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VR-Fit: Walking-in-Place Locomotion with Real Time Step Detection for VR-Enabled Exercise

Sari, S. | Kucukyilmaz, A.

Conference Object | 2019 | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)11673 LNCS , pp.255 - 266

With recent advances in mobile and wearable technologies, virtual reality (VR) found many applications in daily use. Today, a mobile device can be converted into a low-cost immersive VR kit thanks to the availability of do-it-yourself viewers in the shape of simple cardboards and compatible software for 3D rendering. These applications involve interacting with stationary scenes or moving in between spaces within a VR environment. VR locomotion can be enabled through a variety of methods, such as head movement tracking, joystick-triggered motion and through mapping natural movements to translate to virtual locomotion. In this study, . . .we implemented a walk-in-place (WIP) locomotion method for a VR-enabled exercise application. We investigate the utility of WIP for exercise purposes, and compare it with joystick-based locomotion in terms of step performance and subjective qualities of the activity, such as enjoyment, encouragement for exercise and ease of use. Our technique uses vertical accelerometer data to estimate steps taken during walking or running, and locomotes the user’s avatar accordingly in virtual space. We evaluated our technique in a controlled experimental study with 12 people. Results indicate that the way users control the simulated locomotion affects how they interact with the VR simulation, and influence the subjective sense of immersion and the perceived quality of the interaction. In particular, WIP encourages users to move further, and creates a more enjoyable and interesting experience in comparison to joystick-based navigation. © 2019, Springer Nature Switzerland AG Daha fazlası Daha az

A machine learning approach for a scalable, energy-efficient utility-based cache partitioning

Guney, I.A. | Yildiz, A. | Bayindir, I.U. | Serdaroglu, K.C. | Bayik, U. | Kucuk, G.

Conference Object | 2015 | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)9137 LNCS , pp.409 - 421

Inmulti- andmany-core processors, a shared Last Level Cache (LLC) is utilized to alleviate the performance problems resulting from long latency memory instructions. However, an unmanaged LLC may become quite useless when the running threads have conflicting interests. In one extreme, a thread can make benefit from every portion of the cache whereas, in the other end, another thread may just want to thrash the whole LLC. Recently, a variety of way-partitioning mechanisms are introduced to improve cache performance. Today, almost all of the studies utilize the Utility-based Cache Partitioning (UCP) algorithm as their allocation policy . . .. However, the UCP look-ahead algorithm, although it provides a better utility measure than its greedy counterpart, requires a very complex hardware circuitry and dissipates a considerable amount of energy at the end of each decision period. In this study, we propose an offline supervised machine learning algorithm that replaces the UCP lookahead circuitry with a circuitry requiring almost negligible hardware and energy cost.Depending on the cache and processor configuration, our thorough analysis and simulation results show that the proposed mechanism reduces up to 5% of the overall transistor count and 5% of the overall processor energy without introducing any performance penalty. © Springer International Publishing Switzerland 2015 Daha fazlası Daha az

A grouping genetic algorithm using linear linkage encoding for bin packing

Ülker, Ö. | Korkmaz, E.E. | Özcan, E.

Conference Object | 2008 | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)5199 LNCS , pp.1140 - 1149

Linear Linkage Encoding (LLE) is a representation method proposed for grouping problems. It has already been used in solving data clustering, graph coloring and timetabling problems based on multi-objective genetic algorithms. In this study, this novel encoding scheme is investigated on bin packing again using a genetic algorithm. Bin packing benchmark problem instances are used to compare the performance of traditional recombination operators and custom made LLE crossover operators which are hybridized with parametrized placement heuristics. The results denote that LLE is a viable candidate for bin packing problem whenever appropri . . .ate genetic operators are chosen. © 2008 Springer-Verlag Berlin Heidelberg Daha fazlası Daha az

History-based predictive instruction window weighting for SMT processors

Kucuk, G. | Uslu, G. | Yesil, C.

Conference Object | 2014 | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)8488 LNCS , pp.187 - 198

In a Simultaneous Multi-Threaded (SMT) processor environment, threads share datapath resources, and resource allocation policy directly affects the throughput metric. As a way of explicit resource management, resource requirements of threads are estimated based on several runtime statistics, such as cache miss counts, Issue Queue usage and efficiency metrics. Controlling processor resources indirectly by means of a fetch policy is also targeted in many recent studies. A successful technique, Speculative Instruction Window Weighting (SIWW), which speculates the weights of instructions in Issue Queue to indirectly manage SMT resource . . .usage, is recently proposed. SIWW promises better peformance results compared to the well-accepted ICOUNT fetch policy. In this study, we propose an alternative fetch policy that implements SIWW-like logic using a history-based prediction mechanism, History-based Predictive Instruction Window Weighting (HPIWW), avoiding any types of speculation hardware and its inherent complexity. As a result, we show that HPIWW outperforms SIWW by 3% on the average across all simulated workloads, and dissipates 2.5 times less power than its rival. © Springer International Publishing Switzerland 2014 Daha fazlası Daha az

Improving driver behavior using gamification

Helvaci, S. | Senova, A. | Kar, G. | Gören, S.

Conference Object | 2018 | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)10995 LNCS , pp.193 - 204

This work proposes a gamification approach to measure the driving behavior using the in-vehicle data and score drivers. Existing work largely focus on one functionality: either displaying vehicular info or scoring the driver. And some other work just provides navigation or Point of Interest (POI). In our work, we combine these features with minimal distraction for the driver. With this goal, we consider a system that interfaces to the vehicle bus and find the errors of the driver during the drive using multiple criteria. Furthermore, by providing achievements and leader boards, the driver is motivated to have a good score while driv . . .ing. To facilitate this analysis and to evaluate the system, we recorded two trips in real traffic. The results show that we achieve more than 95% accuracy between real-world scenario and the simulation. We also present POI feature that finds nearest preferred locations which are restaurants, hospitals, gas stations, pharmacies, car repair shops. © Springer International Publishing AG, part of Springer Nature 2018 Daha fazlası Daha az

Memes, self-generation and nurse rostering

Özcan, E.

Conference Object | 2006 | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)3867 LNCS , pp.85 - 104

This paper presents an empirical study on memetic algorithms in two parts. In the first part, the details of the memetic algorithm experiments with a set of well known benchmark functions are described. In the second part, a heuristic template is introduced for solving timetabling problems. Two adaptive heuristics that utilize a set of constraint-based hill climbers in a co-operative manner are designed based on this template. A hyper-heuristic is a mechanism used for managing a set of low-level heuristics. At each step, an appropriate heuristic is chosen and applied to a candidate solution. Both adaptive heuristics can be considere . . .d as hyper-heuristics. Memetic algorithms employing each hyper-heuristic separately as a single hill climber are experimented on a set of randomly generated nurse rostering problem instances. Moreover, the standard genetic algorithm and two self-generating multimeme memetic algorithms are compared to the proposed memetic algorithms and a previous study. © Springer-Verlag Berlin Heidelberg 2007 Daha fazlası Daha az

Hill climbers and mutational heuristics in hyperheuristics

Özean, E. | Bilgin, B. | Korkmaz, E.E.

Conference Object | 2006 | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)4193 LNCS , pp.202 - 211

Hyperheuristics are single candidate solution based and simple to maintain mechanisms used in optimization. At each iteration, as a higher level of abstraction, a hyperheuristic chooses and applies one of the heuristics to a candidate solution. In this study, the performance contribution of hill climbing operators along with the mutational heuristics are analyzed in depth in four different hyperheuristic frameworks. Four different hill climbing operators and three mutational operators are used during the experiments. Various subsets of the heuristics are evaluated on fourteen well-known benchmark functions. © Springer-Verlag Berlin . . .Heidelberg 2006 Daha fazlası Daha az

Memetic algorithms for nurse rostering

Özean, E.

Conference Object | 2005 | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)3733 LNCS , pp.482 - 492

Nurse rostering problems represent a subclass of scheduling problems that are hard to solve. The goal is finding high quality shift and resource assignments, satisfying the needs and requirements of employees as well as the employers in healthcare institutions. In this paper, a real case of a nurse rostering problem is introduced. Memetic Algorithms utilizing different type of promising genetic operators and a self adaptive violation directed hierarchical hill climbing method are presented based on a previously proposed framework. © Springer-Verlag Berlin Heidelberg 2005.

Server-based indoor location detection system

Perente, O.K. | Serif, T.

Conference Object | 2018 | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)10995 LNCS , pp.142 - 153

With the advancement of technology and telecommunication services, data consumption rates are increasing ever since. People for long have started using applications with the help of contextual information to improve their user experience. Thus, providing a cross-platform location service to further enrich such applications has become a necessity. For this purpose, numerous client-based indoor location systems on mobile devices are developed to perform this task. Nevertheless, most of the time these systems suffer from elimination of features from operating systems for security purposes. Indeed, with the current security trends, to e . . .nsure the privacy of mobile users, mobile operating system designers are progressively eliminating certain low-level features such as reading RSSI and introducing randomized MAC addresses. Thus, in this study, the authors propose, design and implement a server-based indoor positioning system to eliminate platform dependency and to provide the location detection in wide range of devices. The designed server-based system is scalable and platform independent; hence can run on virtually any family of smart device. Furthermore, the evaluation findings indicate that the proposed system performs in acceptable accuracy to client-based systems compared to more complex and costly implementations. © Springer International Publishing AG, part of Springer Nature 2018 Daha fazlası Daha az

Reducing energy dissipation of wireless sensor processors using silent-store-filtering MoteCache

Kucuk, G. | Basaran, C.

Conference Object | 2006 | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)4148 LNCS , pp.256 - 266

Wireless sensor networks (WSNs) gained increasing interests in recent years; since, they allow wide range of applications from environmental monitoring, to military and medical applications. As most of the sensor nodes (a.k.a. motes) are battery operated, they have limited lifetime, and user intervention is not feasible for most of the WSN applications. This study proposes a technique to reduce the energy dissipation of the processor component of the sensor nodes. We utilize a tiny cache-like structure called MoteCache between the CPU and the SRAM to cache the most recently used data values as well as to filter silent-store instruct . . .ions which write values that exactly match the values that are already stored at the memory address that is being written. A typical WSN application may sense and work on constant data values for long durations, when the environmental conditions are not changing rapidly. This common behavior of WSN applications considerably improves our energy savings. The optimal configuration of MoteCache reduces the total node energy by 24.7% on the average across a variety of simulated sensor benchmarks. The average lifetime of the nodes is also improved by 46% on the average for processor-intensive applications. Using the proposed technique, the lifetime of the nodes that run communication-intensive applications, such as TinyDB and Surge, is also improved as much as 14%. © Springer-Verlag Berlin Heidelberg 2006 Daha fazlası Daha az

Segmenting free-form 3D objects by a function representation in spherical coordinates

Sertel, O. | Ünsalan, G.

Conference Object | 2006 | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)4263 LNCS , pp.343 - 352

Segmenting 3D object surfaces is required for various high level computer vision and computer graphics applications. In computer vision, recognizing and estimating poses of 3D objects heavily depend on segmentation results. Similarly, physically meaningful segments of a 3D object may be useful in various computer graphics applications. Therefore, there are many segmentation algorithms proposed in the literature. Unfortunately, most of these algorithms can not perform reliably on freeform objects. In order to segment free-form objects, we introduce a novel method in this study. Different from previous segmentation methods, we first o . . .btain a function representation of the object surface in spherical coordinates. This representation allows detecting smooth edges on the object surface easily by a zero crossing edge detector. Edge detection results lead to segments of the object. We test our method on diverse free-form 3D objects and provide the segmentation results. © Springer-Verlag Berlin Heidelberg 2006 Daha fazlası Daha az

Detecting Defected Crops: Precision Agriculture Using Haar Classifiers and UAV

Altınbaş, M.D. | Serif, T.

Conference Object | 2019 | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)11673 LNCS , pp.27 - 40

According to recent studies, the world’s population has doubled since 1960. Furthermore, some projections indicate that the world’s population could reach more than ten billion in the next half of this century. As the world is getting increasingly crowded, the ever-growing need for resources is rising. It appears that depletion of natural resources will be three times more than current rates by the mid-century. People would not only consume more resources but also will need more agricultural produce for their everyday life. Hence, in order to meet the ever-increasing demand for farming products, yield should be maximized using top-e . . .nd technologies. Precision agriculture is the application of technologies and methods to obtain data driven crop management of the farmland. In the middle of the 1980s, precision farming techniques initially were used for soil analysis using sensors and evolved to advanced applications that makes use of satellites, handheld devices and aerial vehicles. Drones commonly referred as unmanned aerial vehicles (UAVs) and have been extensively adopted in precision farming. Consequently, in the last two decades, 80 to 90% of the precision farming operations employed UAVs. Accordingly, this paper proposes a prototype UAV based solution, which can be used to hover over tomato fields, collect visual data and process them to establish meaningful information that can used by the farmers to maximize their crop. Furthermore, the findings of the proposed system showed that this was viable solution and identified the defected tomatoes with the success rate of 90%. © 2019, Springer Nature Switzerland AG Daha fazlası Daha az

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