of the laser has passed through the tube.

A simple chaotic system using signum function

Starting from Lorenz System, a simpler system using signum function was obtained, which shows chaotic behavior. The conditions for stability were explained. Lyapunov and bifurcation analyses were carried out to exhibit the chaotic behavior. The system’s suitability for circuit realization was shown with an electronic circuit implementation which is based mainly on opamps, whose simulation results were in harmony with Matlab results. Finally, as an application, a random number generator (RNG) scheme was designed, in which binary data were obtained from x,y,z variables. In order to reveal that the RNG scheme is functional, NIST-Test was applied to the obtained binary data.

A High Consistency Wireless Key Generation Scheme for Vehicular Communication Based on Wiener Filter Extrapolation

A High Consistency Wireless Key Generation Scheme for Vehicular Communication Based on Wiener Filter Extrapolation

A Highly Consistent and High-speed Physical Layer Key Generation Scheme

A Highly Consistent and High-speed Physical Layer Key Generation Scheme

Strategy for Hydrogen Generation from a Photovoltaic System Using a Boost Converter

This research presents two schemes for hydrogen generation by alkaline water electrolysis using a photovoltaic system and a boost converter as a power source. The first scheme aims to extract the maximum power from the photovoltaic system using the P&O algorithm and use this energy to generate hydrogen. Thus, obtaining the maximum efficiency of the photovoltaic-electrolytic cell system. The second scheme aims to generate hydrogen on demand, eliminating the need for storage and reducing the associated costs and risks. Nevertheless, this approach does not fully maximize the power available in the PV system, resulting in lower overall efficiency. Finally, it concludes with the proposal of an approach that combines on-demand hydrogen generation and maximum energy extraction from solar panels. Any unused energy from the electrolytic reactor is stored in a battery bank through a bidirectional converter. However, the simulation and implementation of this third scheme are proposed as future work.

Dual-comb generation with counter-propagating self-injection-locked solitons.

Microresonator-based optical frequency combs have been greatly developed in the last decade and have shown great potential for many applications. A dual-comb scheme is usually required for lidar ranging, spectroscopy, spectrometer and microwave photonic channelizer. However, dual-comb generation with microresonators would require doubled hardware resources and more complex feedback control. Here we propose a novel scheme for dual-comb generation with a single laser diode self-injection locked to a single microresonator. The output of the laser diode is split and pumps the microresonator in clockwise and counter-clockwise directions. The scheme is investigated intensely through numerical simulations based on a set of coupled Lugiato-Lefever equations. Turnkey counter-propagating single soliton generation and repetition rate tuning are demonstrated.

High-throughput interface prediction and generation scheme: The case of β -Ga2O3/AlN interfaces

Solid–solid interfaces play a critical role in determining the performance of materials and devices. However, both experimental and theoretical determinations of interface structures are very challenging. In this Letter, we develop a high-throughput interface prediction and generation (IPG) scheme to predict the atomic structure of interfaces. To demonstrate the performance of the IPG, we apply it to the β-Ga2O3/AlN interface, which has attracted a great deal of attention for its promising application in power devices. Six β-Ga2O3/AlN interfaces are screened from thousands of candidates with the minimum mismatch and area tolerance. All these interfaces are modeled to satisfy the electron counting rules and then optimized by the minimum hopping method. The band alignments at the β-Ga2O3/AlN interfaces are in accordance with the available theoretical and experimental results. The results provide the basis for designing optical and electronic devices based on β-Ga2O3/AlN interfaces.