Development of a Mini Compression Test Machine using Arduino Microcontroller as Texture Analyzer Prototype

A texture analyzer is a standard device to measure food texture properties. This study aims to develop a mini compression test machine prototype that can function as a texture analyzer with simple operation and portability. The components and materials were chosen based on: low cost, manufacturability, and availability in the local Indonesian market. The prototype dimension was 375 × 215 × 450 mm, and used a linear ball screw slider for the translation movement with a 200 mm effective sliding distance. For obtaining the compression load, a 10 kg load cell was used, and it was replaceable by a load cell with a lower capacity if a higher measurement accuracy was required. After calibration, the linear vertical displacement and load cell had ±0.1 mm and ±1 g of accuracy, respectively. The prototype was tested to measure the texture properties of potato chops. As a result, the machine could successfully measure the load and displacement data and detect a crack phenomenon, verifying its usability as a food texture analyzer.A texture analyzer is a standard device to measure food texture properties. This study aims to develop a mini compression test machine prototype that can function as a texture analyzer with simple operation and portability. The components and materials were chosen based on: low cost, manufacturability, and availability in the local Indonesian market. The prototype dimension was 375 × 215 × 450 mm, and used a linear ball screw slider for the translation movement with a 200 mm effective sliding distance. For obtaining the compression load, a 10 kg load cell was used, and it was replaceable by a load cell with a lower capacity if a higher measurement accuracy was required. After calibration, the linear vertical displacement and load cell had ±0.1 mm and ±1 g of accuracy, respectively. The prototype was tested to measure the texture properties of potato chops. As a result, the machine could successfully measure the load and displacement data and detect a crack phenomenon, verifying its usability as a food texture analyzer

Depth map compression via 3D region-based representation

In 3D video, view synthesis is used to create new virtual views between encoded camera views. Errors in the coding of the depth maps introduce geometry inconsistencies in synthesized views. In this paper, a new 3D plane representation of the scene is presented which improves the performance of current standard video codecs in the view synthesis domain. Two image segmentation algorithms are proposed for generating a color and depth segmentation. Using both partitions, depth maps are segmented into regions without sharp discontinuities without having to explicitly signal all depth edges. The resulting regions are represented using a planar model in the 3D world scene. This 3D representation allows an efficient encoding while preserving the 3D characteristics of the scene. The 3D planes open up the possibility to code multiview images with a unique representation.Postprint (author's final draft

ITEM: Inter-Texture Error Measurement for 3D Meshes

We introduce a simple and innovative method to compare any two texture maps, regardless of their sizes, aspect ratios, or even masks, as long as they are both meant to be mapped onto the same 3D mesh. Our system is based on a zero-distortion 3D mesh unwrapping technique which compares two new adapted texture atlases with the same mask but different texel colors, and whose every texel covers the same area in 3D. Once these adapted atlases are created, we measure their difference with ITEM-RMSE, a slightly modified version of the standard RMSE defined for images. ITEM-RMSE is more meaningful and reliable than RMSE because it only takes into account the texels inside the mask, since they are the only ones that will actually be used during rendering. Our method is not only very useful to compare the space efficiency of different texture atlas generation algorithms, but also to quantify texture loss in compression schemes for multi-resolution textured 3D meshes

New approaches to texture coding in segmentation and feature-based image coding schemes

Peer ReviewedPostprint (published version

Texture profile analysis reveals a stiffer ovarian cortex after testosterone therapy : a pilot study

Purpose: The importance of the surrounding ovarian stromal cells and extracellular matrix in the development and maturation of follicles has recently gained attention. An aberrant extracellular matrix has been described in ovaries of patients with polycystic ovary syndrome where a more rigid structural environment, possibly induced by endogenous testosterone, impairs normal folliculogenesis. In this context, we describe the textural parameters of the ovarian cortex of transgender men after prolonged testosterone administration compared to the textural parameters of the non-exposed ovarian cortex originating from female oncological patients. Methods: Texture profile analysis (TPA) was performed on ovarian cortex (5 x 5 mm) of oncological and transgender patients in order to measure stiffness, hardness, cohesiveness, and springiness of the ovarian cortex (LRXplus universal testing system). Statistical analysis was performed using repeated measurements mixed models and the Spearman rank order correlation test (IBM SPSS Statistics 23). Results: A total of 36 frozen-thawed cortical strips (5 x 5 mm) were subjected to TPA. The superficial part of cortex fragments originating from transgender persons (fragments < 1.4 mm; N = 10) appeared to be significantly stiffer compared to cortex derived from oncology patients (fragments < 1.4 mm; N = 7) (6.78 +/- 1.38 N/mm versus 5.41 +/- 0.9 N/mm respectively, p = 0.036). Conclusions: This is the first application of TPA in ovarian cortex to study the physical properties. Comparing the physical properties, we objectively describe an increased cortical stiffness in the most outer part of the ovarian cortex following prolonged testosterone administration in transgender men compared to the ovarian cortex of oncological patients. This preliminary and novel approach could be the start of future research to understand the physical properties of ovarian tissue