NOISE REDUCTION IN CONTOUR LINES AND SLOPE MAPS FROM MEDIUM/HIGH-DENSITY LIDAR DATA

The use of medium/high-density LIDAR (Light Detection And Ranging) data for land modelling and DTM (Digital TerrainModel) is becoming more widespread. This level of detail is difficult to achieve with other means or materials. However,the horizontal and vertical geometric accuracy of the LIDAR points obtained, although high, is not homogeneous.Horizontally you can reach precisions around 30-50 cm, while the vertical precision is rarely greater than 10-15 cm. Theresult of LIDAR flights, are clouds of points very close to each other (30-60 cm) with significant elevation variations, evenif the terrain is flat. And this makes the triangulated models TIN (Triangulated Irregular Network) obtained from such LIDARdata especially chaotic. Since contour lines are generated directly from such triangulated models, their appearance showsexcessive noise, with excessively broken and rapidly closed on themselves. Getting smoothed contour liness, withoutdecreasing accuracy, is a challenge for terrain model software. In addition, triangulated models obtained from LIDAR dataare the basis for future slope maps of the land. And for the same reason explained in the previous paragraph, these slopemaps generated from high or medium density LIDAR point clouds are especially heterogeneous. Achieving uniformity andgreater adjustment to reality by reducing the natural noise of LIDAR data is another added challenge. In this paper, theproblem of excessive noise from LIDAR data of high (around 8 points/m2) and medium density (around 2 points/m2) in thegeneration of contour lines and terrain slope maps is raised and solutions are proposed to reduce this noise. All this, in thearea of specific software for the management of TIN models and GIS (Geographic Information System) and adapting thealternatives proposed by these programmes.

How Wildlife Respond to Natural Noise

Animal sensory systems have evolved in a natural din of noise since the evolution of sensory organs. Anthropogenic noise is a recent addition to the environment, which has had demonstrable, largely negative, effects on wildlife. Yet, we know relatively little about how animals respond to natural sources of noise, which can differ substantially in acoustic characteristics from human-caused noise. Here we review the noise literature and suggest an evolutionary approach for framing the study of novel, anthropogenic sources of noise. We also push for a more quantitative approach to acoustic ecology research. To build a better foundation around the effects of natural noise on wildlife, we experimentally and continuously broadcast whitewater river noise across a landscape for three summers. Additionally, we use spectrally-altered river noise to explicitly test the effects of masking as a mechanism driving patterns. We then monitored bird, bat, and arthropod abundance and activity and assessed predator-prey relationships with bird and bat foraging assays and by counting prey in spider webs. Birds and bats largely avoided high sound levels in noisy environments. Bats also avoided acoustic environments dominated by high frequency noise while birds avoided noise that overlapped with their song, the latter trend suggesting that communication is impaired. Yet, when sound levels were high overlapping noise was not any more disruptive than non-overlapping noise, which suggests that intense noise interferes with more than communication. Avoidance of noise that overlapped in frequency with song was stronger for low-frequency singers. Bats that employ higher frequency echolocation were more likely to avoid high sound level noise; we explore potential explanations for this pattern. Most arthropod Orders responded to noise, yet the directions of effects were not consistent across taxa. Some arthropods increased in abundance in high sound level areas - perhaps in response to the absence of bird and bat predators. Reinforcing this possibility, visually foraging birds and passively listening bats decreased foraging effort beyond what was expected based on declines in abundance and activity. Orb-weaving spiders increased dramatically in high sound level areas, which could be due to a release from predation, an increase in prey capture, or direct attraction to high sound level river noise. Overall, we demonstrated significant changes to many vertebrate and invertebrate taxa during playback of whitewater river noise. We were able to parse out the effects of sound pressure level and background frequency on these individual taxa and predator-prey behaviors. Our results reveal that animals have likely long been affected by particular characteristics of noise, which may help explain contemporary responses to anthropogenic noise. As the spatial and temporal footprint of anthropogenic noise is orders of magnitude greater than intense natural acoustic environments, the insights provided by our data increase the importance of mitigating noise pollution impacts on animals and their habitats. It is clear that natural noise has the power to alter animal abundances and behavior in a way that likely reverberates through entire communities and food webs. Future work should focus on strengthening the relationships between these potential predators and prey and highlight how the structure of the system changes under such noise treatments.

Natural Noise Can Influence Bird Foraging and Vigilance Behavior

Natural sounds are an often overlooked, yet important component of an animal's habitat. The acoustic environment may be especially significant during foraging, because a noisy world can limit auditory surveillance. Here, we investigated how natural noise structures the foraging vigilance trade-off to understand how intense acoustic environments may have shaped antipredator behavior across the evolutionary past, and better inform conservation efforts in the present. First, in Chapter 1, I directly compared the foraging and vigilance behaviors of captive song sparrows (Melospiza melodia) in anthropogenic and natural noise. We recorded foraging trials in 4 playback conditions (roadway traffic, whitewater rivers, whitewater rivers shifted upwards in spectrum, and amplitude-modulated rivers), along with an ambient control to assess which acoustic characteristics make a foraging habitat risky. We found that sparrows increased vigilance or decreased foraging in 4 of 6 behaviors when foraging in higher sound levels, regardless of playback type, indicating a broad role for noise in antipredator behavior. Next, in Chapter 2, I sought to understand the ecological relevance of these findings by examining wild bird behavior. To do so, we broadcast the same whitewater river noise as used in our lab experiment across a riparian landscape. To understand if the spectra of the acoustic environment affected bird behavior, we also presented spectrally-shifted whitewater noise to produce a gradient of frequencies. Using 18 bird feeders placed across this landscape, we recorded and analyzed behavior of the three most common bird species. Black-headed grosbeaks (Pheucticus melanocephalus) and lazuli buntings (Passerina amoena) demonstrated an increase in at least one vigilance behavior in high sound levels, while American goldfinches (Spinus tristis) and grosbeaks altered some behaviors according to background frequency. Clearly, adjusting antipredator behavior in noise is conserved across diverse bird species. Taken together, our findings imply that natural soundscapes have likely shaped behavior long before anthropogenic noise, and that high sound levels negatively affect the foraging vigilance trade-off in both anthropogenic and naturally intense acoustic environments. These results are concerning in light of ever-increasing anthropogenic noise pollution.

Critique on Natural Noise in Recommender Systems

Recommender systems have been upgraded, tested, and applied in many, often incomparable ways. In attempts to diligently understand user behavior in certain environments, those systems have been frequently utilized in domains like e-commerce, e-learning, and tourism. Their increasing need and popularity have allowed the existence of numerous research paths on major issues like data sparsity, cold start, malicious noise, and natural noise, which immensely limit their performance. It is typical that the quality of the data that fuel those systems should be extremely reliable. Inconsistent user information in datasets can alter the performance of recommenders, albeit running advanced personalizing algorithms. The consequences of this can be costly as such systems are employed in abundant online businesses. Successfully managing these inconsistencies results in more personalized user experiences. In this article, the previous works conducted on natural noise management in recommender datasets are thoroughly analyzed. We adequately explore the ways in which the proposed methods measure improved performances and touch on the different natural noise management techniques and the attributes of the solutions. Additionally, we test the evaluation methods employed to assess the approaches and discuss several key gaps and other improvements the field should realize in the future. Our work considers the likelihood of a modern research branch on natural noise management and recommender assessment.

Modern Approaches to Creating Highly Undetectable Stegosystems (HUGO Systems)

This chapter's primary goal is to provide a comprehensive approach to the development of new highly undetectable stegosystems that greatly complicate their steganalysis. The authors propose several implementations of highly undetectable stegosystems, the so-called HUGO systems, using an integrated approach to their synthesis. This approach most fully considers the features of transmitting hidden messages over highly noisy communication channels. At the stage of embedding hidden messages, the authors suggest actively using their discrete transformations. The authors also propose increasing the secrecy of secret messages by converting them to a form that resembles natural noise. The authors use a discrete chaotic decomposition of the Arnold cat map (ACM) to do this. The authors also suggest using highly efficient noise-tolerant encoding and multi-threshold decoding to combat interference in the communication channel and an embedding algorithm. The authors also describe two original stegosystems ±HUGO and ⨁HUGO and test results confirming their effectiveness.

Robust Indoor Localization Methods Using Random Forest-Based Filter against MAC Spoofing Attack

With the development of wireless networks and mobile devices, interest on indoor localization systems (ILSs) has increased. In particular, Wi-Fi-based ILSs are widely used because of the good prediction accuracy without additional hardware. However, as the prediction accuracy decreases in environments with natural noise, some studies were conducted to remove it. So far, two representative methods, i.e., the filtering-based method and deep learning-based method, have shown a significant effect in removing natural noise. However, the prediction accuracy of these methods severely decreased under artificial noise caused by adversaries. In this paper, we introduce a new media access control (MAC) spoofing attack scenario injecting artificial noise, where the prediction accuracy of Wi-Fi-based indoor localization system significantly decreases. We also propose a new deep learning-based indoor localization method using random forest(RF)-filter to provide the good prediction accuracy under the new MAC spoofing attack scenario. From the experimental results, we show that the proposed indoor localization method provides much higher prediction accuracy than the previous methods in environments with artificial noise.

Adaptive Wiener Filter and Natural Noise to Eliminate Adversarial Perturbation

Deep neural network has been widely used in pattern recognition and speech processing, but its vulnerability to adversarial attacks also proverbially demonstrated. These attacks perform unstructured pixel-wise perturbation to fool the classifier, which does not affect the human visual system. The role of adversarial examples in the information security field has received increased attention across a number of disciplines in recent years. An alternative approach is “like cures like”. In this paper, we propose to utilize common noise and adaptive wiener filtering to mitigate the perturbation. Our method includes two operations: noise addition, which adds natural noise to input adversarial examples, and adaptive wiener filtering, which denoising the images in the previous step. Based on the study of the distribution of attacks, adding natural noise has an impact on adversarial examples to a certain extent and then they can be removed through adaptive wiener filter, which is an optimal estimator for the local variance of the image. The proposed improved adaptive wiener filter can automatically select the optimal window size between the given multiple alternative windows based on the features of different images. Based on lots of experiments, the result demonstrates that the proposed method is capable of defending against adversarial attacks, such as FGSM (Fast Gradient Sign Method), C&W, Deepfool, and JSMA (Jacobian-based Saliency Map Attack). By compared experiments, our method outperforms or is comparable to state-of-the-art methods.

Belief Merging Operators as Maximum Likelihood Estimators

We study how belief merging operators can be considered as maximum likelihood estimators, i.e., we assume that there exists a (unknown) true state of the world and that each agent participating in the merging process receives a noisy signal of it, characterized by a noise model. The objective is then to aggregate the agents' belief bases to make the best possible guess about the true state of the world. In this paper, some logical connections between the rationality postulates for belief merging (IC postulates) and simple conditions over the noise model under consideration are exhibited. These results provide a new justification for IC merging postulates. We also provide results for two specific natural noise models: the world swap noise and the atom swap noise, by identifying distance-based merging operators that are maximum likelihood estimators for these two noise models.

Ambient noise from ocean surf drives frequency shifts in non-passerine bird song

Effective communication in birds is often hampered by background noise, with many recent studies focusing on the effect of anthropogenic noise on passerine bird song. Continuous low-frequency natural noise is predicted to drive changes in both frequency and temporal patterning of bird vocalizations, but the extent to which these effects may also affect birds that lack vocal learning is not yet fully understood. Here we use a gradient of exposure to natural low-frequency noise to assess whether it exerts selective pressure on vocalizations in a species whose songs are innate. We tested whether three species of Pogoniulus tinkerbirds adapt their song when exposed to a source of continuous low-frequency noise from ocean surf. We show that dominant frequency increases the closer birds are to the coast in all the three species, and in line with higher noise levels, indicating that ocean surf sound may apply a selective pressure on tinkerbird songs. As a consequence, tinkerbirds adapt their songs with an increase in frequency to avoid the masking effect due to overlapping frequencies with ambient noise, therefore improving long-range communication with intended receivers. Our study provides for the first time, compelling evidence that natural ambient noise affects vocalizations in birds whose songs are developed innately. We believe that our results can also be extrapolated in the context of anthropogenic noise pollution, hence providing a baseline for the study of the effects of low-frequency ambient noise on birds that lack vocal learning.Significance StatementBirdsong is constantly under selection as it mediates key interactions such as mate attraction, competition with same-sex individuals for reproduction and competition with heterospecifics for space-related resources. Any phenomenon that interferes with communication can therefore have a profound impact on individual fitness. Passerines are more likely to avoid the masking effect of background noise because of their higher vocal flexibility. Many non-passerine species lacking such flexibility might therefore be more vulnerable to the negative effects on their fitness of exposure to low-frequency background noise. Species incapable of adapting their signals to background noise are predicted to disappear from noisy areas. Despite this, we show that species that lack song learning may show an adaptive response to natural noise which may develop over evolutionary timescales.