Analysis, Design, and Optimization of Embedded Control Systems

Aminifar, Amir

doi:10.3384/diss.diva-124319

Cited by 16 publications

(11 citation statements)

References 72 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They are typically used in the context of Cyber-Physical Systems (CPS), that is, the software running on the embedded platform controls the movements of the mechanical system that they are coupled with. For this reason they are commonly classified as real-time applications, as they need to respect computation deadlines to ensure the system stability [1].…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Embedded Control Algorithms used in UAVs

Silvestre¹,

Becker²

2020

Anais Estendidos Do X Simpósio Brasileiro De Engenharia De Sistemas Computacionais (SBESC Estendido 2020)

View full text Add to dashboard Cite

Performance analysis of embedded systems is critical when dealing with Cyber-Physical Systems that require stability guarantees. They typically operate having to respect deadlines imposed during the design of the related control system. In a recent past performance analysis was typically done only by executing the code, and making measures, on the target embedded platform. Nowadays, code execution/measuring can also be done on simulation software, which offers greater degree of liberty for designers to configure the system for the desired tests. This paper presents results obtained from analyzing the performance of two control algorithms developed for controlling an Unmanned Aerial Vehicle (UAV) running on simulated and real embedded platforms. Such analysis is important for twofold reasons: better understand the timing behavior or the algorithms and evaluate architectural issues related with the target embedded platform. Raspberry Pi 3 Model B+ (with Cortex-A53 processor) is used as reference platform and serves as basis for creating different simulated versions for the analysis. Initial results highlighted the important role played by cache memory in the performance of the control algorithms and were able to detect a major bottleneck in one of the control algorithms that could compromise system stability.

show abstract

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Embedded Control Algorithms used in UAVs

Silvestre¹,

Becker²

2020

Anais Estendidos Do X Simpósio Brasileiro De Engenharia De Sistemas Computacionais (SBESC Estendido 2020)

View full text Add to dashboard Cite

show abstract

“…To ensure extreme energy efficiency, having knowledge about self and environment is necessary for the system. Self-awareness can be applied in seizure detection systems by fulfilling three major properties: reflecting current situation, predicting the possibilities to improve energy reduction and adopting the right strategy in the system [10]- [12].…”

Section: Introductionmentioning

confidence: 99%

A Self-Aware Epilepsy Monitoring System for Real-Time Epileptic Seizure Detection

Forooghifar

Aminifar

Cammoun³

et al. 2019

Mobile Netw Appl

Self Cite

View full text Add to dashboard Cite

Epilepsy is one of the most prevalent paroxystic neurological disorders that can dramatically degrade the quality of life and may even lead to death. Therefore, real-time epilepsy monitoring and seizure detection has become important over the past decades. In this context, wearable technologies offer a promising solution to pervasive epilepsy monitoring by removing the constraints with respect to time and location. In this paper, we propose a self-aware wearable system for real-time detection of epileptic seizures on a long-term basis. First, we propose a multi-parametric machine learning technique to detect seizures by analyzing both cardiac and respiratory responses to seizures, which are obtained using only the ECG signal. Second, in order to enable long-time epilepsy detection, we introduce the notion of self-awareness in our real-time wearable system. We evaluate the performance of our proposed solution based on an epilepsy database of more than 211 hours of recording, provided by the Lausanne University Hospital (CHUV), on the INYU wearable sensor. Our proposed system achieves a sensitivity of 88.66% and a specificity of 85.65% before applying self-awareness. Moreover, by controlling the energy-quality trade-offs using our self-aware energy-management technique, we can tune the battery lifetime of the wearable system to last between 67.55 and 136.91 days while, still outperforming the state-of-the-art techniques for wearable seizure detection, by achieving from 85.54% to 79.33% geometric mean of specificity and sensitivity.

show abstract

“…As a result, to fulfill continuous high performance, having knowledge about self and environment is necessary for the system. Self-awareness can be applied to many different categories of systems equipped with control mechanisms and units, and usually has three major properties: Self-reflection, Selfprediction and Self-adaptation [3], [4]. According to these properties, each system should be aware of its architecture, execution environment, operational goals, and their corresponding dynamic changes during operation.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Self-Aware Wearable Systems in Epileptic Seizure Detection

Forooghifar

Aminifar

Alonso

2018

2018 21st Euromicro Conference on Digital System Design (DSD)

Self Cite

View full text Add to dashboard Cite

Today, wearable systems are facing fundamental barriers in terms of battery lifetime and quality of their results. The main challenge in wearable systems is to increase the battery lifetime, while maintaining the machine-learning performance of the system. A recently proposed concept for overcoming this challenge is self-awareness, which increases system's knowledge of itself and the surrounding environment. This is precisely what health monitoring wearable systems require to adapt to different situations. To demonstrate the impact of introducing self-awareness in wearable technologies, we consider the epileptic seizure detection problem, as a case study. Epilepsy affects around 1% of the world's population, which can dramatically degrade the quality of life and represents a major public health issue. As a result, detection of epileptic seizures has become more important over the past decades. In this paper, we aim to introduce a new generation of self-aware wearable systems to decrease energy consumption and improve their seizures detection capabilities by introducing the notion of self-awareness in such systems. These techniques include switching to low-power mode to reduce the energy consumption and machine-learning model enhancement to improve detection quality. We incorporated our proposed techniques in the machine learning module, which detects epileptic seizures by monitoring the cardiac and respiratory systems. We evaluated the performance of our approach based on an epilepsy database of more than 141 hours, provided by the Lausanne University Hospital (CHUV). Our self-aware wearable system achieves 36% reduction in computational complexity and 10.51% improvement in detection performance.

show abstract

Analysis, Design, and Optimization of Embedded Control Systems

Cited by 16 publications

References 72 publications

Performance Analysis of Embedded Control Algorithms used in UAVs

Performance Analysis of Embedded Control Algorithms used in UAVs

A Self-Aware Epilepsy Monitoring System for Real-Time Epileptic Seizure Detection

Self-Aware Wearable Systems in Epileptic Seizure Detection

Contact Info

Product

Resources

About