System Architecture for Low-Power Ubiquitously Connected Remote Health Monitoring Applications With Smart Transmission Mechanism

We present a novel smart transmission technique with seamless handoff mechanism to achieve ubiquitous connectivity using multiple on-chip radios targeting remote health monitoring applications. For the first time to the best of our knowledge, a system architecture for low-power ubiquitously connected multiparametric remote health monitoring system is proposed in this paper. The architecture proposed uses a generic adaptive rule engine for classifying the collected multiparametric data from patient and smartly transmit the data when only needed. The on-chip seamless handoff mechanism proposed aids for the ubiquitous connectivity with a very good energy savings by intelligent controlling of the multiple on-chip radios. The performance analysis of the proposed on-chip seamless handoff mechanism along with adaptive rule engine-based smart transmission mechanism achieves on an average of 50.39% of energy saving and 51.01% reduction in duty cycle of transmitter taken over 20 users compared with the continuous transmission. From the hardware complexity analysis made on the proposed seamless handoff controller and adaptive rule engine concludes that they require only 2082 CMOS transistors for real-time implementation

Exploitation of Digital Filters to Advance the Single-Phase T/4 Delay PLL System

With the development of digital signal processing technologies, control and monitoring of power electronics conversion systems have been evolving to become fully digital. As the basic element in the design and analysis phases of digital controllers or filters, a number of unit delays (z-1) have been employed, e.g., in a cascaded structure. Practically, the number of unit delays is designed as an integer, which is related to the sampling frequency as well as the ac signal fundamental frequency (e.g., 50 Hz). More common, the sampling frequency is fixed during operation for simplicity and design. Hence, any disturbance in the ac signal will violate this design rule and it can become a major challenge for digital controllers. To deal with the above issue, this paper first exploits a virtual unit delay (zv-1) to emulate the variable sampling behavior in practical digital signal processors with a fixed sampling rate. This exploitation is demonstrated on a T/4 Delay Phase Locked Loop (PLL) system for a single-phase grid-connected inverter. The T/4 Delay PLL requires to cascade 50 unit delays when implemented (for a 50-Hz system with 10 kHz sampling frequency). Furthermore, digital frequency adaptive comb filters are adopted to enhance the performance of the T/4 Delay PLL when the grid suffers from harmonics. Experimental results have confirmed the effectiveness of the digital filters for advanced control systems

Requirements Problem and Solution Concepts for Adaptive Systems Engineering, and their Relationship to Mathematical Optimisation, Decision Analysis, and Expected Utility Theory

Requirements Engineering (RE) focuses on eliciting, modelling, and analyzing the requirements and environment of a system-to-be in order to design its specification. The design of the specification, usually called the Requirements Problem (RP), is a complex problem solving task, as it involves, for each new system-to-be, the discovery and exploration of, and decision making in, new and ill-defined problem and solution spaces. The default RP in RE is to design a specification of the system-to-be which (i) is consistent with given requirements and conditions of its environment, and (ii) together with environment conditions satisfies requirements. This paper (i) shows that the Requirements Problem for Adaptive Systems (RPAS) is different from, and is not a subclass of the default RP, (ii) gives a formal definition of RPAS, and (iii) discusses implications for future research

ACon: A learning-based approach to deal with uncertainty in contextual requirements at runtime

Context: Runtime uncertainty such as unpredictable operational environment and failure of sensors that gather environmental data is a well-known challenge for adaptive systems. Objective: To execute requirements that depend on context correctly, the system needs up-to-date knowledge about the context relevant to such requirements. Techniques to cope with uncertainty in contextual requirements are currently underrepresented. In this paper we present ACon (Adaptation of Contextual requirements), a data-mining approach to deal with runtime uncertainty affecting contextual requirements. Method: ACon uses feedback loops to maintain up-to-date knowledge about contextual requirements based on current context information in which contextual requirements are valid at runtime. Upon detecting that contextual requirements are affected by runtime uncertainty, ACon analyses and mines contextual data, to (re-)operationalize context and therefore update the information about contextual requirements. Results: We evaluate ACon in an empirical study of an activity scheduling system used by a crew of 4 rowers in a wild and unpredictable environment using a complex monitoring infrastructure. Our study focused on evaluating the data mining part of ACon and analysed the sensor data collected onboard from 46 sensors and 90,748 measurements per sensor. Conclusion: ACon is an important step in dealing with uncertainty affecting contextual requirements at runtime while considering end-user interaction. ACon supports systems in analysing the environment to adapt contextual requirements and complements existing requirements monitoring approaches by keeping the requirements monitoring specification up-to-date. Consequently, it avoids manual analysis that is usually costly in today’s complex system environments.Peer ReviewedPostprint (author's final draft

Deraining and Desnowing Algorithm on Adaptive Tolerance and Dual-tree Complex Wavelet Fusion

Severe weather conditions such as rain and snow often reduce the visual perception quality of the video image system, the traditional methods of deraining and desnowing usually rarely consider adaptive parameters. In order to enhance the effect of video deraining and desnowing, this paper proposes a video deraining and desnowing algorithm based on adaptive tolerance and dual-tree complex wavelet. This algorithm can be widely used in security surveillance, military defense, biological monitoring, remote sensing and other fields. First, this paper introduces the main work of the adaptive tolerance method for the video of dynamic scenes. Second, the algorithm of dual-tree complex wavelet fusion is analyzed and introduced. Using principal component analysis fusion rules to process low-frequency sub-bands, the fusion rule of local energy matching is used to process the high-frequency sub-bands. Finally, this paper used various rain and snow videos to verify the validity and superiority of image reconstruction. Experimental results show that the algorithm has achieved good results in improving the image clarity and restoring the image details obscured by raindrops and snows

Self-Learning Cloud Controllers: Fuzzy Q-Learning for Knowledge Evolution

Cloud controllers aim at responding to application demands by automatically scaling the compute resources at runtime to meet performance guarantees and minimize resource costs. Existing cloud controllers often resort to scaling strategies that are codified as a set of adaptation rules. However, for a cloud provider, applications running on top of the cloud infrastructure are more or less black-boxes, making it difficult at design time to define optimal or pre-emptive adaptation rules. Thus, the burden of taking adaptation decisions often is delegated to the cloud application. Yet, in most cases, application developers in turn have limited knowledge of the cloud infrastructure. In this paper, we propose learning adaptation rules during runtime. To this end, we introduce FQL4KE, a self-learning fuzzy cloud controller. In particular, FQL4KE learns and modifies fuzzy rules at runtime. The benefit is that for designing cloud controllers, we do not have to rely solely on precise design-time knowledge, which may be difficult to acquire. FQL4KE empowers users to specify cloud controllers by simply adjusting weights representing priorities in system goals instead of specifying complex adaptation rules. The applicability of FQL4KE has been experimentally assessed as part of the cloud application framework ElasticBench. The experimental results indicate that FQL4KE outperforms our previously developed fuzzy controller without learning mechanisms and the native Azure auto-scaling