Sensing insole systems are a promising technology for various applications in healthcare and sports. They can provide valuable information about the foot pressure distribution and gait patterns of different individuals. However, designing and implementing such systems poses several challenges, such as sensor selection, calibration, data processing, and interpretation. This paper proposes a sensing insole system that uses force-sensitive resistors (FSRs) to measure the pressure exerted by the foot on different regions of the insole. This system classifies four types of foot deformities: normal, flat, over-pronation, and excessive supination. The classification stage uses the differential values of pressure points as input for a feedforward neural network (FNN) model. Data acquisition involved 60 subjects diagnosed with the studied cases. The implementation of FNN achieved an accuracy of 96.6% using 50% of the dataset as training data and 92.8% using only 30% training data. The comparison with related work shows good impact of using the differential values of pressure points as input for neural networks compared with raw data.
In this paper a system is designed on an FPGA using a Nios II soft-core processor, to detect the colour of a specific surface and moving a robot arm accordingly. The surface being detected is bounded by a starting mark and an ending mark, to define the region of interest. The surface is also divided into sections as rows and columns and each section can have any colour. Such a system has so many uses like for example warehouses or even in stores where their storing areas can be divided to sections and each section is coloured and a robot arm collects objects from these sections according to the section’s colour also the robot arm can organize objects in sections according to the section’s colour.
In this paper a system is designed on an FPGA using a Nios II soft-core processor, to detect the colour of a specific surface and moving a robot arm accordingly. The surface being detected is bounded by a starting mark and an ending mark, to define the region of interest. The surface is also divided into sections as rows and columns and each section can have any colour. Such a system has so many uses like for example warehouses or even in stores where their storing areas can be divided to sections and each section is coloured and a robot arm collects objects from these sections according to the section’s colour also the robot arm can organize objects in sections according to the section’s colour.
In the present investigation, bed porosity and solid holdup in viscous three-phase inverse fluidized bed (TPIFB) are determined for aqueous solutions of carboxy methyl cellulose (CMC) system using polyethylene and polypropylene as a particles with low-density and diameter (5 mm) in a (9.2 cm) inner diameter with height (200 cm) of vertical perspex column. The effectiveness of gas velocity Ug , liquid velocity UL, liquid viscosity μL, and particle density ρs on bed porosity BP and solid holdups εg were determined. The bed porosity increases with "increasing gas velocity", "liquid velocity", and "liquid viscosity". Solid holdup decreases with increasing gas, liquid
... Show MoreThis work investigates generating of pure phase Faujasite-type zeolite Y at the ranges chosen for this study via a static aging step in the absence of seeds synthesis. Nano-sized crystals may result when LUDOX AS-40 is used as a silica source for gel composition of range 6 and the crystallization step may be conducted for a period of 4 to 19 hr at 100 ⁰C. Moreover, large-crystals with high crystallinity pure phase Y zeolite can be obtained at hereinabove conditions but when hydrous sodium metasilicate is used as a silica source. The other selected ranges also offer pure phase Y zeolite at the same controlled conditions.
This work investigates generating of pure phase Faujasite-type zeolite Y at the ranges chosen for this study via a static aging step in the absence of seeds synthesis. Nano-sized crystals may result when LUDOX AS-40 is used as a silica source for gel composition of range 6 and the crystallization step may be conducted for a period of 4 to 19 hr at 100 ⁰C. Moreover, large-crystals with high crystallinity pure phase Y zeolite can be obtained at hereinabove conditions but when hydrous sodium metasilicate is used as a silica source. The other selected ranges also offer pure phase Y zeolite at the same controlled conditions.
Random throwing of industrial waste has a significant impact on the environment unless it takes into account the conditions of engineered destroying and/or re-used. Taking the advantage of re-using waste materials in engineering projects represents a well-planned project in order to resolve a lot of engineering problems for some difficult soils. The objective of this study was to evaluate the capability and effects of Rubber Shreds (RS) from scrap torn belts towards improving the shear strength of soft clay. A direct shear tests were conducted on soft clay-RS mixture. The following parameters were investigated to study the influence of RS content, water content, normal stress, and dilation ratio. From experimental test results it was fou
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Gender classification is a critical task in computer vision. This task holds substantial importance in various domains, including surveillance, marketing, and human-computer interaction. In this work, the face gender classification model proposed consists of three main phases: the first phase involves applying the Viola-Jones algorithm to detect facial images, which includes four steps: 1) Haar-like features, 2) Integral Image, 3) Adaboost Learning, and 4) Cascade Classifier. In the second phase, four pre-processing operations are employed, namely cropping, resizing, converting the image from(RGB) Color Space to (LAB) color space, and enhancing the images using (HE, CLAHE). The final phase involves utilizing Transfer lea
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Codes of red, green, and blue data (RGB) extracted from a lab-fabricated colorimeter device were used to build a proposed classifier with the objective of classifying colors of objects based on defined categories of fundamental colors. Primary, secondary, and tertiary colors namely red, green, orange, yellow, pink, purple, blue, brown, grey, white, and black, were employed in machine learning (ML) by applying an artificial neural network (ANN) algorithm using Python. The classifier, which was based on the ANN algorithm, required a definition of the mentioned eleven colors in the form of RGB codes in order to acquire the capability of classification. The software's capacity to forecast the color of the code that belongs to an ob
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