Edge Defection Prediction by Using Wavelet Compression

A method is presented for robust phase unwrapping based on instantaneous frequency estimation at multiple resolutions. Estimation is done of the frequency difference between resolution levels, resulting in asymptotically zero bias in the aliasing error. The frequency for the highest resolution level is found by the sum of difference frequencies, and the final frequency estimates are input to a least squares phase reconstruction algorithm. A hierarchical algorithm for frequency estimation at all resolution levels, based on the correlation Doppler centroid estimator, is derived. Also, an adaptive multiresolution approach is presented, in which the highest resolution level required for a frequency estimate without aliasing and thus a smooth phase reconstruction, is determined at each point. The method is investigated by simulations, and results show successful phase unwrapping without slope bias, even in the presence of undersampling and low coherence.

Data compression is a key technique for reducing the cost of data transfer from storage to compute nodes. Increasingly, modern data scales necessitate lossy compression techniques, where exactness is sacrificed for a smaller compressed representation. One challenge in lossy compression is that different applications may have different accuracy demands. Today's compression techniques struggle in this setting either forcing the user to compress at the strictest accuracy demand, or to re-encode the data at multiple resolutions. This paper proposes a simple, but effective multiresolution compression algorithm for time series data, where a single encoding can effectively be decompressed at multiple output resolutions. There are a number of benefits over current state-of-the-art techniques for time series compression. (1) The storage footprint of this encoding is smaller than re-encoding the data at multiple resolutions. (2) Similarly, the compression latency is generally smaller than re-encoding at multiple resolutions. (3) Finally, the decompression latency of our encoding is significantly faster than single encodings at the strictest accuracy demand.

Surakarta is one of the rapidly growing Indonesian cities. The pressure towards its peripheral area results in ‘in-situ urbanization’ phenomenon of its rural village surroundings. Gentan is one of Surakarta adjacent rural villages that has been undergoing rapid spatial transformation from rural to urban settlement in the last 20 years (1995-2016). This research aims to clarify the spatial transformation in Gentan village through examinations of its spatial elements on higher resolution level; (1) transformation of its street network connectivity, (2) land use pattern, (3) building density, and (4) public facilities and accessibility. Secondary data from satellite imagery and government institution and primary data from field survey were used in this research as sources. From Gentan’s spatial elements observations, this research concluded that this village was transforming into urban settlements by its spatial elements characteristics. Furthermore, this research provides interesting findings by its analysis on the neighborhood level that while Gentan was transforming into urban settlement, its internal connectivity was decreasing, residential uses dominated its land use, the village was dominated by the formal settlement, and the reach distance of its public facilities fluctuated across the time.

El conocimiento y explicación de la organización espacial y sus variaciones es inherente a la Geografía y constituye su esencia para la expresión gráfica (cartográfica). La organización espacial puede entenderse como una jerarquía encajonada de sistemas y subsistemas a diferentes escalas; por tanto requiere ser clasificada jerárquicamente y representada cartográficamente como espacios geográficos de diversos tamaños, dando cabida á la generalización cartográfica. Sin embargo, al revisar los materiales cartográficos prbducidos por diversas entidades nacionales es evidente el uso de clasificaciones planas o no jerarquizadas (a un único nivel). Este hecho obstaculiza el proceso de generalización cartográfica y presenta serias dificultades tanto conceptuales como de procedimiento en el momento de cambiar la escala cartográfica. En este trabajo se plantea una conceptualización de generalización cartográfica con base en la teoría jerárquicay la noción de nivel de resolución, y se muestra cómo la vocación aplicada o instrumental de la clasificación jerárquica es la cartográfica, puesto que cada nivel jerárquico necesita una dimensiórí espacial suficiente para expresarse.Definida la base conceptual, se propone un procedimiento de generalización cartográfica basado en un modelo jerárquico escalar, que define los conceptos que se desarrollan en cada nivel de la jerarquía y los rangos escalares en los que se representa cada nivel. Además se muestra el empleo correcto de las variables visuales que reflejan el nivel de la información y la clasificación jerárquica. La propuesta anterior se aplica en seis ejemplos de generalización para cartografia temática, en los cuales la clasificación jerárquica es fundamental para, a partir del nivel de mayor resolución, avanzar hacia niveles jerárquicos superiores que se representan en escalas cada vez menores.

Within the past few years considerations concerning the structure of the human retina and the perceptual system have led to increasing interest into hierarchically organized image description schemes (1). Such a hierarchical organization allows for controlling the simultaneous processing of information, on low levels of resolution, by information that itself was acquired on a high level of resolution. For example, the investigation of a certain object in an image can be established in an efficient way by determining the spatial location of that object on low resolution levels, followed by detailed analysis on higher resolution levels. In these and related techniques the resolution decreases with increasing height in the hierarchy and vice versa. As a side-effect the amount of noise may be reduced, or superfluous details suppressed. A degree of similarity of spatial distributions can be obtained from a comparison of their descriptions throughout a range of resolution levels. The greater the resolution range in which spatial distributions match, the greater is the degree of similarity between these distributions.

Stimulated emission depletion (STED), as one of the emerging super-resolution techniques, defines a state-of-the-art image resolution method. It has been developed into a universal fluorescent imaging tool over the past several years. The currently best available lateral resolution offered by STED is around 20 nm, but in real live cell imaging applications, the regular resolution offered through this mechanism is around 100 nm, limited by phototoxicity. Many critical biological structures are below this resolution level. Hence, it will be invaluable to improve the STED resolution through postprocessing techniques. We propose a deep adversarial network for improving the STED resolution significantly, which takes an STED image as an input, relies on physical modeling to obtain training data, and outputs a “self-refined” counterpart image at a higher resolution level. In other words, we use the prior knowledge on the STED point spread function and the structural information about the cells to generate simulated labeled data pairs for network training. Our results suggest that 30-nm resolution can be achieved from a 60-nm resolution STED image, and in our simulation and experiments, the structural similarity index values between the label and output result reached around 0.98, significantly higher than those obtained using the Lucy–Richardson deconvolution method and a state-of-the-art UNet-based super-resolution network.

Most of the content-based image retrieval systems require a distance computation of feature vectors for each candidate image in the image database. This exhaustive search is highly time-consuming and inefficient. This limits the usefulness of such system. Thus there is a growing need for a fast image retrieval system. Multiresolution data-structure algorithm provides a good solution to the above problem. In this paper we propose a wavelet-based multiresolution data-structure algorithm. Wavelet-based multiresolution data-structure further reduce the number of computation by around 50%. In the proposed approach we reuse the information obtained at lower resolution levels to calculate the distance at a higher resolution level. Apart from this, the proposed structure saves memory overheads by about 50% over multiresolution data-structure algorithm. The proposed algorithm can be easily combined with other algorithms for performance enhancement.4 In this paper we use the proposed technique to match luminance histogram for image retrieval. Fuzzy histograms enhances performance by considering the similarity between neighboring bins. We have extended the proposed approach to fuzzy histograms for better performance.

2-D local hp adaptive isogeometric analysis based on hierarchical Fup basis functions

We use the voids-within-voids-within-voids (VVV) simulations, a suite of successive nested N-body simulations with extremely high resolution (denoted, from low to high resolution, by L0 to L7), to test the Press–Schechter (PS), Sheth–Tormen (ST), and extended Press–Schechter (EPS) formulae for the halo abundance over the entire mass range, from minihaloes of 10−6 M⊙, to cluster haloes of 1015 M⊙, at different redshifts, from z = 30 to the present. We find that at z = 0 and z = 2, ST best reproduces the results of L0, which has the mean cosmic density (overdensity δ = 0), at 1011−15 M⊙. The higher resolution levels (L1–L7) are biased underdense regions (δ < −0.6). The EPS formalism takes this into account since it gives the mass function of a region conditioned, in this case, on having a given underdensity. EPS provides good matches to these higher levels, with deviations ≲20 per cent, at 10−6−12.5 M⊙. At z ∼ 7−15, the ST predictions for L0 and the EPS for L1–L7 show somewhat larger deviations from the simulation results. However, at even higher redshifts, z ∼ 30, EPS fits the simulations well again. We confirm our results by picking more subvolumes from the L0 simulation, finding that our conclusions depend only weakly on the size and overdensity of the region. The good agreement of EPS with the higher level simulations implies that PS (or ST) gives an accurate description of the total halo mass function in representative regions of the universe.

Observation of temperature-dependent heavy- and light-hole split direct bandgap and tensile strain from Ge0.985Sn0.015 using photoreflectance spectroscopy

BackgroundGiardia duodenalis (synonyms G. lamblia and G. intestinalis) is an enteric protozoan parasite of a wide range of mammalian hosts, including humans and various domestic and wild animals. There is considerable genetic variability in G. duodenalis and isolates of this parasite have been divided into eight genetic assemblages. Microsatellites markers can be used to discriminate isolates with a high level of sensitivity. This study was conducted to identify and characterize genomic microsatellites (simple sequence repeats—SSRs), sequences of one- to six-nucleotide motifs repeated in tandem, present in the available genomes of G. duodenalis and to develop new markers that can serve as a tool for detection and for characterizing the genetic diversity of this parasite.Methodology/ Principal findingsFor each genetic assemblage, polymorphism levels for the microsatellite markers were evaluated. After performing the analysis using the MISA and SciRoKo software, 1,853 simple sequence repeats (SSRs) were identified. In all the genomes, trinucleotide repeats were the most common class followed by tetranucleotide. Many of the SSR loci are assemblage-specific, and 36 SSR loci shared among all the genomes were identified. Together with hypothetical proteins, variant-specific surface proteins represented nearly half of the annotated SSR loci. The results regarding the most common repeat among the SSRs led us to infer that positive selection occurred to avoid frameshift mutations. Additionally, based on inter- and intra-genetic assemblages polymorphism analyses, we unveiled previously undetected genetic variation, indicating that the microsatellite markers we developed are useful molecular tools for epidemiological inferences based on population genetics patterns and processes.ConclusionsThere is increasing demand for the development of new molecular markers and for the characterization of pathogens at a higher resolution level. In this study, we present 60 G. duodenalis microsatellites markers that exhibited high polymerase chain reaction (PCR) amplification efficiency among the different genetic assemblages. Twenty of these markers presented nucleotide sequence polymorphisms and may be used as a genotyping tool. The monomorphic markers can be used for the detection of the parasite at the species and genetic assemblage level. These polymorphic markers revealed a genetic diversity that was previously undetectable, thus they can be considered valuable molecular tools for high resolution markers in future studies investigating Giardia and may also be used for epidemiological inferences based on populations genetics patterns and processes.

Three field‐identified whitefishCoregonus lavaretusforms in Lake Muddusjärvi, Finland, were compared in morphology, diet and prey size. First, these forms were studied with univariate and multivariate analysis to assess morphological divergence at a higher resolution level than in the field. Second, stomach contents were analysed to estimate diet‐overlap among forms. Finally, the relationship between prey size and morphology was examined. The whitefish were assigned to the initial field‐classification with 99·2% and 98·8% accuracy for morphologic and meristic traits, respectively. The small sparsely‐rakered form (SSR) had the shortest rakers and largest gillraker space, followed by the large sparsely‐rakered form (LSR) with intermediate gillraker length and gillraker space, while densely‐rakered whitefish (DR) had the longest rakers and smallest gillraker space. The two sparsely‐rakered whitefish forms (LSR and SSR), consumed mainly benthic macroinvertebrates, while densely‐rakered whitefish (DR), utilized pelagic food items. Average diet‐overlaps between whitefish forms were low in June‐September (Schoener's α = 0·02 − 0·23). Gillraker number and length were negatively correlated to prey length in the diet (r = −0·73, andr = −0·60), while gillraker space was positively correlated with prey length (r = 0·81). The fact that these whitefish forms were morphologically and ecologically segregated, and that gillraker traits probably have a functional value in food selection, further suggests that natural selection has been important in structuring life‐history trajectories into divergent niche use.

We introduce a compression algorithm using the wavelet transform. The principle of wavelet transform is to decompose hierarchically the input image into a series of successively lower resolution reference images and detail images that contain the information needed to be reconstructed back to the next higher resolution level. The histogram of image sub-bands provides information on the distribution of the coefficient values in this sub image. The sub band images resulting from wavelet transform are not equal significance. Some sub-bands contain more information than others. The total number of available bits describe an image is however, inevitably limited. Therefore, it is desirable to allocate more bits to those sub-band images and can be coded more accurately than others. The objective of such bit allocation method is to optimize the overall coder performance and minimize the quantization error. In determining which wavelet filter is to be used for image compression, some of the properties including vanishing moments are considered. The wavelet transform is implemented using a linear-phase biorthogonal filter (16,4) with 4 level of decomposition. For this study, we used a scalar quantization with uniform threshold quantizers. The quantization method is pulse code modulation (PCM) for the coefficients in all high-pass sub-bands. The coefficients of low-pass sub-band are differential PCM quantized per region.

Additive Watermarking in Filtered Satellite Images Using Alpha Blending

This dissertation presents a new method for the estimation of oceanic surface velocity vector fields using multiresolution. Multiresolution is the ability to analyze a signal at various levels of resolution. In this dissertation, wavelet analysis is used to obtain multiresolution. This method requires the use of two sea surface temperature (SST) images of the same region taken within a known time interval. Wavelet analysis is performed in both images to reveal their components at decreasing resolution levels. The comparison of these components at each level yields vector fields representing local displacements of features within the images. These vector fields are smoothed to eliminate noise and to produce coherent vector fields at each resolution level. Vector fields produced with images at levels with higher resolutions are used as refinements to vector fields found at lower resolution levels. Analysis of the operational parameters for the new method are presented in this dissertation. Artificial SST images and NOAA satellite SST images are used to test its accuracy. Comparison with drifter information is also performed. Extensions to the basic algorithm that improve the accuracy of estimations are also discussed. Finally, foreshadowing of the use of the new technique in GOES satellite images and hurricane imagery concludes this dissertation.