Pixel Research Articles

Selko Suomessa - selkokielen kehitys ja ja sovelluksia

Katsaus käy läpi suomenkielisen selkokielen kehityksen vaiheita Suomessa 1980-luvulta nykypäivään. Katsauksessa kerrotaan käytännön toimista selkokielen kehittämiseksi sekä tutkimuksesta, jota selkokielestä on tehty. Selkokieltä alettiin kehittää Suomessa 1980-luvulla. Äänekkäimmin selkokieltä vaativat kehitysvammahuollon toimijat, vaikkakin selkokielen hyöty monille muillekin ihmisryhmille tunnistettiin. Selkokieltä on kehitetty käytännön työn ja kohderyhmätestauksen avulla. Teoreettisempaa tukea on haettu esimerkiksi erityispedagogiikasta sekä viestinnän- ja muistintutkimuksen aloilta. Selkokielestä kielimuotona on vielä melko vähän tutkimusta, mutta tilanne on muuttumassa selkokielen tunnettuuden ja käytön lisääntyessä. Selkokielen määritelmän mukaan selkokieli on tekstin, sanaston ja rakenteiden tasoilla helpompaa kuin yleiskieli. Selkokieleen liittyy olennaisesti myös visuaalisuus, esimerkiksi kuvituksen ja taiton ohjeistus. Selkokieltä on pitkään sovellettu myös vuorovaikutukseen. Selkokielen tarve voi johtua pysyvistä neurobiologisista syistä (esim. kehitysvamma, autismin kirjo), heikentyneistä kielellisistä taidoista (esim. muistisairaudet ja aivoverenkierron häiriöt) tai todennäköisesti kehittyvistä kielellistä taidoista (esim. uuden kielen opiskelu). Selkokielen tarkoitus on estää kielellistä syrjäytymistä sekä mahdollistaa tasa-arvoisempaa ja itsenäisempää elämää henkilöille, joille yleiskieli on liian vaikeaa. Selkokielellä on julkaistu pitkään esimerkiksi kirjoja ja lehtiä sekä tiedotusmateriaaleja (mm. äänestäminen, Kelan etuudet).

LEARNING INVARIANT COLOUR FEATURES FOR PERSON REIDENTIFICATION

In this examination we have proposed Learning invariant shading highlights for individual recognizable proof utilizing human face for high proficient flag exchange framework applications. In this paper, we have a tendency to propose an information driven approach for taking in shading designs from pixels examined from pictures crosswise over to camera sees. The instinct behind this work is that, even assuming picture element values of same colour would wander across views, they thought to be encoded with indistinguishable qualities. We tend to model colour feature age as a learning drawback by together learning a direct transformation and a wordbook to write in code picture component esteems. We have a tendency to conjointly dissect entirely unexpected estimating invariant shading zones. Abuse shading in light of the fact that the exclusively prompt, we tend to contrast our approach and all the estimating invariant shading zones and show better execution over every one of them. Overwhelming pivoted nearby double example is anticipated yields higher execution. This paper proposes a totally exceptional strategy of characterizing the outer body part misuse Convolutional Neural Network.

Image Texture Detection and Extraction based on Entropy and Eigenvectors of growing seeds

Texture determination, segmentation and extraction is always a field of research and development at image processing and computer vision science. Since there are no clear and general definitions of texture, all known is that texture depends on local pixels gray levels with respect to their spatial position. There has been quite some efforts put on this topic but nearly all of the presented techniques are either very complicated and requiring too much computations, not covering all textures presented in scene or need supervision and databases for training. At this paper a new approach is presented which is completely unsupervised, rapid and adoptable to increase efficiency and accuracy. It also needs no training sequences or databases and can be utilized to identify objects as texture components. The presented approach acquires texture identification features with respect to image areas entropies and basic neighborhoods eigenvectors. After re-defining these parameters for all possible pixel divisions at the image segments with optimal size with respect to the image geometries, certain or random points are chosen as texture seeds and grown up until new additions vary the segment’s entropy dramatically. After this the set of largest grown and separately located areas are introduced in sequence as discovered texture segments. Furthermore different variations of this method can be developed for different functionality properties.

Exploration of temperature effect on videogrammetric technique for displacement monitoring

There has been a sustained interest towards the non-contact structural displacement measurement by means of videogrammetric technique. On the way forward, one of the major concerns is the spurious image drift induced by temperature variation. This study therefore carries out an investigation into the temperature effect of videogrammetric technique, focusing on the exploration of the mechanism behind the temperature effect and the elimination of the temperature-caused measurement error. 2D videogrammetric measurement tests under monotonic or cyclic temperature variation are first performed. Features of measurement error and the casual relationship between temperature variation and measurement error are then studied. The variation of the temperature of digital camera is identified as the main cause of measurement error. An excellent linear relationship between them is revealed. After that, camera parameters are extracted from the mapping between world coordinates and pixels coordinates of the calibration targets. The coordinates of principle point and focal lengths show variations well correlated with temperature variation. The measurement error is thought to be an outcome mainly attributed to the variation of the coordinates of principle point. An approach for eliminating temperature-caused measurement error is finally proposed. Correlation models between camera parameters and temperature are formulated. Thereby, camera parameters under different temperature conditions can be predicted and the camera projective matrix can be updated accordingly. By reconstructing the world coordinates with the updated camera projective matrix, the temperature-caused measurement error is eliminated. A satisfactory performance has been achieved by the proposed approach in eliminating the temperature-caused measurement error.

Skin Diseases Classification Using Deep Leaning Methods.

Due to the high incidence of skin tumors, the development of computer aided-diagnosis methods will become a very powerful diagnosis tool for dermatologists. The skin diseases are initially diagnosed visually, through clinical screening and followed in some cases by dermoscopic analysis, biopsy and histopathological examination. Automatic classification of dermatoscopic images is a challenge due to fine-grained variations in lesions. The convolutional neural network (CNN), one of the most powerful deep learning techniques proved to be superior to traditional algorithms. These networks provide the flexibility of extracting discriminatory features from images that preserve the spatial structure and could be developed for region recognition and medical image classification. In this paper we proposed an architecture of CNN to classify skin lesions using only image pixels and diagnosis labels as inputs. We trained and validated the CNN model using a public dataset of 10015 images consisting of 7 types of skin lesions: actinic keratoses and intraepithelial carcinoma/Bowen disease (akiec), basal cell carcinoma (bcc), benign lesions of the keratosis type (solar lentigine/seborrheic keratoses and lichen-planus like keratosis, bkl), dermatofibroma (df), melanoma (mel), melanocytic nevi (nv) and vascular lesions (angiomas, angiokeratomas, pyogenic granulomas and hemorrhages, vasc).

Function-topological model of urban space sectoral zoning

This study supplements the function-topological model of analysis of city organization and development with a sectoral model of administrative-territorial division. The function-topological model allows revealing unique features of objects of the same profile, located in different space sectors. Besides, significant objects are usually placed in strictly defined sectors. Identification of general principles of spatial arrangement at all levels from geopolitics to the structure of private farmsteads on the cases of various cities, metropolitan areas and architectural ensembles is of great general scientific significance. It deals with the principles described in regional traditions: the Indian Vastu Vidya and the Chinese Feng Shui. The analysis shows that, on an intuitive level, the same principles are implemented in the activities of specialists, designers, who make decisions about the location of industries, residential areas and architectural complexes. Recognition and study of these principles are hindered by the materialistic attitudes of twentieth-century science. The results of the study lead to a radical step in recognizing many spatial elements of the mythological picture of the world as reliable ones.

Компьютерная визуализация русской языковой картины мира

В статье рассматривается визуализация русской языковой картины мира. Языковая картина мира является представлением о мире, сложившимся в обыденном сознании данного языкового коллектива. Концепт представляет собой основную единицу языковой картины мира, которая может реализоваться в слове, словосочетании, высказывании, дискурсе, тексте и т.д. Семантические поля, с помощью которых можно описывать лексическую систему языка, являются отражениями концептов. Существует много способов представления семантических полей: интеллект-карта, сервис RusVectōrēs, Облако тегов и т.д. Анализ словарных толкований слов, в которых отражается заданный концепт, является одной из основных методик представления концептов. В статье мы анализируем русскую языковую картину мира на примере трех концептов: империя, государство, власть с помощью словарных статей слов империи, государства и власти из разных толковых словарей русского языка. В работе описываются разные способов визуализации семантических полей. Рассматрив аются интеллект-карты как один из важных способов визуализации семантических полей слов, в которых отражаются концептов. Также описывается программное обеспечение для формирования интеллект-карт, в частности, MindMeister, XMind, Freemind, MindManager, и Coogle.

Improvement of beam quality by pump homogenization of Innoslab laser amplifier

The effect of pump homogenization on the beam quality of the output laser is studied for a compact partially end-pumped hybrid-cavity slab laser amplifier, i.e. Innoslab laser amplifier. Using ZEMAX simulation software, two pump coupling modes, direct imaging and homogenization with waveguide were designed, and their effects on the uniformity of light intensity distribution in the slow axis (crystal width) direction of laser diode arrays were compared. It is found that the pump uniformity is significantly improved after the waveguide is homogenized and the influence of the laser diode array luminescence dead pixels on the pump imaging is weakened. Furthermore, the effect of pumping homogenization on the performance of Innoslab laser amplifier is verified by experiments: the uniformity of slow-axis pumping increases from 90.6% to 95.4% after homogenization with waveguide, and the quality of output laser beam M2 increases from 2.41 to 1.55 after three-pass amplification, and the self-oscillation in the cavity is effectively suppressed.

«Chaos and Clues» algorithm in routine practice of analyzing dermatoscopic image

The article describes theoretical bases and practical aspects of the use in clinical practice of the «Chaos and Clues» algorithm of dermatoscopic diagnostics of pigmented skin lesions developed by a group of scientists from different countries of the world. The article provides a detailed description of the application of this algorithm in conditions of real clinical practice. It was emphasized that the use of dermatoscopy as a simple and non­invasive method of research in the practice of a dermatologist allows a more accurate assessment of skin tumors with a careful review of important additional details of their morphology, which in turn increases the accuracy of the diagnosis. For the most part, the dermatoscopy is performed for the purpose of differential diagnosis and visualization of the pigmentary or vascular structures of the epidermis and upper layers of the dermis. Increasingly, dermatoscopy is successfully used to diagnose unpigmented elements, but this article focuses on the diagnostic algorithm for pigmented neoplasms of the skin. The purpose of this publication is to inform specialists about a clear, concise, understandable and consistent algorithm, necessary for the analysis of patterns visible in dermatoscopy. The article proves that the structure of the «Chaos and Clues» algorithm is extremely simple and convenient for implementation in everyday practice. It is noted that the main advantage of this algorithm is the clarity and logic of using terminology to name elements of a dermatoscopic picture, and an original way to use these characteristics for the purpose of establishing a diagnosis. These characteristics are extremely relevant today, when in various sources of information we come across many visual and descriptive terms that, instead of explaining, introduce misunderstandings in the diagnostic process, unreasonably complicate the terminology, which may mislead an insufficiently experienced medical specialist.

Studying the expertise reversal of the multimedia signaling effect at a process level: evidence from eye tracking

The purpose of this study was to shed light on the cognitive processes underlying the expertise reversal effect related to multimedia signaling. Multimedia signals highlight correspondences between text and pictures, which is supposed to support text-picture integration and thus learning from multimedia. Previous research suggests that learners’ prior knowledge moderates the multimedia signaling effect in that they only aid learners with low prior knowledge (LPK). We conducted an eye tracking study with students in secondary education who learned with a digital textbook in one of the two versions: (a) a basic version with mostly text signals only (e.g., bold face), or (b) an extended version with additional multimedia signals that aimed at supporting text-picture integration (e.g., color coding of corresponding text and picture elements). In addition to learning outcomes, we assessed students’ cognitive load and gaze behavior as process measures. Results revealed that only LPK learners were supported in learning whereas HPK learners were not affected by multimedia signals (partial expertise reversal). A moderated mediation analysis revealed that multimedia signals affected gaze behavior of LPK students in that they looked earlier at pictures. For high prior knowledge students multimedia signals lead to a higher subjective germane cognitive load. Thus, multimedia signals affected processing of materials. However, the process measures did not explain the expertise reversal of the signaling effect regarding learning outcome.

ATTRIBUTIVE SPACE OF THE SACRED CENTER OF UKRAINIAN CHRISTMAS CAROLS (WORLD TREE – CHURCH)

Study of Christmas carols as an archaic ritual genre of Ukrainian folklore is an actual problem of modern linguistics. A comprehensive analysis of the attributive space of key images of a calendar ritual song, in particular sacred centers, will help reconstruct the mythological model of the Ukrainian world and allow the identification of fragments of folk culture that are present.The purpose of our exploration is to study the symbolic space of the sacred center of the carol songs, taking into account the semiosphere of the calendar holiday. The object of the research is the language of Ukrainian caroling texts. The subject is the semantic and functional features of the epithets on the designation of the mythological tree and the church in the Ukrainian carolytic texts.The tree is one of the main elements of the archaic picture of the world, embodying the spatial and temporal coordinates, symbolizing the structure of the universe. The mythological tree is denoted by a number of epithets that characterize it according to various parameters (height, latitude, diameter), indicate deep roots, crown delusion, flowering and simulate the triune vertical structure of the universe. Such definitions always have a symbolic meaning. It is determined that the image of the mythological tree is often transformed into an image of a church characterized by a greater structural variability. The image of the church is more representative of the higher space.It is proved that the symbolism of the images of the tree and the church is directly related to the semiosphere of Christmas and New Year holidays and the pragmatics of the genre, attributive space reflects the ancient mythological notions of the structure of the world. The proposed exploration outlines the prospects for further analysis of the peculiarities of the figurative system of calendar songs, taking into account the peculiarities of the mythological outlook and ritual contexts.

Measurement and prediction of tomato canopy apparent

Given the lack of technical conditions and research methods, instruments that can measure the canopy apparent photosynthetic rate have low precision and are rarely studied. Comparative studies on canopy apparent photosynthetic rate and single leaf photosynthetic rate are also relatively few. This study aims to measure and predict the canopy apparent photosynthetic rate of tomato. A canopy apparent photosynthetic rate measuring system, which was comprised of a wireless sensor network (WSN), an assimilation chamber, and a LI-6400XT photosynthetic rate instrument was established. The system was used to determine the greenhouse environmental parameters and CO2 absorptive capacity of the whole growth stage of tomato. A semi-closed assimilation chamber was designed as a side opening to conveniently measure the canopy apparent photosynthetic rate. WSN nodes were placed in the chamber to monitor environmental parameters, including air temperature, air humidity, and assimilation chamber temperature. A grid and pixel conversion method was used to measure the whole plant leaf areas of tomato. As a semi-closed measurement system, the assimilation chamber was used to calculate the canopy apparent photosynthetic rate. To conduct a comparative research on the canopy apparent photosynthetic rate and the single leaf photosynthetic rate, the LI-6400XT portable photosynthesis system was used to measure the single leaf photosynthetic rate, and the support vector machine was used to establish the prediction model of canopy apparent photosynthetic rate. The experimental results indicated that the correlation coefficients of the photosynthesis prediction model in the seeding and flowering stages were 0.9420 and 0.9226, respectively, showing the high accuracy of the SVM model. Keywords: photosynthetic rate, tomato, assimilation chamber, SVM, photosynthesis prediction model DOI: 10.25165/j.ijabe.20191205.4982 Citation: Yin J, Liu X Y, Miao Y L, Gao Y, Qiu R C, Zhang M, et al. Measurement and prediction of tomato canopy apparent photosynthetic rate. Int J Agric & Biol Eng, 2019; 12(5): 156–161.

Region-Based Segmentation and Object Detection

Object identification and multi-object picture separation are two firmly related processes and it can be enhanced when understood jointly by supporting data from one assignment to the next. Be that as it may, current best in object models are different portrayal for each space creation joint objects and leaving the categorization of numerous part of the scene uncertain. Picture element appearance highlights enable us to do well on classifying formless foundation classes, while the express portrayal of districts encourage the calculation of increasingly complex highlights essential for object detection. Vitally, our model gives a solitary bound together portrayal of the scene we clarify each picture elements of image and authorize it contains in the web between every random variable in our model.

Modified Kernel Based Fuzzy Clustering for MR Brain Image Segmentation using Deep Learning

The segmentation procedure might cause error in diagnosing MR images due to the artifacts and noises that exist in it. This may lead to misclassifying normal tissue as abnormal tissue. In addition, it is also required to model the ontogenesis of a tumour, as they propagate at distinctive rates in contrast to their surroundings. Hence, it is still a challenging task to segment MR brain images due to possible noise presence, bias field and impact of partial volume. This article presents an efficient approach for segmenting MR brain images using a modified kernel based fuzzy clustering (MKFC) algorithm. In addition, this approach computes the weight of each picture element based on the local mutation coefficient (LMC). The proposed system would reflexively group normal tissues like white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF) respectively, from abnormal tissue, such as a tumour region, in MR brain images. Simulation outcomes have shown that the performance of the proposed segmentation approach is superior to the existing segmentation algorithms in terms of both ocular and quantitative analysis.

Electron-Excited X-ray Microanalysis by Energy Dispersive Spectrometry at 50: Analytical Accuracy, Precision, Trace Sensitivity, and Quantitative Compositional Mapping.

2018 marked the 50th anniversary of the introduction of energy dispersive X-ray spectrometry (EDS) with semiconductor detectors to electron-excited X-ray microanalysis. Initially useful for qualitative analysis, EDS has developed into a fully quantitative analytical tool that can match wavelength dispersive spectrometry for accuracy in the determination of major (mass concentration C > 0.1) and minor (0.01 ≤ C ≤ 0.1) constituents, and useful accuracy can extend well into the trace (0.001 < C < 0.01) constituent range even when severe peak interference occurs. Accurate analysis is possible for low atomic number elements (B, C, N, O, and F), and at low beam energy, which can optimize lateral and depth spatial resolution. By recording a full EDS spectrum at each picture element of a scan, comprehensive quantitative compositional mapping can also be performed.

Depth recovery for unstructured farmland road image using an improved SIFT algorithm

Road visual navigation relies on accurate road models. This study was aimed at proposing an improved scale-invariant feature transform (SIFT) algorithm for recovering depth information from farmland road images, which would provide a reliable path for visual navigation. The mean image of pixel value in five channels (R, G, B, S and V) were treated as the inspected image and the feature points of the inspected image were extracted by the Canny algorithm, for achieving precise location of the feature points and ensuring the uniformity and density of the feature points. The mean value of the pixels in 5×5 neighborhood around the feature point at an interval of 45o in eight directions was then treated as the feature vector, and the differences of the feature vectors were calculated for preliminary matching of the left and right image feature points. In order to achieve the depth information of farmland road images, the energy method of feature points was used for eliminating the mismatched points. Experiments with a binocular stereo vision system were conducted and the results showed that the matching accuracy and time consuming for depth recovery when using the improved SIFT algorithm were 96.48% and 5.6 s, respectively, with the accuracy for depth recovery of –7.17%-2.97% in a certain sight distance. The mean uniformity, time consuming and matching accuracy for all the 60 images under various climates and road conditions were 50%-70%, 5.0-6.5 s, and higher than 88%, respectively, indicating that performance for achieving the feature points (e.g., uniformity, matching accuracy, and algorithm real-time) of the improved SIFT algorithm were superior to that of conventional SIFT algorithm. This study provides an important reference for navigation technology of agricultural equipment based on machine vision. Keywords: scale-invariant feature transform (sift), feature matching, canny operator, energy method of feature point, farmland road, depth recovery, visual navigation DOI: 10.25165/j.ijabe.20191204.4821 Citation: Yao L J, Hu D, Yang Z D, Li H B, Qian M B. Depth recovery for unstructured farmland road image using an improved SIFT algorithm. Int J Agric & Biol Eng, 2019; 12(4): 141–147.

FD‐based detector for medical image watermarking

In this study, the authors propose a novel technique for medical image watermark detection using the concept of the fractional differentiator (FD). The feature of FD as a non-linear high-pass filter helps in watermark detection. In the region of non-interest, the watermark image has been added in a mid-band frequency range of the discrete cosine transform coefficients of different blocks by generating direct spread spectrum sequence. Their scheme produces noise-free watermarked medical images. Furthermore, they derive the test statistics of the proposed detector, which is characterised by the fractional order q. The average errors in pixels (PEs), peak-signal-to-noise ratio (PSNR), structural similarity index (SSIM) and cross-correlation coefficient (CC) have been used to quantify the capability of the proposed technique over some state-of-the-art techniques. The proposed technique shows that at a particular value of fractional order q, there is a significant reduction in average PEs. It causes an increment in PSNR, SSIM and CC. The proposed technique is tested on a large number of medical images and it is found that their proposed technique works better or comparable with other state-of-the-art techniques.

Pixel Size and Revisit Rate Requirements for Monitoring Power Plant CO2 Emissions from Space

The observational requirements for space-based quantification of anthropogenic CO 2 emissions are of interest to space agencies and related organizations that may contribute to a possible satellite constellation to support emission monitoring in the future. We assess two key observing characteristics for space-based monitoring of CO2 emissions: pixel size and revisit rate, and we introduce a new method utilizing multiple images simultaneously to significantly improve emission estimates. The impact of pixel size ranging from 2–10 km for space-based imaging spectrometers is investigated using plume model simulations, accounting for biases in the observations. Performance of rectangular pixels is compared to square pixels of equal area. The findings confirm the advantage of the smallest pixels in this range and the advantage of square pixels over rectangular pixels. A method of averaging multiple images is introduced and demonstrated to be able to estimate emissions from small sources when the individual images are unable to distinguish the plume. Due to variability in power plant emissions, results from a single overpass cannot be directly extrapolated to annual emissions, the most desired timescale for regulatory purposes. We investigate the number of overpasses required to quantify annual emissions with a given accuracy, based on the mean variability from the 50 highest emitting US power plants. Although the results of this work alone are not sufficient to define the full architecture of a future CO 2 monitoring constellation, when considered along with other studies, they may assist in informing the design of a space-based system to support anthropogenic CO 2 emission monitoring.

Design of Two Layer Depth-encoding Detector Module with SiPM for PET

A depth-encoding detector module with silicon photomultipliers(SiPMs) using two layers of scintillation crystal array was designed, and the position measurement capability was verified using DETECT2000. The depth of interaction of the crystal pixels with the gamma rays was tracked through the image acquired with the combination of surface treatment of the crystal pixels and reflectors. The bottom layer was treated as a reflector except for the optically coupled surfaces, and the crystals of top layer were optically coupled each other except for the outer surfaces so that the light sharing was made easier than the bottom layer. Flood images were obtained through the combination of specular reflectors and random reflectors, grounded and polished surfaces of crystal pixels, and the positions at which layer images were generated were measured and analyzed. The images were reconstructed using the Anger algorithm, whose the SiPM signals were reduced as the 16-channels to 4-channels. In the combination of the grounded surface and all reflectors, the depth positions were discriminated into two layers, whereas it was impossible to separate the two layers in the all polished surface combinations. Therefore, using the combination of grounded surface crystal pixels and reflectors could improve the spatial resolution at the outside of the field of view by measuring the depth position in preclinical positron emission tomography.

視聽說敎材《家有兒女》練習題考察

Audio visual teaching textbooks are popular among students and teachers for theirs vividly picture, authentic language, interesting content and rich culture elements, but there are still lack of researches on exercises of audio visual teaching textbooks. It is considered that exercises are the essential elements of language learning and the important part of textbooks. In this article we investigated the exercises of 「Home With Kids」 by statistical analysis and questionnaire survey.