Generation Scheme Research Articles

Nonclassicalities of hybrid coherent states

We address the non-classicality of hybrid coherent (HC) states, i.e. states expressed as a superposition of coherent state and single-photon-added coherent (SPAC) state. In particular, we evaluate their photon statistics, squeezing, and negativity of the Wigner function. Our results indicated that the HC states could exhibit larger non-classicalities than the SPAC state. We also suggest a generation scheme for HC states, which involves Kerr nonlinearity and postselection.

A VAE-Bayesian deep learning scheme for solar power generation forecasting based on dimensionality reduction

The advancements in distributed generation (DG) technologies such as solar panels have led to a widespread integration of renewable power generation in modern power systems. However, the intermittent nature of renewable energy poses new challenges to the network operational planning with underlying uncertainties. This paper proposes a novel probabilistic scheme for renewable solar power generation forecasting by addressing data and model parameter uncertainties using Bayesian bidirectional long short-term memory (BiLSTM) neural networks, while handling the high dimensionality in weight parameters using variational auto-encoders (VAE). The forecasting performance of the proposed method is evaluated using various deterministic and probabilistic evaluation metrics such as root-mean square error (RMSE), Pinball loss, etc. Furthermore, reconstruction error and computational time are also monitored to evaluate the dimensionality reduction using the VAE component. When compared with benchmark methods, the proposed method leads to significant improvements in weight reduction, i.e., from 76,4224 to 2,022 number of weight parameters, quantifying to 97.35% improvement in weight parameters reduction and 37.93% improvement in computational time for 6 months of solar power generation data.

Adaptive Scheme of Denoising Autoencoder for Estimating Indoor Localization Based on RSSI Analytics in BLE Environment

In indoor environments, estimating localization using a received signal strength indicator (RSSI) is difficult because of the noise from signals reflected and refracted by walls and obstacles. In this study, we used a denoising autoencoder (DAE) to remove noise in the RSSI of Bluetooth Low Energy (BLE) signals to improve localization performance. In addition, it is known that the signal of an RSSI can be exponentially aggravated when the noise is increased proportionally to the square of the distance increment. Based on the problem, to effectively remove the noise by adapting this characteristic, we proposed adaptive noise generation schemes to train the DAE model to reflect the characteristics in which the signal-to-noise ratio (SNR) considerably increases as the distance between the terminal and beacon increases. We compared the model’s performance with that of Gaussian noise and other localization algorithms. The results showed an accuracy of 72.6%, a 10.2% improvement over the model with Gaussian noise. Furthermore, our model outperformed the Kalman filter in terms of denoising.

TouchKey

Secure device pairing is important to wearables. Existing solutions either degrade usability due to the need of specific actions like shaking, or they lack universality due to the need of dedicated hardware like electrocardiogram sensors. This paper proposes TouchKey, a symmetric key generation scheme that exploits the skin electric potential (SEP) induced by powerline electromagnetic radiation. The SEP is ubiquitously accessible indoors with analog-to-digital converters widely available on Internet of Things devices. Our measurements show that the SEP has high randomness and the SEPs measured at two close locations on the same human body are similar. Extensive experiments show that TouchKey achieves a high key generation rate of 345 bit/s and an average success rate of 99.29%. Under a range of adversary models including active and passive attacks, TouchKey shows a low false acceptance rate of 0.86%, which outperforms existing solutions. Besides, the overall execution time and energy usage are 0.44 s and 2.716 mJ, which make it suitable for resource-constrained devices.

Automatic Generation of 3D Indoor Navigation Networks from Building Information Modeling Data Using Image Thinning

Navigation networks are a common form of indoor map that provide the basis for a wide range of indoor location-based services, intelligent tasks for indoor robots, and three-dimensional (3D) geographic information systems. The majority of current indoor navigation networks are manually modeled, resulting in a laborious and fallible process. Building Information Modeling (BIM) captures design information, allowing for the automated generation of indoor maps. Most existing BIM-based navigation systems for floor-level wayfinding rely on well-defined spatial semantics, and do not adapt well to buildings with irregular 3D shapes, which can make cross-floor path generation difficult. This research introduces an innovative approach to generating 3D indoor navigation networks automatically from BIM data using image thinning, which is referred to as GINIT. Firstly, GINIT extracts grid-based maps for floors from BIM data using only two types of semantics, i.e., slabs and doors. Secondly, GINIT captures cross-floor paths from building components by projecting 3D forms onto a 2D image, thinning the 2D image to capture the 2D projection path, and crossing over the 2D routes with 3D routes to restore the 3D path. Finally, to demonstrate the effectiveness of GINIT, experiments were conducted on three real-world multi-floor buildings, evaluating its performance across eight types of cross-layer architectural component. GINIT overcomes the dependency of space definitions in current BIM-based navigation network generation schemes by introducing image thinning. Due to the adaptability of navigation image thinning to any binary image, GINIT is capable of generating navigation networks from building components with diverse 3D shapes. Moreover, the current studies on indoor navigation network extraction mainly use geometry theory, while this study is the first to generate 3D indoor navigation networks automatically using image thinning theory. The results of this study will offer a unique perspective and foster the exploration of imaging theory applications of BIM.

Metabolome shifts triggered by chlorine sanitisation induce Escherichia coli on fresh produce into the viable but nonculturable state

Facing the increasing occurrence of “big six” Escherichia coli outbreaks linked to fresh produce, chlorine-based sanitisers are widely used for fresh produce decontamination in recent years. However, latest finding that chlorine may induce E. coli cells into a viable not nonculturable (VBNC) state is bringing a new challenge to the fresh produce industry. VBNC cells are undetectable by the plate count test, and yet they retain pathogenicity and are more antibiotic-resistant than culturable cells. As a result, their eradication is critical to ensure the safety of fresh produce. Understanding VBNC cells at the metabolic level may provide a breakthrough for their eradication. Therefore, this study was carried out to collect the VBNC pathogenic E. coli (O26:H11, O121:H19, and O157:H7) cells from chlorine-treated pea sprouts and characterise them using NMR-based metabolomics. From the globally increased metabolite contents detected in the VBNC E. coli cells as compared to the culturable cells, mechanisms underlying E. coli’s VBNC induction were elucidated. These include rendering the energy generation scheme to become more compatible with the lowered energy needs, disaggregating protein aggregates to release amino acids for osmoprotection and later resuscitation, as well as increasing cAMP content to downregulate RpoS. These identified metabolic characteristics can inspire future development of targeted measures for VBNC E. coli cell inhibition. Our methods can also be applied to other pathogens to help lower the risk of overall foodborne diseases.

Photonic filter-free adjustable frequency sextupling scheme for V-band vector mm-wave signal generation based on a DP-MZM with precoding

In this work, a new filter-free adjustable scheme for the generation of frequency sextupling vector millimeter-wave (mm-wave) signal without optical filter which can widen the adjustable frequency range. In the process of vector mm-wave frequency multiplication, the frequency is linearly multiplication and the phase would be simultaneous linearly multiplication, which makes the information carried by the phase distorted. Phase precoding is employed to eliminate constellation overlapping caused by phase multiplication. Simulations are done to generate V-band 60 GHz sextupling phase shift keying (QPSK) modulated vector mm-wave signals using a 10 GHz radio frequency source and the BER performance is analyzed in detail. The results show that BER of the generated 5/10-Gbit/s vector millimeter-wave signal is below 3.8×10−3, when the input optical power for into photo-detector is higher than −20.67 dBm.

Experimental entanglement generation using multiport beam splitters

Multipartite entanglement plays a central role in optical quantum technologies. One way to entangle two photons is to prepare them in orthogonal internal states, for example, in two polarisations, and then send them through a balanced beam splitter. Post-selecting on the cases where there is one photon in each output port results in a maximally entangled state. This idea can be extended to schemes for the post-selected generation of larger entangled states. Typically, switching between different types of entangled states requires different arrangements of beam splitters and so a new experimental setup. Here, we demonstrate a simple and versatile scheme to generate different types of genuine tripartite entangled states with only one experimental setup. We send three photons through a three-port splitter and vary their internal states before post-selecting on certain output distributions. This results in the generation of tripartite W, G and GHZ states. We obtain fidelities of up to with regard to the respective ideal states, confirming a successful generation of genuine tripartite entanglement.

Stochastic unit commitment to determine frequency response ramp rate including wind turbines with synthetic inertia and virtual synchronous generator

In this paper, the dynamic frequency constraints are derived for power systems incorporating full-converter wind turbines. These wind turbines could be equipped with a virtual inertia by either synthetic inertia control or virtual synchronous generator concept. In the former scheme, deviations in the wind turbines rotor speed remain in synchronism with the system frequency. However, the wind turbines start to restore their rotor speed before the system frequency recovery initiation, in virtual synchronous generator scheme. The restriction on the virtual inertia are determined considering a limit variable on wind turbines rotor speed drop. The frequency response requirements are derived in terms of governor’s base power, ramp rate and reserve power. A linear frequency nadir constraint is also proposed for the synthetic inertia strategy. Then, the constraints are integrated into the stochastic unit commitment to be consistently and economically met over 24-hour period. As such, the contingency size management are coordinated with the virtual inertia provision. The unit commitment results of IEEE 118-bus system including multiple wind farms reveal that the virtual synchronous generator concept can be an optimal solution for future low-inertia grid’s challenges. Finally, accuracy of the unit commitment results is validated through time-domain simulations of the detailed system model.

Performance study of thermal-nuclear co-generation system based on exergy and thermal economy analysis

The thermal-nuclear co-generation system realizes the effective complementarity of power generating efficiency and pollutant emission reduction while simultaneously reducing the consumption of fossil fuels and pollutant emissions. First, four potential combined thermal-nuclear power generation schemes are proposed in this research. The performance of each scheme is examined, and the system is compared and examined based on two indicators: nuclear energy share and nuclear energy generation efficiency. For the chosen ideal combination scheme, exergy analysis and a thermo-economic structural theory analysis are carried out. The examination of the assembly for the Direct heating scheme, which the study's findings indicated was the optimum option, revealed that the combined unit could achieve 38.14% assembly efficiency, which is 2.29% greater than that of the nuclear unit alone. The feasibility of combining thermal and nuclear power is demonstrated by the study of the influence of the key parameters on the cost per unit of assembly and the calculation that the co-generation system's economic efficiency is higher than that of nuclear or thermal power units built separately in terms of fuel economy.

Generation of an ultrashort pulse train through ultrafast parity-time symmetry switching.

We propose a scheme for the direct generation of an ultrashort pulse train as well as the further compression of pulsed lasers based on the nonlinearity inherent to parity-time (PT) symmetric optical systems. Implementation of optical parametric amplification in a directional coupler of χ(2) waveguides enables ultrafast gain switching through pump-controlled breaking of PT symmetry. We theoretically demonstrate that pumping such a PT symmetric optical system with a periodically amplitude-modulated laser enables periodic gain switching, which can directly convert a continuous-wave signal laser into a train of ultrashort pulses. We further demonstrate that by engineering the PT symmetry threshold, an apodized gain switching that enables the production of ultrashort pulses without side lobes. This work suggests a new approach for exploring the non-linearity inherent to various PT symmetric optical structures to extend optical manipulation capabilities.

Importance guided stream surface generation and feature exploration

Exploring flow features and patterns hidden behind the data has received extensive academic attention in flow visualization. In this paper, we introduce an importance-guided surface generation and exploration scheme to explore the features and their connections. The features are expressed as an importance field, which can either be derived from a scalar field or be specified as a flow pattern. Guided by the importance field, we sample a pool of seeding curves along the binormal direction and construct stream surfaces to fit the regions of high- importance values. Our scheme evaluates candidate seeding curves by collecting importance scores from the curve and corresponding streamlines. The candidate seeding curves are refined using the high-score segments to identify the optimal surfaces. Comparative visualization among different kinds of flow features across time steps can be easily derived for flow structure analysis. In order to reduce the visual complexity, we leverage SurfRiver to achieve clearer observation by flattening and aligning the surface. Finally, we apply our surface generation scheme guided by flow patterns and scalar fields to evaluate the effectiveness of the proposed tool.

Genetic-simulated annealing optimization for surface wave inversion of shear-wave velocity profiles of geotechnical sites

A new hybrid genetic-simulated-annealing (GSA) optimization algorithm is introduced to solve the multivariable minimization problem for surface wave inversion. The algorithm is effective for both global and local searches due to its combination of the reproduction and selective generation schemes from genetic algorithms (GA) with the nonlinear scaling fitness function and perturbation scheme from simulated annealing (SA). The hybrid GSA algorithm can reduce the risk of a solution becoming trapped in a local minimum and improve global searching efficiency. A mathematical test function as well as surface wave examples are used to examine the advantages and performance of the GSA algorithm. Comparisons of GA, SA, and GSA inversion results demonstrates that GSA can yield the smallest uncertainty and greatest efficiency, and improve the statistical confidence of using surface wave testing for shear-wave velocity profiling.

On multiplexing in physical random number generation, and conserved total entropy content

In the current article, we use a random supercontinuum based on a random Raman distributed feedback laser to investigate the generation of random numbers by spectrally demultiplexing the broad supercontinuum spectrum in parallel channels. By tuning the spectral separation between two independent channels, we test the most typically used statistical tests’ abilities to identify the required minimum spectral separation between channels, especially after the use of post-processing steps. Out of all the tests that were investigated, the cross-correlation across channels using the raw data appears to be the most robust. We also demonstrate that the use of post-processing steps, either least significant bits extraction or exclusive-OR operations, hinders the ability of these tests to detect the existing correlations. As such, performing these tests on post-processed data, often reported in literature, is insufficient to properly establish the independence of two parallel channels. We therefore present a methodology, which may be used to confirm the true randomness of parallel random number generation schemes. Finally, we demonstrate that, while tuning a single channel’s bandwidth can modify its potential randomness output, it also affects the number of available channels, such that the total random number generation bitrate is conserved.

Digital DSM-Enabled DAC-Free Gigabit FBMC-VLC System With FWHT-Based Interleaved Block Precoding

Digital-to-analog converters (DACs) are widely used in bandwidth-limited visible-light communication (VLC) systems for high spectral-efficiency multi-carrier signal generation. To reduce the implementation complexity of the transmitter, a digital 1-bit low-pass delta-sigma modulation (DSM)-enabled DAC-free filter-bank multi-carrier (FBMC) signal generation scheme is proposed and experimentally investigated in a red light-emitting diode-based VLC system. In this scheme, one field programmable gate array gigabit transceiver in combination with a low-pass filter is employed to realize the 609 MHz baseband FBMC signal generation. Besides, a low-complexity fast Walsh-Hadamard transform (FWHT)-based interleaved block precoding (IBP) technique is also proposed to reduce peak-to-average power ratio and achieve a uniform SNR profile, and then improve the bit error rate (BER) performance. The experimental results exhibit that the net data rate of 2.38-Gbit/s 16-ary quadrature amplitude modulation-encoded FBMC signal, generated by the proposed scheme, can transmit 2.3-m free space with the BER less than 3.8×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-3</sup> . With the help of the FWHT-IBP technique, about 2-dB improvement in receiver sensitivity is achieved. In addition, the BER performance can provide a slight improvement in the BER performance with the proposed scheme, compared to the conventional DAC-based FBMC-VLC.

Robust scheme for high-fidelity generation of mesoscopic entangled cat state

We here study a hybrid quantum system of one solid-state electronic spin coupled to a mechanical resonator (MR) using the crystal strain, with the purpose of engineering the mesoscopic spin-phonon entangled cat state. The feature of this work to be mentioned is that it proposes a different and feasible method to achieve the entanglement, especially with the higher fidelity. This scheme mainly contains two steps, namely the coherent population trapping (CPT) initialization and large-detuning evolution (LDE). By taking all of the adverse decoherence factors into our considerations, the numerical simulations indicate that its fidelity can reach more than 0.995 (or 0.999) when the spin-mechanical coupling strength is 10 (or 100) times larger than spin dephasing and mechanical resonator dissipation rate, respectively. Because of the cooperation of CPT and LDE, this proposal also exhibits a considerable robustness for engineering the entangled cat state, and which may be considered as a general attempt that may be suitable for different systems.

Highly Reliable and Secure PUF Using Resistive Memory Integrated Into a 28 nm CMOS Process

Owing to the increased demand for secure communication channels and authentication steps, physical unclonable functions (PUFs) are increasingly important for hardware security. In this article, we report a novel PUF architecture and generation scheme that utilizes the inherent program-time variation of resistive random access memory (ReRAM) cells as an entropy source. ReRAM cells are integrated into a standard 28 nm CMOS manufacturing process, and also show robust non-volatile memory operation. We generated a PUF data set larger than 10 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^{\text{8}}$</tex-math> </inline-formula> bits and verified its randomness using a standard NIST test suite. Further verifications such as inter-and intra-hamming distance, 150 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^{\circ}$</tex-math> </inline-formula> C data retention of PUF bits, and spatial co-relation tests confirm the high reliability of the generated PUF keys.

Metabolic Anomaly Appearance Aware U-Net for Automatic Lymphoma Segmentation in Whole-Body PET/CT Scans.

Positron emission tomography-computed tomography (PET/CT) is an essential imaging instrument for lymphoma diagnosis and prognosis. PET/CT image based automatic lymphoma segmentation is increasingly used in the clinical community. U-Net-like deep learning methods have been widely used for PET/CT in this task. However, their performance is limited by the lack of sufficient annotated data, due to the existence of tumor heterogeneity. To address this issue, we propose an unsupervised image generation scheme to improve the performance of another independent supervised U-Net for lymphoma segmentation by capturing metabolic anomaly appearance (MAA). Firstly, we propose an anatomical-metabolic consistency generative adversarial network (AMC-GAN) as an auxiliary branch of U-Net. Specifically, AMC-GAN learns normal anatomical and metabolic information representations using co-aligned whole-body PET/CT scans. In the generator of AMC-GAN, we propose a complementary attention block to enhance the feature representation of low-intensity areas. Then, the trained AMC-GAN is used to reconstruct the corresponding pseudo-normal PET scans to capture MAAs. Finally, combined with the original PET/CT images, MAAs are used as the prior information for improving the performance of lymphoma segmentation. Experiments are conducted on a clinical dataset containing 191 normal subjects and 53 patients with lymphomas. The results demonstrate that the anatomical-metabolic consistency representations obtained from unlabeled paired PET/CT scans can be helpful for more accurate lymphoma segmentation, which suggest the potential of our approach to support physician diagnosis in practical clinical applications.

Time and toll trade-off with heterogeneous users: A continuous time surplus maximization bi-objective user equilibrium model

This paper presents a continuous time surplus maximization bi-objective user equilibrium (C-TSmaxBUE) model, in which the users’ variability toward the time and toll trade-off in a tolled road network is explicitly considered. Different users are assigned with different ratios of the time saved per unit of money (RTSMs), the values of which represent the different expectations to trade more money for less travel time. Infinite indifference curves are then generated by considering continuously distributed RTSMs in the population. Accordingly, we extend the definition of the path-based time surplus for the C-TSmaxBUE model, which can be formulated as a Beckmann-type mathematical program operating in the space of RTSM boundaries. To solve the problem, we develop a path-based single-boundary adjustment (SBA) algorithm that fits the non-additive structure of the model. The algorithm involves a Newton-type flow equilibration procedure that simultaneously adjusts RTSM boundaries and path flows, as well as a column generation scheme that firstly enumerates all efficient paths and then identifies the paths that have the potential to reduce generalized travel times. Numerical results on a small network show the feature of the equilibrium flow pattern, whereas instances of practical size confirm that SBA is an efficient tool to obtain high-quality equilibrium solutions. Compared to the original TSmaxBUE problem, we found that a larger set of efficient paths will be used when the users’ heterogeneity is considered.

Wireless Key Generation Scheme Based on Random Permutation and Perturbation in Quasistatic Environments

The wireless key generation using wireless channel reciprocity has attracted considerable attention in the past two decades. However, there are many challenges for the key generation in quasistatic wireless environments. The key generation rate (KGR) in a quasistatic environment is low, and the randomness of the key is insufficient, which is difficult to meet the secure communication requirements. To tackle these issues, a random permutation and perturbation-based wireless key generation (RPP-WKG) scheme is proposed to improve the KGR and randomness in quasistatic environments. Unlike existing key generation schemes, the RPP-WKG scheme allows the two legitimate users to generate the same secret key based on their random permuted channel measurements. Besides, the perturbed key sequence will be obtained by combining the initial key generated after quantization and the permutation order sequence through the XOR operation. Simulation results show that the proposed RPP-WKG scheme can generate secret keys with a high generation rate, sufficient randomness, a low mismatch rate, and a low correlation coefficient in quasistatic environments.