Maximum likelihood-based adaptive differential evolution identification algorithm for multivariable systems in the state-space form

Parameter estimation plays an important role in the field of system control. This article is concerned with the parameter estimation methods for multivariable systems in the state-space form. For the sake of solving the identification complexity caused by a large number of parameters in multivariable systems, we decompose the original multivariable system into some subsystems containing fewer parameters and study identification algorithms to estimate the parameters of each subsystem. By taking the maximum likelihood criterion function as the fitness function of the differential evolution algorithm, we present a maximum likelihood-based differential evolution (ML-DE) algorithm for parameter estimation. To improve the parameter estimation accuracy, we introduce the adaptive mutation factor and the adaptive crossover factor into the ML-DE algorithm and propose a maximum likelihood-based adaptive differential evolution algorithm. The simulation study indicates the efficiency of the proposed algorithms.     

A fog-based security framework for intelligent traffic light control system

Multimedia Tools and Applications - The world is facing many problems including that of traffic congestion. To highlight the issue of traffic congestion worldwide specially in urban areas and to...     

The low process yield of some PNP power transistors

A study is presented of the symptoms of the devices in a slice of PNP power transistors with a poor yield. The failure modes over the slice are analysed. In specific areas the electrical characteristics and optical appearance of the devices were studied and failure statistics are presented. The low yield is ascribed to cracks in the phosphosilicate glass used for the base diffusion. The effect of these cracks is just visible in the optical and scanning electron microscope.     

Algebraic properties of cryptosystem PGM

In the late 1970s Magliveras invented a private-key cryptographic system calledPermutation Group Mappings (PGM). PGM is based on the prolific existence of certain kinds of factorization sets, calledlogarithmic signatures, for finite permutation groups. PGM is an endomorphic system with message space ℤ_|G| for a given finite permutation groupG. In this paper we prove several algebraic properties of PGM. We show that the set of PGM transformations ℐ _G is not closed under functional composition and hence not a group. This set is 2-transitive on ℤ_|G| if the underlying groupG is not hamiltonian and not abelian. Moreover, if the order ofG is not a power of 2, then the set of transformations contains an odd permutation. An important consequence of these results is that the group generated by the set of transformations is nearly always the symmetric group ℒ_|G|. Thus, allowing multiple encryption, any permutation of the message space is attainable. This property is one of the strongest security conditions that can be offered by a private-key encryption system. S. S. Magliveras was supported in part by NSF/NSA Grant Number MDA904-82-H0001, by U.S. West Communications, and by the Center for Communication and Information Science of the University of Nebraska.     

Breakdown probabilities for thin heterostructure avalanche photodiodes

The recurrence theory for the breakdown probability in avalanche photodiodes (APDs) is generalized to heterostructure APDs that may have multiple multiplication layers. The generalization addresses layer-boundary effects such as the initial energy of injected carriers as well as the layer-dependent profile of the dead space in the multiplication region. Reducing the width of the multiplication layer serves to both downshift and sharpen the breakdown probability curve as a function of the applied reverse-bias voltage. In structures where the injected carriers have an initial energy that is comparable to the ionization threshold energy, the transition from linear mode to Geiger-mode is more abrupt than in structures in which such initial energy is negligible. The theory is applied to two recently fabricated Al/sub 0.6/Ga/sub 0.4/As-GaAs heterostructure APDs and to other homostructure thin GaAs APDs and the predictions of the breakdown-voltage thresholds are verified.     

Improving security of the Internet of Things via RF fingerprinting based device identification system

Neural Computing and Applications - Security is one of the primary concerns when designing wireless networks. Along detecting user identity, it is also important to detect the devices at the...     

Characterization of acoustic signals due to surface discharges on H.V. glass insulators using wavelet radial basis function neural networks

A hybrid model incorporating wavelet and radial basis function neural network is presented which is used to detect, identify and characterize the acoustic signals due to surface discharge activity and hence differentiate abnormal operating conditions from the normal ones. The tests were carried out on cleaned and polluted high voltage glass insulators by using surface tracking and erosion test procedure of international electrotechnical commission 60587. A laboratory experiment was conducted by preparing the prototypes of the discharges. This study suggests a feature extraction and classification algorithm for surface discharge classification, which when combined together reduced the dimensionality of the feature space to a manageable dimension, by “marrying” the wavelet to radial basis function neural network very high levels of classification are achieved. Wavelet signal treatment toolbox is used to recover the surface discharge acoustic signals by eliminating the noisy portion and to reduce the dimension of the feature input vector. A radial basis function neural network classifier was used to classify the surface discharge and assess the suitability of this feature vector in classification. This learning method is proved to be effective by applying the wavelet radial basis function neural network in the classification of surface discharge fault data set. The test results show that the proposed approach is efficient and reliable.     

Effects of Silicon on Growth,Yield and Fruit Quality of Cantaloupe under Drought Stress

Silicon - Silicon (Si) plays an important role in mitigating adverse effects of various biotic and abiotic stresses including drought. Polyhouse experiment was conducted to evaluate the effects of...     

Rheological properties of poly(vinyl chloride) / epoxidized natural rubber blends. Part II: The effect of processing variables

The effect of processing variables on the rheological properties of PVC/ENR blends was investigated. The role of crosslinking in determining the flow behavior of blends was also examined by means of dynamically cured blends. It was found that PVC/ENR blends yield melts that are power law fluids. The flow of the melts improves with an increase in temperature and shear rate. However, the introduction of crosslinks reverses this trend, although under more rigorous conditions, the influence of crosslinks is superseded, and subsequently, flow becomes shear rate and temperature dependent. PVC/ENR systems also manifested elastic phenomena. The dependence of the elastic phenomena such as die swell and melt fracture on L/D ratio of the die was demonstrated.