Nima Taherinejad

Comprehensive Observation and its Role in Self-Awareness; An Emotion Recognition System Example.

Observation plays a crucial role in self-awareness. In many scenarios, such as the Observe-Decide-Act (ODA) loops, self-awareness is founded upon observations of the system. In other words, observation generates the understanding of the system from the status and behavior of its self and its environment. Although recently more focus has been put on comprehensive and competent observations, we believe that further attention and work is due, especially in the field of cyberphysical systems. Hence, in this paper, we discuss our position on various aspects of observation methods. In a short list, the major aspects are Abstraction, Disambiguation, Desirability, Relevance, Data Reliability, Confidence, Attention, and History. We elaborate and anticipate the potential of these factors in improving the quality of the observation of the system, decreasing the processing load of higher layers, increasing the reliability of decisions, and consequently the overall performance of the system. To put these aspects into perspective, we elaborate them in the context of their potentials in our emotion recognition system under development.

Self-awareness in remote health monitoring systems using wearable electronics

In healthcare, effective monitoring of patients plays a key role in detecting health deterioration early enough. Many signs of deterioration exist as early as 24 hours prior having a serious impact on the health of a person. As hospitalization times have to be minimized, in-home or remote early warning systems can fill the gap by allowing in-home care while having the potentially problematic conditions and their signs under surveillance and control. This work presents a remote monitoring and diagnostic system that provides a holistic perspective of patients and their health conditions. We discuss how the concept of self-awareness can be used in various parts of the system such as information collection through wearable sensors, confidence assessment of the sensory data, the knowledge base of the patients health situation, and automation of reasoning about the health situation. Our approach to self-awareness provides (i) situation awareness to consider the impact of variations such as sleeping, walking, running, and resting, (ii) system personalization by reflecting parameters such as age, body mass index, and gender, and (iii) the attention property of self-awareness to improve the energy efficiency and dependability of the system via adjusting the priorities of the sensory data collection. We evaluate the proposed method using a full system demonstration.

Enhancing the Early Warning Score System Using Data Confidence

Early Warning Score (EWS) systems are utilized in hospitals by health-care professionals to interpret vital signals of patients. These scores are used to measure and predict amelioration or deterioration of patients’ health status to intervene in an appropriate manner when needed. Based on an earlier work presenting an automated Internet-of-Things based EWS system, we propose an architecture to analyze and enhance data reliability and consistency. In particular, we present a hierarchical agent-based data confidence evaluation system to detect erroneous or irrelevant vital signal measurements. In our extensive experiments, we demonstrate how our system offers a more robust EWS monitoring system.

Memristors' Potential for Multi-bit Storage and Pattern Learning

Memristor is a two-terminal device, termed as fourth element, and characterized by a varying resistance depending on the charge (current) flown through it. This leads to many interesting characteristics, including a memory of its past states, demonstrated in its resistance. Smaller area and power consumed by memristors compared to conventional memories makes them a more suitable choice for applications needing large memory. In this paper we explore one of the unique properties of memristors which extends their suitability by allowing storage of multi-bit data in a single memristor. Their ability of storing multi-bit patterns will be shown via a simplified proof and simulations. This characteristic can be advantageous for many applications. In this paper particularly, we briefly discuss its advantages in pattern learning applications.

Assessment of Physiological Signals During Happiness, Sadness, Pain or Anger

With the advancement of technology, non-intrusive monitoring of some physiological signals through smart watches and other wearable devices are made possible. This provides us with new opportunities of exploring newer fields of information technology applied in our everyday lives. One application which can help individuals with difficulty in expressing their emotions, e.g. autistic individuals, is emotion recognition through bio-signal processing. To develop such systems, however, a significant amount of measurement data is necessary to establish proper paradigms, which enable such analyses. Given the sparsity of the available data in the literature, specifically the ones using portable devices, we conducted a set of experiments to help in enriching the literature. In our experiments, we measured physiological signals of various subjects during four different emotional experiences; happiness, sadness, pain, and anger. Measured bio-signals are Electrodermal activity (EDA), Skin Temperature, and Heart rate. In this paper, we share our measurement results and our findings regarding their relation with happiness, sadness, anger, and pain.

SAMBA – an architecture for adaptive cognitive control of distributed Cyber-Physical Production Systems based on its self-awareness

Factories in Industry 4.0 are growing in complexity due to the incorporation of a large number of Cyber-Physical System (CPSs) which are logically and often physically distributed. Traditional monolithic control and monitoring structures are not able to address the increasing requirements regarding flexibility, operational time, and efficiency as well as resilience. Self-Aware health Monitoring and Bio-inspired coordination for distributed Automation systems (SAMBA) is a cognitive application architecture which processes information from the factory floor and interacts with the Manufacturing Execution System (MES) to enable automated control and supervision of decentralized CPSs. The proposed architecture increases the ability of the system to ensure the quality of the process by intelligently adapting to rapidly changing environments and conditions.ZusammenfassungIndustrie 4.0-Fabriken nehmen rasch an Komplexität zu aufgrund der Einbeziehung einer großen Anzahl von cyber-physischer Systeme (CPS), die logisch und oft physisch verteilt sind. Traditionelle monolithische Kontrolle und Überwachungsstrukturen sind nicht in der Lage, den steigenden Anforderungen hinsichtlich Flexibilität, Betriebszeit und Effizienz sowie auch Belastbarkeit gerecht zu werden. ,,Self-Aware Health Monitoring and Bio-inspired coordination for distributed Automation systems“ (SAMBA) ist eine kognitive Anwendungsarchitektur, die Informationen von der Fabrik verarbeitet und mit dem Manufacturing Execution System (MES) zur automatisierten Kontrolle und Überwachung von dezentralen CPS interagiert. Die vorgeschlagene Architektur erhöht die Fähigkeit eines Systems, durch intelligente Anpassung an eine sich schnell verändernde Umgebung bzw. Bedingungen die Qualität des Prozesses zu gewährleisten.

Self-aware sensing and attention-based data collection in Multi-Processor System-on-Chips

Self-awareness is the foundation for many of the nowadays desired system characteristics, such as self-optimization and self-adaption. This awareness is rooted in observation and sensory data obtained by the system regarding itself and its environment. Given the important role which data collection plays in creating this awareness, we believe that it merits more attention than it has so far received. For example, increasing the amount of collected data can overload the system with increased computational cost, communication load, and power consumption. Self-awareness can help the system by making data collection smarter and better oriented. In this paper, we propose an attention-based data collection method, inspired by self-awareness, and exploit its potential in the context of Multi-Processor System-on-Chips (MPSoCs). Our case study shows that this method can reduce the computation and communication load related to processing sensory data up to 95%, at the cost of a negligible overhead at the sensor node.

Self-Awareness in Cyber-Physical Systems

The concept of self-awareness has become a hot research topic in a variety of disciplines such as robotics, artificial intelligence, control theory, networked systems, and so on. Its applicability has been explored in various application domains such as automotive, consumer electronics, industrial control, medical equipment, and so forth. The topic owes its attractiveness to many examples in insects, animals and humans, where self-awareness is attributed to facilitate highly resilient and outstandingly efficient behaviors. Thus, self-awareness holds the promise to promote dependability in all types of smart gadgets and artificial agents in the interconnected world of future.This tutorial will introduce the concepts surrounding self-awareness in the context of Cyber-Physical Systems (CPS). A key facet of CPS is the inherent control function where the environment is sensed, the system is analyzed, and adaptions are applied to respect constraints and achieve desired goals. In control theory the design and analysis of autonomous systems has long been subject of intensive research. In recent years self-awareness has been proposed as a handle to equip traditional control systems with a deeper understanding about itself and its environment in the context of the goals of the system. The design community of complex hardware integrated circuits, embedded and cyber-physical systems has also recognized self-awareness as a means to improve dependable behavior in the presence of uncertainties, faulty components and unexpected changes of the environment.

Fully digital write-in scheme for multi-bit memristive storage

Memristors have been used in various applications, including single- and multi-bit storage units. The non-linear voltage-current relation in memristors is often seen as a problem, necessitating complex circuits and methods for a reliable write-in. In this paper, we take advantage of this phenomenon for storing more than one bit of information in a single memristor using digital bit streams. First, we demonstrate how two bits of information can be stored and read back from a single memristor unit. Then, we propose encoding schemes that can enhance the reliability of digitally writing two and three bits of data in a single memristor. To verify the reliability of this method for multi-bit data storage, we have run simulations based on the most prominent simulation models available.