A Review on Brain-Controlled Home Automation

A "smart home" employs ambient intelligence to keep tabs on things around the house so that the owner may get services tailored to their specific needs and control their home appliances from afar. Home automation for the elderly and handicapped focuses on enabling older persons and those with disabilities to live safely and comfortably at home. Additionally, the integration of this technology with a brain-computer interface (BCI) is perhaps of tremendous usefulness to those who are either old or disabled. These BCI-based brain-controlled home automation (BCHA) systems have emerged as a viable option for people with neuro disorders to remain in their homes rather than move to assisted living facilities. To summarize, BCI-based BCHA for the elderly and handicapped people is transforming people's lives every day. Most individuals prefer a simple approach to save time and effort. Automating the house is the simplest way for individuals to save time and effort. The brain-computer interface, often known as a BCI, is an innovative method of human-computer connection that does not rely on conventional output channels (muscle tissue and peripheral nerve). Over the course of the last three decades, it has attracted the attention of industry experts and developed into a thriving centre for research. Brain-controlled home automation (BCHA), as a typical BCI application, may provide physically challenged people with a new communication route with the outside world. However, the primary challenge that BCHA faces is to rapidly decipher multi-degree-of-freedom control instructions extracted from an electroencephalogram (EEG). The BCHA's research has made significant headway in a short amount of time during the last fifteen years. This study investigates the BCHA from several viewpoints, including the pattern of instructions for the control system, the type of signal acquisition, and the operational mechanism of the control system itself. This paper a concise description of the building blocks of smart homes and how they may be used to construct BCI-controlled home automation to assist disabled individuals. It is a compilation of information pertaining to communication protocols, multimedia devices, sensors, and systems that are often used in the process of putting smart homes into action. A comprehensive strategy for developing a functional and sustainable BCI-controlled home automation system is laid out in this paper as well, which could be useful to researchers in the future

Composite - its endless journey.

A composite, in its general term, is a solid material that results when two or more different substances, each with its own characteristics and properties, are combined to create a new substance whose properties are superior to those of the original components in a specific application, Composites are of greatest use in the aerospace industry in which their stiffness, lightness, and heat resistance make them the materials of choice in reinforcing the engine cowls, wings, doors, and flaps of aircraft. Composite materials are also used in rackets and other sports equipment, in cutting tools, and in certain parts of automotive engines

Real-time speech emotion analysis for smart home assistants

Artificial Intelligence (AI) based Speech Emotion Recognition (SER) has been widely used in the consumer field for control of smart home personal assistants, with many such devices on the market. However, with the increase in computational power, connectivity and the need to enable people to live in the home for longer though the use of technology, then smart home assistants that could detect human emotion will improve the communication between a user and the assistant enabling the assistant of offer more productive feedback. Thus, the aim of this work is to analyze emotional states in speech and propose a suitable method considering performance verses complexity for deployment in Consumer Electronics home products, and to present a practical live demonstration of the research. In this paper, a comprehensive approach has been introduced for the human speech-based emotion analysis. The 1-D convolutional neural network (CNN) has been implemented to learn and classify the emotions associated with human speech. The paper has been implemented on the standard datasets (emotion classification) Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) and Toronto Emotional Speech Set database (TESS) (Young and Old). The proposed approach gives 90.48%, 95.79% and94.47% classification accuracies in the aforementioned datasets. We conclude that the 1-D CNN classification models used in speaker-independent experiments are highly effective in the automatic prediction of emotion and are ideal for deployment in smart home assistants to detect emotion

Recognize basic emotional statesin speech by machine learning techniques using mel-frequency cepstral coefficient features

Speech Emotion Recognition (SER) has been widely used in many fields, such as smart home assistants commonly found in the market. Smart home assistants that could detect the user’s emotion would improve the communication between a user and the assistant enabling the assistant to offer more productive feedback. Thus, the aim of this work is to analyze emotional states in speech and propose a suitable algorithm considering performance verses complexity for deployment in smart home devices. The four emotional speech sets were selected from the Berlin Emotional Database (EMO-DB) as experimental data, 26 MFCC features were extracted from each type of emotional speech to identify the emotions of happiness, anger, sadness and neutrality. Then, speaker-independent experiments for our Speech emotion Recognition (SER) were conducted by using the Back Propagation Neural Network (BPNN), Extreme Learning Machine (ELM), Probabilistic Neural Network (PNN) and Support Vector Machine (SVM). Synthesizing the recognition accuracy and processing time, this work shows that the performance of SVM was the best among the four methods as a good candidate to be deployed for SER in smart home devices. SVM achieved an overall accuracy of 92.4% while offering low computational requirements when training and testing. We conclude that the MFCC features and the SVM classification models used in speaker-independent experiments are highly effective in the automatic prediction of emotion

La communication sans fil dans un réseau électrique intelligent (smart grid) : méthodologie de développement

L'évolution des réseaux électriques combinés avec les réseaux et les infrastructures des technologies de l'information et de la communication a donné lieu à une nouvelle génération de réseaux appelés Réseaux électriques intelligents (Smart grids). Ces réseaux ont des exigences relativement aux architectures de communication et des services. Cette génération de réseaux requiert également la mise en œuvre de services pour leur monitoring et leur surveillance. Avec la venue des compteurs intelligents, il devient possible de faire communiquer les foyers avec un centre de contrôle et de monitoring, ce qui permet de gérer et de contrôler à distance les appareils se trouvant dans le foyer. Les réseaux intelligents offrent également la possibilité d'intégrer des énergies alternatives et renouvelables comme l'énergie solaire ou l'énergie éolienne. Tout foyer connecté au réseau peut non seulement consommer de l'énergie provenant d'un réseau électrique comme celui d'Hydro-Québec, mais également produire de l'énergie et la rendre disponible aux autres foyers. Dans ce mémoire, nous nous sommes intéressés à plusieurs de ces aspects et nous formulons des propositions pour répondre aux nouveaux besoins crées par ces réseaux. Nous avons proposé des infrastructures de communication impliquant des réseaux sans fil et une architecture permettant de connecter les différents éléments du réseau et les équipements des foyers (appareils électriques, appareils électroménagers, etc.). Nous avons proposé plusieurs services pour effectuer le contrôle et le monitoring du réseau et des équipements des foyers à travers les compteurs intelligents. À cet effet, nous avons utilisé des services Web. Nous avons proposé une procédure d'ordonnancement des appareils électriques et électroménagers des foyers en vue de gérer leurs consommations et faire face aux situations de panne, notamment dans les chutes de tension. Nous avons également développé des services et une application pour donner accès à ces services à travers des appareils portables, notamment les téléphones mobiles intelligents. Pour permettre aux fournisseurs d'énergie de mettre à la disposition des usagers du réseau le surplus d'énergie qu'ils produisent, nous avons mis sur pied une infrastructure et des mécanismes d'échange pour la négociation de l'offre et de la demande d'énergie. Ce mécanisme utilise les agents mobiles. Ces agents sont des programmes qui voyagent à travers le réseau grâce à un protocole de routage spécifique en vue de trouver les meilleurs fournisseurs qui répondent aux demandes des foyers qui ont un besoin en énergie. Ce protocole de routage est évalué par simulation, et les résultats sont présentés et discutés dans ce mémoire, démontrant ainsi des bonnes performances.\ud ______________________________________________________________________________ \ud MOTS-CLÉS DE L’AUTEUR : réseaux électriques intelligents, compteurs intelligents, monitoring et contrôle de la distribution d'énergie, gestion de la consommation électrique, ordonnancement, communication sans fil, agents mobiles

Enhancing the efficiency of electricity utilization through home energy management systems within the smart grid framework

The concept behind smart grids is the aggregation of “intelligence” into the grid, whether through communication systems technologies that allow broadcast/data reception in real-time, or through monitoring and systems control in an autonomous way. With respect to the technological advancements, in recent years there has been a significant increment in devices and new strategies for the implementation of smart buildings/homes, due to the growing awareness of society in relation to environmental concerns and higher energy costs, so that energy efficiency improvements can provide real gains within modern society. In this perspective, the end-users are seen as active players with the ability to manage their energy resources, for example, microproduction units, domestic loads, electric vehicles and their participation in demand response events. This thesis is focused on identifying application areas where such technologies could bring benefits for their applicability, such as the case of wireless networks, considering the positive and negative points of each protocol available in the market. Moreover, this thesis provides an evaluation of dynamic prices of electricity and peak power, using as an example a system with electric vehicles and energy storage, supported by mixed-integer linear programming, within residential energy management. This thesis will also develop a power measuring prototype designed to process and determine the main electrical measurements and quantify the electrical load connected to a low voltage alternating current system. Finally, two cases studies are proposed regarding the application of model predictive control and thermal regulation for domestic applications with cooling requirements, allowing to minimize energy consumption, considering the restrictions of demand, load and acclimatization in the system