Output Entropy Research Articles

Classical capacity of quantum non-Gaussian attenuator and amplifier channels

We consider a quantum bosonic channel that couples the input mode via a beam splitter or two-mode squeezer to an environmental mode that is prepared in an arbitrary state. We investigate the classical capacity of this channel, which we call a non-Gaussian attenuator or amplifier channel. If the environment state is thermal, we of course recover a Gaussian phase-covariant channel whose classical capacity is well known. Otherwise, we derive both a lower and an upper bound to the classical capacity of the channel, drawing inspiration from the classical treatment of the capacity of non-Gaussian additive-noise channels. We show that the lower bound to the capacity is always achievable and give examples where the non-Gaussianity of the channel can be exploited so that the communication rate beats the capacity of the Gaussian-equivalent channel (i.e. the channel where the environment state is replaced by a Gaussian state with the same covariance matrix). Finally, our upper bound leads us to formulate and investigate conjectures on the input state that minimizes the output entropy of non-Gaussian attenuator or amplifier channels. Solving these conjectures would be a main step toward accessing the capacity of a large class of non-Gaussian bosonic channels.

An effective parametric sensitivity analysis to improve electrical and thermal energy/exergy efficiency of PVT system using nanofluid

A 3D steady-state model of a photovoltaic/Thermal system with nanofluid is studied to investigate the electrical and thermal performance of PVT by utilizing a combination of statistical modeling and numerical simulation. The 3D CAD (Computer Aided Design) modeling of the photovoltaic thermal (PV/T) is performed using Ansys Space Claim. The current study is based on using a 2-way coupling FSI approach for modeling nanofluid-based PVT systems. Five output responses electrical power, thermal power, electrical exergy, thermal exergy, and entropy generation are chosen against four input parameters, solar radiation (G_sun), ambient temperature (Tamb), mass flow rate (ṁ), and nanoparticles concentration % (φ) to analyze system performance. A statistical prediction model is developed using response surface methodology (RSM) that starts from the design of experiment (DOE). The predicted outcomes obtained from the statistical model and the numerical simulation of the heat transfer model exhibit the existence of good fitness. Furthermore, optimization is performed using RSM for various optimization objectives to suggest the ideal design parameter values for optimal system performance. Sensitivity analysis illustrates that a rise in ambient temperature causes a drop in electrical power production and leads to an increase in thermal power. However, the increase in solar radiation boosts electrical power and causes a drop in thermal exergy, entropy generation increases with an increase in solar irradiation. Similarly, an increase in mass flow rate leads to an increase in thermal power. Multi-objective optimization with the highest composite desirability values depicts the influence of input design parameters and has a favorable effect on the objective of concurrently maximizing electrical and thermal power.

Influence of impeller energy dissipation characteristics of multi-blade axial flow pump impeller by the cascade density

Abstract This paper investigates the influence of cascade density on energy dissipation in the impeller of a multi-blade axial-flow pump. Three-dimensional transient numerical simulations were conducted using the SST k-ω turbulence model for impeller schemes with different cascade density, corresponding to different blade chord length and pitch. Entropy production theory was applied to locate the regions with high energy loss in the impeller. The relationships between local entropy generation, energy loss, and unsteady flow were analyzed for different cascade density. The results indicate that impeller entropy output of the multi-blade axial-flow pump is highly consistent with the head loss during testing. Turbulence dissipation accounts for more than 50% of the total energy loss, followed by wall friction, while direct dissipation is the smallest contributor. The relative loss of the impeller can be minimized by decreasing the number of blades or increasing the blade chord length. By comparing energy dissipation in blade rotation with different chord length and pitch, it is found that turbulent dissipation ability can be controlled more effectively by changing chord length, while the ability of direct dissipation can be controlled more effectively by changing the pitch.

DynamicKD: An effective knowledge distillation via dynamic entropy correction-based distillation for gap optimizing

The knowledge distillation uses a high-performance teacher network to guide the student network. However, the performance gap between the teacher and student networks can affect the student’s training. This paper proposes a novel knowledge distillation algorithm based on dynamic entropy correction, which adjusts the student instead of the teacher to reduce the gap. Firstly, the effect of changing the output entropy (short for output information entropy) on the distillation loss in the student is analyzed in theory. This paper shows that correcting the output entropy can reduce the gap. Then, a knowledge distillation algorithm based on dynamic entropy correction is created, which can correct the output entropy in real-time with an entropy controller updated dynamically by the distillation loss. The proposed algorithm is validated on the CIFAR100, ImageNet, and PASCAL VOC 2007. The comparison with various state-of-the-art distillation algorithms shows impressive results, especially in the experiment on the CIFAR100 regarding teacher–student pair resnet32x4–resnet8x4. The proposed algorithm raises 2.64 points over the traditional distillation algorithm and 0.87 points over the state-of-the-art algorithm CRD in classification accuracy, demonstrating its effectiveness and efficiency.

Mbit/s-range alkali vapour spin noise quantum random number generators

Spin noise based quantum random number generators first appeared in 2008 and have since then garnered little further interest, in part because their bit rate is limited by the transverse relaxation time T2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$T_{2}$\\end{document} which for coated alkali vapour cells is typically in the kbit/s range. Here we present two advances. The first is an improved bit generation protocol that allows generating bits at rates exceeding 1/T2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$1/T_{2}$\\end{document} with only a minor increase of serial correlations. The second is a significant reduction of the time T2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$T_{2}$\\end{document} itself by removing the coating, increasing the vapour temperature and introducing a magnetic-field gradient. In this way we managed to increase the bit generation rate to 1.04 Mbit/s. We analyse the quality of the generated random bits using entropy estimation and we discuss the extraction methods to obtain high-entropy bitstreams. We accurately predict the entropy output of the device backed with a stochastic model and numerical simulations.

Stochastic Adder Circuits with Improved Entropy Output.

Random pulse computing (RPC), the third paradigm along with digital and quantum computing, draws inspiration from biology, particularly the functioning of neurons. Here, we study information processing in random pulse computing circuits intended for the summation of numbers. Based on the information-theoretic merits of entropy budget and relative Kolmogorov-Sinai entropy, we investigate the prior art and propose new circuits: three deterministic adders with significantly improved output entropy and one exact nondeterministic adder that requires much less additional entropy than the previous art. All circuits are realized and tested experimentally, using quantum entropy sources and reconfigurable logic devices. Not only the proposed circuits yield a precise mathematical result and have output entropy near maximum, which satisfies the need for building a programmable random pulse computer, but also they provide affordable hardware options for generating additional entropy.

Thermal and entropy behavior of sustainable solar energy in water solar collectors due to non-Newtonian power-law hybrid nanofluids

Introduction: Nanofluids, hybrid nanofluid possesses thermophysical features that boost the fluid performance. This research work is motivated by the utilization of water solar collectors that incorporate non-Newtonian, power-law hybrid nanofluid in a three-dimensional model, considering the two-phase model.Method: The primary objective of this study is to transform the governing equations of the flow model into a set of ordinary differential equations by employing the three-parameters group technique. Based on the innovative discoveries, two models incorporating new associated functions have been successfully developed for two distinct scenarios characterized by the power-law index, n. The impact of physical factors on the velocity profile, temperature distribution, concentration field, and entropy output of the system is clearly illustrated through a variety of graphs.Results: The results indicated that the inclination angle of 20° had the best thermal characteristics compared to other inclinations. The entropy generation reached its maximum value at temperature difference of 13 K due to irreversibility of the system, which indicates that the system is more efficient.Discussion: Furthermore, the increasing percentage in Nusselt number is predicted to be 28.18% when the Prandtl number is taken a range. The Sherwood number enhanced up to 18.61% with a range of Brownian motion. A quantitative comparison is conducted between the present results and the literature in order to validate the superior efficiency of the used method.

Response of the GE Entropy™ monitor to neuromuscular block in awake volunteers

BackgroundThe GE Entropy™ monitor analyses the frontal electroencephalogram (EEG) and generates two indices intended to represent the degree of anaesthetic drug effect on the brain. It is frequently used in the context of neuromuscular block. We have shown that a similar device, the Bispectral Index monitor (BIS), does not generate correct values in awake volunteers when neuromuscular blocking drugs are administered. MethodsWe replayed the EEGs recorded during awake paralysis from the original study to an Entropy monitor via a calibrated electronic playback system. Each EEG was replayed 30 times to evaluate the consistency of the Entropy output. ResultsBoth State Entropy and Response Entropy decreased during periods of neuromuscular block to values consistent with anaesthesia, despite there being no change in conscious state (State Entropy <60 in eight of nine rocuronium trials and nine of 10 suxamethonium trials). Entropy values did not return to pre-test levels until after the return of movement. Entropy did not generate exactly the same results when the same EEG was replayed multiple times, which is primarily because of a cyclical state within the Entropy system itself. ConclusionsThe GE Entropy™ monitor requires muscle activity to generate correct values in an awake subject. It could therefore be unreliable at detecting awareness in patients who have been given neuromuscular blocking drugs. In addition, Entropy does not generate the same result each time it is presented with the same EEG.

An Evaluation On The Entropy Supplying Capability Of Smartphone Sensors

Abstract Random numbers are very important for the security of computer system. However, generating qualified random numbers is difficult because we cannot always successfully introduce dedicated random number hardware into computer system. Although most operating systems provide random number generation capabilities, the effective entropy supply is still dependent on the hardware platform including memory and clocks etc. However, obtaining hardware events such as clocks requires system privileges, which is not conducive for entropy estimation at the application layer. In contrast, data related to the sensor hardware can be extracted directly at the application layer. These sensor data contain some randomness and may be used as a noise source. In this way, applications can use these sensors to implement their own proprietary random number generators. Before taking these sensors as the noise source, it is necessary to fully evaluate their entropy supply capability. In this paper, 300 Android smartphones and 30 iOS smartphones are selected as samples and their sensor entropy supply capabilities are comprehensively evaluated. Based on the entropy evaluation results, we give some suggestions on how to generate random numbers using these sensor data. We first design a framework for evaluating the entropy supply capability for smartphone sensors, based on the min-entropy estimation method proposed in NIST SP 800-90B. According to this framework, we simulate stationary and mobile working states for each smartphone, and collect sufficient sensor data as the min-entropy estimation dataset. The min-entropy estimation results show that in the stationary working state, each ACCELEROMETER sensor data collection can obtain at least 1.5 bits of entropy in Android, while each GYROSCOPE sensor data collection can obtain at least 20 bits of entropy in iOS. In the mobile working state, each ACCELEROMETER sensor data collection can obtain at least 1.9 bits of entropy, while each GYROSCOPE sensor data acquisition in iOS system can obtain at least 27 bits of entropy. This means that we can still get a stable entropy output from the sensor even when the smartphone is in stationary working state. Statistical analysis of the data using cross correlation methods suggests it is hard for an attacker to guess or predict the random numbers generated by a smartphone through another smartphone put in the similar external environment.

Enhancing Quality and Security of the PLL-TRNG

Field Programmable Gate Arrays (FPGAs) are used more and more frequently to implement cryptographic systems, which need random number generators (RNGs) to be embedded in the same device. The main challenge related to the implementation of a generator running inside FPGAs is that the physical source of randomness, such as jittered clock generator, is implemented in the configurable logic area, i.e. in the close vicinity of noisy running algorithms, which can have significant impact on generated numbers or even serve to attack the generator. A possible approach to prevent such influence is the use of Phase-Lock Loops (PLLs), which are separated from the re-configurable logic area inside the FPGA chip. In this paper, we propose a new architecture of the PLL-based TRNG including a method to avoid correlation in the output through control of timing in the sampling process, as well as new embedded tests based on the enhanced stochastic model. We also propose a workflow to help find the best parameters, such as output bitrate and entropy rate. We show that bitrates of around 400 kb/s or more can be achieved, while guaranteeing min-entropy rates per bit higher than 0.98 as required by the latest security standards.

Influence of power-law index and hybrid-nanoparticles concentrations on the behavior of non-Newtonian hybrid nanofluid inside water solar collector

Nowadays, there is great attention given to solar collectors (SCs) for their important applications based on the advantages of nanotechnology and solar radiation. Hybrid nanofluid (HNF) is our first option due to its thermophysical properties that help in improving the overall performance, unlike other nanofluids. This paper gives a detailed novel analysis of SCs with the existence of Newtonian, power-law HNF in unsteady conditions and a three-dimensional model under the consideration of Brownian motion and thermophoresis parameter. In this research, the group transformation method (GTM) and similarity transformation steady state fluid dynamics are used to transform the mathematical model into a simpler system. This coupled system of ordinary differential equations with the related functions, dimensionless entropy generation and Bejan number is achieved at two cases of power-law index. The impact of involved parameters on velocity profile, temperature distribution, concentration field, entropy output of the system and Bejan number is depicted prominently by various graphs. The fluid velocity shows improvement with higher values of power-law index and shape factor, while it diminishes with magnetic parameter and Prandtl number. Enhancing the values of magnetic field and shape factor, results in increase of temperature characteristic which decreases with Prandtl number and power-law index. Increment in the concentration ratio parameter leads to maximize the entropy generation, whereas entropy generation diminishes with higher values of temperature ratio and magnetic parameter. The obtained results and the previously published work are compared qualitatively and quantitatively to each other to validate that the applied method is more efficient. It is predicted that the Nusselt number improves by 28.18% when the Prandtl number is taken range [Formula: see text]. The percentage of increasing in Sherwood number is noted to be 18.61% for range [Formula: see text] of Brownian motion parameter.

Detection of motor imagery based on short-term entropy of time–frequency representations

BackgroundMotor imagery is a cognitive process of imagining a performance of a motor task without employing the actual movement of muscles. It is often used in rehabilitation and utilized in assistive technologies to control a brain–computer interface (BCI). This paper provides a comparison of different time–frequency representations (TFR) and their Rényi and Shannon entropies for sensorimotor rhythm (SMR) based motor imagery control signals in electroencephalographic (EEG) data. The motor imagery task was guided by visual guidance, visual and vibrotactile (somatosensory) guidance or visual cue only.ResultsWhen using TFR-based entropy features as an input for classification of different interaction intentions, higher accuracies were achieved (up to 99.87%) in comparison to regular time-series amplitude features (for which accuracy was up to 85.91%), which is an increase when compared to existing methods. In particular, the highest accuracy was achieved for the classification of the motor imagery versus the baseline (rest state) when using Shannon entropy with Reassigned Pseudo Wigner–Ville time–frequency representation.ConclusionsOur findings suggest that the quantity of useful classifiable motor imagery information (entropy output) changes during the period of motor imagery in comparison to baseline period; as a result, there is an increase in the accuracy and F1 score of classification when using entropy features in comparison to the accuracy and the F1 of classification when using amplitude features, hence, it is manifested as an improvement of the ability to detect motor imagery.

Security-enhanced electro-optic chaotic communication system based on the logistic map feedback and dynamic key

In this paper, a novel electro-optic chaotic system based on the logistic map feedback (EOLM) is proposed. The logistic map is used to introduce additional nonlinear effects into the electro-optic feedback loop. The simulation results show that, with the increase of logistic map iterations N, the bandwidth and permutation entropy of the chaotic output can be significantly increased, and the spectrum is flatter. The time-delay signature (TDS) of the system can be concealed within the appropriate range of values of parameters, which ensures the security of the key. Synchronization results show that the system is not only sensitive to time delay T but is also sensitive to the feedback intensity β, so β is also the key of the system. Utilizing the sensitivity to β, a dynamic EOLM communication system with changing key (DEOLM) is designed. Based on chaotic self-control, the chaotic optical signal is transformed nonlinearly to generate the control signal, which drives the gain coefficient of the amplifier to change dynamically, so as to realize the changing of β. Simulation of communication performance shows that the DEOLM system greatly raises the difficulty for the eavesdropper to crack the message and improves the confidentiality of communication.

Provably Secure Randomness Generation from Switching Probability of Magnetic Tunnel Junctions

In recent years, true random number generators (TRNGs) based on magnetic tunnel junctions (MTJs) have become increasingly attractive. This is because MTJ-based TRNGs offer some advantages over traditional CMOS-based TRNGs, such as smaller area and simpler structure. However, there has been no work thus far that has quantified the quality of the raw output of a MTJ-based TRNG and performed suitable randomness extraction to produce provably secure random bits, unlike the case for CMOS-based TRNGs. In the work presented here, we implement a MTJ-based TRNG and characterize the entropy of the raw output. Using this information, we perform postprocessing to extract a set of random bits that are provably secure.

Local additivity revisited

We make a number of simplifications in Gour and Friedland’s proof of local additivity of the minimum output entropy of a quantum channel. We follow them in reframing the question as one about the entanglement entropy of bipartite states associated with a dB × dE matrix. We use a different approach to reduce the general case to that of a square positive definite matrix. We use the integral representation of the log to obtain expressions for the first and second derivatives of the entropy, and then exploit the modular operator and functional calculus to streamline the proof following their underlying strategy. We also extend this result to the maximum relative entropy with respect to a fixed reference state, which has important implications for studying the superadditivity of the capacity of a quantum channel to transmit classical information.

Deep causal factorization network: A novel domain generalization method for cross-machine bearing fault diagnosis

Domain generalization (DG) has attracted much attention in bearing fault diagnosis since it can generalize the prior diagnostic knowledge to invisible working conditions. However, previous DG-based approaches are easy to introduce personalized bias due to the specificity of the mechanical equipment, which worsens the generalization performance. To solve this problem, a deep causal factorization network (DCFN) is proposed for cross-machine bearing diagnosis without the involvement of target domain data in training. Specifically, by leveraging the structural causal model of bearing fault signal generation, DCFN defines the cross-machine generalized fault representations as causal factors and the domain-related representations as non-causal factors. Afterwards, taking advantage of the causal properties that can be preserved in data distribution shifts, causal task factorization and feature factorization modules are designed to reconstruct causal mechanisms. To separate the two types of underlying factors, causal task factorization aims to maximize the predicted output entropy of the domain classifier using learned causal factors as input, and simultaneously maximizes the entropy of the fault classifier using learned non-causal factors as input. Moreover, causal feature factorization highlights that the ideal causal factors desire cross-domain consistency and inter-dimensional independence, thereby learning causal factors with stable and sufficient distinguishing features. Finally, under broad bearing fault datasets including public, laboratory and simulation datasets, the effectiveness of the proposed DCFN is verified by comparing with various state-of -the-arts diagnosis methods.

Torque Regulation Is Influenced by the Nature of the Isometric Contraction

The present study aimed to investigate the effects of a continuous visual feedback and the isometric contraction nature on the complexity and variability of force. Thirteen healthy and young male adults performed three MVCs followed by three submaximal isometric force tasks at a target force of 40% of their MVC for 30 s, as follows: (i) push isometric task with visual feedback (Pvisual); (ii) hold isometric task with visual feedback (Hvisual); (iii) hold isometric task without visual feedback (Hnon-visual). Force complexity was evaluated through sample entropy (SampEn) of the force output. Force variability was analyzed through the coefficient of variation (CV). Results showed that differences were task-related, with Pvisual showing higher complexity (i.e., higher SampEn) and decreased variability (i.e., lower CV) when compared with the remaining tasks. Additionally, no significant differences were found between the two hold isometric force tasks (i.e., no influence of visual feedback). Our results are promising as we showed these two isometric tasks to induce different motor control strategies. Furthermore, we demonstrated that visual feedback's influence is also dependent on the type of isometric task. These findings should motivate researchers and physiologists to shift training paradigms and incorporate different force control evaluation tasks.

Study on the Impact of Financial Agglomeration on the Development of Digital Economy

The digital economy has become an important engine for the high-quality development of China's economy. This paper uses panel data of six central provinces in China from 2013 to 2020 as samples, and measures the financial agglomeration level of each province by using the location entropy of financial output, and explores the influence of financial agglomeration on the development of digital economy by constructing a progressive stepwise regression of fixed-effect model. The results of the analysis show that the financial support for high-tech enterprises' investment and financing still cannot keep up with the rapid development of the digital economy, and put forward policy suggestions such as promoting the digital transformation of inclusive finance, promoting new financial service models such as "artificial intelligence + big data", and accelerating the construction of digital industry.

Optimal piston motion paths for a light-driven engine with generalized radiative law and maximum ecological function

Searching optimal piston motion path (OPMP) by using optimal control theory for various thermal cycles is an important task in finite time thermodynamics. Some optimization objectives have been used in this type of researches, including maximum work output (MWO) and minimum entropy generation (MEG). This paper introduces maximum ecological function (MEF) into OPMP problem for light-driven engine (LDE) with generalized radiative heat transfer law (HTL) [q∝Δ(Tn)]. The LDE is with working fluid of reacting system [A]⇌[B] and irreversibilities of piston friction and heat conduction. Numerical examples of OPMPs at MEF with Newtonian (n=1), linear phenomenological (n=−1) and radiative (n=4) HTLs are provided. The results are compared with those obtained by MWO and MEG objectives as well as different HTLs. Utilizing MEF as objective can effectively accomplish entropy generation reduction with a little decrease in work output. Time-parametrized piston velocity, working fluid temperature and piston position for MEF are situated between those for MEG and MWO. Moreover, OPMPs for MEF with different HTLs are also quite different. Although all of the values of ecological function are negative for three special HTLs, work output and ecological function decrease with the increase in n, and entropy generation increases when n increases.

Concentration estimates for random subspaces of a tensor product and application to quantum information theory

Given a random subspace Hn chosen uniformly in a tensor product of Hilbert spaces Vn ⊗ W, we consider the collection Kn of all singular values of all norm one elements of Hn with respect to the tensor structure. A law of large numbers has been obtained for this random set in the context of W fixed and the dimension of Hn, Vn tending to infinity at the same speed by Belinschi, Collins, and Nechita [Commun. Math. Phys. 341(3), 885–909 (2016)]. In this paper, we provide measure concentration estimates in this context. The probabilistic study of Kn was motivated by important questions in quantum information theory and allowed us to provide the smallest known dimension for the dimension of an ancilla space, allowing for Minimum Output Entropy (MOE) violation. With our estimates, we are able, as an application, to provide actual bounds for the dimension of spaces where the violation of MOE occurs.