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Jimenez, Lara Lorna
Publications (5 of 5) Show all publications
Karvonen, N., Jimenez, L. L., Gomez Simon, M., Nilsson, J., Kikhia, B. & Hallberg, J. (2017). Classifier Optimized for Resource-constrained Pervasive Systems and Energy-efficiency. Paper presented at 10th International Conference on Ubiquitous Computing and Ambient Intelligence (UCAmI), San Bartolome de Tirajana, Spain, Nov 29-Dec 2, 2016. International Journal of Computational Intelligence Systems, 10(1), 1272-1279
Open this publication in new window or tab >>Classifier Optimized for Resource-constrained Pervasive Systems and Energy-efficiency
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2017 (English)In: International Journal of Computational Intelligence Systems, ISSN 1875-6891, E-ISSN 1875-6883, Vol. 10, no 1, p. 1272-1279Article in journal (Refereed) Published
Abstract [en]

Computational intelligence is often used in smart environment applications in order to determine a user’scontext. Many computational intelligence algorithms are complex and resource-consuming which can beproblematic for implementation devices such as FPGA:s, ASIC:s and low-level microcontrollers. Thesetypes of devices are, however, highly useful in pervasive and mobile computing due to their small size,energy-efficiency and ability to provide fast real-time responses. In this paper, we propose a classi-fier, CORPSE, specifically targeted for implementation in FPGA:s, ASIC:s or low-level microcontrollers.CORPSE has a small memory footprint, is computationally inexpensive, and is suitable for parallel processing.The classifier was evaluated on eight different datasets of various types. Our results show thatCORPSE, despite its simplistic design, has comparable performance to some common machine learningalgorithms. This makes the classifier a viable choice for use in pervasive systems that have limitedresources, requires energy-efficiency, or have the need for fast real-time responses.

Place, publisher, year, edition, pages
Atlantis Press, 2017
Keywords
Cellular Automata, FPGA, Energy-efficient
National Category
Computer Sciences Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Pervasive Mobile Computing; Industrial Electronics
Identifiers
urn:nbn:se:ltu:diva-65869 (URN)10.2991/ijcis.10.1.86 (DOI)000415593600032 ()
Conference
10th International Conference on Ubiquitous Computing and Ambient Intelligence (UCAmI), San Bartolome de Tirajana, Spain, Nov 29-Dec 2, 2016
Note

Konferensartikel i tidskrift

Available from: 2017-09-28 Created: 2017-09-28 Last updated: 2018-10-15Bibliographically approved
Karvonen, N., Kikhia, B., Jimenez, L. L., Gomez Simon, M. & Hallberg, J. (2016). A Computationally Inexpensive Classifier Merging Cellular Automata and MCP-Neurons. In: Carmelo R. García, Pino Caballero-Gil, Mike Burmester, Alexis Quesada-Arencibia (Ed.), Ubiquitous Computing and Ambient Intelligence: 10th International Conference, UCAmI 2016, San Bartolomé de Tirajana, Gran Canaria, Spain, November 29 – December 2, 2016, Part II. Paper presented at 10th International Conference, UCAmI 2016, San Bartolomé de Tirajana, Gran Canaria, Spain, November 29 – December 2, 2016 (pp. 368-379). Springer, 2
Open this publication in new window or tab >>A Computationally Inexpensive Classifier Merging Cellular Automata and MCP-Neurons
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2016 (English)In: Ubiquitous Computing and Ambient Intelligence: 10th International Conference, UCAmI 2016, San Bartolomé de Tirajana, Gran Canaria, Spain, November 29 – December 2, 2016, Part II / [ed] Carmelo R. García, Pino Caballero-Gil, Mike Burmester, Alexis Quesada-Arencibia, Springer, 2016, Vol. 2, p. 368-379Conference paper, Published paper (Refereed)
Abstract [en]

There is an increasing need for personalised and context-aware services in our everyday lives and we rely on mobile and wearable devices to provide such services. Context-aware applications often make use of machine-learning algorithms, but many of these are too complex or resource-consuming for implementation on some devices that are common in pervasive and mobile computing. The algorithm presented in this paper, named CAMP, has been developed to obtain a classifier that is suitable for resource-constrained devices such as FPGA:s, ASIC:s or microcontrollers. The algorithm uses a combination of the McCulloch-Pitts neuron model and Cellular Automata in order to produce a computationally inexpensive classifier with a small memory footprint. The algorithm consists of a sparse binary neural network where neurons are updated using a Cellular Automata rule as the activation function. Output of the classifier is depending on the selected rule and the interconnections between the neurons. Since solving the input-output mapping mathematically can not be performed using traditional optimization algorithms, the classifier is trained using a genetic algorithm. The results of the study show that CAMP, despite its minimalistic structure, has a comparable accuracy to that of more advanced algorithms for the datasets tested containing few classes, while performing poorly on the datasets with a higher amount of classes. CAMP could thus be a viable choice for solving classification problems in environments with extreme demands on low resource consumption

Place, publisher, year, edition, pages
Springer, 2016
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 10070
National Category
Media and Communication Technology
Research subject
Mobile and Pervasive Computing
Identifiers
urn:nbn:se:ltu:diva-60640 (URN)10.1007/978-3-319-48799-1_42 (DOI)000389507400042 ()2-s2.0-85009786711 (Scopus ID)978-3-319-48798-4 (ISBN)978-3-319-48799-1 (ISBN)
Conference
10th International Conference, UCAmI 2016, San Bartolomé de Tirajana, Gran Canaria, Spain, November 29 – December 2, 2016
Available from: 2016-11-23 Created: 2016-11-23 Last updated: 2018-07-10Bibliographically approved
Simon, M. G. & Jimenez, L. L. (2015). Analysis of End-User programming platforms (ed.). Paper presented at .
Open this publication in new window or tab >>Analysis of End-User programming platforms
2015 (English)Report (Other academic)
Abstract [en]

End-user programming platforms allow end-users with and without programming experienceto build applications using a user-friendly graphical environment. This study reviews dif-ferent types of end-user platforms focusing on the features obtained from previous end-usersoftware engineering studies: What You See Is What You Get(WYSIWYG), What You TestIs What You Get (WTISWYG), how the learning by examples methodology is implementedand how the performance of end-user programmers is increased through reusable code. Thestudy also establishes the dierence between end-user programming platforms and tradi-tional programming platforms based on the programmer's interaction. In this report, a newin-between category is dened as End-User Professional Programming Platform, which rep-resents the end-user programming platforms that require the end-user programmer to havea certain programming knowledge. Finally, the research discusses current trends and de-nes new features for the future of end-user platforms, in particular the denition of a newconcept, which is What You SAy Is What You Get(WYSAIWYG).

Publisher
p. 28
National Category
Media and Communication Technology
Research subject
Mobile and Pervasive Computing
Identifiers
urn:nbn:se:ltu:diva-22347 (URN)285135d1-b18a-4808-89d9-8738bd567156 (Local ID)285135d1-b18a-4808-89d9-8738bd567156 (Archive number)285135d1-b18a-4808-89d9-8738bd567156 (OAI)
Note
Godkänd; 2015; 20150520 (andbra)Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2018-01-10Bibliographically approved
Jimenez, L. L., Simon, M. G., Schelén, O., Kristiansson, J., Synnes, K. & Åhlund, C. (2015). CoMA: Resource Monitoring of Docker Containers (ed.). In: (Ed.), (Ed.), Proceedings of the 5th International Conference on Cloud Computing and Services Science (CLOSER 2015): . Paper presented at International Conference on Cloud Computing and Services Science : 20/05/2015 - 22/05/2015 (pp. 145-154). : SCITEPRESS Digital Library, 1
Open this publication in new window or tab >>CoMA: Resource Monitoring of Docker Containers
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2015 (English)In: Proceedings of the 5th International Conference on Cloud Computing and Services Science (CLOSER 2015), SCITEPRESS Digital Library , 2015, Vol. 1, p. 145-154Conference paper, Published paper (Refereed)
Abstract [en]

This research paper presents CoMA, a Container Monitoring Agent, that oversees resource consumption of operating system level virtualization platforms, primarily targeting container-based platforms such as Docker. The core contribution is CoMA, together with a quantitative evaluation verifying the validity of the measurements reported by the agent for three metrics: CPU, memory and block I/O. The proof-of-concept is implemented for Docker-based systems and consists of CoMA, the Ganglia Monitoring System and the Host sFlow agent. This research is in line with the rising trend of container adoption which is due to the resource efficiency and ease of deployment. These characteristics have set containers in a position to topple virtual machines as the reigning virtualization technology in data centers.

Place, publisher, year, edition, pages
SCITEPRESS Digital Library, 2015
Keywords
Docker, Containers, OS-level virtualization, Cloud Computing, Information technology - Computer science, Informationsteknik - Datorvetenskap
National Category
Media and Communication Technology
Research subject
Mobile and Pervasive Computing
Identifiers
urn:nbn:se:ltu:diva-31992 (URN)10.5220/0005448001450154 (DOI)65372d05-6cc9-4916-b19f-df00f4a146dc (Local ID)978-989-758-104-5 (ISBN)65372d05-6cc9-4916-b19f-df00f4a146dc (Archive number)65372d05-6cc9-4916-b19f-df00f4a146dc (OAI)
Conference
International Conference on Cloud Computing and Services Science : 20/05/2015 - 22/05/2015
Projects
Cloudberry Datacenters
Note
Godkänd; 2015; Bibliografisk uppgift: The full text of this paper is only available to INSTICC members. ; 20150821 (larjim)Available from: 2016-09-30 Created: 2016-09-30 Last updated: 2018-05-09Bibliographically approved
Kikhia, B., Simon, M. G., Jimenez, L. L., Hallberg, J., Karvonen, N. & Synnes, K. (2014). Analyzing Body Movements within the Laban Effort Framework using a Single Accelerometer (ed.). Paper presented at . Sensors, 14(3), 5725-41
Open this publication in new window or tab >>Analyzing Body Movements within the Laban Effort Framework using a Single Accelerometer
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2014 (English)In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 14, no 3, p. 5725-41Article in journal (Refereed) Published
Abstract [en]

This article presents a study on analyzing body movements by using a single accelerometer sensor. The investigated categories of body movements belong to the Laban Effort Framework: Strong - Light, Free – Bound and Sudden - Sustained. All body movements were represented by a set of activities used for data collection. The calculated accuracy of detecting the body movements was based on collecting data from a single wireless tri-axial accelerometer sensor. Ten healthy subjects collected data from three body locations (chest, wrist and thigh) simultaneously in order to analyze the locations comparatively. The data was then processed and analyzed using Machine Learning techniques. The wrist placement was found to be the best single location to record data for detecting (Strong – Light) body movements using the Random Forest classifier. The wrist placement was also the best location for classifying (Bound – Free) body movements using the SVM classifier. However, the data collected from the chest placement yielded the best results for detecting (Sudden – Sustained) body movements using the Random Forest classifier. The study shows that the choice of the accelerometer placement should depend on the targeted type of movement. In addition, the choice of the classifier when processing data should also depend on the chosen location and the target movement.

National Category
Media and Communication Technology
Research subject
Mobile and Pervasive Computing
Identifiers
urn:nbn:se:ltu:diva-14175 (URN)10.3390/s140305725 (DOI)000336783300101 ()24662408 (PubMedID)d869bee7-d398-4f28-a6fc-b928a102737c (Local ID)d869bee7-d398-4f28-a6fc-b928a102737c (Archive number)d869bee7-d398-4f28-a6fc-b928a102737c (OAI)
Note
Validerad; 2014; 20140311 (basel)Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2018-10-15Bibliographically approved
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