| 1. |
- Guang, Liang, et al.
(författare)
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A review of dynamic power management methods in NoC under emerging design considerations
- 2009
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Ingår i: 2009 NORCHIP. ; , s. 1-6
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Konferensbidrag (refereegranskat)abstract
- A review of dynamic and adaptive techniques for power management of on-chip interconnects, under emerging design considerations, is presented. The progress of IC technology has introduced novel methods, architectures and new challenges for power-aware design exploration. An examination of stateof-the-art power management techniques enables feasible and efficient design of future NoC platforms. This review first analyzes the new challenges, architectures and technologies, including PVT (process, voltage, temperature) variations, rapidly increasing leakage power, multiple on-chip PDN (power delivery network) as well as other architectures, which bring new considerations in low-power design exploration. A wide selection of dynamic power-saving techniques for onchip interconnects are examined, classified into several categories including run-time datapath configuration, supply configuration and adaptive encoding. The effects and feasibility of these methods, especially their potentials in future technology, are judiciously analyzed. An outlook on generic power management paradigms in next-generation NoCs concludes the review.
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| 2. |
- Guang, Liang, et al.
(författare)
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Autonomous DVFS on Supply Islands for Energy-Constrained NoC Communication
- 2009
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Ingår i: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). - Berlin, Heidelberg : Springer Berlin Heidelberg. ; , s. 183-194
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Konferensbidrag (refereegranskat)abstract
- An autonomous-DVFS-enabled supply island architecture on network-on-chip platforms is proposed. This architecture exploits the temporal and spatial network traffic variations in minimizing the communication energy while constraining the latency and supply management overhead. Each island is equipped with autonomous DVFS mechanism, which traces the local and nearby network conditions. In quantitative simulations with various types of representative traffic patterns, this approach achieves greater energy efficiency than two other low-energy architectures (typically 10% - 27% lower energy). With autonomous supply management on a proper granularity as demonstrated in this study, the communication energy can be minimized in a scalable manner for many-core NoCs.
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| 3. |
- Guang, Liang, et al.
(författare)
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Hierarchical agent monitoring design approach towards self-aware parallel systems-on-chip
- 2010
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Ingår i: ACM Transactions on Embedded Computing Systems. - : Association for Computing Machinery (ACM). - 1539-9087 .- 1558-3465. ; 9:3
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Tidskriftsartikel (refereegranskat)abstract
- Hierarchical agent framework is proposed to construct a monitoring layer towards self-aware parallel systems-on-chip (SoCs). With monitoring services as a new design dimension, systems are capable of observing and reconfiguring themselves dynamically at all levels of granularity, based on application requirements and platform conditions. Agents with hierarchical priorities work adaptively and cooperatively to maintain and improve system performance in the presence of variations and faults. Function partitioning of agents and hierarchical monitoring operations on parallel SoCs are analyzed. Applying the design approach on the Network-on-Chip (NoC) platform demonstrates the design process and benefits using the novel approach.
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| 4. |
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| 5. |
- Guang, Liang, et al.
(författare)
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Interconnection alternatives for hierarchical monitoring communication in parallel SoCs
- 2010
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Ingår i: Microprocessors and microsystems. - : Elsevier BV. - 0141-9331 .- 1872-9436. ; 34:5, s. 118-128
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Tidskriftsartikel (refereegranskat)abstract
- Interconnection architectures for hierarchical monitoring communication in parallel System-on-Chip (SoC) platforms are explored. Hierarchical agent monitoring design paradigm is an efficient and scalable approach for the design of parallel embedded systems. Between distributed agents on different levels, monitoring communication is required to exchange information, which forms a prioritized traffic class over data traffic. The paper explains the common monitoring operations in SoCs, and categorizes them into different types of functionality and various granularities. Requirements for on-chip interconnections to support the monitoring communication are outlined. Baseline architecture with best-effort service, time division multiple access (TDMA) and two types of physically separate interconnections are discussed and compared, both theoretically and quantitatively on a Network-on-Chip (NoC)-based platform. The simulation uses power estimation of 65 nm technology and NoC microbenchmarks as traffic traces. The evaluation points out the benefits and issues of each interconnection alternative. In particular, hierarchical monitoring networks are the most suitable alternative, which decouple the monitoring communication from data traffic, provide the highest energy efficiency with simple switching, and enable flexible reconfiguration to tradeoff power and performance.
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| 6. |
- Guang, Liang, et al.
(författare)
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Low-latency and Energy-efficient Monitoring Interconnect for Hierarchical-agent-monitored NoCs
- 2008
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Ingår i: Norchip - 26th Norchip Conference, Formal Proceedings. - 9781424424931 ; , s. 227-232
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Konferensbidrag (refereegranskat)abstract
- This paper presents quantitative analysis of monitoring interconnect architecture alternatives in hierarchical agent-based NoC platform. Hierarchical monitoring design methodology provides scalable dynamic management services with agents monitoring different levels. To enable low-latency and lowenergy agent communication, we examined three interconnect alternatives: TDM-based virtual channeling, unified dedicated monitoring network, and separate dedicated monitoring networks. With Orion and Cadence simulators, we estimated the energy and latency of monitoring communications on the three architectures for an 8*8 mesh network in 65nm technology. The results suggest that separate dedicate links mostly minimize the communication delay and energy consumption (66.7% and 82.1% respectively compared to TDM-based interconnect), while incurring moderate area penalty.
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| 7. |
- Guang, Liang, et al.
(författare)
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Run-time communication bypassing for energy-efficient, low-latency per-core DVFS on Network-on-Chip
- 2010
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Ingår i: Proceedings - IEEE International SOC Conference, SOCC 2010. ; , s. 481-486
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Konferensbidrag (refereegranskat)abstract
- System-level exploration of a novel Network-on-Chip (NoC) architecture with run-time communication bypassing is presented. Fine-grained DVFS (Dynamic Voltage and Frequency Scaling) is an effective power reduction technique. We propose run-time reconfigurable interconnect on each inter-router channel to minimize the latency and energy overhead. When two routers are running on the same frequency, FIFO-channel is bypassed by direct interconnect. Distributed algorithm is designed for per-core DVFS. Proper power delivery and clocking scheme are integrated. Simulation shows significant energy and latency saving.
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| 8. |
- Guang, Liang, et al.
(författare)
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System-level exploration of run-time clusterization for energy-efficient on-chip communication
- 2009
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Ingår i: 2nd International Workshop on Network on Chip Architectures, NoCArc 2009, In conjunction with the 42nd Annual IEEE/ACM International Symposium on Microarchitecture, MICRO42. - New York, NY, USA : ACM. ; , s. 63-68
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Konferensbidrag (refereegranskat)abstract
- System-level exploration of run-time power clusterization for energy-efficient on-chip communication is presented. Facilitated by multiple on-chip power-delivery-networks, areas of heavy or low traffics can be dynamically identified and adaptively supplied with new power schemes. This method is superior to design-time voltage island partitioning, in dealing with unpredictable spatial and temporal variations of communication traffics in large NoCs. Architectural design of the platform and online iterative configuration process are presented. The effectiveness of the proposed approach is demonstrated quantitatively on a NoC simulator with 65nm power models. With synthetic traffic traces characterizing various communication patterns, run-time power clusterization achieves considerable energy benefits compared to existing energy-efficient architectures (9% - 42% lower). The latency penalty is predictable and moderately bounded with minimal area overhead. The proposed architecture presents an ideal tradeoff, prioritizing energy efficiency, for massively parallel on-chip computing.
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| 9. |
- Kelati, Amleset, et al.
(författare)
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Biosignal Feature Extraction Techniques for IoT Healthcare Platform
- 2016
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Ingår i: IEEE Conference on Design and Architectures for Signal and Image Processing (DASIP2016). - Rennes, France.
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Konferensbidrag (populärvet., debatt m.m.)abstract
- In IoT healthcare platform, a variety of biosignals are acquired from its sensors and appropriate feature extraction techniques are crucial in order to make use of the acquired biosignal data and help the healthcare scientist or bio-engineer to reach at optimal decisions. This work reviews the existing biosignal feature extraction and classification methods for different healthcare applications. Due the enormous amount of different biosignals and since most healthcare applications uses electrocardiogram (ECG), electroencephalogram (EEG), electromyogram (EMG), Electrogastrogram (EGG), we focus the review on feature extractions and classification method for these biosignals. The review also includes a summary of Blood Oxygen Saturation determined by Pulse Oximetry (SpO2), Electrooculography and eye movement (EOG), and Respiration (RSP) signals. Its discussion and analysis focuses on advantages, performance and drawbacks of the techniques.
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| 10. |
- Kelati, Amleset
(författare)
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Classification of Pain level using Zygomaticus and Corrugator EMG Features: Machine Learning Approach
-
Tidskriftsartikel (refereegranskat)abstract
- A real-time recognition of facial expressions is required to certify the accurate pain assess-8 ment of patients in ICU, infants, and other patients who may not be able to communicate verbally 9 or even express the sensation of pain. Facial expression is a key pain-related behavior that may 10 unlock the answer to objective pain measurement tool. In this work, a machine learning based pain 11 level classification using data collected from facial electromyograms (EMG) is presented. The da-12 taset is acquired from part of Bio Vid Heat Pain database [1] to evaluated facial expression from emg 13 corrugator and emg zygomaticus and an EMG signal processing and data analysis flow is adapted 14 for continuous pain estimation. The extracted pain-associated facial electromyography (fEMG) fea-15 tures classification is performed by a supervised ML algorithm, on the KNN by choosing the value 16 of k and that depends on the nonlinear models. The presentation of the accuracy estimation is per-17 formed with and considerable growth in classification accuracy is noticed when the subject matter 18 from the features is omitted from the analysis. The ML algorithm for classification of the amount of 19 pain in patients could deliver valuable evidence for the health care providers and aid the treatment 20 assessment. Performances of 99.4% shown on the binary classification for the dis-crimination be-21 tween the baseline and the pain tolerance level (P0 verse P4) without the influence of on a subject 22 bias. Moreover, the result of the classification accuracy is clearly showing the relevance of the pro-23 posed approach.
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| 11. |
- Kelati, Amleset
(författare)
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Data-driven Implementations for Enhanced Healthcare Internet-of-Things Systems
- 2021
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Healthcare monitoring systems based on the Internet of Things (IoT) areemerging as a potential solution for reducing healthcare costs by impacting and improving the quality of health care delivery. The rising numberof elderly and chronic patient population in the world and the associatedhealthcare costs urges the application of IoT technology to improve andsupport the health care services. This thesis develops and integrates twoIoT-based healthcare systems aiming to support elderly independent livingat home. The first one involves using IoT-based remote monitoring for paindetection, while the second one detects behavioral changes caused by illnessvia profiling the appliances’ energy usage.In the first approach, an Electromyography (EMG )sensor node with aWireless Fidelity (Wi-Fi) radio module is designed for monitoring the painof patients living at home. An appropriate feature-extraction and classification algorithm is applied to the EMG signal. The classification algorithmachieves 98.5% accuracy for the experimental data collected from the developed EMG sensor node, while it achieves 99.4% classification accuracy forthe clinically approved pain intensity dataset. Moreover, the experimentalresults clearly show the relevance of the proposed approaches and provetheir suitability for real-life applications. The developed sensor node for thepain level classification method is beneficial for continuous pain assessmentto the smart home-care community.As a complement to the first approach, in the second approach, an IoTbased smart meter and a set of appliance-level load profiling methods aredeveloped to detect the electricity usage of users’ daily living at home, whichindirectly provides information about the subject’s health status. The thesishas formulated a novel methodology by integrating Non-intrusive ApplianceLoad Monitoring (NIALM) analysis with Machine Learning- (ML) basedclassification at the fog layer. The developed method allows the detectionof a single appliance with high accuracy by associating the user’s Activitiesof Daily Living (ADL). The appliances detection is performed by employinga k-Nearest Neighbors (k-NN) classification algorithm. It achieves 97.4% accuracy, demonstrating its high detection performance. Due to the low cost and reusability advantages of Field Programmable Gate Arrays (FPGA),the execution of k-NN for appliances classification model is performed onan FPGA. Its classification performance was comparable with other computing platforms, making it a cost-effective alternative for IoT-based healthcare assessment of daily living at home. The developed methods have haspractical application in assisting real-time e-health monitoring of any individual who can remain in the comfort of their normal living environment.
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| 12. |
- Kelati, Amleset, et al.
(författare)
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Real-Time Classification of Pain Level Using Zygomaticus and Corrugator EMG Features
- 2022
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Ingår i: Electronics. - : MDPI AG. - 2079-9292. ; 11:11, s. 1671-1671
-
Tidskriftsartikel (refereegranskat)abstract
- The real-time recognition of pain level is required to perform an accurate pain assessment of patients in the intensive care unit, infants, and other subjects who may not be able to communicate verbally or even express the sensation of pain. Facial expression is a key pain-related behavior that may unlock the answer to an objective pain measurement tool. In this work, a machine learning-based pain level classification system using data collected from facial electromyograms (EMG) is presented. The dataset was acquired from part of the BioVid Heat Pain database to evaluate facial expression from an EMG corrugator and EMG zygomaticus and an EMG signal processing and data analysis flow is adapted for continuous pain estimation. The extracted pain-associated facial electromyography (fEMG) features classification is performed by K-nearest neighbor (KNN) by choosing the value of k which depends on the nonlinear models. The presentation of the accuracy estimation is performed, and considerable growth in classification accuracy is noticed when the subject matter from the features is omitted from the analysis. The ML algorithm for the classification of the amount of pain experienced by patients could deliver valuable evidence for health care providers and aid treatment assessment. The proposed classification algorithm has achieved a 99.4% accuracy for classifying the pain tolerance level from the baseline (P0 versus P4) without the influence of a subject bias. Moreover, the result on the classification accuracy clearly shows the relevance of the proposed approac
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| 13. |
- Kelati, Amleset, et al.
(författare)
-
Signal Processing Based BioSignal Feature Extraction and Classification Techniques for IoT Healthcare Platform: Survey
- 2016
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Ingår i: IEEE Conference on Design and Architectures for Signal and Image Processing (DASIP2016). - Rennes, France.
-
Konferensbidrag (populärvet., debatt m.m.)abstract
- In IoT healthcare platform, a variety of biosignals are acquired from its sensors and appropriate feature extraction techniques are crucial in order to make use of the acquired biosignal data and help the healthcare scientist or bio-engineer to reach at optimal decisions. This work reviews the existing biosignal feature extraction and classification methods for different healthcare applications. Due the enormous amount of different biosignals and since most healthcare applications uses electrocardiogram (ECG), electroencephalogram (EEG), electromyogram (EMG), Electrogastrogram (EGG), we focus the review on feature extractions and classification method for these biosignals. The review also includes a summary of Blood Oxygen Saturation determined by Pulse Oximetry (SpO2), Electrooculography and eye movement (EOG), and Respiration (RSP) signals. Its discussion and analysis focuses on advantages, performance and drawbacks of the techniques.
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| 14. |
- Latif, Khaled, et al.
(författare)
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Enhancing performance sustainability of fault tolerant routing algorithms in NoC-based architectures
- 2011
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Ingår i: Proceedings - 2011 14th Euromicro Conference on Digital System Design: Architectures, Methods and Tools, DSD 2011. - 9780769544946 ; , s. 626-633
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Konferensbidrag (refereegranskat)abstract
- Reliability of embedded systems and devices is becoming a challenge with technology scaling. To deal with the reliability issues, fault tolerant solutions are needed. The design paradigm for future System-on-Chip (SoC) implementation is Network-on-Chip (NoC). Fault tolerance in NoC can be achieved at many abstraction levels. Many fault tolerant architectures and routing algorithms have already been proposed for NoC but the utilization of resources, affected indirectly by faults is yet to be addressed. In this paper, we propose a NoC architecture, which sustains the overall system performance by utilizing resources, which cannot be used by other architectures under faults. An approach towards a proper virtual-channel (VC) sharing strategy is proposed, based on communication bandwidth requirements. The technique can be applied to any NoC architecture, including 3-D NoCs. Extensive quantitative experiments with synthetic benchmarks, including uniform, transpose and negative exponential distribution (NED), demonstrate considerable improvement in terms of performance sustainability under faulty conditions compared to existing VC-based NoC architectures.
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| 15. |
- Mekuria, Fisseha, 1962-, et al.
(författare)
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Rescuing the Fresh Water Lakes of Africa through the Use of Drones and Underwater Robots
- 2021
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Ingår i: 2021 International Conference on Information and Communication Technology for Development for Africa (ICT4DA). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781665436663 ; , s. 154-159
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Konferensbidrag (refereegranskat)abstract
- In this paper, we present a conceptual system architecture for real-time monitoring, predicting and controlling of invasive water hyacinth in freshwater bodies through the use of emerging technologies. The proposed system is planned to be deployed as one of the rescue efforts to preserve the fresh water lakes of Africa. The case study and the system presented in this paper are based on the Lake Tana, situated near the city of Bahir Dar, in Ethiopia. The rescuing efforts of Lake Tana so far focused on removal of the weed by hand and using harvesting machines. With the weed invasion doubling every two weeks, the current approaches will not be able to control the rapid invasion of the weed, which is causing considerable socioeconomic losses. The proposed system architecture employs networked underwater robots, aerial drones and other environmental sensors for better mapping of the weed coverage in real-time, predicting the floating paths of the weed, and learning the favourable environmental conditions of the lake for eradicating the invasive weed. The advantages of the proposed technical intervention lie not only in accurate monitoring and fast removal of the weed, but also in facilitating data collection for better understanding of the underlying environmental and chemical conditions that facilitate the rapid infestation and growth of the invasive weed.
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| 16. |
- Nigussie, Ethiopia, et al.
(författare)
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IoT Architecture for Enhancing Rural Societal Services in Sub-Saharan Africa
- 2020
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Ingår i: Procedia Computer Science. - : Elsevier. - 1877-0509. ; 177, s. 338-344
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Tidskriftsartikel (refereegranskat)abstract
- The potential of IoT in contributing towards sustainable economic development in Sub-Saharan Africa (SSA) through digital transformation and effective service delivery is widely accepted. However, the unreliability/unavailability of connectivity and power grid infrastructure as well as the unaffordability of the overall system hinders the implementation of a multi-layered IoT architecture for rural societal services in SSA. In this work, affordable IoT architecture that operates without reliance on broadband connectivity and power grid is developed. The architecture employs energy harvesting system and performs data processing, actuation decisions and network management locally by integrating a customized low- cost computationally capable device with the gateway. The sharing of this device among the water resource and quality management, healthcare and agriculture applications further reduces the overall system cost. The evaluation of LPWAN technologies reveals that LoRaWAN has lower cost with added benefits of adaptive data rate and largest community support while providing comparable performance and communication range with the other technologies. The relevant results of the analysis is communicated to end-users’ mobile device via 2G/3G GPRS. Hence, the proposed IoT architecture enables the implementation of IoT systems for improving efficiency in three key application areas at low cost.
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| 17. |
- Rahimi Moosavi, Sanaz, et al.
(författare)
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SEA : A Secure and Efficient Authentication and Authorization Architecture for IoT-Based Healthcare Using Smart Gateways
- 2015
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Ingår i: Procedia Computer Science. - : Elsevier. - 1877-0509. ; 52, s. 452-459
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, a secure and efficient authentication and authorization architecture for IoT-based healthcare is developed. Security and privacy of patients’ medical data are crucial for the acceptance and ubiquitous use of IoT in healthcare. Secure authentication and authorization of a remote healthcare professional is the main focus of this work. Due to resource constraints of medical sensors, it is infeasible to utilize conventional cryptography in IoT-based healthcare. In addition, gateways in existing IoTs focus only on trivial tasks without alleviating the authentication and authorization challenges. In the presented architecture, authentication and authorization of a remote end-user is done by distributed smart e-health gateways to unburden the medical sensors from performing these tasks. The proposed architecture relies on the certificate-based DTLS handshake protocol as it is the main IP security solution for IoT. The proposed authentication and authorization architecture is tested by developing a prototype IoT-based healthcare system. The prototype is built of a Pandaboard, a TI SmartRF06 board and WiSMotes. The CC2538 module integrated into the TI board acts as a smart gateway and the WisMotes act as medical sensor nodes. The proposed architecture is more secure than a state-of-the-art centralized delegation-based architecture because it uses a more secure key management scheme between sensor nodes and the smart gateway. Furthermore, the impact of DoS attacks is reduced due to the distributed nature of the architecture. Our performance evaluation results show that compared to the delegation-based architecture, the proposed architecture reduces communication overhead by 26% and communication latency from the smart gateway to the end-user by 16%.
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