Detection of an artificial target with low radar cross-section in presence of backscatter

This paper presents an approach to improve the detection of an artificial target with low radar cross-section in presence of clutter. The target proposed in the paper modulates the phase response of the circularly polarized incident signal by means of rotation. The same physical phenomenon can be used to steer the modulated response in a non-specular direction. The bi-static measurements of the response of the target have demonstrated good agreement with theoretical prediction as well as with full-wave simulation.

Artificial urinary sphincter implantation in the irradiated patient:safety, efficacy and satisfaction

Objective To compare the long-term outcome of artificial urinary sphincter (AUS) implantation in patients after prostatectomy, with and with no history of previous irradiation.

Patients and methods The study included 98 men (mean age 68 years) with urinary incontinence after prostatectomy for prostate cancer (85 radical, 13 transurethral resection) who had an AUS implanted. Twenty-two of the patients had received adjuvant external beam irradiation before AUS implantation. Over a mean (range) follow-up of 46 (5-118) months, the complication and surgical revision rates were recorded and compared between irradiated and unirradiated patients. The two groups were also compared for the resolution of incontinence and satisfaction, assessed using a questionnaire.

Results Overall, surgical revision was equally common in irradiated (36%) and unirradiated (24%) patients. After activating the AUS, urethral atrophy, infection and erosion requiring surgical revision were more common in irradiated patients (41% vs 11%; P <0.05); 70% of patients reported a significant improvement in continence, regardless of previous irradiation. Patient satisfaction remained high, with >80% of patients stating that they would undergo surgery again and/or recommend it to others, despite previous Irradiation and/or the need for surgical revision.

Conclusions Despite higher complication and surgical revision rates in patients who have an AUS implanted and have a history of previous Irradiation, the long-term continence and patient satisfaction appear not to be adversely affected.

Predicting forest attributes In southeast Alaska using artificial neural networks

Artificial neural network (ANN) methods are used to predict forest characteristics. The data source is the Southeast Alaska (SEAK) Grid Inventory, a ground survey compiled by the USDA Forest Service at several thousand sites. The main objective of this article is to predict characteristics at unsurveyed locations between grid sites. A secondary objective is to evaluate the relative performance of different ANNs. Data from the grid sites are used to train six ANNs: multilayer perceptron, fuzzy ARTMAP, probabilistic, generalized regression, radial basis function, and learning vector quantization. A classification and regression tree method is used for comparison. Topographic variables are used to construct models: latitude and longitude coordinates, elevation, slope, and aspect. The models classify three forest characteristics: crown closure, species land cover, and tree size/structure. Models are constructed using n-fold cross-validation. Predictive accuracy is calculated using a method that accounts for the influence of misclassification as well as measuring correct classifications. The probabilistic and generalized regression networks are found to be the most accurate. The predictions of the ANN models are compared with a classification of the Tongass national forest in southeast Alaska based on the interpretation of satellite imagery and are found to be of similar accuracy.