Indoor Positioning by LED Visible Light Communication and Image Sensors

High power white LEDs are expected to replace the existing lighting technologies in near future which are also suggested for visible light communication (VLC). We proposed an algorithm for high precision indoor positioning using lighting LEDs, VLC and image sensors. In the proposed algorithm, four LEDs transmitted their three-dimensional coordinate information which were received and demodulated by two image sensors near the unknown position. The unknown position was then calculated from the geometrical relations of the LED images created on the image sensors. We described the algorithm in details. Simulation of the proposed algorithm was done and presented in this paper. This technique did not require any angular measurement which was needed in contemporary positioning algorithms using LED and image sensor. Simulation results showed that the proposed system could estimate the unknown position within the accuracy of few centimeters. Positioning accuracy could be increased by using high resolution image sensors or by increasing the separation between the image sensors.DOI:http://dx.doi.org/10.11591/ijece.v1i2.16

Activation of the Renin–Angiotensin System Disrupts the Cytoskeletal Architecture of Human Urine-Derived Podocytes

High blood pressure is one of the major public health problems that causes severe disorders in several tissues including the human kidney. One of the most important signaling pathways associated with the regulation of blood pressure is the renin–angiotensin system (RAS), with its main mediator angiotensin II (ANGII). Elevated levels of circulating and intracellular ANGII and aldosterone lead to pro-fibrotic, -inflammatory, and -hypertrophic milieu that causes remodeling and dysfunction in cardiovascular and renal tissues. Furthermore, ANGII has been recognized as a major risk factor for the induction of apoptosis in podocytes, ultimately leading to chronic kidney disease (CKD). In the past, disease modeling of kidney-associated diseases was extremely difficult, as the derivation of kidney originated cells is very challenging. Here we describe a differentiation protocol for reproducible differentiation of sine oculis homeobox homolog 2 (SIX2)-positive urine-derived renal progenitor cells (UdRPCs) into podocytes bearing typical cellular processes. The UdRPCs-derived podocytes show the activation of the renin–angiotensin system by being responsive to ANGII stimulation. Our data reveal the ANGII-dependent downregulation of nephrin (NPHS1) and synaptopodin (SYNPO), resulting in the disruption of the podocyte cytoskeletal architecture, as shown by immunofluorescence-based detection of α-Actinin. Furthermore, we show that the cytoskeletal disruption is mainly mediated through angiotensin II receptor type 1 (AGTR1) signaling and can be rescued by AGTR1 inhibition with the selective, competitive angiotensin II receptor type 1 antagonist, losartan. In the present manuscript we confirm and propose UdRPCs differentiated to podocytes as a unique cell type useful for studying nephrogenesis and associated diseases. Furthermore, the responsiveness of UdRPCs-derived podocytes to ANGII implies potential applications in nephrotoxicity studies and drug screening

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Empirical Analysis of Learnable Image Resizer for Large-Scale Medical Classification and Segmentation

Deep Convolutional Neural Networks demonstrate state-of-art performance in computer vision and medical image tasks. However, handling a large-scale image is still a challenging task that usually deals with resizing and patching methods to embed in the lower dimensional space. Recently, Learnable Resizer (LR) has been proposed to analyze large-scale images for computer vision tasks. This study proposes two DCNN models for classification and segmentation tasks constructed with LR in combination with successful classification and segmentation architectures. The performance of the proposed models is evaluated for the Diabetic Retinopathy (DR) analysis and skin cancer segmentation tasks. The proposed model demonstrated better performance than the existing methods for segmentation and classification tasks. For classification tasks, the proposed architectures achieved a 5.34% improvement in accuracy compared to ResNet50. Besides, around 0.62% accuracy over the base model and 0.28% in Intersection-over-Union (IoU) from state-of-the-art performance. The proposed model with the resizer network enhances the capability of the existing R2U-Net for medical image segmentation tasks. Moreover, the proposed methods enable a significant advantage in learning better with a few samples. The experimental results reveal that the proposed models are better than the current approaches

The illusion of creation of the text

Is it really possible to create a (literary) text? It is actually impossible as to create an authentic text we need an authentic context which is impossible to create. So all the literary txets we have are not authentic and are not created authentically

The illusion of creation of the text

Is it really possible to create a (literary) text? It is actually impossible as to create an authentic text we need an authentic context which is impossible to create. So all the literary txets we have are not authentic and are not created authentically