No abstract

A technique is described that allows unimodal function optimization methods to be extended to locate all optima of multimodal problems efficiently. We describe an algorithm based on a traditional genetic algorithm (GA). This technique involves iterating the GA but uses knowledge gained during one iteration to avoid re-searching, on subsequent iterations, regions of problem space where solutions have already been found. This gain is achieved by applying a fitness derating function to the raw fitness function, so that fitness values are depressed in the regions of the problem space where solutions have already been found. Consequently, the likelihood of discovering a new solution on each iteration is dramatically increased. The technique may be used with various styles of GAs or with other optimization methods, such as simulated annealing. The effectiveness of the algorithm is demonstrated on a number of multimodal test functions. The technique is at least as fast as fitness sharing methods. It provides an acceleration of between 1 and l0p on a problem with p optima, depending on the value of p and the convergence time complexity.

At present, WLANs supporting broadband multimedia communication are being developed and standardized around the world. Standards include HIPERLAN/2, defined by ETSI BRAN, 802.11a, defined by the IEEE, and HiSWANa defined by MMAC. These systems provide channel adaptive data rates up to 54 Mb/s (in a 20 MHz channel spacing) in the 5 GHz radio band. In this article an overview of the HIPERLAN/2 and 802.11a standards is presented together with software simulated physical layer performance results for each of the defined transmission modes. Furthermore, the differences between these two standards are highlighted (packet size, upper protocol layers etc.), and the effects of these differences on throughput are analyzed and discussed.

No abstract

Restoring a scene distorted by atmospheric turbulence is a challenging problem in video surveillance. The effect, caused by random, spatially varying, perturbations, makes a model-based solution difficult and in most cases, impractical. In this paper, we propose a novel method for mitigating the effects of atmospheric distortion on observed images, particularly airborne turbulence which can severely degrade a region of interest (ROI). In order to extract accurate detail about objects behind the distorting layer, a simple and efficient frame selection method is proposed to select informative ROIs only from good-quality frames. The ROIs in each frame are then registered to further reduce offsets and distortions. We solve the space-varying distortion problem using region-level fusion based on the dual tree complex wavelet transform. Finally, contrast enhancement is applied. We further propose a learning-based metric specifically for image quality assessment in the presence of atmospheric distortion. This is capable of estimating quality in both full- and no-reference scenarios. The proposed method is shown to significantly outperform existing methods, providing enhanced situational awareness in a range of surveillance scenarios.

Recent improvements in the frequency, type, and availability of satellite images mean it is now feasible to routinely study volcanoes in remote and inaccessible regions, including those with no ground‐based monitoring. In particular, Interferometric Synthetic Aperture Radar data can detect surface deformation, which has a strong statistical link to eruption. However, the data set produced by the recently launched Sentinel‐1 satellite is too large to be manually analyzed on a global basis. In this study, we systematically process >30,000 short‐term interferograms at over 900 volcanoes and apply machine learning algorithms to automatically detect volcanic ground deformation. We use a convolutional neutral network to classify interferometric fringes in wrapped interferograms with no atmospheric corrections. We employ a transfer learning strategy and test a range of pretrained networks, finding that AlexNet is best suited to this task. The positive results are checked by an expert and fed back for model updating. Following training with a combination of both positive and negative examples, this method reduced the number of interferograms to ∼100 which required further inspection, of which at least 39 are considered true positives. We demonstrate that machine learning can efficiently detect large, rapid deformation signals in wrapped interferograms, but further development is required to detect slow or small deformation patterns which do not generate multiple fringes in short duration interferograms. This study is the first to use machine learning approaches for detecting volcanic deformation in large data sets and demonstrates the potential of such techniques for developing alert systems based on satellite imagery.

No abstract

The goal of segmentation is to partition an image into disjoint regions, in a manner consistent with human perception of the content. For unsupervised segmentation of general images, however, there is the competing requirement not to make prior assumptions about the scene. Here, a two-stage method for general image segmentation is proposed, which is capable of processing both textured and nontextured objects in a meaningful fashion. The first stage extracts texture features from the subbands of the dual-tree complex wavelet transform. Oriented median filtering is employed, to circumvent the problem of texture feature response at step edges in the image. From the processed feature images, a perceptual gradient function is synthesised, whose watershed transform provides an initial segmentation. The second stage of the algorithm groups together these primitive regions into meaningful objects. To achieve this, a novel spectral clustering technique is proposed, which introduces the weighted mean cut cost function for graph partitioning. The ability of the proposed algorithm to generalize across a variety of image types is demonstrated.