Abstract
[No abstract available]

Abstract
Greenhouse control computers are an essential part of modern greenhouse operation. Climate, irrigation and nutrient supply must be controlled, in an economical way, to produce the best crop conditions. Current research on CO2 enrichment and optimal growth strategies implies the use of powerful tools, either based on hardware or software. This paper describes the design and implementation of a distributed data acquisition and control system for computerised agricultural management systems that is being developed at Universidade of Tras-os-Montes and Alto Douro, Vila Real. Different communications platform concepts, such as Controller Area Network (CAN), Wireless Technologies, Ethernet and Internet tools supported by Transfer Control Protocol/Internet Protocol (TCP/IP) and e-mail tools supported by Simple Mail Transfer Protocol (SMTP) were used to achieve a network with a low-cost, flexible, and functional characteristics. The system management and maintenance tasks are divided into two types of performance levels. At a lower supervision level, a Local Controller placed in the greenhouses performs the monitoring/control and communications actions. The management decisions are performed at the higher level. The techniques and tools, which provide to the user a transparent, friendly and intuitive Graphical User Interface (GUI) will be presented.

Abstract
Analog-to-digital converter (ADC) characterization is usually performed using stationary stimuli like sine waves. However, the use of a non-stationary stimulus, besides providing testing conditions closer to those found in real applications, can lead to interesting improvements in ADC testing speed, This kind of signal needs proper processing techniques in order to extract useful information. In this paper we propose the use of joint time-frequency analysis (JTFA) for this purpose. The basic principles of the technique, and how it can be used in ADC testing are presented. in particular, a method for characterising an ADC on its entire bandwidth using a single stimulus is described.

Abstract
The concepts of Instantaneous Frequency (IFt) and Instantaneous Bandwidth (IBt) have traditionally been considered as independent spectral features. Recent work has suggested, however, that the spectral dynamics of the signal (and, hence, its IFt) may have an impact on IBt and on the achievable spectral resolution. In this article, we consider model based AR spectral estimators. Simulations are done with several distinct time-frequency tools, to confirm the theoretical predictions.

Abstract
This paper reviews the problem of translating signals into symbols preserving maximally the information contained in the signal time structure. In this context, we motivate the use of nonconvergent dynamics for the signal to symbol translator. We then describe a biologically realistic model of the olfactory system proposed by Walter Freeman that has locally stable dynamics but is globally chaotic. We show how we can discretize Freemans model using digital signal processing techniques, providing an alternative to the more conventional Runge-Kutta integration. This analysis leads to a direct mixed-signal {analog amplitude/discrete time) implementation of the dynamical building block that simplifies the implementation of the interconnect. We present results of simulations and measurements obtained from a fabricated analog very large scale integration (VLSI) chip. © 2001 IEEE.

Abstract
In this paper, we present and compare two approaches to model supply and demand curves of a sealed-bid auction market. Both the Linear Hinges model and the Sigmo model are able to extract the relevant information from these bidding curves, without losing significant market information. We discuss their main similarities and important differences using an unified framework, to highlight their main strengths and weakness. A practical comparative study based on real supply functions from the Californian electricity market has been included to derive practical conclusions. © 2001 IEEE.