A critical discussion is given of existing methods for the computation of noise in single injection space−charge−limited (SCL) devices: the salami method, the Langevin method, and the impedance field method. In addition, a new method is set forth, which in one form (finite volume divisions Δ3r) presents a lumped network description of noise and electrical parameters, whereas in another form (Δ3r→0) it presents a continuous media transport picture which is characterized by a transfer impedance tensor. The first form ties in with a modified salami method, whereas the second form is the substratum from which the more global impedance field formulas can be derived. A necessary and sufficient condition under which the noise is expressible as generalized Nyquist noise is obtained. For the simplest device, the thermal electron trap−free insulator, this is applied to one−dimensional as well as some three−dimensional geometries.

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The noise of single injection diodes of varying degrees of complexity is studied: the trap−free insulator, the trap−free semiconductor, structures with traps, and three−dimensional insulators. Where possible, results for hot as well as thermal carriers are derived. The one−dimensional results are consistently obtained with the transfer impedance method and the emphasis is on results valid in the entire characteristic [Ohmic, space−charge−limited (SCL), and mixed conduction]. Agreement with other work in limiting current regimes is generally observed. For devices with traps explicit new formulas for the trapping noise are obtained.

Structural Health Monitoring (SHM) has reached a high importance in numerous fields of civil and mechanical engineering. Promising damage detection approaches like the Damage Index Method, Gapped Smoothing Technique and Modal Strain Energy Method require the structure's mode shapes [1].Long term modal data acquisition on real life structures requires a computational efficient system based on a measuring method that can easily be installed. Systems using the Random Decrement Method (RDM) are composed of a decentralized network of smart acceleration sensors applied for both, triggering and pure measuring. They allow the reduction of cabling effort and computational costs to a minimum.In order to design a RDM measuring network efficiently, an approved procedure for defining hardware as well as measuring settings is required. In addition, optimal sensor positions have to be defined. However, today those decisions are mostly based on expert's knowledge. In this paper a systematic and analytical procedure for defining the hardware requirements and measuring settings as well as optimal sensor positions is presented. The proposed routine uses the outcome of an Experimental Modal Analysis (EMA).Due to different requirements for triggering and non-triggering sensors in the RDM network a combination of two approaches for sensor placement has to be used in order to find the best distribution of measurement points over the structure. A controllability based technique is used for placing triggering sensors, whereas the Effective Independence (EI) is utilized for the placement of non-triggering sensors.The combination of these two techniques selects the best set of measuring points for a given number of sensors out of all possible sensor positions.Damage detection itself is not considered within the scope of this paper.

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