Adaptable and Dynamic Access Control Decision-Enforcement Approach Based on Multilayer Hybrid Deep Learning Techniques in BYOD Environment

Organizations are adopting the Bring Your Own Device(BYOD)concept to enhance productivity and reduce expenses.However,this trend introduces security challenges,such as unauthorized access.Traditional access control systems,such as Attribute-Based Access Control(ABAC)and Role-Based Access Control(RBAC),are limited in their ability to enforce access decisions due to the variability and dynamism of attributes related to users and resources.This paper proposes a method for enforcing access decisions that is adaptable and dynamic,based on multilayer hybrid deep learning techniques,particularly the Tabular Deep Neural Network Tabular DNN method.This technique transforms all input attributes in an access request into a binary classification(allow or deny)using multiple layers,ensuring accurate and efficient access decision-making.The proposed solution was evaluated using the Kaggle Amazon access control policy dataset and demonstrated its effectiveness by achieving a 94%accuracy rate.Additionally,the proposed solution enhances the implementation of access decisions based on a variety of resource and user attributes while ensuring privacy through indirect communication with the Policy Administration Point(PAP).This solution significantly improves the flexibility of access control systems,making themmore dynamic and adaptable to the evolving needs ofmodern organizations.Furthermore,it offers a scalable approach to manage the complexities associated with the BYOD environment,providing a robust framework for secure and efficient access management.

Novel Adaptive Memory Event-Triggered-Based Fuzzy Robust Control for Nonlinear Networked Systems via the Differential Evolution Algorithm

This article mainly investigates the fuzzy optimization robust control issue for nonlinear networked systems characterized by the interval type-2(IT2)fuzzy technique under a differential evolution algorithm.To provide a more reasonable utilization of the constrained communication channel,a novel adaptive memory event-triggered(AMET)mechanism is developed,where two event-triggered thresholds can be dynamically adjusted in the light of the current system information and the transmitted historical data.Sufficient conditions with less conservative design of the fuzzy imperfect premise matching(IPM)controller are presented by introducing the Wirtinger-based integral inequality,the information of membership functions(MFs)and slack matrices.Subsequently,under the IPM policy,a new MFs intelligent optimization technique that takes advantage of the differential evolution algorithm is first provided for IT2 TakagiSugeno(T-S)fuzzy systems to update the fuzzy controller MFs in real-time and achieve a better system control effect.Finally,simulation results demonstrate that the proposed control scheme can obtain better system performance in the case of using fewer communication resources.

Robust adaptive fuzzy tracking control for a class of strict-feedback nonlinear systems based on backstepping technique

In this paper, the robust adaptive fuzzy tracking control problem is discussed for a class of perturbed strict-feedback nonlinear systems. The fuzzy logic systems in Mamdani type are used to approximate unknown nonlinear functions. A design scheme of the robust adaptive fuzzy controller is proposed by use of the backstepping technique. The proposed controller guarantees semi-global uniform ultimate boundedness of all the signals in the derived closed-loop system and achieves the good tracking performance. The possible controller singularity problem which may occur in some existing adaptive control schemes with feedback linearization techniques can be avoided. In addition, the number of the on-line adaptive parameters is not more than the order of the designed system. Finally, two simulation examples are used to demonstrate the effectiveness of the proposed control scheme.

Stereotactic radiotherapy and the potential role of magnetic resonance-guided adaptive techniques for pancreatic cancer

BACKGROUND Pancreatic cancer is a malignancy with one of the poorest prognoses amongst all cancers.Patients with unresectable tumours either receive palliative care or undergo various chemoradiotherapy regimens.Conventional techniques are often associated with acute gastrointestinal toxicities,as adjacent critical structures such as the duodenum ultimately limits delivered doses.Stereotactic body radiotherapy(SBRT)is an advanced radiation technique that delivers highly ablative radiation split into several fractions,with a steep dose fall-off outside target volumes.AIM To discuss the latest data on SBRT and whether there is a role for magnetic resonance-guided techniques in multimodal management of locally advanced,unresectable pancreatic cancer.METHODS We conducted a search on multiple large databases to collate the latest records on radiotherapy techniques used to treat pancreatic cancer.Out of 1229 total records retrieved from our search,36 studies were included in this review.RESULTS Studies indicate that SBRT is associated with improved clinical efficacy and toxicity profiles compared to conventional radiotherapy techniques.Further dose escalation to the tumour with SBRT is limited by the poor soft-tissue visualisation of computed tomography imaging during radiation planning and treatment delivery.Magnetic resonance-guided techniques have been introduced to improve imaging quality,enabling treatment plan adaptation and re-optimisation before delivering each fraction.CONCLUSION Therefore,SBRT may lead to improved survival outcomes and safer toxicity profiles compared to conventional techniques,and the addition of magnetic resonance-guided techniques potentially allows dose escalation and conversion of unresectable tumours to operable cases.

Robust adaptive synchronization of chaotic neural networks by slide technique

In this paper, we focus on the robust adaptive synchronization between two coupled chaotic neural networks with all the parameters unknown and time-varying delay. In order to increase the robustness of the two coupled neural networks, the key idea is that a sliding-mode-type controller is employed. Moreover, without the estimate values of the network unknown parameters taken as an updating object, a new updating object is introduced in the constructing of controller. Using the proposed controller, without any requirements for the boundedness, monotonicity and differentiability of activation functions, and symmetry of connections, the two coupled chaotic neural networks can achieve global robust synchronization no matter what their initial states are. Finally, the numerical simulation validates the effectiveness and feasibility of the proposed technique.

Multidither coherent optical adaptive technique for deep tissue two-photon microscopy

Two-photon microscopy normally suffers from the scattering of the tissue in biological imaging.Multidither coberent optical adaptive technique(COAT)can correct the scattered wavefront in parallel.However,the determination of the corrective phases may not be completely accurate using conventional method,which undermines the performance of this technique.In this paper,we theoretically demonstrate a method that can obtain more accurate corrective phases by determining the phase values from the square root of the fuorescence signal.A numnerical simulation model is established to study the performance of adaptive optics in two-photon micros-copy by combining scalar diffraction theory with vector diffraction theory.The results show that the distortion of the wavefront can be corrected more thoroughly with our method in two-photon imaging.In our simulation,with the scattering from a 450-mn-thick mouse brain tissue,excitation focal spots with higher peak-to background ratio(PBR)and images with higher contrast can be obtained.Hence,further enhancement of the multidither COAT correction performance in two-photon imaging can be expected.

A low-cost and high-performance technique for adaptive optics static wavefront correction

Non-Common Path Error(NCPE) is one of the factors that limit an Adaptive Optics(AO)system from delivering ultra-high performance. To correct the NCPE associated static aberration, we propose a simple but robust and high-performance pupil-plane based wavefront measurement and correction technique, which can copy a single-mode fiber generated perfect wavefront to the AO system via an iteration optimization process, and the NCPE can be effectively corrected by directly commanding the Deformable Mirror(DM) of the AO system. Compared with the previous focal-plane based approach that uses focal plane based Point Spread Function(PSF) for correction evaluation, the pupil-plane based approach can be reliably and rapidly converged to a global optimization result and provides better performance, in particular for an AO system with a large initial static wavefront error. This technique we proposed can be implemented in astronomical AO systems where extremely high performance is required.

ON THE MOVING ADAPTIVE INTERPOLATION TECHNIQUE TO IMPROVE THE VERTICAL RESOLUTION OF PICTURES IN TV

This paper deals with the method of moving adaptive interpolation technique. The interlaced scanning that structures the pictures in today’s television systems is changed into progressive scanning, which helps to greatly improve the vertical resolution of pictures.

USING Hybrid Adaptive Techniques to Reduce Multipath Effects in S-PCN Mobile Terminals

Multipath signal processing is a promising technique for increasing the capacity of downlink frequency of satellite communication networks (S-PCN). The paper presents an approach to processing and reducing multipath signals received from S-PCN typified of mobile terminal users in clustered or mountainous environment. Use of hybrid linear adaptive antenna array technique and adaptive filtering technique provides improved performance by eliminating uncorrelated signal residing in antenna sidelobes.

A SELF-ADAPTIVE TECHNIQUE FORA KIND OF NONLINEAR CONJUGATEGRADIENT METHODS

Conjugate gradient methods. are a class of important methods for unconstrained optimization, especially when the dimension is large. In 2001, Dai and Liao have proposed a new conjugate condition, based on it two nonlinear conjugate gradient methods are constructed. With trust region idea, this paper gives a self-adaptive technique for the two methods. The numerical results show that this technique works well for the given nonlinear optimization test problems.

An effective technique to improve photosynthetic productivity and adaptability of rice

High photosynthetic productivity and widephotosynthetic adaptability are two importantphysiological characteristics for the sustained highyield and wider growing range of rice.Preliminaryexperiments indicated that the utilization ofbeterosis between two different ecotype rices,US

Adaptive Threshold Wave Front Sensing for Portable Adaptive Optics System Driven by Hybrid Parallel Technique

Our Portable Adaptive Optics(PAO)system designed for high-contrast imaging of exoplanets with current 2-4 m class telescopes achieves a correction speed of nearly 1000 Hz,utilizing a Shack-Hartmann Wave Front Sensor(WFS)in a 9×9 sub-aperture configuration.As we look towards adapting the PAO system for larger telescopes,an increase in the number of sub-apertures in the WFS and enhanced precision in wave front detection are imperative.Originally programmed in LabVIEW,our initial PAO software is based on a traditional centroid calculation module for nighttime wave front sensing and lacks adaptive processing of background noise.To address these limitations and to boost the PAO system's performance and accuracy in wave front detection,we propose a compressive neural network(Th-Net)combined with a specialized hybrid parallel programming approach for wave front detection.Our experimental results indicate that this hybrid parallel technique and Th-Net significantly enhance the PAO system's operational speed and wave front detection precision under uneven background noise.This work paves the way so that a duplicable and low-cost PAO system can be used for direct imaging of exoplanets with large telescopes.

THE GREENEST BUILDING (IS THE ONE THAT YOU DON’T BUILD!) Effective Techniques for Sustainable Adaptive Reuse/Renovation

As an advocate for the older building,I offer the following article as a compendium of useful arguments for the caring institution manager,trustee,or architect.They can be used to support your efforts to convince others to fund the reuse of your wonderful existing buildings from a“green”point of view!Not everyone appreciates the quirks and qualities of older buildings,and,to be fair,they do require more time and attention than brand new facilities.However,there are many advantages to adaptive reuse or renovation.

Adaptive Proportional-Derivative Sliding Mode Control Law With Improved Transient Performance for Underactuated Overhead Crane Systems

In this paper, an adaptive proportional-derivative sliding mode control(APD-SMC) law, is proposed for 2D underactuated overhead crane systems. The proposed controller has the advantages of simple structure, easy to implement of PD control, strong robustness of SMC with respect to external disturbances and uncertain system parameters, and adaptation for unknown system dynamics associated with the feedforward parts. In the proposed APD-SMC law, the PD control part is used to stabilize the controlled system, the SMC part is used to compensate the external disturbances and system uncertainties,and the adaptive control part is utilized to estimate the unknown system parameters. The coupling behavior between the trolley movement and the payload swing is enhanced and, therefore, the transient performance of the proposed controller is improved.The Lyapunov techniques and the La Salle's invariance theorem are employed in to support the theoretical derivations. Experimental results are provided to validate the superior performance of the proposed control law.

Adaptive neural network tracking control for a class of unknown nonlinear time-delay systems

For a class of unknown nonlinear time-delay systems, an adaptive neural network （NN） control design approach is proposed. Backstepping, domination and adaptive bounding design technique are combined to construct a robust memoryless adaptive NN tracking controller. Unknown time-delay functions are approximated by NNs, such that the requirement on the nonlinear time-delay functions is relaxed. Based on Lyapunov-Krasoviskii functional, the sem-global uniformly ultimately boundedness （UUB） of all the signals in the closed-loop system is proved. The arbitrary output tracking accuracy is achieved by tuning the design parameters. The feasibility is investigated by an illustrative simulation example.

Adaptive Fuzzy Backstepping Tracking Control for Flexible Robotic Manipulator

In this paper,an adaptive fuzzy state feedback control method is proposed for the single-link robotic manipulator system.The considered system contains unknown nonlinearfunction and actuator saturation.Fuzzy logic systems(FLSs)and a smooth function are used to approximate the unknownnonlinearities and the actuator saturation,respectively.By com-bining the command-filter technique with the backsteppingdesign algorithm,a novel adaptive fuuzy tracking backsteppingcontrol method is developed.It is proved that the adaptive fuuzycontrol scheme can guarantee that all the variables in the closed-loop system are bounded,and the system output can track thegiven reference signal as close as possible.Simulation results areprovided to illustrate the effectiveness of the proposed approach.

Adaptive broadband beamformer for nonuniform linear array based on second order cone programming

Adaptive broadband beamforraing is a key issue in array applications. The adaptive broadband beamformer with tapped delay line （TDL） structure for nonuniform linear array （NLA） is designed according to the rule of minimizing the beamformer＇s output power while keeping the distortionless response （DR） in the direction of desired signal and keeping the constant beamwidth （CB） with the prescribed sidelobe level over the whole operating band. This kind of beamforming problem can be solved with the interior-point method after being converted to the form of standard second order cone programming （SOCP）. The computer simulations are presented which illustrate the effectiveness of our beamformer.

Combined Adaptive Multiple Subtraction Based on Optimized Event Tracing and Extended Wiener Filtering

The surface-related multiple elimination(SRME) method is based on feedback formulation and has become one of the most preferred multiple suppression methods used. However, some differences are apparent between the predicted multiples and those in the source seismic records, which may result in conventional adaptive multiple subtraction methods being barely able to effectively suppress multiples in actual production. This paper introduces a combined adaptive multiple attenuation method based on the optimized event tracing technique and extended Wiener filtering. The method firstly uses multiple records predicted by SRME to generate a multiple velocity spectrum, then separates the original record to an approximate primary record and an approximate multiple record by applying the optimized event tracing method and short-time window FK filtering method. After applying the extended Wiener filtering method, residual multiples in the approximate primary record can then be eliminated and the damaged primary can be restored from the approximate multiple record. This method combines the advantages of multiple elimination based on the optimized event tracing method and the extended Wiener filtering technique. It is an ideal method for suppressing typical hyperbolic and other types of multiples, with the advantage of minimizing damage of the primary. Synthetic and field data tests show that this method produces better multiple elimination results than the traditional multi-channel Wiener filter method and is more suitable for multiple elimination in complicated geological areas.

Stochastic parallel gradient descent based adaptive optics used for a high contrast imaging coronagraph

An adaptive optics （AO） system based on a stochastic parallel gradient descent （SPGD） algorithm is proposed to reduce the speckle noises in the optical system of a stellar coronagraph in order to further improve the contrast. The principle of the SPGD algorithm is described briefly and a metric suitable for point source imaging optimization is given. The feasibility and good performance of the SPGD algorithm is demonstrated by an experimental system featured with a 140-actuator deformable mirror and a Hartrnann-Shark wavefront sensor. Then the SPGD based AO is applied to a liquid crystal array （LCA） based coronagraph to improve the contrast. The LCA can modulate the incoming light to generate a pupil apodization mask of any pattern. A circular stepped pattern is used in our preliminary experiment and the image contrast shows improvement from 10^-3 to 10^-4.5 at an angular distance of 2A/D after being corrected by SPGD based AO.

Portable adaptive optics for exoplanet imaging

The portable adaptive optics(PAO)device is a low-cost and compact system,designed for 4-meter class telescopes that have no adaptive optics(AO)system,because of the physical space limitation at the Nasmyth or Cassegrain focus and the historically high cost of conventional AO.The initial scientific observations of the PAO are focused on the direct imaging of exoplanets and sub-stellar companions.This paper discusses the concept of PAO and the associated high-contrast imaging performance in our recent observational runs.PAO deliver a Strehl ratio better than 60%in H band under median seeing conditions of 1".Combined with our dedicated image rotation and subtraction(IRS)technique and the optimized IRS(O-IRS)algorithm,the averaged contrast ratio for a 5≤V_(mag)≤9 primary star is 1.3×10^(-5)and3.3×10^(-6)at angular distance of 0.36"with exposure time of 7 minutes and 2 hours,respectively.PAO has successfully revealed the known exoplanet ofκAnd b in our recent observation with the 3.5-meter ARC telescope at Apache Point Observatory.We have performed the associated astrometry and photometry analysis of the recoveredκAnd b planet,which gives a projected separation of 0.98"±0.05",a position angle of 51.1°±0.5°and a mass of 10.15_(-1.255)^(+2.19) MJup.These results demonstrate that PAO can be used for direct imaging of exoplanets with medium-sized telescopes.