Entropy coders and 3D-Hadamard coefficients sequency scan order for a fast embedded color video codec

China’s South–North Water Transfer Project has been questioned as it has resulted in significantly negative issues. Drawing on the notion of hydrosocial territories, this article examines the contested hydraulic configuration and counter-imaginaries from local water users’ perspectives and their specific adaptation strategies in the South–North Water Transfer Project. This article argues that local water users in a Chinese context can only adopt adaptation strategies that are determined by their socio-economic backgrounds. This has led to significant social and environmental injustice. Addressing these issues is crucial for tackling inequities in the South–North Water Transfer Project and achieving the ambitious development goals of the project.

Fixed length entropy codes (FLC) have been employed as an alternative to the usual variable length codes (VLC) in video codecs. The effect of a transmission error in an FLC coded stream does not propagate beyond the corrupted codeword because of its fixed parsing structure. This paper presents a new decoding algorithm for FLC compressed video streams that fully exploits the error resilience properties of FLC codes combined with spatial synchronization points. While maintaining the compression efficiency of VLC coded video streams, the proposed solution is a novel idea to combat the consequences of transmission errors and it is totally compatible with more traditional solutions such as error concealment and forward error correcting codes.

The authors present a new technique for coding gray-scale images for facsimile transmission and printing on a laser printer. They use a gray-scale image encoder so that it is only at the receiver that the image is converted to a binary pattern and printed. The conventional approach is to transmit the image in halftoned form, using entropy coding (e.g., CCITT Group 3 or JBIG). The main advantages of the new approach are that one can get higher compression rates and that the receiver can tune the halftoning process to the particular printer. They use a perceptually based subband coding approach. It uses a perceptual masking model that was empirically derived for printed images using a specific printer and halftoning technique. In particular, they used a 300 dots/inch write-black laser printer and a standard halftoning scheme ("classical") for that resolution. For nearly transparent coding of gray-scale images, the proposed technique requires lower rates than the standard facsimile techniques.

Surviving the Covid-19 Pandemic: An Examination of Adaptive Coping Strategies

Very fast image and video codecs are a pursued goal both in the academia and the industry. This paper presents a complexity scalable and parallel bitplane coding engine for wavelet-based image codecs. The proposed method processes the coefficients in parallel, suiting hardware architectures based on vector instructions. Our previous work is extended with a mechanism that provides complexity scalability to the system. Such a feature allows the coder to regulate the throughput achieved at the expense of slightly penalizing compression efficiency. Experimental results suggests that, when using the fastest speed, the method almost doubles the throughput of our previous engine while penalizing compression efficiency by about 10%

esults from this work enable researchers, engineers, and communities to better understand how permafrost landscapes may change in the near future and what specific mitigation and adaptation strategies may be necessary to mitigate the effects of permafrost landslides on infrastructure and the environment.This project documented the distribution and initiation of recent acceleration of permafrost landslides in the central Mackenzie Valley.A close association was established between past forest fires and recent landslides.This includes novel styles of permafrost landslides that are larger and occur more rapidly than traditional forms of permafrost slope failure.

Manual wheelchairs (MWC) users have limited mobility during winter months as they encounter many environmental barriers that restrict their community participation. This paper outlines the creation and standardization of an outdoor environment designed to simulate the real-life conditions and obstacles experienced by MWC users in winter. This study consisted of four phases. In Phase 1, researchers used a qualitative ethnographic approach to document the specific challenges and adaptive strategies used by MWC users in winter conditions. In Phase 2, key informants with expertise in MWC winter mobility were invited to co-design the Standardized Navigation Of Winter Mobility & Accessibility Network (SNOWMAN) course. Participants reviewed draft design solutions and offered their input and suggestions to expand upon the initial design. A second co-design workshop included additional key informants, including service providers, policymakers, and professionals with expertise in landscape architecture and engineering, to validate the design solution. The workshops resulted in a detailed illustration of the SNOWMAN course, including five sections: platforms with side slopes, a miniature ice rink, curbs and curb cuts, a path with uneven winter surfaces, and modular ramps at various slopes. Phases 3 and 4 marked the conclusion of the study and involved fabrication of the SNOWMAN course and establishment of a standardized protocol for course setup and maintenance. The project aimed to offer several additional potential benefits, supported by the various stakeholders across the study phases, that extend beyond creation of a controlled and safe environment for wheelchair users to develop their winter mobility skills. Practicing wheelchair skills in this area may assist wheelchair users in gaining confidence which may ultimately translate to increased participation in the community.

Understanding local climate variability and change is necessary for improving future climate forecasts and also aids preparation of informed area specific climate mitigation and adaptation strategies. Climate change at local scale is best revealed by studying observed variabilities and trends in rainfall and temperature data through statistical techniques. Therefore, this study employed standard deviation and coefficient of variability and Mann-Kendall test and Sen slope determination non-parametric techniques to perform variability and trends analyses across multiple temporal scales on climate data obtained at Imphal (Tulihal) station. The results indicate prevalence of different patterns between rainfall and temperature trends. Except for the positive trends in the month May (2mm/yr) and in the pre-monsoon season (9.49mm/yr), no other discernable patterns in rainfall data were observed. Temperature trends, on the other hand, witnessed significant positive increase in maximum, minimum and mean values. For mean temperature, all months registered significant increasing trends. At the annual and seasonal scales also, maximum, minimum and mean temperatures have increased although with varying rates. It is noteworthy to mention that temperature change has occurred at two distinct phases; before 1993 slow warming and after 1993 rapid warming. Temporal distribution of annual mean temperature captures this pattern more vividly as warming rate before 1993 was less than 0.01 compared to 0.450c/year in the latter phase. Overall, it can be said that rainfall has higher variability with very little or no pattern but temperature distribution suggests existence of strong trends in the observed data.

The risk of climate change impacts occurring is a function of a socioecological system's exposure and vulnerability to climate-related hazards. Vulnerability itself is the result of a system's sensitivity and adaptive capacity. The potential climate change driven biophysical impacts on the municipality Totonicapn in the western highlands of Guatemala are well documented in outcome vulnerability studies and projected to be severe. They include droughts, frosts, forest fires and life zone changes which also represent important hazards to the municipality's population. Yet, recent detailed socioeconomic information on the municipality's contextual vulnerability is scarce. Moreover, social capital which is central to the yet successful management of the unique communal coniferous forests is poorly understood. The present study evaluates the contextual vulnerability of the municipality's population and communal forests using 5 interviews and 167 household surveys from 3 communities for 15 socioeconomic indicators. Qualitative analysis of the interviews urges for further investigation into the link between emigration to the USA, the loss of social capital and communal forest management. Quantitative analysis of the indicators and their aggregation into a vulnerability index by Principal Component Analysis demonstrates that education is the most important vulnerability factor, followed by income which was negatively related to natural resource dependency. An overarching theme was gender inequality. The study is a plea for location and population specific research and adaptation strategies as it identifies significant differences even between communities of the same municipality.

Block-matching motion estimation (BME) is the most computationally expensive process in every video codec. The algorithm proposed in this paper takes into account almost all key elements of BME including integer-pixel ME (IPME), sub-pixel ME (SPME), variable block-size ME (VBSME) and multiple reference frame ME (MRFME). The algorithm is developed by adding MRFME method to the multi-path adaptive computation-aware ME strategy (MPS) introduced in our previous papers. The algorithm was implemented in the H.264/AVC reference software and tested against the fast full search (FFS) method achieving comparable results within less than 3% of execution time.

Multi-exposure image fusion (MEF) algorithms have been used to merge a stack of low dynamic range images with various exposure levels into a well-perceived image. However, little work has been dedicated to predicting the visual quality of fused images. In this work, we propose a novel and efficient objective image quality assessment (IQA) model for MEF images of both static and dynamic scenes based on superpixels and an information theory adaptive pooling strategy. First, with the help of superpixels, we divide fused images into large- and small-changed regions using the structural inconsistency map between each exposure and fused images. Then, we compute the quality maps based on the Laplacian pyramid for large- and small-changed regions separately. Finally, an information theory induced adaptive pooling strategy is proposed to compute the perceptual quality of the fused image. Experimental results on three public databases of MEF images demonstrate the proposed model achieves promising performance and yields a relatively low computational complexity. Additionally, we also demonstrate the potential application for parameter tuning of MEF algorithms.

Maize is the leading food crop produced in most parts of the world and a staple food crop in Kenya. Majority of the maize farmers are smallholders due to reduced land sizes as a result of land fragmentation. Maize farming is rain-fed making it more vulnerable to rainfall variability. Over the years, the farmers have increased their resilience and adaptive potential through indigenous knowledge so as to cope with the climate related risks. However, the increasing rainfall variability, frequency and more severe shocks likely surpass their current adaptive strategies. The study assessed existing adaptive strategies of the small-holder maize farmers in Kieni East sub-County in Nyeri Couny to the seasonal rainfall variability effects. The research adopted a mixed research design. Data was obtained from primary and secondary sources. Primary data was obtained using household structured questionnaires. Data was analyzed using descriptive methods and presented using charts and tables. SWOT analysis analyzed several underlying aspects affecting maize farmers in the sub-County. The study found that the farmers have several specific adaptive strategies to the effects of seasonal rainfall variability at their disposal which include but not limited to the use of varieties of maize seeds, seeking training, water harvesting, use of manure and fertilizers. Farmers also respond to rainfall variability by planting maize varieties that are early maturing and drought resistant as well as engaging in alternative farming activities and other economic activities.

Accurate characterization of sample dielectric property is essential for scientists and engineers to analyze and evaluate the performance of the invested sample. This article proposes a learning-based (LB) method for quantitative subsurface imaging via coaxial-resonator-based near-field scanning microwave microscopy (CR-NFSMM) in a nondestructive way. To efficiently generate the database, a well-designed forward solver is critical since many samples will be included in the database, each sample needs a scanning procedure, and each scanning point involves solving the forward problem once. A fast forward problem solver is used to evaluate the tip–sample interaction in CR-NFSMM that avoids repeated meshing during the scanning process and it can apply to an arbitrary tip shape. Once the neural network is trained, it generates the reconstructed image within 1 s. Numerical and experimental results show that the proposed LB method could improve the resolution of the image and recover the dielectric properties of the subsurface perturbation pixel-by-pixel. Moreover, the proposed method outperforms the traditional objective function approach in terms of image resolution and time cost. The proposed method is promising to realize a nondestructive and real-time local dielectric evaluation of the subsurface object.

Computed Tomography (CT) has proven itself as a powerful technique for analysing the internal structure of cultural heritage objects. The process followed by conservators and technical art historians for investigating an object is explorative: each time a new question is asked based on the outcome of the previous investigation. This workflow however conflicts with the static nature of CT imaging, where the planning, execution and image analysis for a single CT scan can take days, or even weeks. A new question often requires conducting a new experiment, repeating the process of planning, execution and image analysis. This means that the time that is needed to complete the investigation is often longer than originally anticipated. In addition, it brings up more practical challenges such as the transportation of the object, facility availability and dependence on the imaging operator, as well as the cost of running additional experiments. A much needed interactive imaging process, where the user can adapt the CT scanning process based on the insights discovered on the spot, is hard to accomplish. Therefore, in this paper we show how a time-efficient explorative workflow can be created for CT investigation of art objects, where the object can be inspected in 3D while still in the scanner, and based on the observations and the resulting new questions, the scanning procedure can be iteratively refined. We identify the technical requirements for a CT scanner that can address the diversity in cultural heritage objects (size, shape, material composition), and the need for adaptive steering of the scanning process required for an explorative workflow. Our approach has been developed through the interdisciplinary research projects The See-Through Museum and Impact4Art. We demonstrate the key concepts by showing results of art objects scanned at the FleX-ray Laboratory at CWI, Amsterdam.

Date Presented Accepted for AOTA INSPIRE 2021 but unable to be presented due to online event limitations. Physical activity and dietary-related behaviors are important for enhancing health and quality of life among adults with stroke, yet the prevalence of these healthy behaviors is low. In this qualitative study, adults with stroke identified how stroke-related physical, cognitive, and sensory deficits impact performance of these healthy behaviors and identified specific adaptive and accessible equipment and compensatory strategies for overcoming these barriers that can be utilized in clinical practice. Primary Author and Speaker: Ryan Bailey Contributing Authors: Jennifer Stevenson