Neural network-based vehicle image classification for IoT devices

Convolutional Neural Networks (CNNs) have previously provided unforeseen results in automatic image analysis and interpretation, an area which has numerous applications in both consumer electronics and industry. However, the signal processing related to CNNs is computationally very demanding, which has prohibited their use in the smallest embedded computing platforms, to which many Internet of Things (IoT) devices belong. Fortunately, in the recent years researchers have developed many approaches for optimizing the performance and for shrinking the memory footprint of CNNs. This paper presents a neuralnetwork-based image classifier that has been trained to classify vehicle images into four different classes. The neural network is optimized by a technique called binarization, and the resulting binarized network is placed to an IoT-class processor core for execution. Binarization reduces the memory footprint of the CNN by around 95% and increases performance by more than 6×. Furthermore, we show that by utilizing a custom instruction 'popcount' of the processor, the performance of the binarized vehicle classifier can still be increased by more than 2×, making the CNN-based image classifier suitable for the smallest embedded processors.©2029 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.fi=vertaisarvioitu|en=peerReviewed

Nigeria Paper Currency Serial Number Pattern Recognition System for Crimes Control

Only secured and conducive environment void of robbery, kidnapping, fake currency and all forms of insurgencies will foster production and distribution of goods, investment and saving that enhance national economic growth and development. This is a mirage in a country generally believed and tagged the giant of African; Nigeria. Crime, in whatever name or nomenclature, has a significant negative impact on the welfare and economy prosperities of our society. The urge to get rich promotes Crime like armed robbery, kidnapping for ransom and production of counterfeit banknotes to mention but a few. Innocent people have suffered psychological distress, fear, anger, depression, physical harm, financial loss and in most cases untimely death during the operations by these hoodlums. Banks, Cash-In-Transit Vehicle, and ATM points are often robbed by gangs in search for paper currency. Kidnappers as well demand for paper currency as ransom while some other gangs are involved in the production of counterfeit banknotes so as to enrich themselves no minding the negative effect on the nation’s economy.  The banknotes collected during the operations by the hoodlums are taken to banks. Yet, the banks will not detect or recognize any of these notes which attest to the fact that our system lacks check and balance. The system is very porous without a recourse to this era of technology when machine is trained to do virtually everything for our convenience. Currency as an entity has a unique identification number. The identification number is an alphanumeric currency issuance of about 10 digits comprises two (2) capital letters and eight (8) numbers usually positioned at a strategic location on either front or back of the 5, 10, 20, 50, 100, 200, 500 and 1000 naira notes. It is a reliable and intelligent system developed to track banknotes unique identifiers numbers- serial numbers, in order to control financial related crimes. Keywords: Nigeria Paper Currency Serial Number, Pattern Recognition DOI: 10.7176/IKM/11-3-04 Publication date: April 30th 202

A Vietnamese Handwritten Text Recognition Pipeline for Tetanus Medical Records

Machine learning techniques are successful for optical character recognition tasks, especially in recognizing handwriting. However, recognizing Vietnamese handwriting is challenging with the presence of extra six distinctive tonal symbols and vowels. Such a challenge is amplified given the handwriting of health workers in an emergency care setting, where staff is under constant pressure to record the well-being of patients. In this study, we aim to digitize the handwriting of Vietnamese health workers. We develop a complete handwritten text recognition pipeline that receives scanned documents, detects, and enhances the handwriting text areas of interest, transcribes the images into computer text, and finally auto-corrects invalid words and terms to achieve high accuracy. From experiments with medical documents written by 30 doctors and nurses from the Tetanus Emergency Care unit at the Hospital for Tropical Diseases, we obtain promising results of 2% and 12% for Character Error Rate and Word Error Rate, respectively

Text Extraction from Historical Document Images by the Combination of Several Thresholding Techniques

This paper presents a new technique for the binarization of historical document images characterized by deteriorations and damages making their automatic processing difficult at several levels. The proposed method is based on hybrid thresholding combining the advantages of global and local methods and on the mixture of several binarization techniques. Two stages have been included. In the first stage, global thresholding is applied on the entire image and two different thresholds are determined from which the most of image pixels are classified into foreground or background. In the second stage, the remaining pixels are assigned to foreground or background classes based on local analysis. In this stage, several local thresholding methods are combined and the final binary value of each remaining pixel is chosen as the most probable one. The proposed technique has been tested on a large collection of standard and synthetic documents and compared with well-known methods using standard measures and was shown to be more powerful

Analysis and review of the possibility of using the generative model as a compression technique in DNA data storage: review and future research agenda

The amount of data in this world is getting higher, and overwriting technology also has severe challenges. Data growth is expected to grow to 175 ZB by 2025. Data storage technology in DNA is an alternative technology with potential in information storage, mainly digital data. One of the stages of storing information on DNA is synthesis. This synthesis process costs very high, so it is necessary to integrate compression techniques for digital data to minimize the costs incurred. One of the models used in compression techniques is the generative model. This paper aims to see if compression using this generative model allows it to be integrated into data storage methods on DNA. To this end, we have conducted a Systematic Literature Review using the PRISMA method in selecting papers. We took the source of the papers from four leading databases and other additional databases. Out of 2440 papers, we finally decided on 34 primary papers for detailed analysis. This systematic literature review (SLR) presents and categorizes based on research questions, namely discussing machine learning methods applied in DNA storage, identifying compression techniques for DNA storage, knowing the role of deep learning in the compression process for DNA storage, knowing how generative models are associated with deep learning, knowing how generative models are applied in the compression process, and knowing latent space can be formed. The study highlights open problems that need to be solved and provides an identified research direction