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3S Smart Salón & Spa es un salón de belleza y spa dedicado al cuidado de las mujeres de 25 a 45 años del NSE AB que viven en Lima Moderna, poniendo a su disposición un ambiente armonioso con soluciones tecnológicas para mantener y mejorar la imagen personal de sus clientes y eliminar el estrés. 3S busca que la mujer sienta bienestar, descanso, cuidado de su cuerpo, rostro y cabello y que además pueda encontrar elementos electrónicos que puedan incrementar su experiencia de servicio con el respaldo de equipos modernos de alta tecnología y productos de calidad de las marcas más reconocidas del sector de salones de belleza y spa. La necesidad de este tipo de servicios surge debido a que la imagen se ha convertido en un factor decisivo en el mundo de hoy, tomando gran importancia tanto en el ámbito social como personal y laboral de las personas. Las mujeres buscan cada vez verse y sentirse bien. Esto alineado al constante avance tecnológico da como resultado el primer salón de belleza tecnológico del Perú, en el cual se minimiza el riesgo de un cambio de look pudiendo ver el resultado antes de realizarse un cambio irreversible, acompañado del ambiente tecnológico de la infraestructura y otros elementos que serán parte de la plataforma tecnológica del local. La fidelidad de las clientas y la atracción de nuevas, se captará a través de nuestro servicio diferenciado y constante actualización tecnológica, siendo consistentes en la calidad del servicio brindado con un staff altamente calificado, acompañado de un plan de marketing vía internet, redes sociales, medios y eventos. / Tesis

Med den snabba utvecklingen av Internetteknologi växer området förhemautomatisering alltmer. Intresset och efterfrågan för teknik inom smarta hemhar ökat avsevärt i Sverige. Teknik inom smarta hem förser hushåll medintelligenta levnadsmiljöer för daglig bekvämlighet. Syftet med denna studie är attframställa information från svenska konsumenter om deras uppfattade fördelar ochrisker kring smarta hem. För att kunna generera denna data har vi genomfört enenkätundersökning och detta dokumentet lyfter fram de upplevda fördelarna ochriskerna med smarta hem från flera perspektiv av svenska konsumenter runt omhela Sverige. Resultaten av studien kan vara till hjälp för framtida svenskalagstiftande organ, beslutsfattare och tillverkare av smarta hem för att mildra desäkerhetsfrågor och risker som svenska konsumenter lyfter fram. / With the rapid development of Internet technology, the field of home automation isexpanding. The interest and demand for smart home technologies has increasedsignificantly in Sweden. Smart home technology provides households withintelligent living environments for daily convenience. The purpose of this study is toextract the information from Swedish consumers about their perceived benefits andrisks of smart homes. To be able to generate this data we have executed a surveyand this paper highlights the perceived benefits and risks of smart homes frommultiple perspectives of Swedish consumers. The results of the study can behelpful in future for Swedish law making agencies, policymakers and smart homedevice manufacturers to mitigate the security issues and risks mentioned bySwedish consumers.

Wissen über Digitalisierung, Automatisierung und über „smarte“ Technologien sind Schlüssel für einen stabilen und nachhaltigen Erfolg von Klein- und Mittelunternehmen in der Region Salzburg. Die Fachhochschule Salzburg beantragte aus diesem Grund die Initiative Salzburg Smart Factory Bootcamp. Moderne und mobile Industrierobotik, kommunikative Software-Architekturen und OT-Sicherheit bilden die Basis für die Umsetzung aktueller Smart-Factory-Technologien und deren Potentiale in Unternehmen. Ziel des Bootcamps ist die gezielte Weiterqualifizierung mit modernsten Smart-Factory-Technologien von Mitarbeiterinnen und Mitarbeitern der beteiligten Unternehmen. Zusätzlich kann langfristig, auch über die beantragte Projektlaufzeit hinaus, die Zusammenarbeit zwischen Hochschule und Salzburger Unternehmen etabliert und weitergeführt werden.

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Recently, there has been an increase in the deployment of Phasor Measurement Units (PMUs) which has enabled real time, wide area monitoring of power systems. PMUs can synchronously measure operating parameters across the grid at typically 30 samples per second, compared to 1 sample per 2-5 seconds of a conventional Supervisory Control And Data Acquisition (SCADA) system. Such an explosion of data in power systems has provided an opportunity to make electrical grids more reliable. Additionally, it has brought a challenge to extract information from the massive amount of data. In this research, several data mining algorithms are used to extract information from synchrophasor data for improving situational awareness of power systems. The extracted information can be used for event detection, for reducing the dimension of data without losing information, and also to use it as heuristic to process future measurements. The methods proposed in this research work can be broadly classified into two parts: a) stream mining and b) dimension reduction. Stream mining algorithms provide solution utilizing state-of-the-art data stream mining algorithms such as Hoeffding Trees (HT). HT algorithm builds a decision tree by scanning the incoming data stream only once. The tree itself holds sufficient statistics in its leaves to grow the tree and also to make classification decisions of incoming data. Instead of using a large number of samples, which leads to a tree too large to accommodate in memory, the number of samples that are needed to split at each node is determined using Hoeffding bound (HB). HB keeps the size of the decision tree within bounds while also maintaining accuracies statistically competitive to traditional decision trees. Dimension reduction algorithms reduce dimension of the synchrophasor data by extracting maximum information from a huge data set without losing information. In this dissertation, both online and offline dimension reduction algorithms have been studied. The online dimension reduction uses an unsupervised method using principal components of the time series data. The offline method optimizes unique mutual information between the state of the power system and synchrophasor measurements. It optimizes the criteria by reducing redundant information while maximizing relevant information.

Background and Objectives Smart healthcare systems use technologies such as wearable devices, Internet of Medical Things and mobile internet technologies to dynamically access health information, connect patients to health professionals and health institutions, and to actively manage and respond intelligently to the medical ecosystem's needs. However, smart healthcare systems are affected by many challenges in their implementation and maintenance. Key among these are ensuring the security and privacy of patient health information. To address this challenge, several mitigation measures have been proposed and some have been implemented. Techniques that have been used include data encryption and biometric access. In addition, blockchain is an emerging security technology that is expected to address the security issues due to its distributed and decentralized architecture which is similar to that of smart healthcare systems. This study reviewed articles that identified security requirements and risks, proposed potential solutions, and explained the effectiveness of these solutions in addressing security problems in smart healthcare systems. Methods This review adhered to the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines and was framed using the Problem, Intervention, Comparator, and Outcome (PICO) approach to investigate and analyse the concepts of interest. However, the comparator is not applicable because this review focuses on the security measures available and in this case no comparable solutions were considered since the concept of smart healthcare systems is an emerging one and there are therefore, no existing security solutions that have been used before. The search strategy involved the identification of studies from several databases including the Cumulative Index of Nursing and Allied Health Literature (CINAL), Scopus, PubMed, Web of Science, Medline, Excerpta Medical database (EMBASE), Ebscohost and the Cochrane Library for articles that focused on the security for smart healthcare systems. The selection process involved removing duplicate studies, and excluding studies after reading the titles, abstracts, and full texts. Studies whose records could not be retrieved using a predefined selection criterion for inclusion and exclusion were excluded. The remaining articles were then screened for eligibility. A data extraction form was used to capture details of the screened studies after reading the full text. Of the searched databases, only three yielded results when the search strategy was applied, i.e., Scopus, Web of science and Medline, giving a total of 1742 articles. 436 duplicate studies were removed. Of the remaining articles, 801 were excluded after reading the title, after which 342 after were excluded after reading the abstract, leaving 163, of which 4 studies could not be retrieved. 159 articles were therefore screened for eligibility after reading the full text. Of these, 14 studies were included for detailed review using the formulated research questions and the PICO framework. Each of the 14 included articles presented a description of a smart healthcare system and identified the security requirements, risks and solutions to mitigate the risks. Each article also summarized the effectiveness of the proposed security solution. Results The key security requirements reported were data confidentiality, integrity and availability of data within the system, with authorisation and authentication used to support these key security requirements. The identified security risks include loss of data confidentiality due to eavesdropping in wireless communication mediums, authentication vulnerabilities in user devices and storage servers, data fabrication and message modification attacks during transmission as well as while the data is at rest in databases and other storage devices. The proposed mitigation measures included the use of biometric accessing devices; data encryption for protecting the confidentiality and integrity of data; blockchain technology to address confidentiality, integrity, and availability of data; network slicing techniques to provide isolation of patient health data in 5G mobile systems; and multi-factor authentication when accessing IoT devices, servers, and other components of the smart healthcare systems. The effectiveness of the proposed solutions was demonstrated through their ability to provide a high level of data security in smart healthcare systems. For example, proposed encryption algorithms demonstrated better energy efficiency, and improved operational speed; reduced computational overhead, better scalability, efficiency in data processing, and better ease of deployment. Conclusion This systematic review has shown that the use of blockchain technology, biometrics (fingerprints), data encryption techniques, multifactor authentication and network slicing in the case of 5G smart healthcare systems has the potential to alleviate possible security risks in smart healthcare systems. The benefits of these solutions include a high level of security and privacy for Electronic Health Records (EHRs) systems; improved speed of data transaction without the need for a decentralized third party, enabled by the use of blockchain. However, the proposed solutions do not address data protection in cases where an intruder has already accessed the system. This may be potential avenues for further research and inquiry.

<p>  </p> <p>The rise of Internet of Things (IoT) technologies into the substantial commercial market that it is today comes with several challenges. Not only do these systems face the traditional challenges of security and reliability faced by traditional information technology (IT) products, but they also face the challenge of loss of privacy. The concern of user data privacy is most prevalent when these technologies come into the home environment. In this dissertation quasi-experimental research is conducted on the ability of users to protect private data in a heterogeneous smart home network. For this work the experiments are conducted and verified on eight different smart home devices using network traffic analysis and discourse analysis to identify privacy concerns. The results of the research show that data privacy within the confines of the user’s home often cannot be ensured while maintaining smart home device functionality. This dissertation discusses how those results can inform users and manufacturers alike in the use and development of future smart home technologies to better protect privacy concerns.</p>

<p>The manufacturing industry widely employs sound monitoring inspired by the ability of operators that can detect problems based on the sounds that machines emit. This monitoring serves as an integral component for predictive maintenance and productivity estimation. To facilitate real-time monitoring, edge devices are employed to manage and collect sound data. A streamlined Convolutional Neural Network (CNN) model was proposed, designed to execute all necessary computations for predictions, taking into consideration the limited computational resources of edge devices. Comparative analysis with renowned CNN models, namely VGG16, VGG19, ResNet-50, and YAMNet, reveals that the proposed CNN model is highly effective in event prediction from sound classification. Remarkably, the proposed model only required 2% of the prediction time as compared to the slowest and most complex model, while preserving an overall prediction accuracy of 98.9%. To balance the minor accuracy trade-off due to the simplicity of the proposed CNN architecture, an algorithm based on the First-In, First-Out (FIFO) queue system was developed. This method led to a reduction in the prediction error rate by up to 25% within a certain interval between the queue elements, in contrast to systems that do not implement this algorithm. The input feature adopted was the normalized Log-Mel spectrum with a duration of one second. A grid search method was utilized for hyperparameter tuning, with the aim of identifying the optimal hyperparameter combination within the constraints of the simplified CNN model architecture. To substantiate the real-time monitoring performance and superiority of the proposed CNN model, the same workflow was applied to the grain leg and plasma cutting machine using sound data collected from each. The results affirmed that the combination of the proposed CNN model and the developed algorithm exhibited exceptional performance under real-world conditions. In conclusion, for real-time monitoring that employs edge devices, the usage of a simplified CNN model and a customized algorithm is advocated to ensure continuous real-time monitoring devoid of errors or network instability.  </p>

Abstract Society has changed immensely due to the revolution in information technology. The question is how this technology can be useful to man. My interest lies in how to apply radar to traffic. People in general relate radar to military matters while the focus here is on traffic accidents. Accidents happen and therefore police and ambulance need to be on site as fast as possible. This master thesis presents a new design with hardware and software combined in one device. The combination is essential in traffic technology. I designed a new and smart traffic light that can be effective in a traffic system. I design, discuss and examine the radar up to the laboratory stage. It is based on the Doppler effect and has a sound sensor that can identify a police car and also detect vehicle speed. The status of the traffic lights can then be changed to give priority to the police car, based on speed limits and other restrictions. Difficulties as well as advantages with the design are discussed.