“SmartMonitor”— An Intelligent Security System for the Protection of Individuals and Small Properties with the Possibility of Home Automation

Perimeter detection systems detect intruders penetrating protected areas, but modern solutions require the combination of smart detectors, information networks and controlling software to reduce false alarms and extend detection range. The current solutions available to secure a perimeter (infrared and motion sensors, fiber optics, cameras, radar, among others) have several problems, such as sensitivity to weather conditions or the high failure alarm rate that forces the need for human supervision. The system exposed in this paper overcomes these problems by combining a perimeter security system based on CEMF (control of electromagnetic fields) sensing technology, a set of video cameras that remain powered off except when an event has been detected. An autonomous drone is also informed where the event has been initially detected. Then, it flies through computer vision to follow the intruder for as long as they remain within the perimeter. This paper covers a detailed view of how all three components cooperate in harmony to protect a perimeter effectively, without having to worry about false alarms, blinding due to weather conditions, clearance areas, or privacy issues. The system also provides extra information of where the intruder is or has been, at all times, no matter whether they have become mixed up with more people or not during the attack.

Section: Introductionmentioning

confidence: 99%

Secured Perimeter with Electromagnetic Detection and Tracking with Drone Embedded and Static Cameras

Teixidó¹,

Gómez-Galán

Caballero

et al. 2021

“…However, even though the technological ecosystem concept has gained importance within the research community in recent years [20,21], there is still a lack of consensus on what a technological care and assistance ecosystem is, which components of this type of ecosystem are relevant in the care and assistance domain, what architectures are the most appropriate for developing this type of ecosystem, or even how the community of users interacts with each other. Until recently, most research and developments in care and assistance services have been focused on the development of techno-centric and/or isolated services (e.g., faint/fall detection [22], seniors’ motion monitoring [23], physiological monitoring [24,25], cardiac early warning systems [26] measuring ambient air quality [27], smart appliances [28], etc.). However, the importance of the role of communities and other forms of collaborative networks involving all stakeholders, operating as an ecosystem, is being recognized [3,29].…”

Section: Introductionmentioning

confidence: 99%

Technological Ecosystems in Care and Assistance: A Systematic Literature Review

Marcos-Pablos

García-Peñalvo

2019

Applying the concepts of technological ecosystems to the care and assistance domain is an emerging field that has gained interest during the last years, as they allow to describe the complex relationships between actors in a technologically boosted care domain. In that context, this paper presents a systematic review and mapping of the literature to identify, analyse and classify the published research carried out to provide care and assistance services under a technological ecosystems’ perspective. Thirty-seven papers were identified in the literature as relevant and analysed in detail (between 2003–2018). The main findings show that it is indeed an emerging field, as few of the found ecosystem proposals have been developed in the real world nor have they been tested with real users. In addition, a lot of research to date reports the proposal of platform-centric architectures developed over existing platforms not specifically developed for care and services provision. Employed sensor technologies for providing services have very diverse natures depending on the intended services to be provided. However, many of these technologies do not take into account medical standards. The degree of the ecosystems’ openness to adding new devices greatly depends on the approach followed, such as the type of middleware considered. Thus, there is still much work to be done in order to equate other more established ecosystems such as business or software ecosystems.

“…It can be used to complement the well-known sound source localization (direction and elevation only) methods [1][2][3][4][5][6][7][8][9] to enhance their localization accuracy and improve their effectiveness. For example, in a security breach situation where there is a gunshot, an intelligent surveillance system [10] can detect the sound of the gunshot, localize its azimuth, and estimate its distance in addition. This could provide information to assist the police and other emergency personnel by reducing their emergency response time [11].…”

Section: Introductionmentioning

confidence: 99%

Sound Source Distance Estimation Using Deep Learning: An Image Classification Approach

Yiwere

Rhee

2019

This paper presents a sound source distance estimation (SSDE) method using a convolutional recurrent neural network (CRNN). We approach the sound source distance estimation task as an image classification problem, and we aim to classify a given audio signal into one of three predefined distance classes—one meter, two meters, and three meters—irrespective of its orientation angle. For the purpose of training, we create a dataset by recording audio signals at the three different distances and three angles in different rooms. The CRNN is trained using time-frequency representations of the audio signals. Specifically, we transform the audio signals into log-scaled mel spectrograms, allowing the convolutional layers to extract the appropriate features required for the classification. When trained and tested with combined datasets from all rooms, the proposed model exhibits high classification accuracies; however, training and testing the model in separate rooms results in lower accuracies, indicating that further study is required to improve the method’s generalization ability. Our experimental results demonstrate that it is possible to estimate sound source distances in known environments by classification using the log-scaled mel spectrogram.