A new method for feature extraction is presented in this paper for speech recognition using a combination of discrete wavelet transform (DWT) and mel Frequency Cepstral Coefficients (MFCCs). The objective of this method is to enhance the performance of the proposed method by introducing more features from the signal. The performance of the Wavelet-based mel Frequency Cepstral Coefficients method is compared to mel Frequency Cepstral Coefficients based method for features extraction. Wavelet transform is applied to the speech signal where the input speech signal is decomposed into various frequency channels using the properties of wavelet transform. then Mel-Frequency Cepstral Coefficients (MFCCs) of the wavelet channels are calculated. A new set of features can be generated by concatenating both features. The speech signals are sampled directly from the microphone. Neural Networks (NN) are used in the proposed methods for classification. The proposed method is implemented for 15 male speakers uttering 10 isolated words each which are the digits from zero to nine. each digit is repeated 15 times.

Deep lean is a novel approach that is concerned with the profound analysis for waste’s behavior at hidden layers in manufacturing processes to enhance processes’ reliability level at the upstream. Ideal Standard Co. for bathtubs suffered from defects and cost losses in the spraying section, due to differences in the painting cover thickness due to bubbles, caused by eddies, which move toward the bathtubs through hoses. These bubbles and their movement are considered as a form of lean’s waste. The spraying liquid inside the tanks and hoses must move with uniform velocity, viscosity, pressure, feed rate and suitable Reynolds circulation values to eliminate the eddy causes. These factors are tackled through the adoption Internet of Things (IoT) technologies that are aided by neural networks (NN) when an abnormal flow rate is detected using sensor data in real-time that can reduce the defects. The NN aimed at forecasting eddies’ movement lines that carry bubbles and works on being blasted before entering the hoses through using Design of Experiment (DOE). This paper illustrates a deep lean perspective as driven by the define, measure, analysis, improvement and control (DMAIC) methodology to improve reliability. The eddy moves downstream slowly with an anti-clockwise flow for some of the optimal values for the influencing factors, whereas the circulation of Ω increases, whether for vertical or horizontal travel.

One of the most challenging problems related to the operation of smart microgrids is the optimal home energy management scheme with multiple and conflicting objectives. Moreover, there is a noticeable increase in homes equipped with renewable energy sources (RESs), where the coordination of loads and generation can achieve extra savings and minimize peak loads. In this paper, a solar-powered smart home with optimal energy management is designed in an affordable and secure manner, allowing the owner to control the home from remote and local sites using their smartphones and PCs. The Raspberry Pi 4 B is used as the brain of the proposed smart home automation management system (HAMS). It is used to collect the data from the existing sensors and store them, and then take the decision. The home is monitored using a graphical interface that monitors room temperature, humidity, smoke, and lighting through a set of sensors, as well as PIR sensors to monitor the people movement. This action enables remote control of all home appliances in a safe and emission-free manner. This target is reached using Cayenne, which is an IoT platform, in addition to building some codes related to some appliances and sensors not supported in Cayenne from scratch. Convenience for people with disabilities is considered by using the Amazon Echo Dot (Alexa) to control home appliances and the charging point by voice, implementing the associated code for connecting the Raspberry pi with Alexa from scratch, and simulating the system on LabVIEW. To reach the optimal operation and reduce the operating costs, an optimization framework for the home energy management system (HEMS) is proposed. The operating costs for the day amounted to approximately 16.039 €. There is a decrease in the operating costs by about 23.13%. The consumption decreased after using the smart HAMS by 18.161 kWh. The results of the optimization also show that the least area that can be used to install solar panels to produce the desired energy with the lowest cost is about 118.1039 m2, which is about 23.62% of the total surface area of the home in which the study was conducted. The obtained results prove the effectiveness of the proposed system in terms of automation, security, safety, and low operating costs.

No abstract

The demand for cloud computing resources is increasing due to its accessibility everywhere at any time. When the number of clients for cloud services increases, the load on the cloud nodes becomes high. This status requires load balancing to evenly distribute client requests among the available Virtual Machines (VMs) in a Data Center (DC). There are different standard dynamic load balancing techniques, such as Throttled and Active Monitoring. In this paper, a Genetic Algorithm (GA) is incorporated with a throttled to improve load balancing. The improvement is achieved by enhancing the overall response time, the data center processing time, and the maximum resource utilization. Simulation results show the improved performance of the proposed method compared to the ESCE and Throttled. Keyword-Cloud Computing, Load Balancing, Genetic Algorithm, Cloud Analyst I. INTRODUCTION Cloud computing technology has seen a speedy growth in recent years. It has affected the growth in several sub-technologies, like storage, distributed networks, virtualization, participation, and connectivity. In [1, 2], a cloud is considered as a distributed system that can handle diverse resource requirements by users. The rule system of a cloud-user relationship is planned by the Service Level Agreement (SLA), which is an agreement between a user and a service provider. As indicated in[3], the physical structure and repairing system can be achieved by the cloud, not the user. This automatically decreases the total cost and increases system efficiency. As indicated in [4], cloud computing gives an easy way to hold data and files, and it includes the following features: virtualization, distributed computing, and web services. Any complex task that calls for enormous computational resources can be serviced by cloud computing using distributed resources in a decentralized style [5]. Although cloud computing has many countenances, there are obstacles as load balancing over the resources and task scheduling [6]. Task scheduling in a cloud environment is a problem of specifying tasks to an appropriate machine to finish their work. A task should be done within a given period. The cloud task scheduler restores the information from the cloud service manager about the case of available resources [7]. Therefore, the scheduling of task problem can be qualified as the method of finding out a model mapping for execution of user tasks with the aim of reaching the desired goals [8]. Algorithms of task scheduling in cloud computing can be done depending on diverse objectives such as balancing the load, minimizing waiting time, and maximizing the utilization of resources and the throughput of the full system. Therefore, an efficient task scheduling algorithm aims to balance diverse and conflicting parameters together at the same time[9]. Moreover, resources are not utilized efficiently due to the rise in the load so for that reason load balancing is required [10]. Load balancing is the approach of redistributing the whole load into separate nodes to guarantee th...

Late order loss for difficulty handling (loading and unloading) activities left an alarm message to make traditional transportation handling for distribution e-commerce more accessible through mitigating it to semi-auto actions. This article discusses the idea of Vehicle Containers made up of Permutational Drawers, i.e., VCPD, that ensure ergonomic handling. The proposed Ergonomic Digital Twin (EDT) manages the VCPD by the Internet of things, i.e., IoT. The VCPD object has two dimensions: drawers' size and motion mechanism. These targets are implemented via establishing the digital twins' model, i.e., DT, for these drawers to test its qualifying for implementation, mainly if supported by IoT. There is still much confusion regarding the DT and how it will apply to the VCPD in medium-sized schemes for transportation enterprises. This work activates the IoT to bolster and simplify transportation activities through designing VCPD and control via a unified framework having several standard steps to reduce execution time, effort, and transportation costs.

No abstract

No abstract