Auto Encoders based Neural Networks to Predict Faultiness of VLSI Circuits
Smart Electrical Vehicle
Development of Smart Electronic System to Implement Smart Home
Multilingual Speaker Identification System through Multiple Features Analysis of Speech Signal in Multilingual Environment
Photographing a Black Hole
Development of an Intelligent Battery Charging System Based on PIC16F877A Microcontroller
Blockchain 3.0: Towards a Secure Ballotcoin Democracy through a Digitized Public Ledger in Developing Countries
Brief Introduction to Modular Multilevel Converters and Relative Concepts and Functionalities
Fetal ECG Extraction from Maternal ECG using MATLAB
Detection of Phase to Phase Faults and Identification of Faulty Phases in Series Capacitor Compensated Six Phase Transmission Line using the Norm of Wavelet Transform
A Novel Approach to Reduce Deafness in Classical Earphones: MUEAR
A novel mathematical ECG signal analysis approach for features extraction using LabVIEW
Filtering of ECG Signal Using Adaptive and Non Adaptive Filters
Application of Polynomial Approximation Techniques for Smoothing ECG Signals
A Novel Approach to Improve the Wind Profiler Doppler Spectra Using Wavelets
Wearable Health Monitoring Smart Gloves
Cardless automated teller machine (CATM) is an electronic gadget that empowers the bank’s clients to perform monetary transactions such as dispensing cash to their clients, pay bills and transfer of money. The customary approach of an ATM utilizes the use of debit card for its transactions have its limitation. The challenges in utilizing such system are extortion, security and high probability of users forgetting their passwords. In this work, we propose a straightforward paradigm called CATM. The proposed CATM model used a five tuple finite machine. In the proposed platform a thumb print (Biometric) framework is utilized. The platform will improve services to clients, also secure, effective and efficient systems will be achieved.
Understanding infants’ needs through crying is a skill acquired by health care givers as well as parents from training and experiences. However, errors may evolve due to variations in judgment and limitations on the human sensory system. Various approaches have been proposed to mimic the classical human based method which also tied results to system dominant errors. This work uses the Autoregressive (AR) model coefficient as features for recognizing infant cry. First, a dataset of infant cry consisting of Hunger, Pain and Normal cry was obtained. Each cry was framed and widowed with overlap to enable the processing of the rapidly changing cry signal. Then AR model coefficients (features) were extracted from the trained Artificial Neural Network (ANN). The extracted features were then used to train an Artificial Neural Network recognition system. The performance of this system was tested using three different activation functions, sampling frequencies and various threshold values. Results show the appropriateness of this new approach.
Adaptive Traffic Control System (ATCS) serves as a main element in the constituents with which traffic control flow is achieved in fast developing, and developed urban areas. ATCS, however causes more delays on vehicles due to the fact that it is made up of intersecting points. Ensuring maximum efficiency at intersections has remained a challenge due to its dynamic nature of traffic. Additionally, a number of different methods that can be used to achieve higher performance at road traffic intersections have been recently proposed to engineers. In this study, a new and different method based on modified round robin scheduling algorithm through genetic algorithm technique to optimize the performance (in terms of timing) of a signalized intersection in one of the busiest and most crowded roads of Minna, Niger State – Nigeria (at Obasanjo shopping complex area). The technique uses an initial timing pattern to generate newer offspring (in terms of delay duration) to analyze cost function and to check if a global optimum is reached. This technique outweighs current techniques because the data upon which the nature of the system is built is relatively more phenomenal, as it puts into consideration the exact nature of the lane in many possible occurrences. In this work, a global optimum was reached at only a few number of iteration on the whole Genetic Algorithm process.
This paper reviews scholarly articles on the application of blockchain technology for secure electronic voting (e-voting). Furthermore, the feasibility of using blockchain technology to replace the existing manual or semidigitized voting system in developing countries with Nigeria as a case study is performed. To analyse the current state and preparedness of adopting Blockchain Enabled E-voting (BEEV) system in Nigeria, this paper employs the qualitative SWOT (Strengths, Weaknesses, Opportunities and Threats) and PEST (Political, Economic, Social and Technological) analysis approach. This evaluation leads us to identify internal and external factors and the strategic direction in adopting BEEV in Nigeria. It is the authors’ opinion that this approach could also be tailored to evaluate situations of other developing countries.
Applying Machine Learning to a problem which involves medical, financial, or other types of sensitive data needs careful attention in order to maintaining data privacy and security. This paper presents a model for privacy preserving classification and demonstrated that, by using a decision tree classifier, it is possible to perform a privacy preserving classification operation on an encrypted data residing on an untrusted server using the technique of Fully Homomorphic Encryption. First, the paper presented a model for the design and implementation of privacy preserving decision tree classifier over encrypted data. Also, Fully Homomorphic Encryption technique was used to secretly carry out classification on ciphertext using decision tree model built out of confidential medical data. The classifier was implemented using the SEAL homomorphic library and evaluation was done using encrypted medical datasets. The experimental results demonstrated high accuracy of the ciphertext classifier (when compared to the plaintext data equivalent) and efficiency (compared to other classifier on similar tasks). It takes less than 5 seconds (depending on the depth) to perform classification over an encrypted hepatitis feature vector dataset.