Sistem Pemandu Pengemudi Berbasis Kamera Embeded

Keamanan dan kenyamanan berkendara merupakan salah satu aspek penting yang harus diperhatikan oleh industri otomotif. Sebuah sistem yang mampu memberikan peringatan dini pada pengemudi akan membantu mencegah terjadinya kecelakaan. Sistem pemandu pengemudi (Driver assistance system) merupakan sistem yang dikembangkan untuk menyediakan fungsi tersebut. Sistem pemandu pengemudi berbasis kamera merupakan sistem yang berkembang cukup pesat, seiring dengan perkembangan teknologi di bidang teknik pengolahan citra digital dan sistem komputer. Penelitian ini bertujuan untuk mengembangkan sistem pemandu pengemudi berbasis kamera yang mampu mendeteksi kelelahan dan konsentrasi/pandangan mata pengemudi, rambu-rambu lalu lintas, dan marka jalan, serta objek atau kendaraan yang berada di depan. Pada peneltian di tahun pertama, dikembangkan sistem pendeteksi kelelahan pengemudi menggunakan kamera embeded yang dipasang di ruang kemudi kendaraan. Sebuah sistem komputer embeded digunakan sebagai pengolah utama dalam proses pendeteksian berbasis kamera tersebut. Dengan menggunakan sistem embeded ini, implementasi sistem di kendaraan dapat dilakukan dengan mudah dan murah

Implementation of Face Detection and Tracking on A Low Cost Embedded System Using Fusion Technique

Abstract—This paper presents the fusion techniques for detecting and tracking the face. The proposed method combines the Viola-Jones method, the CamShift tracking, and the Kalman Filter tracking. The objective is to increase the face detection rate, while reduce the computation cost. The proposed method is implemented on a low cost embedded system based-on the Raspberry Pi module. The experimental results show that the average detection rate of 98.3% is achieved, and it is superior compared to the existing techniques. The proposed system achieves the frame rate of 7.09 fps in the real-time face detection. Index Terms—Face detection, Viola-Jones, CamShift, Kalman Filter, Raspberry Pi

Artificial Intelligence Enabled Project Management: A Systematic Literature Review

In the Industry 5.0 era, companies are leveraging the potential of cutting-edge technologies such as artificial intelligence for more efficient and green human-centric production. In a similar approach, project management would benefit from artificial intelligence in order to achieve project goals by improving project performance, and consequently, reaching higher sustainable success. In this context, this paper examines the role of artificial intelligence in emerging project management through a systematic literature review; the applications of AI techniques in the project management performance domains are presented. The results show that the number of influential publications on artificial intelligence-enabled project management has increased significantly over the last decade. The findings indicate that artificial intelligence, predominantly machine learning, can be considerably useful in the management of construction and IT projects; it is notably encouraging for enhancing the planning, measurement, and uncertainty performance domains by providing promising forecasting and decision-making capabilities

Application of a Big Data Framework for Data Monitoring on a Smart Campus

At present, university campuses integrate technologies such as the internet of things, cloud computing, and big data, among others, which provide support to the campus to improve their resource management processes and learning models. Integrating these technologies into a centralized environment allows for the creation of a controlled environment and, subsequently, an intelligent environment. These environments are ideal for generating new management methods that can solve problems of global interest, such as resource consumption. The integration of new technologies also allows for the focusing of its efforts on improving the quality of life of its inhabitants. However, the comfort and benefits of technology must be developed in a sustainable environment where there is harmony between people and nature. For this, it is necessary to improve the energy consumption of the smart campus, which is possible by constantly monitoring and analyzing the data to detect any anomaly in the system. This work integrates a big data framework capable of analyzing the data, regardless of its format, providing effective and efficient responses to each process. The method developed is generic, which allows for its application to be adequate in addressing the needs of any smart campus

Sundanese Cultural Values of Local Wisdom: Integrated to Develop a Model of Learning Biology

This study aims to determine the ability of student groups to develop biology teaching model that integrates the values ??of local wisdom Sunda on student teachers of biology education. The research method using descriptive quantitative method. The sampling technique used purposive sampling technique as much as 27 groups of students in the subject of Biology Education Innovation learning Biology. The results showed that students in the group's ability to develop biology teaching model included into the category fairly with the average score of the group amounted to 76.44. A total of 79.63% of student groups capable of developing learning models based on creativity (original) included in both categories, 80% of student groups capable of developing learning models and can be applied in learnin

Prioritization-based adaptive emergency traffic medium access control protocol for wireless body area networks

Wireless Body Area Networks (WBANs) provide continuous monitoring of a patient by using heterogeneous Bio-Medical Sensor Nodes (BMSNs). WBANs pose unique constraints due to contention-based prioritized channel access, sporadic emergency traffic handling and emergency-based traffic adaptivity. In the existing medium access control protocols, the available contention-based prioritized channel access is incomplete due to the repetitions in backoff period ranges. The emergency traffic is considered based on traffic generation rate as well as sporadic emergency traffic that is not handled at multiple BMSNs during contention. In an emergency situation, non-emergency traffic is ignored, traffic is not adjusted dynamically with balanced throughput and energy consumption, and the energy of non-emergency traffic BMSNs is not preserved. In this research, prioritization-based adaptive emergency traffic Medium Access Control (MAC) protocol was designed to consider contention-based prioritized channel access for heterogenous BMSNs along with sporadic emergency traffic handling and dynamic adjustment of traffic in sporadic emergency situation. Firstly, a Traffic Class Prioritization based slotted-CSMA/CA (TCP-CSMA/CA) scheme was developed to provide contention-based prioritized channel access by removing repetitions in backoff period ranges. Secondly, an emergency Traffic Class Provisioning based slotted-CSMA/CA (ETCP-CSMA/CA) scheme was presented to deliver the sporadic emergency traffic instantaneously that occurs either at a single BMSN or multiple BMSNs, with minimum delay and packet loss without ignoring non-emergency traffic. Finally, an emergency-based Traffic Adaptive slotted-CSMA/CA (ETA-CSMA/CA) scheme provided dynamic adjustment of traffic to accommodate the variations in heterogeneous traffic rates along with energy preservation of non-emergency traffic BMSNs, creating a balance between throughput and energy in the sporadic emergency situation. Performance comparison was conducted by simulation using NS-2 and the results revealed that the proposed schemes were better than ATLAS, PLA-MAC, eMC-MAC and PG-MAC protocols. The least improved performances were in terms of packet delivery delay 10%, throughput 14%, packet delivery ratio 21%, packet loss ratio 28% and energy consumption 37%. In conclusion, the prioritization-based adaptive emergency traffic MAC protocol outperformed the existing protocols

On-demand offloading collaboration framework based on LTE network virtualisation

Recently, there has been a significant increase in data traffic on mobile networks, due to the growth in the numbers of users and the average data volume per user. In a context of traffic surge and reduced revenues, operators face the challenge of finding costless solutions to increase capacity and coverage. Such a solution should necessarily rule out any physical expansion, and mainly conceive real-time strategies to utilise the spectrum more efficiently, such as network offload and Long-term Evolution (LTE) network virtualisation. Virtualisation is playing a significant role in shaping the way of networking now and in future, since it is being devised as one of the available technologies heading towards the upcoming 5G mobile broadband. Now, the successful utilisation of such innovative techniques relies critically on an efficient call admission control (CAC) algorithm. In this work, framework is proposed to manage the operation of a system in which CAC, virtualisation and Local break out (LBO) strategies are collaboratively implemented to avoid congestion in a mobile network, while simultaneously guaranteeing that measures of quality of service (QoS) are kept above desired thresholds. In order to evaluate the proposed framework, two simulation stages were carried out. In the first stage, MATLAB was used to run a numerical example, with the purpose of verifying the mathematical model of the proposed framework in air interface level. The second stage involved of using open source applications such as, Emulated Virtual Environment (EVE) and Wireshark, for emulating the traffic in the network for different scenarios inside the core network. The results confirm the effectiveness of the proposed framework

Exploring attributes, sequences, and time in Recommender Systems: From classical to Point-of-Interest recommendation

Tesis Doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Ingenieria Informática. Fecha de lectura: 08-07-2021Since the emergence of the Internet and the spread of digital communications throughout the world, the amount of data stored on the Web has been growing exponentially. In this new digital era, a large number of companies have emerged with the purpose of ltering the information available on the web and provide users with interesting items. The algorithms and models used to recommend these items are called Recommender Systems. These systems are applied to a large number of domains, from music, books, or movies to dating or Point-of-Interest (POI), which is an increasingly popular domain where users receive recommendations of di erent places when they arrive to a city. In this thesis, we focus on exploiting the use of contextual information, especially temporal and sequential data, and apply it in novel ways in both traditional and Point-of-Interest recommendation. We believe that this type of information can be used not only for creating new recommendation models but also for developing new metrics for analyzing the quality of these recommendations. In one of our rst contributions we propose di erent metrics, some of them derived from previously existing frameworks, using this contextual information. Besides, we also propose an intuitive algorithm that is able to provide recommendations to a target user by exploiting the last common interactions with other similar users of the system. At the same time, we conduct a comprehensive review of the algorithms that have been proposed in the area of POI recommendation between 2011 and 2019, identifying the common characteristics and methodologies used. Once this classi cation of the algorithms proposed to date is completed, we design a mechanism to recommend complete routes (not only independent POIs) to users, making use of reranking techniques. In addition, due to the great di culty of making recommendations in the POI domain, we propose the use of data aggregation techniques to use information from di erent cities to generate POI recommendations in a given target city. In the experimental work we present our approaches on di erent datasets belonging to both classical and POI recommendation. The results obtained in these experiments con rm the usefulness of our recommendation proposals, in terms of ranking accuracy and other dimensions like novelty, diversity, and coverage, and the appropriateness of our metrics for analyzing temporal information and biases in the recommendations producedDesde la aparici on de Internet y la difusi on de las redes de comunicaciones en todo el mundo, la cantidad de datos almacenados en la red ha crecido exponencialmente. En esta nueva era digital, han surgido un gran n umero de empresas con el objetivo de ltrar la informaci on disponible en la red y ofrecer a los usuarios art culos interesantes. Los algoritmos y modelos utilizados para recomendar estos art culos reciben el nombre de Sistemas de Recomendaci on. Estos sistemas se aplican a un gran n umero de dominios, desde m usica, libros o pel culas hasta las citas o los Puntos de Inter es (POIs, en ingl es), un dominio cada vez m as popular en el que los usuarios reciben recomendaciones de diferentes lugares cuando llegan a una ciudad. En esta tesis, nos centramos en explotar el uso de la informaci on contextual, especialmente los datos temporales y secuenciales, y aplicarla de forma novedosa tanto en la recomendaci on cl asica como en la recomendaci on de POIs. Creemos que este tipo de informaci on puede utilizarse no s olo para crear nuevos modelos de recomendaci on, sino tambi en para desarrollar nuevas m etricas para analizar la calidad de estas recomendaciones. En una de nuestras primeras contribuciones proponemos diferentes m etricas, algunas derivadas de formulaciones previamente existentes, utilizando esta informaci on contextual. Adem as, proponemos un algoritmo intuitivo que es capaz de proporcionar recomendaciones a un usuario objetivo explotando las ultimas interacciones comunes con otros usuarios similares del sistema. Al mismo tiempo, realizamos una revisi on exhaustiva de los algoritmos que se han propuesto en el a mbito de la recomendaci o n de POIs entre 2011 y 2019, identi cando las caracter sticas comunes y las metodolog as utilizadas. Una vez realizada esta clasi caci on de los algoritmos propuestos hasta la fecha, dise~namos un mecanismo para recomendar rutas completas (no s olo POIs independientes) a los usuarios, haciendo uso de t ecnicas de reranking. Adem as, debido a la gran di cultad de realizar recomendaciones en el ambito de los POIs, proponemos el uso de t ecnicas de agregaci on de datos para utilizar la informaci on de diferentes ciudades y generar recomendaciones de POIs en una determinada ciudad objetivo. En el trabajo experimental presentamos nuestros m etodos en diferentes conjuntos de datos tanto de recomendaci on cl asica como de POIs. Los resultados obtenidos en estos experimentos con rman la utilidad de nuestras propuestas de recomendaci on en t erminos de precisi on de ranking y de otras dimensiones como la novedad, la diversidad y la cobertura, y c omo de apropiadas son nuestras m etricas para analizar la informaci on temporal y los sesgos en las recomendaciones producida