Stack-run adaptive wavelet image compression

We report on the development of an adaptive wavelet image coder based on stack-run representation of the quantized coefficients. The coder works by selecting an optimal wavelet packet basis for the given image and encoding the quantization indices for significant coefficients and zero runs between coefficients using a 4-ary arithmetic coder. Due to the fact that our coder exploits the redundancies present within individual subbands, its addressing complexity is much lower than that of the wavelet zerotree coding algorithms. Experimental results show coding gains of up to 1:4dB over the benchmark wavelet coding algorithm

PDF Estimation and Liquid Water Content Based Attenuation Modeling for Fog in Terrestrial FSO Links

Terrestrial Free-space optical communication (FSO) links have yet to achieve a mass market success due to the ever elusive 99.999% availability requirement. The terrestrial FSO links are heavily affected by atmospheric fog. To design systems which can achieve high availability and reliability in the presence of fog, accurate and better models of fog attenuation need to be developed. The current article puts forth appropriate probability density function estimates for received signal strength (hereafter RSS) under fog conditions, where variations in the RSS during foggy events have been statistically characterized. Moreover, from the surface observations of fog density, liquid water content (hereafter LWC) of fog is estimated. The actual measured optical attenuations are then compared with the optical attenuations estimated from LWC. The results presented suggest that fog density measurements carried out are accurate representation of the fog intensity and the attenuation predictions obtained by the LWC estimate match the actual measured optical attenuations. This suggests that the LWC is a useful parameter besides visibility range to predict optical attenuations in the presence of hydrometeors

Evaluation of social personalized adaptive E-Learning environments : end-user point of view

The use of adaptations, along with the social affordances of collaboration and networking, carries a great potential for improving e-learning experiences. However, the review of the previous work indicates current e-learning systems have only marginally explored the integration of social features and adaptation techniques. The overall aim of this research, therefore, is to address this gap by evaluating a system developed to foster social personalized adaptive e-learning experiences. We have developed our first prototype system, Topolor, based on the concepts of Adaptive Educational Hypermedia and Social E-Learning. We have also conducted an experimental case study for the evaluation of the prototype system from different perspectives. The results show a considerably high satisfaction of the end users. This paper reports the evaluation results from end user point of view, and generalizes our method to a component-based evaluation framework

Talking the talk, but not walking the walk: a comparison of self-reported and observed prosocial behavior

The claim that Public Service Motivation (PSM) is an antecedent of prosocial behaviour has often been empirically tested and supported. However, closer inspection of this literature reveals large disparities in relating the two constructs. One reason that could explain such differences is that the relationship between PSM and prosocial behaviours has been primarily tested using self‐reported cross‐sectional, single‐rater and same‐survey data. While all of these are widely used methodological approaches in social sciences, they are also susceptible to potential biases. We conduct two comparative studies to re‐examine this relationship. Study 1 utilizes self‐reported cross‐sectional, single‐rater and same‐survey data linking PSM and prosocial behaviour, revealing a positive relationship with PSM's Compassion dimension. Study 2 involves observing actual prosocial behaviour in a real‐life setting. Then, the correlation between PSM and prosocial behaviour disappears. We conclude by discussing the possible reasons that could lead to the differences found across the two studies

Economics of nitrogen and integrated weed management in dry seeded rice

Dry-seeded rice (DSR) is an emerging production technology in many Asian countries, whose profitability is higher than puddled transplanted rice. However, weed infestations are severe in the DSR. To increase the competitiveness with weeds and achieve the yield potential of rice, weed management in DSR needs an integration of herbicides and higher nitrogen (N) fertilizer rates. Field experiments were conducted in the aman (wet season) 2012 and 2013 in Bangladesh to evaluate the effect of N rates (0, 80, 120, and 160 kg ha) and weed control methods [one hand weeding (HW); pendimethalin 1000 g ai ha followed by (fb) ethoxysulfuron 20 g ai ha; pendimethalin fb ethoxysulfuron fb one HW; and weed-free] on weed growth and crop yield in DSR. The experiment was arranged in a split-plot design with three replications. The highest grain yield (5.3 to 5.5 t ha) was recorded in the season-long manual weed free treatment when N rate was160 kg ha; however, because of the high cost of labor, this method was not profitable. The use of pendimethalin fb ethoxysulfuron fb one HW effectively controlled weeds and produced a similar yield with the weed-free treatment for all levels of N. However, weed management cost was also higher because of the involvement of one HW. Although pendimethalin fb ethoxysulfuron treatment had always lower yielded than the pendimethalin fb ethoxysulfuron fb one HW, grain yield increased and net profit was similar when N rate increased from 120 to 160 kg ha. Considering weed control efficiency, yield, and economics, pendimethalin fb ethoxysulfuron fb one HW with 120 kg N ha may be recommended to growers. However, if laborers are not available for hand weeding, pendimethalin fb ethoxysulfuron with 160 kg N ha is the best option to achieve high yield in DSR

Probabilistic Model for Free-Space Optical Links Under Continental Fog Conditions

The error characteristics of a free-space optical (FSO) channel are significantly different from the fiber based optical links and thus require a deep physical understanding of the propagation channel. In particular different fog conditions greatly influence the optical transmissions and thus a channel model is required to estimate the detrimental fog effects. In this paper we shall present the probabilistic model for radiation fog from the measured data over a 80 m FSO link installed at Graz, Austria. The fog events are classified into thick fog, moderate fog, light fog and general fog based on the international code of visibility range. We applied some probability distribution functions (PDFs) such as Kumaraswamy, Johnson SB and Logistic distribution, to the actual measured optical attenuations. The performance of each distribution is evaluated by Q-Q and P-P plots. It is found that Kumaraswamy distribution is the best fit for general fog, while Logistic distribution is the optimum choice for thick fog. On the other hand, Johnson SB distribution best fits the moderate and light fog related measured attenuation data. The difference in these probabilistic models and the resultant variation in the received signal strength under different fog types needs to be considered in designing an efficient FSO system

Vestibular schwannomas: Clinical presentation, management and outcome

Objective: To review the demographic trends clinical spectrum, diagnosis, management and outcome of patients with vestibular Schwannoma and to identify areas where improvements are needed.Methods: All patients with vestibular schwannoma admitted to the Aga Khan University Hospital over the past 11 years were reviewed retrospectively.Results: The age range of majority of 22 patients analyzed, was 41-50 years (23%). Hearing loss was the most common presenting symptom (96%). Other clinical features included cranial nerve palsies (59%) and headache (55%). Fifty percent had signs of raised intracranial pressure. Neuroimaging revealed Stage IV b (tumor distorting the brainstem and compressing the 4th ventricle) in 50% cases. Neurosurgical intervention was carried out in 86%; mainly using the retrosigmoid approach. Postoperative complications included facial nerve palsy in 13 (65%) and hydrocephalus in 5 (25%) patients. Hearing determined clinically was preserved in three patients (14%). One patient died during the inpatient stay.Conclusion: Presentation of these patients is late and the outcome is poor

Deep Learning and Image data-based surface cracks recognition of laser nitrided Titanium alloy

Laser nitriding, a high-precision surface modification process, enhances the hardness, wear resistance and corrosion resistance of the materials. However, laser nitriding process is prone to appearance of cracks when the process is performed at high laser energy levels. Traditional techniques to detect the cracks are time consuming, costly and lack standardization. Thus, this research aims to put forth deep learning-based crack recognition for the laser nitriding of Ti–6Al–4V alloy. The process of laser nitriding has been performed by varying duty cycles, and other process parameters. The laser nitrided sample has then been processed through optical 3D surface measurements (Alicona Infinite Focus G5), creating high resolution images. The images were then pre-processed which included 2D conversion, patchification, image augmentation and subsequent removal of anomalies. After preprocessing, the investigation focused on employing robust binary classification method based on CNN models and its variants, including ResNet-50, VGG-19, VGG-16, GoogLeNet (Inception V3), and DenseNet-121, to recognize surface cracks. The performance of these models has been optimized by fine tuning different hyper parameters and it is found that CNN base model along with models having less trainable parameters like VGG-19, VGG-16 exhibit better performance with accuracy of more than 98% to recognize cracks. Through the achieved results, it is found that VGG-19 is the most preferable model for this crack recognition problem to effectively recognize the surface cracks on laser nitrided Ti–6Al–4V material, owing to its best accuracy and lesser parameters compared to complex models like ResNet-50 and Inception-V3