New Driver Workload Prediction Using Clustering-Aided Approaches

Awareness of driver workload (DW) plays a paramount role in enhancing driving safety and convenience for intelligent vehicles. The DW prediction systems proposed so far learn either from individual driver's data (termed personalized system) or existing drivers' data indiscriminately (termed average system). As a result, they either do not work or lead to a limited performance for new drivers without labeled data. To this end, we develop clustering-aided approaches exploiting group characteristics of the existing drivers' data. Two clustering aided predictors are proposed. The first is clustering-aided regression (CAR) model, where the regression model for the cluster with the highest likelihood is adopted. The second is clustering-aided multiple model regression model, where the concept of multiple models is further augmented to CAR. A recent dataset from real-world driving experiments is adopted to validate the algorithms. Comparative results against the conventional average system demonstrate that by incorporating clustering information, both the proposed approaches significantly improve workload prediction performance

Implicit personalization in driving assistance: State-of-the-art and open issues

In recent decades, driving assistance systems have been evolving towards personalization for adapting to different drivers. With the consideration of driving preferences and driver characteristics, these systems become more acceptable and trustworthy. This article presents a survey on recent advances in implicit personalized driving assistance. We classify the collection of work into three main categories: 1) personalized Safe Driving Systems (SDS), 2) personalized Driver Monitoring Systems (DMS), and 3) personalized In-vehicle Information Systems (IVIS). For each category, we provide a comprehensive review of current applications and related techniques along with the discussion of industry status, benefits of personalization, application prospects, and future focal points. Both relevant driving datasets and open issues about personalized driving assistance are discussed to facilitate future research. By creating an organized categorization of the field, we hope that this survey could not only support future research and the development of new technologies for personalized driving assistance but also facilitate the application of these techniques within the driving automation community</h2

High Dimensional Data Clustering using Self-Organized Map

As the population grows and e economic development, houses could be one of basic needs of every family. Therefore, housing investment has promising value in the future. This research implements the Self-Organized Map (SOM) algorithm to cluster house data for providing several house groups based on the various features. K-means is used as the baseline of the proposed approach. SOM has higher silhouette coefficient (0.4367) compared to its comparison (0.236). Thus, this method outperforms k-means in terms of visualizing high-dimensional data cluster. It is also better in the cluster formation and regulating the data distribution

Power, Performance, and Energy Management of Heterogeneous Architectures

abstract: Many core modern multiprocessor systems-on-chip offers tremendous power and performance optimization opportunities by tuning thousands of potential voltage, frequency and core configurations. Applications running on these architectures are becoming increasingly complex. As the basic building blocks, which make up the application, change during runtime, different configurations may become optimal with respect to power, performance or other metrics. Identifying the optimal configuration at runtime is a daunting task due to a large number of workloads and configurations. Therefore, there is a strong need to evaluate the metrics of interest as a function of the supported configurations. This thesis focuses on two different types of modern multiprocessor systems-on-chip (SoC): Mobile heterogeneous systems and tile based Intel Xeon Phi architecture. For mobile heterogeneous systems, this thesis presents a novel methodology that can accurately instrument different types of applications with specific performance monitoring calls. These calls provide a rich set of performance statistics at a basic block level while the application runs on the target platform. The target architecture used for this work (Odroid XU3) is capable of running at 4940 different frequency and core combinations. With the help of instrumented application vast amount of characterization data is collected that provides details about performance, power and CPU state at every instrumented basic block across 19 different types of applications. The vast amount of data collected has enabled two runtime schemes. The first work provides a methodology to find optimal configurations in heterogeneous architecture using classifiers and demonstrates an average increase of 93%, 81% and 6% in performance per watt compared to the interactive, ondemand and powersave governors, respectively. The second work using same data shows a novel imitation learning framework for dynamically controlling the type, number, and the frequencies of active cores to achieve an average of 109% PPW improvement compared to the default governors. This work also presents how to accurately profile tile based Intel Xeon Phi architecture while training different types of neural networks using open image dataset on deep learning framework. The data collected allows deep exploratory analysis. It also showcases how different hardware parameters affect performance of Xeon Phi.Dissertation/ThesisMasters Thesis Engineering 201

Aerospace Medicine and Biology: A continuing bibliography, supplement 191

A bibliographical list of 182 reports, articles, and other documents introduced into the NASA scientific and technical information system in February 1979 is presented

Power-Performance Modeling and Adaptive Management of Heterogeneous Mobile Platforms​

abstract: Nearly 60% of the world population uses a mobile phone, which is typically powered by a system-on-chip (SoC). While the mobile platform capabilities range widely, responsiveness, long battery life and reliability are common design concerns that are crucial to remain competitive. Consequently, state-of-the-art mobile platforms have become highly heterogeneous by combining a powerful SoC with numerous other resources, including display, memory, power management IC, battery and wireless modems. Furthermore, the SoC itself is a heterogeneous resource that integrates many processing elements, such as CPU cores, GPU, video, image, and audio processors. Therefore, CPU cores do not dominate the platform power consumption under many application scenarios. Competitive performance requires higher operating frequency, and leads to larger power consumption. In turn, power consumption increases the junction and skin temperatures, which have adverse effects on the device reliability and user experience. As a result, allocating the power budget among the major platform resources and temperature control have become fundamental consideration for mobile platforms. Dynamic thermal and power management algorithms address this problem by putting a subset of the processing elements or shared resources to sleep states, or throttling their frequencies. However, an adhoc approach could easily cripple the performance, if it slows down the performance-critical processing element. Furthermore, mobile platforms run a wide range of applications with time varying workload characteristics, unlike early generations, which supported only limited functionality. As a result, there is a need for adaptive power and performance management approaches that consider the platform as a whole, rather than focusing on a subset. Towards this need, our specific contributions include (a) a framework to dynamically select the Pareto-optimal frequency and active cores for the heterogeneous CPUs, such as ARM big.Little architecture, (b) a dynamic power budgeting approach for allocating optimal power consumption to the CPU and GPU using performance sensitivity models for each PE, (c) an adaptive GPU frame time sensitivity prediction model to aid power management algorithms, and (d) an online learning algorithm that constructs adaptive run-time models for non-stationary workloads.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

Dwarfs on Accelerators: Enhancing OpenCL Benchmarking for Heterogeneous Computing Architectures

For reasons of both performance and energy efficiency, high-performance computing (HPC) hardware is becoming increasingly heterogeneous. The OpenCL framework supports portable programming across a wide range of computing devices and is gaining influence in programming next-generation accelerators. To characterize the performance of these devices across a range of applications requires a diverse, portable and configurable benchmark suite, and OpenCL is an attractive programming model for this purpose. We present an extended and enhanced version of the OpenDwarfs OpenCL benchmark suite, with a strong focus placed on the robustness of applications, curation of additional benchmarks with an increased emphasis on correctness of results and choice of problem size. Preliminary results and analysis are reported for eight benchmark codes on a diverse set of architectures -- three Intel CPUs, five Nvidia GPUs, six AMD GPUs and a Xeon Phi.Comment: 10 pages, 5 figure

Big data analytics:Computational intelligence techniques and application areas

Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment

A machine learning based personalized system for driving state recognition

Reliable driving state recognition (e.g. normal, drowsy, and aggressive) plays a significant role in improving road safety, driving experience and fuel efficiency. It lays the foundation for a number of advanced functions such as driver safety monitoring systems and adaptive driving assistance systems. In these applications, state recognition accuracy is of paramount importance to guarantee user acceptance. This paper is mainly focused on developing a personalized driving state recognition system by learning from non-intrusive, easily accessible vehicle related measurements and its validation using real-world driving data. Compared to conventional approaches, this paper first highlights the necessities of adopting a personalized system by analysing feature distribution of individual driver’s data and all drivers’ data via advanced data visualization and statistical analysis. If significant differences are identified, a dedicated personalized model is learnt to predict the driver’s driving state. Spearman distance is also drawn to evaluate the differences between individual driver’s data and all drivers’ data in a quantitative manner. In addition, five categories of classifiers are tested and compared to identify a suitable one for classification, where random forest with Bayesian parameter optimization outperforms others and therefore is adopted in this paper. A recently collected dataset from real-world driving experiments is adopted to evaluate the proposed system. Comparative experimental results indicate that the personalized learning system with road information significantly outperforms conventional approaches without considering personalized characteristics or road information, where the overall accuracy increases from 81.3% to 91.6%. It is believed that the newly developed personalized learning system can find a wide range of applications where diverse behaviours exist

Cross-Layer Rapid Prototyping and Synthesis of Application-Specific and Reconfigurable Many-accelerator Platforms

Technological advances of recent years laid the foundation consolidation of informatisationof society, impacting on economic, political, cultural and socialdimensions. At the peak of this realization, today, more and more everydaydevices are connected to the web, giving the term ”Internet of Things”. The futureholds the full connection and interaction of IT and communications systemsto the natural world, delimiting the transition to natural cyber systems and offeringmeta-services in the physical world, such as personalized medical care, autonomoustransportation, smart energy cities etc. . Outlining the necessities of this dynamicallyevolving market, computer engineers are required to implement computingplatforms that incorporate both increased systemic complexity and also cover awide range of meta-characteristics, such as the cost and design time, reliabilityand reuse, which are prescribed by a conflicting set of functional, technical andconstruction constraints. This thesis aims to address these design challenges bydeveloping methodologies and hardware/software co-design tools that enable therapid implementation and efficient synthesis of architectural solutions, which specifyoperating meta-features required by the modern market. Specifically, this thesispresents a) methodologies to accelerate the design flow for both reconfigurableand application-specific architectures, b) coarse-grain heterogeneous architecturaltemplates for processing and communication acceleration and c) efficient multiobjectivesynthesis techniques both at high abstraction level of programming andphysical silicon level.Regarding to the acceleration of the design flow, the proposed methodologyemploys virtual platforms in order to hide architectural details and drastically reducesimulation time. An extension of this framework introduces the systemicco-simulation using reconfigurable acceleration platforms as co-emulation intermediateplatforms. Thus, the development cycle of a hardware/software productis accelerated by moving from a vertical serial flow to a circular interactive loop.Moreover the simulation capabilities are enriched with efficient detection and correctiontechniques of design errors, as well as control methods of performancemetrics of the system according to the desired specifications, during all phasesof the system development. In orthogonal correlation with the aforementionedmethodological framework, a new architectural template is proposed, aiming atbridging the gap between design complexity and technological productivity usingspecialized hardware accelerators in heterogeneous systems-on-chip and networkon-chip platforms. It is presented a novel co-design methodology for the hardwareaccelerators and their respective programming software, including the tasks allocationto the available resources of the system/network. The introduced frameworkprovides implementation techniques for the accelerators, using either conventionalprogramming flows with hardware description language or abstract programmingmodel flows, using techniques from high-level synthesis. In any case, it is providedthe option of systemic measures optimization, such as the processing speed,the throughput, the reliability, the power consumption and the design silicon area.Finally, on addressing the increased complexity in design tools of reconfigurablesystems, there are proposed novel multi-objective optimization evolutionary algo-rithms which exploit the modern multicore processors and the coarse-grain natureof multithreaded programming environments (e.g. OpenMP) in order to reduce theplacement time, while by simultaneously grouping the applications based on theirintrinsic characteristics, the effectively explore the design space effectively.The efficiency of the proposed architectural templates, design tools and methodologyflows is evaluated in relation to the existing edge solutions with applicationsfrom typical computing domains, such as digital signal processing, multimedia andarithmetic complexity, as well as from systemic heterogeneous environments, suchas a computer vision system for autonomous robotic space navigation and manyacceleratorsystems for HPC and workstations/datacenters. The results strengthenthe belief of the author, that this thesis provides competitive expertise to addresscomplex modern - and projected future - design challenges.Οι τεχνολογικές εξελίξεις των τελευταίων ετών έθεσαν τα θεμέλια εδραίωσης της πληροφοριοποίησης της κοινωνίας, επιδρώντας σε οικονομικές,πολιτικές, πολιτιστικές και κοινωνικές διαστάσεις. Στο απόγειο αυτής τη ςπραγμάτωσης, σήμερα, ολοένα και περισσότερες καθημερινές συσκευές συνδέονται στο παγκόσμιο ιστό, αποδίδοντας τον όρο «Ίντερνετ των πραγμάτων».Το μέλλον επιφυλάσσει την πλήρη σύνδεση και αλληλεπίδραση των συστημάτων πληροφορικής και επικοινωνιών με τον φυσικό κόσμο, οριοθετώντας τη μετάβαση στα συστήματα φυσικού κυβερνοχώρου και προσφέροντας μεταυπηρεσίες στον φυσικό κόσμο όπως προσωποποιημένη ιατρική περίθαλψη, αυτόνομες μετακινήσεις, έξυπνες ενεργειακά πόλεις κ.α. . Σκιαγραφώντας τις ανάγκες αυτής της δυναμικά εξελισσόμενης αγοράς, οι μηχανικοί υπολογιστών καλούνται να υλοποιήσουν υπολογιστικές πλατφόρμες που αφενός ενσωματώνουν αυξημένη συστημική πολυπλοκότητα και αφετέρου καλύπτουν ένα ευρύ φάσμα μεταχαρακτηριστικών, όπως λ.χ. το κόστος σχεδιασμού, ο χρόνος σχεδιασμού, η αξιοπιστία και η επαναχρησιμοποίηση, τα οποία προδιαγράφονται από ένα αντικρουόμενο σύνολο λειτουργικών, τεχνολογικών και κατασκευαστικών περιορισμών. Η παρούσα διατριβή στοχεύει στην αντιμετώπιση των παραπάνω σχεδιαστικών προκλήσεων, μέσω της ανάπτυξης μεθοδολογιών και εργαλείων συνσχεδίασης υλικού/λογισμικού που επιτρέπουν την ταχεία υλοποίηση καθώς και την αποδοτική σύνθεση αρχιτεκτονικών λύσεων, οι οποίες προδιαγράφουν τα μετα-χαρακτηριστικά λειτουργίας που απαιτεί η σύγχρονη αγορά. Συγκεκριμένα, στα πλαίσια αυτής της διατριβής, παρουσιάζονται α) μεθοδολογίες επιτάχυνσης της ροής σχεδιασμού τόσο για επαναδιαμορφούμενες όσο και για εξειδικευμένες αρχιτεκτονικές, β) ετερογενή αδρομερή αρχιτεκτονικά πρότυπα επιτάχυνσης επεξεργασίας και επικοινωνίας και γ) αποδοτικές τεχνικές πολυκριτηριακής σύνθεσης τόσο σε υψηλό αφαιρετικό επίπεδο προγραμματισμού,όσο και σε φυσικό επίπεδο πυριτίου.Αναφορικά προς την επιτάχυνση της ροής σχεδιασμού, προτείνεται μια μεθοδολογία που χρησιμοποιεί εικονικές πλατφόρμες, οι οποίες αφαιρώντας τις αρχιτεκτονικές λεπτομέρειες καταφέρνουν να μειώσουν σημαντικά το χρόνο εξομοίωσης. Παράλληλα, εισηγείται η συστημική συν-εξομοίωση με τη χρήση επαναδιαμορφούμενων πλατφορμών, ως μέσων επιτάχυνσης. Με αυτόν τον τρόπο, ο κύκλος ανάπτυξης ενός προϊόντος υλικού, μετατεθειμένος από την κάθετη σειριακή ροή σε έναν κυκλικό αλληλεπιδραστικό βρόγχο, καθίσταται ταχύτερος, ενώ οι δυνατότητες προσομοίωσης εμπλουτίζονται με αποδοτικότερες μεθόδους εντοπισμού και διόρθωσης σχεδιαστικών σφαλμάτων, καθώς και μεθόδους ελέγχου των μετρικών απόδοσης του συστήματος σε σχέση με τις επιθυμητές προδιαγραφές, σε όλες τις φάσεις ανάπτυξης του συστήματος. Σε ορθογώνια συνάφεια με το προαναφερθέν μεθοδολογικό πλαίσιο, προτείνονται νέα αρχιτεκτονικά πρότυπα που στοχεύουν στη γεφύρωση του χάσματος μεταξύ της σχεδιαστικής πολυπλοκότητας και της τεχνολογικής παραγωγικότητας, με τη χρήση συστημάτων εξειδικευμένων επιταχυντών υλικού σε ετερογενή συστήματα-σε-ψηφίδα καθώς και δίκτυα-σε-ψηφίδα. Παρουσιάζεται κατάλληλη μεθοδολογία συν-σχεδίασης των επιταχυντών υλικού και του λογισμικού προκειμένου να αποφασισθεί η κατανομή των εργασιών στους διαθέσιμους πόρους του συστήματος/δικτύου. Το μεθοδολογικό πλαίσιο προβλέπει την υλοποίηση των επιταχυντών είτε με συμβατικές μεθόδους προγραμματισμού σε γλώσσα περιγραφής υλικού είτε με αφαιρετικό προγραμματιστικό μοντέλο με τη χρήση τεχνικών υψηλού επιπέδου σύνθεσης. Σε κάθε περίπτωση, δίδεται η δυνατότητα στο σχεδιαστή για βελτιστοποίηση συστημικών μετρικών, όπως η ταχύτητα επεξεργασίας, η ρυθμαπόδοση, η αξιοπιστία, η κατανάλωση ενέργειας και η επιφάνεια πυριτίου του σχεδιασμού. Τέλος, προκειμένου να αντιμετωπισθεί η αυξημένη πολυπλοκότητα στα σχεδιαστικά εργαλεία επαναδιαμορφούμενων συστημάτων, προτείνονται νέοι εξελικτικοί αλγόριθμοι πολυκριτηριακής βελτιστοποίησης, οι οποίοι εκμεταλλευόμενοι τους σύγχρονους πολυπύρηνους επεξεργαστές και την αδρομερή φύση των πολυνηματικών περιβαλλόντων προγραμματισμού (π.χ. OpenMP), μειώνουν το χρόνο επίλυσης του προβλήματος της τοποθέτησης των λογικών πόρων σε φυσικούς,ενώ ταυτόχρονα, ομαδοποιώντας τις εφαρμογές βάση των εγγενών χαρακτηριστικών τους, διερευνούν αποτελεσματικότερα το χώρο σχεδίασης.Η αποδοτικότητά των προτεινόμενων αρχιτεκτονικών προτύπων και μεθοδολογιών επαληθεύτηκε σε σχέση με τις υφιστάμενες λύσεις αιχμής τόσο σε αυτοτελής εφαρμογές, όπως η ψηφιακή επεξεργασία σήματος, τα πολυμέσα και τα προβλήματα αριθμητικής πολυπλοκότητας, καθώς και σε συστημικά ετερογενή περιβάλλοντα, όπως ένα σύστημα όρασης υπολογιστών για αυτόνομα διαστημικά ρομποτικά οχήματα και ένα σύστημα πολλαπλών επιταχυντών υλικού για σταθμούς εργασίας και κέντρα δεδομένων, στοχεύοντας εφαρμογές υψηλής υπολογιστικής απόδοσης (HPC). Τα αποτελέσματα ενισχύουν την πεποίθηση του γράφοντα, ότι η παρούσα διατριβή παρέχει ανταγωνιστική τεχνογνωσία για την αντιμετώπιση των πολύπλοκων σύγχρονων και προβλεπόμενα μελλοντικών σχεδιαστικών προκλήσεων