Edge Techniques Research Articles

RETRACTED ARTICLE: Development of video compression using EWNS linear transformation and un-repetition simulated contrary based resurgence procedure

In the recent years, video transmission area endures several failures owing to the limited amount of a cutting edge technique to store large sized videos. For this reason, video compression method is used. For compression of videos, frame formation can be done by splitting the video frames by A_Frames, B_Frames and C_Frames. These frames will stay unchanged entire process; it will also utilize the memory for computational purpose. The proposed work consists of 2 phases. In the first phase, Acclimatize Frame Formation (AFF) is used to expand the characteristic of video coding and EWNS linear transformation (ELT) is initiated for substituting B_Frames with neither A_Frame nor C_Frame. In the second phase, un-repetition simulated contrary based resurgence procedure (URSCRP) is proposed for restoration of videos that demonstrates tiny convolution and time necessity together with conservation of restoration feature. Simulation results shows that URSCRP provides better accuracy and optimization compared to other related procedures. URSCRP provides PSNR of 30 dB, accuracy of 98% and minimized amount of elapsed time compared to related procedures.

Knife edges technique for focused beam profiling of the photothermal imaging system

In this paper, we demonstrate knife edge techniques for measuring the focused beam profile of the photothermal imaging system. This quick and simple method of profiling the focused beam from Helium-Neon (He-Ne) laser is presented and detected by the PVDF thin film as a sensor. The study of focused beam profile is crucial so as to provide information of the focused beam such as beam resolution, depth of focus and the level of astigmatism of the beam. In knife edge technique, the blade moves across the beam in x and y-direction at varying axial position.

The innovation of cryo-SEM freeze-fracturing methodology demonstrated on high pressure frozen biofilm

In this study we present an innovative method for the preparation of fully hydrated samples of microbial biofilms of cultures Staphylococcus epidermidis, Candida parapsilosis and Candida albicans. Cryo-scanning electron microscopy (cryo-SEM) and high-pressure freezing (HPF) rank among cutting edge techniques in the electron microscopy of hydrated samples such as biofilms. However, the combination of these techniques is not always easily applicable. Therefore, we present a method of combining high-pressure freezing using EM PACT2 (Leica Microsystems), which fixes hydrated samples on small sapphire discs, with a high resolution SEM equipped with the widely used cryo-preparation system ALTO 2500 (Gatan). Using a holder developed in house, a freeze-fracturing technique was applied to image and investigate microbial cultures cultivated on the sapphire discs. In our experiments, we focused on the ultrastructure of the extracellular matrix produced during cultivation and the relationships among microbial cells in the biofilm. The main goal of our investigations was the detailed visualization of areas of the biofilm where the microbial cells adhere to the substrate/surface. We show the feasibility of this technique, which is clearly demonstrated in experiments with various freeze-etching times.

Effect of noise on MTF calculations using different phantoms.

In this work, the effect of noise on the precision in the modulation transfer function (MTF) calculation using different phantoms is investigated. Three different techniques are studied, based on three different phantoms: edge, bar, and star bar. For each technique, theoretical expressions for the standard deviation and the signal to noise ratio (SNR) of the MTF are derived. In addition, Monte Carlo simulations are carried out to study the accuracy and precision of all the three techniques. Using the analytic expressions for the MTF calculation, statistical fluctuations of noise in the phantom images are propagated to MTF values. Then, for each frequency the standard deviation and the SNR of the MTF are calculated. Monte Carlo simulations are carried out for each phantom image and for different noise level. Images are artificially generated following a procedure that simulates sampling, blurring, and noise present in a real phantom image and takes into account the attenuation of each phantom. Then, oversampling procedures are applied to these images to obtain presampled MTFs. In all three techniques the standard deviation of the MTF is proportional to that of the noise in the image. For the edge image, the standard deviation is proportional to the frequency f, whereas for the bar and star bar techniques it is proportional to the square root of f. Regarding the precision, theoretical expressions and simulations results agree that the bar technique shows the best precision for all the frequencies and all the noise levels analyzed in this study. In addition, simulation results show how, for large levels of noise, the edge technique gives rise to large bias errors at high and medium frequencies. Finally, the precision of the star bar technique is similar to that of the bar technique for isotropic systems, but worse for nonisotropic ones. The uncertainties in the MTF calculation with the edge technique are larger than with the bar technique, and the differences increase with the frequency. On the one hand, the stronger dependency on the frequency shown by the standard deviation of the MTF makes the precision gets worse faster in the edge technique. On the other hand, in the case of the edge and for high levels of noise, differentiation produces line spread functions (LSF) with a poor SNR, increasing erroneously the MTF and worsening accuracy.

Energy Efficient Big Data Networks: Impact of Volume and Variety

In this paper, we study the impact of big data’s volume and variety dimensions on energy efficient big data networks (EEBDN) by developing a mixed integer linear programming (MILP) model to encapsulate the distinctive features of these two dimensions. First, a progressive energy efficient edge, intermediate, and central processing technique is proposed to process big data’s raw traffic by building processing nodes (PNs) in the network along the way from the sources to datacenters. Second, we validate the MILP operation by developing a heuristic that mimics, in real time, the behavior of the MILP for the volume dimension. Third, we test the energy efficiency limits of our green approach under several conditions where PNs are less energy efficient in terms of processing and communication compared to data centers. Fourth, we test the performance limits in our energy efficient approach by studying a “software matching” problem where different software packages are required to process big data. The results are then compared to the classical big data networks (CBDN) approach where big data is only processed inside centralized data centers. Our results revealed that up to 52% and 47% power saving can be achieved by the EEBDN approach compared to the CBDN approach, under the impact of volume and variety scenarios, respectively. Moreover, our results identify the limits of the progressive processing approach and in particular the conditions under which the CBDN centralized approach is more appropriate given certain PNs energy efficiency and software availability levels.

Wearable Inset-Fed FR4 Microstrip Patch Antenna Design

This project proposes the design of a wireless body area network (WBAN) microstrip patch antenna covered by the jeans fabric as the outer layer operating at the center frequency, fc of 2.40 GHz. Precisely, the microstrip patch antenna with the inset-fed edge technique is designed and simulated systematically by using the Keysight Advanced Design System (ADS) software where the FR4 board with the dielectric constant, ϵr of 4.70, dissipation factor or loss tangent, tan δ of 0.02 and height, h of 1.60 mm is the chosen dielectric substrate. The wearable microstrip patch antenna design is then fabricated using the FR4 printed circuit board (PCB) material, hidden inside the jeans fabric, and attached to clothing, such as a jacket accordingly. Simulation and fabrication measurement results show that the designed microstrip patch antenna characteristics can be applied significantly within the industrial, scientific, and medical (ISM) radio band, which is at fc = 2.40 GHz.

Exploiting Advanced Hydrogel Technologies to Address Key Challenges in Regenerative Medicine.

Regenerative medicine aims to tackle a panoply of challenges from repairing focal damage to articular cartilage to preventing pathological tissue remodeling after myocardial infarction. Hydrogels are water‐swollen networks formed from synthetic or naturally derived polymers and are emerging as important tools to address these challenges. Recent advances in hydrogel chemistries are enabling researchers to create hydrogels that can act as 3D ex vivo tissue models, allowing them to explore fundamental questions in cell biology by replicating tissues' dynamic and nonlinear physical properties. Enabled by cutting edge techniques such as 3D bioprinting, cell‐laden hydrogels are also being developed with highly controlled tissue‐specific architectures, vasculature, and biological functions that together can direct tissue repair. Moreover, advanced in situ forming and acellular hydrogels are increasingly finding use as delivery vehicles for bioactive compounds and in mediating host cell response. Here, advances in the design and fabrication of hydrogels for regenerative medicine are reviewed. It is also addressed how controlled chemistries are allowing for precise engineering of spatial and time‐dependent properties in hydrogels with a look to how these materials will eventually translate to clinical applications.

On-machine measurement of cutting tool edge profile using fluorescence excited by laser

Tool management is extremely important to promote mass-producing of micro-scale machine parts. Thus three-dimensional form measurement technique for edge of micro-tools needs to be developed. In this research, we propose a three-dimensional form measurement by detecting fluorescence from cutting fluids on surface of the tools. This technique enables to measure the edge of a tool more precisely and quickly without disturbance of the cutting fluids. To improve the applicability of the proposed measurement method, we detected the fluorescence signal from the water-soluble cutting fluid and measured the sample surface position.

Flow Cytometry for the Immunotoxicologist.

Assessing the immunotoxicity of xenobiotics by current regulatory testing has revealed compounds that can cause immunosuppression and stimulation. Flow cytometry is a cutting edge technique that can provide data on how toxicants can alter the quality and quantity of the immune response after exposure. Here we describe protocols for how to use flow cytometry to measure the immune response in multiple rodent organs (blood and lymphoid and nonlymphoid) as well as in novel models recently being utilized in the field of toxicology. These methods can be used for current testing and to determine mechanisms by which a xenobiotic can cause immunotoxicity.

Visible Light Based Occupancy Inference Using Ensemble Learning

As a key component of building management and security, occupancy inference through smart sensing has attracted a lot of research attention for nearly two decades. Recently, a cutting edge technique visible light sensing (VLS) that utilizes the LED luminaires as light sensors has shown its promising application potentials in occupancy inference as it piggybacks on pervasive lighting infrastructure without extra equipment deployment. Although existing inference algorithms based on the VLS data set can achieve high accuracy, the performance degrades when the occupants are moving. This paper focuses on the occupancy inference issue and presents an ensemble learning algorithm to improve the inference accuracy. We use heterogeneous learning algorithms to generate diverse learners. Consequently, we adopt forward sequential pruning to enhance the ensemble that pursues inference error minimization. We conduct extensive experiments based on the field data. The experiment results show that the proposed algorithm is able to improve inference accuracy, especially for highly dynamic occupancy data set.

Using the Medipix3 detector for direct electron imaging in the range 60 keV to 200 keV in electron microscopy

Hybrid pixel sensor technology such as the Medipix3 represents a unique tool for electron imaging. We have investigated its performance as a direct imaging detector using a Transmission Electron Microscope (TEM) which incorporated a Medipix3 detector with a 300 μm thick silicon layer compromising of 256×256 pixels at 55 μm pixel pitch. We present results taken with the Medipix3 in Single Pixel Mode (SPM) with electron beam energies in the range, 60–200 keV . Measurements of the Modulation Transfer Function (MTF) and the Detective Quantum Efficiency (DQE) were investigated. At a given beam energy, the MTF data was acquired by deploying the established knife edge technique. Similarly, the experimental data required to determine DQE was obtained by acquiring a stack of images of a focused beam and of free space (flatfield) to determine the Noise Power Spectrum (NPS).

Interim FDG-PET Imaging as an Early Response Biomarker for Pathologic Response to Chemoradiation in Esophageal Cancer

To evaluate predictors of complete pathologic response to chemoradiation (CRT) in esophageal cancer (EC) treatment using fluorodeoxyglucose (FDG) positron emission tomography (PET) imaging changes before, during, and after CRT. Previous research shows that change in maximum standardized uptake value (SUVmax) does not predict for response to CRT. We evaluated other PET parameters in this prospective clinical trial. Forty-two EC patients were enrolled in this ongoing prospective imaging study. For this report, we report on the 20 patients who had surgery after neoadjuvant CRT. Outcome was assessed pathologically based on tumor regression grade (TRG, 1 = pathologic complete response, 2 = near complete response, and ≥3 = more than minimal residual). FDG PET imaging was acquired before treatment (baseline, BL), approximately 2 weeks after starting chemoradiation (interim, IM), and at first follow-up (FU) 4-6 weeks after completing CRT. The 3D tumor was contoured on the PET images using gradient edge technique using MIM software (MIM Software Inc. Cleveland, OH). Summary statistics for SUVmax, SUVmean, metabolic tumor volume (MTV), and total lesion glycolysis (TLG) were determined from the contoured volume at each time point. Relative changes in each tumor PET parameter from BL to IM and FU scan were calculated and compared by outcome status defined as TRG1 vs. TRG2/TRG≥3 using Wilcoxon rank-sum test. Multiple logistic regression was used to evaluate receiver operating curve (ROC) characteristics for predicting pathologic response status based on analysis of DSUVmax, DSUVmean, DMTV, and DTLG values. TRG 1, 2, and 3 after CRT and surgery were seen in 5 (29%), 9 (43%), and 6 pts (29%), respectively. Tumors that had a complete pathologic response exhibited significantly greater change in TLG (P = 0.0117) from the baseline to interim scan, with a threshold of 59% decrease. Change in SUVmax (P = 0.232, P = 0.832), SUVmean (P = 0.086, P = 0.550), and MTV from BL to IM or BL to FU scans were not predictive of pathologic CR, respectively. Predicting outcome status based on change in TLG at the IM scan yielded AUROC of 0.888 (95% CI: 0.742-1.000). Youden’s optimal sensitivity (specificity) were 1.000 (0.813) for DTLG at the IM scan. Our results indicate that DTLG on PET imaging after 2 weeks of CRT may predict pathologic complete response in esophageal cancer. Early response assessment with FDG-PET is potentially useful for stratification of EC pts to treatment intensification or non-surgical management.

Advanced MRI Techniques of the Shoulder Joint: Current Applications in Clinical Practice.

We are fortunate to live in a time when real advances in medicine are happening at an increasingly rapid pace. This is especially true in the field of radiology, and keeping abreast of these advances is one of the main challenges of clinical practice. Traditionally, cutting edge techniques in our field have been researched and validated at major academic medical centers before slowly making their way into the armamentarium of routine clinical practice. However, the improved ability to communicate and disseminate information in our modern age has facilitated more rapid implementation of new techniques to allow us to better serve our patients. As such, this article aims to review the current standards for MRI of the shoulder used in routine practice. Furthermore, we will discuss some of the most recent advances in shoulder MRI, with particular emphasis on the applicability of an additional axial 3D T1-weighted FLASH sequence with Dixon-based water-fat separation in routine clinical practice that can be useful in characterizing several commonly encountered pathologic processes of the shoulder joint.

A Time-Walk Correction Method for PET Detectors Based on Leading Edge Discriminators.

The leading edge timing pick-off technique is the simplest timing extraction method for PET detectors. Due to the inherent time-walk of the leading edge technique, corrections should be made to improve timing resolution, especially for time-of-flight PET. Time-walk correction can be done by utilizing the relationship between the threshold crossing time and the event energy on an event by event basis. In this paper, a time-walk correction method is proposed and evaluated using timing information from two identical detectors both using leading edge discriminators. This differs from other techniques that use an external dedicated reference detector, such as a fast PMT-based detector using constant fraction techniques to pick-off timing information. In our proposed method, one detector was used as reference detector to correct the time-walk of the other detector. Time-walk in the reference detector was minimized by using events within a small energy window (508.5 - 513.5 keV). To validate this method, a coincidence detector pair was assembled using two SensL MicroFB SiPMs and two 2.5 mm × 2.5 mm × 20 mm polished LYSO crystals. Coincidence timing resolutions using different time pick-off techniques were obtained at a bias voltage of 27.5 V and a fixed temperature of 20 °C. The coincidence timing resolution without time-walk correction were 389.0 ± 12.0 ps (425 -650 keV energy window) and 670.2 ± 16.2 ps (250-750 keV energy window). The timing resolution with time-walk correction improved to 367.3 ± 0.5 ps (425 - 650 keV) and 413.7 ± 0.9 ps (250 - 750 keV). For comparison, timing resolutions were 442.8 ± 12.8 ps (425 - 650 keV) and 476.0 ± 13.0 ps (250 - 750 keV) using constant fraction techniques, and 367.3 ± 0.4 ps (425 - 650 keV) and 413.4 ± 0.9 ps (250 - 750 keV) using a reference detector based on the constant fraction technique. These results show that the proposed leading edge based time-walk correction method works well. Timing resolution obtained using this method was equivalent to that obtained using a reference detector and was better than that obtained using constant fraction discriminators.

Research trends on Big Data in Marketing: A text mining and topic modeling based literature analysis

Given the research interest on Big Data in Marketing, we present a research literature analysis based on a text mining semi-automated approach with the goal of identifying the main trends in this domain. In particular, the analysis focuses on relevant terms and topics related with five dimensions: Big Data, Marketing, Geographic location of authors’ affiliation (countries and continents), Products, and Sectors. A total of 1560 articles published from 2010 to 2015 were scrutinized. The findings revealed that research is bipartite between technological and research domains, with Big Data publications not clearly aligning cutting edge techniques toward Marketing benefits. Also, few inter-continental co-authored publications were found. Moreover, findings show that research in Big Data applications to Marketing is still in an embryonic stage, thus making it essential to develop more direct efforts toward business for Big Data to thrive in the Marketing arena.

State of the Art in Edge and Trail Bundling Techniques

AbstractBundling techniques provide a visual simplification of a graph drawing or trail set, by spatially grouping similar graph edges or trails. This way, the structure of the visualization becomes simpler and thereby easier to comprehend in terms of assessing relations that are encoded by such paths, such as finding groups of strongly interrelated nodes in a graph, finding connections between spatial regions on a map linked by a number of vehicle trails, or discerning the motion structure of a set of objects by analyzing their paths. In this state of the art report, we aim to improve the understanding of graph and trail bundling via the following main contributions. First, we propose a data‐based taxonomy that organizes bundling methods on the type of data they work on (graphs vs trails, which we refer to as paths). Based on a formal definition of path bundling, we propose a generic framework that describes the typical steps of all bundling algorithms in terms of high‐level operations and show how existing method classes implement these steps. Next, we propose a description of tasks that bundling aims to address. Finally, we provide a wide set of example applications of bundling techniques and relate these to the above‐mentioned taxonomies. Through these contributions, we aim to help both researchers and users to understand the bundling landscape as well as its technicalities.

Neurobiology of autism spectrum disorders

Autism Spectrum Disorders (ASD) is a group of neurodevelopmental disorders with heterogeneous etiology characterized by deficits in social cognition, communication, and behavioral flexibility. Disturbances on molecular and cellular level in early brain development incl. intercellular communication, an unbalanced ratio between certain neuronal populations and maturation/differentiation process, oxidative stress, happening in embryonal stages, might be promising candidates to explain the development of autistic symptoms.In order to get a deeper understanding of these processes, valid “disease models” are pivotal. A new cutting edge technique, named brain organoids, has been highlighted as a promising candidate for obtaining a better “disease model”.Brain organoids derived from patients induced pluripotent stem cells (iPSC) follow in vivo timeline development; they also have the ability to recreate the right complexity of the brains, developmental stages. On the cellular and gene expression level, organoids demonstrate a high similarity to the developing brain in vivo and can therefore recapitulate early stages of the neurogenesis. To date organoids are the most relevant cellular in vitro platform for the understanding of the mechanisms behind ADS pathology. Investigations of “mini brains” at different time points in their development will give a wider and more detailed picture of the disease dynamic and thus the development of therapeutic and prevention strategies. It is a tool that can be used for effective high throughput screening of chemical compounds as potential drugs (“in sphero” drug testing). Organoids are a good modeling system for elucidating the role of epigenetic and environmental factors for development of ASD.Disclosure of interestThe authors declare that they have no competing interest.

Optical Submicrometer Displacement Sensor Based on the Herriot Cell

In this paper, we present a displacement sensor using a Herriott cell designed with the cylindrical deformable mirrors. A movable object modifies one of the mirror’s curvature and position in the cell, thus changing the angular path of the output beam that is measured with the knife edge technique. The design results in a compact device with easy manufacturing process for the mirrors. We achieve a sensitivity of $- 1.7~\mu \text{W}/\mu \text{m}$ over the range of $100~\mu \text{m}$ with a precision of $3.0~\mu \text{m}$ . The use of a Herriott cell to measure displacements allows the modification of the sensitivity and range of the sensor according to the number of incidences.

Complex Analysis of Edge Cutting Machining of Periodic Profiles

The technological capabilities of complex analysis technique for edge cutting machining of periodic profiles are presented. The examples of method application for the most widespread forming processes are described. It is stated that the method creates a virtual environment for the synthesis of the research results in the field of machine-building industry, metal cutting, and cutting tool, which can be integrated into the unified system in order to forecast and manage the item quality properties. The method provides maximal flexibility to work with source data and allows transforming virtual processes sequentially for selecting the most effective option of removing the allowance by varying the parameters of the process.

An Advanced Omic Approach to Identify Co-Regulated Clusters and Transcription Regulation Network with AGCT and SHOE Methods.

To obtain the global picture of genetic machinery for massive high-throughput gene expression data, novel data-driven unsupervised learning approaches are becoming essentially important. For this purpose, basic analytic workflow has been established and should include two steps: first, unsupervised clustering to identify genes with similar behavior upon exposure to a signal, and second, identification of transcription factors regulating those genes. In this chapter, we will describe an advanced tool that can be used for analyzing and characterizing large-scale time-series gene expression composed of a two-step approach. For the first step, we developed an original method "A Geometric Clustering Tool" (AGCT) that unveils the complex architecture of large-scale time-series gene expression data in a real-time manner using cutting edge techniques of low dimension manifold learning, data clustering, and visualization. For the second step, we established an original method "Sequence Homology in Eukaryotes" (SHOE) executing comparative genomic analysis on humans, mice, and rats.