Abstract
This work addresses the signal propagation and the fractional-order dynamics during, the evolution of a genetic algorithm (GA), for generating a robot manipulator trajectory. The GA objective is to minimize the trajectory space/time ripple without exceeding the torque requirements. In order to investigate the phenomena involved in the GA population evolution, the mutation is exposed to excitation perturbations and the corresponding fitness variations are evaluated. The chaos-like noise and the input/output signals are studied revealing a fractional-order dynamics, characteristic of a long-term system memory.

Abstract
This work addresses the fractional-order dynamics during the evolution of a Genetic Algorithm population (GA) for generating a robot manipulator trajectory. The GA objective is to minimize the trajectory space/time ripple without exceeding the torque requirements. In order to investigate the phenomena involved in the GA population evolution, the mutation is exposed to excitation perturbations and the corresponding fitness variations are evaluated. The input/output signals are studied revealing a fractional-order dynamic evolution, characteristic of a long-term system memory.

Abstract
A new optimisation algorithm is proposed that results from merging a split adaptive particle swarm optimisation algorithm with the differential evolution algorithm. The proposed technique adopts the crossover type of operator used within the differential evolution algorithm to update the swarm particle velocity vectors. The hybrid algorithm is tested in a well known benchmark continuous function and its performance compared with the particle swarm optimisation and differential evolution algorithms. Copyright (C) 2003 IFAC.

Abstract
This work addresses the fractional-order dynamics during the evolution of a GA, which generates a robot manipulator trajectory. In order to investigate the phenomena involved in the GA population evolution, the crossover is exposed to excitation perturbations and the corresponding fitness variations are evaluated. The input/output signals are studied revealing a fractional-order dynamic evolution, characteristic of a long-term system memory.

Abstract
This paper describes a CMOS mixed-signal interface with a RF transmitter. This die is assembled in a Multi-Chip-Module (MCM) micro-system together with the micromachined soil moisture sensor to achieve a cost-effective solution with accurate and reliable measurements for soil moisture in agriculture. The soil moisture probe, based on Dual-Probe Heat-Pulse (DPHP) method, is fabricated in bulk-micromachining technology. The DPHP method is based on the measurement of the maximum temperature rise at some distance from the heater, after applying a heat-pulse. The measurement of the temperature rise is obtained by subtracting soil temperature from the probe temperature. The mixed-signal interface is based on a pre-amplification stage and first-order sigma-delta modulator. The bit-stream output of the modulator is then applied to a counter as a first order decimation filter thus providing a 12-bit readout sample. Prior to transmission, data is encoded as a pulse width modulated (PWM) signal and then transmitted by means of an amplitude shift-keying (ASK) modulation. ne transmitter features a VCO phase locked to the quartz crystal reference of 13.56 MHz to achieve a carrier frequency of 433.92 MHz. A RF power amplifier based on class E topology was chosen. The CMOS mixed-signal interface with a RF transmitter has been implemented in a single-chip using a standard CMOS process (AMI 0.7 mum, n-well, 2 metals and I Poly).

Abstract
The number of applications using machine vision and digital image processing techniques in the agricultural sector is increasing rapidly. These applications include land/aerial remote sensing of crops, detection and recognition of pathological stress conditions, shape and color characterization of fruits, among many other topics. In fact, quantification of the visual properties of horticultural products and plants can play an important role to improve and automate agricultural management tasks. In this paper, is described a plant leaf characterization system based on a personal computer. This system uses a digital scanner to acquire leaf images with a resolution of 150dpi. These images are afterwards processed in order to compute some leaf characteristic parameters, such as: leaf area and perimeter, existence of holes, width and length. With the implemented algorithms the errors between the measurements and the real values were typically less than +/-3% and +/-2.5% for the area and linear measurements, respectively. These tests and results were realized using sets of known size images and leaf images that were measured with the proposed system and with a commercial calibrated leaf area system LiCor from Delta-T Devices.