Prototyping Feed-Forward Artificial Neural Network on Spartan 3S1000 FPGA for Blood Type Classification

In this research, a Feed-Forward Artificial Neural Network design was implemented on Xilinx Spartan 3S1000 Field Programable Gate Array using XSA-3S Board and prototyped blood type classification device. This research uses blood sample images as a system input. The system was built using VHSIC Hardware Description Language to describe the feed-forward propagation with a backpropagation neural network algorithm. We use three layers for the feed-forward ANN design with two hidden layers. The hidden layer designed has two neurons. In this study, the accuracy of detection obtained for four-type blood image resolutions results from 86%-92%, respectively.

Facial Skincare Products’ Recommendation with Computer Vision Technologies

Acne is a skin issue that plagues many young people and adults. Even if it is cured, it leaves acne spots or acne scars, which drives many individuals to use skincare products or undertake medical treatment. On the contrary, the use of inappropriate skincare products can exacerbate the condition of the skin. In view of this, this work proposes the use of computer vision (CV) technology to realize a new business model of facial skincare products. The overall framework is composed of a finger vein identification system, skincare products’ recommendation system, and electronic payment system. A finger vein identification system is used as identity verification and personalized service. A skincare products’ recommendation system provides consumers with professional skin analysis through skin type classification and acne detection to recommend skincare products that finally improve skin issues of consumers. An electronic payment system provides a variety of checkout methods, and the system will check out by finger-vein connections according to membership information. Experimental results showed that the equal error rate (EER) comparison of the FV-USM public database on the finger-vein system was the lowest and the response time was the shortest. Additionally, the comparison of the skin type classification accuracy was the highest.

Vehicle Type Classification using Hybrid Features and a Deep Neural Network

Currently, considerable research has been done in vehicle type classification, especially due to the success of deep learning in many image classification problems. In this research, a system incorporating hybrid features is proposed to improve the performance of vehicle type classification. The feature vectors are extracted from the pre-processed images using Gabor features, a histogram of oriented gradients and a local optimal oriented pattern. The hybrid set of features contains complementary information that could help discriminate between the classes better, further, an ant colony optimizer is utilized to reduce the dimension of the extracted feature vectors. Finally, a deep neural network is used to classify the types of vehicles in the images. The proposed approach was tested on the MIO vision traffic camera dataset and another more challenging real-world dataset consisting of videos of multiple lanes of a toll plaza. The proposed model showed an improvement in accuracy ranging from 0.28% to 8.68% in the MIO TCD dataset when compared to well-known neural network architectures.

Last glacial maximum hydro-climate and cyclone characteristics in the Levant: a regional modelling perspective

Proxy-based hydro-climatic reconstructions over the Levant suggest enhanced water availability during the Last Glacial Maximum (LGM) compared to present-day conditions. To date, the governing hypothesis is that additional water availability may be directly linked to increased Cyprus Low frequency and intensity over the region. However, this paradigm has not been tested in a modelling framework. With this aim, we analyzed results from a weather type classification algorithm and regional climate simulations. The weather type classification is applied to ERA5 Reanalysis data for present-day (1979-2018) and two PMIP3/PMIP4 pre-industrial and LGM model runs. Dynamical downscaling of the two models with the regional WRF model shows that the present hydro-climate can largely be reproduced. Our simulations suggest that both evaporation and precipitation were lower in the LGM compared to pre-industrial conditions, and that their relative changes can thus most likely explain the additional water availability during that time. Indeed, evaporation in the eastern Mediterranean is reduced to a higher degree (~-33%) as compared to precipitation (~-20%) during the LGM. Particularly, lower evaporation during LGM summer may have sustained the year-round wetter conditions in the Levant. In addition, we find significant changes in Cyprus Low characteristics for the LGM. The simulated daily precipitation associated with Cyprus Lows is significantly lower than pre-industrial values (reduction of 26 - 29%), whereas the wind intensity is stronger (increase of 7 - 8%). Finally, a significant increase in Cyprus Low frequency during LGM winter is likely (+22%). Indeed, our findings are in line with a plethora of proxy-based reconstructions, and provide a reinterpretation of the driving mechanism of water availability, i.e., strong changes in evaporation rather than precipitation. This study places projected hydro-climatic drying of the Levant in a long timescale perspective. As such, it improves our understanding of the physical processes influencing the hydrological cycle in this vulnerable region, situated on the border between sub-tropical and mid-latitude climatic zones.

Seismic inversion with deep learning

This article investigates bypassing the inversion steps involved in a standard litho-type classification pipeline and performing the litho-type classification directly from imaged seismic data. We consider a set of deep learning methods that map the seismic data directly into litho-type classes, trained on two variants of synthetic seismic data: (i) one in which we image the seismic data using a local Radon transform to obtain angle gathers, (ii) and another in which we start from the subsurface-offset gathers, based on correlations over the seismic data. Our results indicate that this single-step approach provides a faster alternative to the established pipeline while being convincingly accurate. We observe that adding the background model as input to the deep network optimization is essential in correctly categorizing litho-types. Also, starting from the angle gathers obtained by imaging in the Radon domain is more informative than using the subsurface offset gathers as input.