Implementation of a Coin Recognition System for Mobile Devices with Deep Learning

This paper examines the application of a deep learning approach to automatic coin recognition, via a mobile device and client-server architecture. We show that a convolutional neural network is effective for coin identification. During the training phase, we determine the optimum size of the training dataset necessary to achieve high classification accuracy with low variance. In addition, we propose a client-server architecture that enables a user to identify coins by photographing it with a smartphone. The image provided by the user is matched with the neural network on a remote server. A high correlation suggests that the image is a match. The application is a first step towards the automatic identification of coins and may help coin experts in their study of coins and reduce the associated expense of numismatic applications

Aplicación móvil para el reconocimiento de moneda colombiana con retroalimentación de audio para personas con discapacidad visual

Context: According to the census conducted by the National Department of Statistics (DANE) in 2018, 7.1% of the Colombian population has a visual disability. These people face conditions with limited autonomy, such as the handling of money. In this context, there is a need to create tools to enable the inclusion of visually impaired people in the financial sector, allowing them to make payments and withdrawals in a safe and reliable manner. Method: This work describes the development of a mobile application called CopReader. This application enables the recognition of coins and banknotes of Colombian currency without an Internet connection, by means of convolutional neural network models. CopReader was developed to be used by visually impaired people. It takes a video or photographs, analyzes the input data, estimates the currency value, and uses audio feedback to communicate the result. Results: To validate the functionality of CopReader, integration tests were performed. In addition, precision and recall tests were conducted, considering the YoloV5 and MobileNet architectures, obtaining 95 and 93% for the former model and 99% for the latter. Then, field tests were performed with visually impaired people, obtaining accuracy values of 96%. 90% of the users were satisfied with the application’s functionality. Conclusions: CopReader is a useful tool for recognizing Colombian currency, helping visually impaired people gain to autonomy in handling money.Contexto: Según el censo realizado por el Departamento Nacional de Estadística (DANE) en 2018, el 7.1 % de la población colombiana tiene una discapacidad visual. Estas personas enfrentan condiciones con autonomía limitada, como lo es el manejo de dinero. En este contexto, es necesario crear herramientas que permitan la inclusión de las personas con discapacidad visual en el sector financiero, permitiéndoles realizar pagos y retiros de manera segura y confiable. Método: Este trabajo describe el desarrollo de una aplicación móvil llamada CopReader. Esta aplicación permite el reconocimiento de monedas y billetes de la moneda colombiana sin conexión a Internet, mediante modelos de redes neuronales convolucionales. CopReader fue desarrollada para ser utilizada por personas con discapacidad visual: toma un video o fotografías, analiza los datos de entrada, estima el valor de la moneda y utiliza retroalimentación auditiva para comunicar el resultado. Resultados: Para validar la funcionalidad de CopReader, se realizaron pruebas de integración. Además, se llevaron a cabo pruebas de precisión y recall, considerando las arquitecturas YoloV5 y MobileNet, donde se obtuvo 95 y 93 % para el primer modelo y 99 % para el segundo. Luego, se realizaron pruebas de campo con personas visualmente discapacitadas, obteniendo valores de exactitud del 96 %. El 90 % de los usuarios quedaron satisfechos con la funcionalidad de la aplicación. Conclusiones: CopReader es una herramienta útil para el reconocimiento de la moneda colombiana, ayudando a las personas con discapacidad visual a ganar autonomía en el manejo del dinero

An Efficient and Lightweight Deep Learning Model for Human Activity Recognition Using Smartphones

Traditional pattern recognition approaches have gained a lot of popularity. However, these are largely dependent upon manual feature extraction, which makes the generalized model obscure. The sequences of accelerometer data recorded can be classified by specialized smartphones into well known movements that can be done with human activity recognition. With the high success and wide adaptation of deep learning approaches for the recognition of human activities, these techniques are widely used in wearable devices and smartphones to recognize the human activities. In this paper, convolutional layers are combined with long short-term memory (LSTM), along with the deep learning neural network for human activities recognition (HAR). The proposed model extracts the features in an automated way and categorizes them with some model attributes. In general, LSTM is alternative form of recurrent neural network (RNN) which is famous for temporal sequences’ processing. In the proposed architecture, a dataset of UCI-HAR for Samsung Galaxy S2 is used for various human activities. The CNN classifier, which should be taken single, and LSTM models should be taken in series and take the feed data. For each input, the CNN model is applied, and each input image’s output is transferred to the LSTM classifier as a time step. The number of filter maps for mapping of the various portions of image is the most important hyperparameter used. Transformation on the basis of observations takes place by using Gaussian standardization. CNN-LSTM, a proposed model, is an efficient and lightweight model that has shown high robustness and better activity detection capability than traditional algorithms by providing the accuracy of 97.89%

Rethinking affordance

n/a – Critical survey essay retheorising the concept of 'affordance' in digital media context. Lead article in a special issue on the topic, co-edited by the authors for the journal Media Theory

Chronic Kidney Disease Android Application

Chronic kidney disease is increasingly recognized as a leading public health problem over the world that affects more than 10 percent of the population worldwide, where electrolytes and wastes can build up in your system. Kidney failure might not be noticeable until more advanced stages where it may then become fatal if not for artificial filtering or a transplant. As a result, it is important to detect kidney disease early on to prevent it from progressing to kidney failure. The current main test of the disease is a blood test that measures the levels of a waste product called creatine and needs information such as age, size, gender, and ethnicity. They may be uncomfortable, can lead to infections, and are inconvenient and expensive. I will re-engineer an Android application for Chronic Kidney Disease detection by working on test strip detection zone localization, detection zone focus, capture quality, and dynamic model loading. This uses a smartphone’s camera and allows users to manually focus on an area of the view to analyze. The camera detects where the test strip and its detection zone is and checks if it is in focus. The pixels are sent to the machine learning algorithm. The application can quickly determine the health of a users kidney and can display it. By only requiring a few drops of blood and an Android smartphone, it is very important for those who cannot afford insurance or live in developing countries. This can make a huge difference in early detection of CDK in these areas where people would otherwise disregard the tests in fear of not having enough money

HANDLING WORK FROM HOME SECURITY ISSUES IN SALESFORCE

Security is a vital component when it is identified with an endeavor record or our genuine materials. To protect our home or valuable things like gold, cash we use bank storage administrations or underground secret storage spaces at home. Similarly, IT enterprises put tremendous measure of capital in expanding security to its business and the archives. Associations use cryptography procedures to get their information utilizing progressed encryption calculations like SHA-256, SHA-512, RSA-1024, RSA-2048 pieces’ key encryption and Elliptic Curve Cryptography (ECC) calculations. These industry standard calculations are difficult to break. For instance, to break RSA-2048-piece encryption key, an old-style PC needs around 300 trillion years. As indicated by the continuous examination, a quantum PC can break it in 10seconds, yet such a quantum PC doesn\u27t yet exist. Despite the fact that these cryptographic calculations guarantee an awesome degree of safety, there will be dependably a space for breaking the security. Programmers will attempt new techniques to break the security. Thus, the association likewise should continue to utilize new strategies to build the level and nature of the security. Now it is time to check how the security aspect is taken care of when the IT employees are at work from home. The 2020 year has made many professionals work from home because of the Covid-19 pandemic. The Covid-19 has transformed almost all organizations to work from home, this has become standard advice, and technology plays an important role during work from home to monitor the employee works and provide security when the work is being carried away from their respective organization. Employees\u27 information security awareness will become one of the most important parts of safeguarding against nefarious information security practices during this work from home. Most of the workers like the expediency of work from home and the flexibility provided for the employees. But in this situation, workers need guarantees that their privacy is secured when using company laptops and phones. Cyber security plays an important role in maintaining a secured environment when working from home. This work focusses on managing the security break attack in the course of work from home. The focus of the study is on dealing with security breaches that occur when salespeople operate from home. The problem of security isn\u27t new. Security issues existed prior to the lockdown or pandemic, but because the staff was working from the office at the time, the system administrator was available to address them. However, how can an employee\u27s laptop and account be secured when working from home? MFH\u27s salesforce has leveraged a variety of innovative technologies to address security concerns during their tenure. Because the IT behemoth Salesforce has made it possible for all employees, including freshly hired ones, to seek WFH on a permanent basis. To address the security breach difficulties faced by employees, the organization used a number of new approaches, including tracking working hours, raising password difficulty, employing VPN (virtual private network), mandating video during meetings, continuously checking right to use control, and MFA (multi-factor authentication). Improvement of existing multi-factor authentication (MFA) is the focused topic discussed in the thesis. To add an additional step of protection to the login process Blockchain technology is proposed and to identify the employee identification a hybrid recognition model is proposed using face and fingerprint recognition. This leads to the employee going through multiple processes to authenticate his or her identity in numerous ways in order to access the business laptop. This procedure entails connecting his or her laptop to his or her mobile phone or email account. Keywords: MFA, WFH, Cyber Security, Encryption, Decryption

A Contactless Health Monitoring System for Vital Signs Monitoring, Human Activity Recognition and Tracking

Integrated sensing and communication technologies provide essential sensing capabilities that address pressing challenges in remote health monitoring systems. However, most of today’s systems remain obtrusive, requiring users to wear devices, interfering with people’s daily activities, and often raising privacy concerns. Herein, we present HealthDAR, a low-cost, contactless, and easy-to-deploy health monitoring system. Specifically, HealthDAR encompasses three interventions: i) Symptom Early Detection (monitoring of vital signs and cough detection), ii) Tracking & Social Distancing, and iii) Preventive Measures (monitoring of daily activities such as face-touching and hand-washing). HealthDAR has three key components: (1) A low-cost, low-energy, and compact integrated radar system, (2) A simultaneous signal processing combined deep learning (SSPDL) network for cough detection, and (3) A deep learning method for the classification of daily activities. Through performance tests involving multiple subjects across uncontrolled environments, we demonstrate HealthDAR’s practical utility for health monitoring