Search

An Artificial Intelligence(AI) study was conducted to calculate overtopping discharges for various coastal structures. The Deep Neural Network(DNN), one of the artificial intelligence methods, was employed in the study. The neural network was trained, validated and tested using the EurOtop database containing the experimental data collected from all over the world. To improve the accuracy of the deep neural network results, all data were non-dimensionalized and max-min normalized as a preprocessing process. L2 regularization was also introduced in the cost function to secure the convergence of iterative learning, and the cost function was optimized using RMSProp and Adam techniques. In order to compare the performance of DNN, additional calculations based on the multiple linear regression model and EurOtop’s overtopping formulas were done as well, using the data sets which were not included in the network training. The results showed that the predictive performance of the AI technique was relatively superior to the two other methods.

In this letter, we aim to apply Nuttall's power-law processor, for which available data are raised to a power prior to summation, to the detection of variable length transient signals. The available data are often couched in frequency domain, where aggregation of transient signal energy into relatively small number of frequency bins is achieved by matching the DFT size with the transient length. However, without prior knowledge of the transient duration, performance degrades due to mismatch and the spread of transient energy over frequency bins as a result. A simple segmentation scheme is proposed to deal with the mismatch problem by exploiting robustness of power-law processor and contiguity of transients in time domain. Performance of the proposed segmentation scheme is evaluated, and is compared with two time domain detectors with and without the prior knowledge of transient length.

Being able to use a single app across multiple devices can bring novel experiences to the users in various domains including entertainment and productivity. For instance, a user of a video editing app would be able to use a smart pad as a canvas and a smartphone as a remote toolbox so that the toolbox does not occlude the canvas during editing. However, existing approaches do not properly support the single-app multi-device execution due to several limitations, including high development cost, device heterogeneity, and high performance requirement. In this paper, we introduce FLUID-XP, a novel cross-platform multi-device system that enables UIs of a single app to be executed across heterogeneous platforms, while overcoming the limitations of previous approaches. FLUID-XP provides flexible, efficient, and seamless interactions by addressing three main challenges: i) how to transparently enable a single-display app to use multiple displays, ii) how to distribute UIs across heterogeneous devices with minimal network traffic, and iii) how to optimize the UI distribution process when multiple UIs have different distribution requirements. Our experiments with a working prototype of FLUID-XP on Android confirm that FLUID-XP successfully supports a variety of unmodified real-world apps across heterogeneous platforms (Android, iOS, and Linux). We also conduct a lab study with 25 participants to demonstrate the effectiveness of FLUID-XP with real users.

These days, the term of Internet of Things (IoT) becomes popular to interact and cooperate with individual smart objects, and one of the most critical challenges for IoT is to achieve efficient resource sharing as well as ensure safety-stringent timing constraints. To design such reliable real-time IoT, this paper focuses on the concept of mixed-criticality (MC) introduced to address the low processor utilization on traditional real-time systems. Although different worst-case execution time estimates depending on criticality are proven effective on processor scheduling, the MC concept is not yet mature on distributed systems (such as IoT), especially with end-to-end deadline guarantee. To the best of our knowledge, this paper presents the first attempt to apply the MC concept into interference (or jitter), which is a complicated source of pessimism when analyzing the schedulability of distributed systems. Our goal is to guarantee the end-to-end deadlines of high-criticality flows and minimize the deadline miss ratio of low-criticality flows in distributed systems. To achieve this goal, we introduce a jitter-based MC (JMC) scheduling framework, which supports node-level mode changes in distributed systems. We present an optimal feasibility condition (subject to given schedulability analysis) and two policies to determine jitter-threshold values to achieve the goal in different conditions. Via simulation results for randomly generated workloads, JMC outperforms an existing criticality-monotonic scheme in terms of achieving higher schedulability and fewer deadline misses.

Full-spreading non-orthogonal multiple access (FS-NOMA) is one category of the candidate technologies designed to support massive connectivity in wireless communication systems. Before it can handle the massive volume of user connections, it is important for the FS-NOMA to develop a receiver that successfully decodes target data from non-orthogonally overlapped receiving signals. However, the decoding performance of conventional interference-cancellation (IC)-based receivers is far from optimal because of error-propagation problems. To improve the decoding performance, we propose a novel FS-NOMA receiver based on the tabu-search (TS) algorithm which is a sort of machine-learning algorithm. Specifically, a novel TS mechanism and a diversification scheme are proposed to overcome the inherent adverse conditions of FS-NOMA systems which lead the TS algorithm to local optima. Simulation results demonstrate that the proposed TS-based receiver has decoding performance that is superior to that of the conventional IC-based receiver. The results also show that the proposed receiver accommodates a higher number of user connections with a given packet drop rate threshold.

In recent years, several designs that use in-memory processing to accelerate machine-learning inference problems have been proposed. Such designs are also a perfect fit for discrete, dynamic, and distributed systems that can solve large-dimensional optimization problems using iterative algorithms. For in-memory computations, ferroelectric field-effect transistors (FerroFETs) owing to their compact area and distinguishable multiple states offer promising possibilities. We present a distributed architecture that uses FerroFET memory and implements in-memory processing to solve a template problem of least squares minimization. Through this architecture, we demonstrate an improvement of $21 \times $ in energy efficiency and $3 \times $ in compute time compared to a static random access memory (SRAM)-based processing-in-memory (PIM) architecture.

In this paper, we propose a novel location-based operation strategy for Device-to-Device (D2D) communication which exploits successive interference cancellation (SIC). First, we investigate the characteristics of SIC-enabled D2D, and then propose a new resource selection scheme to fully exploit cellular uplink (UL) resources and downlink (DL) resources using only location information. Second, we propose a new power allocation scheme designed to overcome the problem of excess outages in cellular users. Simulation results show that the proposed schemes show good D2D throughput performance compared to the existing schemes, while guaranteeing reliability for cellular users.

This letter proposes a new orthogonal frequency division multiplexing (OFDM)-based waveform, called time spread-windowed OFDM (TS-W-OFDM). TS-W-OFDM applies a windowing procedure to improve spectral efficiency and a time spreading method to create a block signal structure. Numerical results show that TS-W-OFDM has higher spectral efficiency and nearly same error rate compared to the conventional cyclic prefix-OFDM, windowed-OFDM (W-OFDM), weighted overlap and add-based OFDM (WOLA-OFDM), and filtered-OFDM (F-OFDM). In addition, they also show that TS-W-OFDM has lower complexity than W-OFDM, WOLA-OFDM, and F-OFDM.

Real-time scheduling for Mixed-Criticality (MC) systems has received a growing attention as real-time embedded systems accommodate various tasks with different levels of criticality. While many studies have addressed how to guarantee timing requirements for MC systems with uniprocessor and multiprocessors, most of them have focused on supporting preemptive tasks. On the other hand, there have been few studies to address non-preemptive scheduling especially for MC multiprocessor platforms, in which the jobs under execution cannot be preempted by other jobs. In this paper, we develop schedulability tests for non-preemptive scheduling, which is the first attempt for MC multiprocessor systems. To this end, we first generalize an existing NP-EDF (Non-Preemptive Earliest Deadline First) schedulability test developed for single-criticality multiprocessor systems, towards for MC multiprocessor systems. For the generalization, we introduce new timing guarantee techniques for the system transition between two different criticalities, which is one of the key features in MC systems. We next extend the proposed NP-EDF schedulability test towards NP-EDFVD (NP-EDF with Virtual Deadlines) that is specialized for MC systems, and pose a virtual deadline assignment problem. We develop an optimal virtual deadline assignment policy using a control knob of the system-level deadline-reduction parameter and then a suboptimal one for the task-level parameter. Our simulation results demonstrate that the NP-EDFVD schedulability test with the proposed virtual deadline assignment policies finds a number of additional schedulable task sets, which are not schedulable by the NP-EDF schedulability test.

The contention-free policy has received attention in real-time multiprocessor scheduling owing to its wide applicability and significant improvement in offline schedulability guarantees. Utilizing the notion of contention-free slots in which the number of active jobs is smaller than or equal to the number of processors, the policy improves the schedulability by offloading executions in contending time slots to contention-free ones. In this paper, we propose the multi-level contention-free policy by exploiting a new, generalized notion of multi-level contention-free slots. In a case study, we present how the multi-level contention-free policy is applied to EDF (Earliest Deadline First) scheduling and develop a schedulability test for EDF that adopts the new policy. Our evaluation results demonstrate that the multi-level contention-free policy significantly improves the schedulability by up to 4188% and 127%, compared to vanilla EDF and EDF adopting the existing contention-free policy, respectively.

Recent embedded systems are becoming integrated systems with components of different criticality. To tackle this, mixed-criticality systems aim to provide different levels of timing assurance to components of different criticality levels while achieving efficient resource utilization. Many approaches have been proposed to execute more lower-criticality tasks without affecting the timeliness of higher-criticality tasks. Those previous approaches however have at least one of the two limitations; i) they penalize all lower-criticality tasks at once upon a certain situation, or ii) they make the decision how to penalize lower-criticality tasks at design time. As a consequence, they under-utilize resources by imposing an excessive penalty on low-criticality tasks. Unlike those existing studies, we present a novel framework, called MC-ADAPT, that aims to minimally penalize lower-criticality tasks by fully reflecting the dynamically changing system behavior into adaptive decision making. Towards this, we propose a new scheduling algorithm and develop its runtime schedulability analysis capable of capturing the dynamic system state. Our proposed algorithm adaptively determines which task to drop based on the runtime analysis. To determine the quality of task dropping solution, we propose the speedup factor for task dropping while the conventional use of the speedup factor only evaluates MC scheduling algorithms in terms of the worst-case schedulability. We apply the speedup factor for a newly-defined task dropping problem that evaluates task dropping solution under different runtime scheduling scenarios. We derive that MC-ADAPT has a speedup factor of 1.619 for task drop. This implies that MC-ADAPT can behave the same as the optimal scheduling algorithm with optimal task dropping strategy does under any runtime scenario if the system is sped up by a factor of 1.619.

With the widespread adoption of multi-core architectures, it is becoming more important to develop software in ways that takes advantage of such parallel architectures. This particularly entails a shift in programming paradigms towards fine-grained, thread-parallel computing. Many parallel programming models have been introduced for targeting such intra-task thread-level parallelism. However, most successful results on traditional multi-core real-time scheduling are focused on sequential programming models. For example, thread-level parallelism is not properly captured into the concept of interference, which is key to many schedulability analysis techniques. Thereby, most interference-based analysis techniques are not directly applicable to parallel programming models. Motivated by this, we extend the notion of interference to capture thread-level parallelism more accurately. We then leverage the proposed notion of parallelism-aware interference to derive efficient EDF schedulability tests that are directly applicable to parallel task models, including DAG models, on multi-core platforms, without knowing an optimal schedule. Our evaluation results indicate that the proposed analysis significantly advances the state-of-the-art in global EDF schedulability analysis for parallel tasks. In particular, we identify that our proposed schedulability tests are adaptive to different degrees of thread-level parallelism and scalable to the number of processors, resulting in substantial improvement of schedulability for parallel tasks on multi-core platforms. MOE (Min. of Education, S’pore) Accepted version

Traffic redundancy elimination (RE) is an attractive approach to improve the throughput in bandwidth-limited networks. While previous studies show that the RE is useful for improving the throughput in such networks, we observed that the RE would not be an effective solution in wireless networks. We found the TCP congestion control cannot take advantage of the RE, without knowing how the underlying RE system manipulates each TCP packet. In this letter, we present a novel technique called REboost to enable the TCP layer to be aware of the underlying RE system and improve the throughput. Our evaluation with a prototype shows that REboost significantly improves the throughput compared with the previous RE systems.

Battery aging is one of the critical issues in battery-powered electric systems. However, this issue has not received much attention in the real-time systems community. In this paper, we present the first attempt to translate the problem of minimizing battery aging subject to timing requirements into a real-time scheduling problem, addressing the following issues. (i) Can scheduling make a systematic impact on battery aging? If so, which scheduling principles are favorable to minimizing battery aging? (ii) If there exists any, how can we build upon the scheduling principle to guarantee real-time requirements? For (i), we first illuminate the connection between task scheduling and battery aging minimization and then derive a principle for task scheduling from abstracting the complicated dynamics of battery aging, which is to minimize the variance of total power consumption over time. In addition, we implement a battery aging simulator and use it to verify the effectiveness of the proposed principle in minimizing battery aging and its impact on quantitative improvement. For (ii), we propose a scheduling framework that separates control for timing guarantees from that for battery aging minimization. Such a separation allows reducing the complexity significantly such that we can employ existing scheduling algorithm and schedulability analysis for real-time guarantee and tailor the proposed scheduling principle to decelerate battery aging without taking real-time guarantees into accounts. Our simulation results show that the proposed framework can extend the battery lifespan by up to 144.4%.

The growing trend of multi-device ownerships creates a need and an opportunity to use applications across multiple devices. However, in general, the current app development and usage still remain within the single-device paradigm, falling far short of user expectations. We present FLUID, a new multi-device platform that allows users to migrate or replicate individual user interfaces (UIs) of a single app on multiple devices. In addition, FLUID aims to require no extra development effort to support a wide range of legacy apps that follow the trend of using custom-made UIs. To this end, FLUID analyzes which UI states are necessary to correctly render UI objects, deploys only those states on different devices, and supports cross-device function calls transparently. In this demo, we demonstrate several interesting use cases supported by our Android-based FLUID prototype.

The growing trend of multi-device ownerships creates a need and an opportunity to use applications across multiple devices. However, in general, the current app development and usage still remain within the single-device paradigm, falling far short of user expectations. For example, it is currently not possible for a user to dynamically partition an existing live streaming app with chatting capabilities across different devices, such that she watches her favorite broadcast on her smart TV while real-time chatting on her smartphone. In this paper, we present FLUID, a new Android-based multi-device platform that enables innovative ways of using multiple devices. FLUID aims to i) allow users to migrate or replicate individual user interfaces (UIs) of a single app on multiple devices (high flexibility), ii) require no additional development effort to support unmodified, legacy applications (ease of development), and iii) support a wide range of apps that follow the trend of using custom-made UIs (wide applicability). Previous approaches, such as screen mirroring, app migration, and customized apps utilizing multiple devices, do not satisfy those goals altogether. FLUID, on the other hand, meets the goals by carefully analyzing which UI states are necessary to correctly render UI objects, deploying only those states on different devices, supporting cross-device function calls transparently, and synchronizing the UI states of replicated UI objects across multiple devices. Our evaluation with 20 unmodified, real-world Android apps shows that FLUID can transparently support a wide range of apps and is fast enough for interactive use.

A data race in a kernel is an important class of bugs, critically impacting the reliability and security of the associated system. As a result of a race, the kernel may become unresponsive. Even worse, an attacker may launch a privilege escalation attack to acquire root privileges. In this paper, we propose Razzer, a tool to find race bugs in kernels. The core of Razzer is in guiding fuzz testing towards potential data race spots in the kernel. Razzer employs two techniques to find races efficiently: a static analysis and a deterministic thread interleaving technique. Using a static analysis, Razzer identifies over-approximated potential data race spots, guiding the fuzzer to search for data races in the kernel more efficiently. Using the deterministic thread interleaving technique implemented at the hypervisor, Razzer tames the non-deterministic behavior of the kernel such that it can deterministically trigger a race. We implemented a prototype of Razzer and ran the latest Linux kernel (from v4.16-rc3 to v4.18-rc3) using Razzer. As a result, Razzer discovered 30 new races in the kernel, with 16 subsequently confirmed and accordingly patched by kernel developers after they were reported.

We present a 55nm test-chip prototype of a cryptosystem (encryption and decryption) implementing Homomorphic Encryption that can enable computation on encrypted data. Test-chip measurements show 50nJ/encode and 13nJ/decode thus making the cryptosystem suitable for sensor-nodes and IoT applications.

This paper proposes a new scheme to mitigate the effects of interference in generalized frequency division multiplexing (GFDM) systems. We take advantage of the tabu search (TS) process that was originally designed to find approximate solutions for combinatorial optimization problems. We find the initial solution through the application of conventional GFDM receivers such as matched filter and zero-forcing receivers. Starting from the initial solution, we apply the TS algorithm to find a new solution that has lower cost in terms of maximum likelihood costs. In this way, the proposed system can alleviate the interference inherent in GFDM systems. Simulation results show that the proposed system outperforms conventional GFDM systems and orthogonal frequency division multiplexing (OFDM) systems in a multi-path channel regardless of the roll-off factor of the prototype filter.

The development of complex cyber-physical systems necessitates real-time networking with timing guarantees even in the presence of a link fault. Targeting firm real-time flows with the maximum allowable number of continuous deadline misses, this paper introduces FR-SDN, a fault-resilient SDN (Software-Defined Networking) framework that satisfies the timing requirements of firm real-time flows. To this end, we first investigate individual steps for path restoration: fault recognition, path recalculation, and path reassignment. We then design novel system architecture that reduces the delay of the fault recognition and path reassignment steps to potentially assign more time budget to the path recalculation step. Based on the calculation of tight upper-bounds on the delays in individual steps under the proposed system design, we derive a necessary feasibility condition that guarantees the timing requirements of firm real-time flows, and we calculate a time budget for the path recalculation step. Finally, we develop a multi-constrained path finding algorithm that can dynamically adjust the scope of flows to reroute according to the time budget. To the best of our knowledge, FR-SDN is the first study on adaptive path restoration for real-time flows, taking into account path restoration delay and fault tolerance constraints in case of link fault. We have implemented and evaluated FR-SDN on top of Open vSwitch to demonstrate its effectiveness, achieving an order of magnitude reduction in path restoration delay. In addition, we have deployed FR-SDN into a 1/10 scale autonomous vehicle and have shown, via an in-depth case study of adaptive cruise control, that FR-SDN is able to meet all fault tolerance requirements so that it can behave similarly as if there were no link failure.

In this paper we present an algorithm-hardware co-design for camera-based autonomous flight in small drones. We show that the large write-latency and write-energy for nonvolatile memory (NVM) based embedded systems makes them unsuitable for real-time reinforcement learning (RL). We address this by performing transfer learning (TL) on meta-environments and RL on the last few layers of a deep convolutional network. While the NVM stores the meta-model from TL, an on-die SRAM stores the weights of the last few layers. Thus all the real-time updates via RL are carried out on the SRAM arrays. This provides us with a practical platform with comparable performance as end-to-end RL and 83.4% lower energy per image frame.

As network systems become widespread in emerging time-sensitive domains, latency becomes an important performance factor. Thus, there is a new demand for ultra-low latency (ULL), which requires an extra-ordinarily low latency even in a deterministic manner. To support the ULL requirement, it is mandatory that each network device strictly guarantees this latency. As a result, there is an increasing demand for a precise latency measurement tool that achieves two conflicting goals: high time precision and flexible traffic control. To support these goals, this paper proposes a novel measurement tool, named Formullar , which has a hybrid architecture composed of hardware ( Formullar FPGA) and software ( Formullar controller) layers. Formullar generates packets in an accurate timing, according to the various traffic patterns. The packet generation is controlled in a fully programmable manner that makes the testing fully automated and flexible. Then, latency is measured based on packet generation and reception timing; the measurement is done by the hardware layer, providing highly precise measurement results. Using this carefully constructed system, a Formullar prototype was implemented on top of the commodity FPGA board NetFPGA-SUME. The evaluation indicates that Formullar provides highly precise measurements with nanoseconds precision at a bandwidth of 10 Gbps.

A four-year project started in 2013 and supported by the Samsung Research Funding Center aims to revolutionize acoustic array systems through the construction and combination of smart sound devices. This "self-networked smart sound sticker" project uses novel acoustic array transducers made from graphene, as well as smart loudspeakers with self-sensing abilities, to offer installation and operational benefits over conventional arrays of nonflexible configurations.

An efficient interference-based analysis for multi-threaded tasks is introduced.The optimal thread-level priority assignment (OTPA) algorithm is proposed.A novel priority assignment based on deadline adjustment is suggested.Robust experiments that show great performance of our proposed approaches are given. The advent of multi- and many-core processors offers enormous performance potential for parallel tasks that exhibit sufficient intra-task thread-level parallelism. With a growth of novel parallel programming models (e.g., OpenMP, MapReduce), scheduling parallel tasks in the real-time context has received an increasing attention in the recent past. While most studies focused on schedulability analysis under some well-known scheduling algorithms designed for sequential tasks, little work has been introduced to design new scheduling policies that accommodate the features of parallel tasks, such as their multi-threaded structure. Motivated by this, we refine real-time scheduling algorithm categories according to the basic unit of scheduling and propose a new priority assignment method for global task-wide thread-level fixed-priority scheduling of parallel task systems. Our evaluation results show that a finer-grained, thread-level fixed-priority assignment, when properly assigned, significantly improves schedulability, compared to a coarser-grained, task-level assignment.

In indoor environments, there exists a few distinctive indoor spaces’ features (ISFs). However, up to our knowledge, there is no algorithm that fully utilizes ISF for accurate 3-D SLAM. In this letter, we suggest a sensor system that efficiently captures ISF and propose an algorithm framework that accurately estimates sensor’s 3-D poses by utilizing ISF. Experiments conducted in six representative indoor spaces show that the accuracy of the proposed method is better than the previous method. Furthermore, the proposed method shows robust performances in a sense that a set of adjusted parameters of the related algorithms does not need to be recalibrated as target environment changes. We also demonstrate that the proposed method not only generates 3-D depth maps but also builds a dense 3-D RGB-D map.

In this paper, we present the first approach to support mixed-criticality (MC) flow scheduling on switched Ethernet networks leveraging an emerging network architecture, Software-Defined Networking (SDN). Though SDN provides flexible and programmatic ways to control packet forwarding and scheduling, it yet raises several challenges to enable real-time MC flow scheduling on SDN, including i) how to handle (i.e., drop or reprioritize) out-of-mode packets in the middle of the network when the criticality mode changes, and ii) how the mode change affects end-to-end transmission delays. Addressing such challenges, we develop MC-SDN that supports real-time MC flow scheduling by extending SDN-enabled switches and OpenFlow protocols. It manages and schedules MC packets in different ways depending on the system criticality mode. To this end, we carefully design the mode change protocol that provides analytic mode change delay bound, and then resolve implementation issues for system architecture. For evaluation, we implement a prototype of MC-SDN on top of Open vSwitch, and integrate it into a real world network testbed as well as a 1/10 autonomous vehicle. Our extensive evaluations with the network testbed and vehicle deployment show that MC-SDN supports MC flow scheduling with minimal delays on forwarding rule updates and it brings a significant improvement in safety in a real-world application scenario.

With the omnipresence of computing devices in our daily lives, interests in ubiquitous computing interfaces have grown. In response to this, various studies have introduced on-surface input techniques which use the surfaces of surrounding objects as a touch interface. However, these methods are yet struggling to support ubiquitous interaction due to their dependency on specific hardware or environments. In this paper, we propose UbiTap, an input method that turns solid surfaces into a touch input space, through the use of sound (i.e., with microphones already present in the commodity devices). More specifically, we develop a novel touch localization technique which leverages the physical phenomenon, referred to as dispersion, a characteristic of sound as it travels through solid surfaces, so as to address challenges which limit existing acoustic-based solutions in terms of portability, accuracy, usability, robustness, and responsiveness. Our extensive experiments with a prototype of UbiTap show that we can support sub-centimeter accuracy on various surfaces with minor user calibration effort. In our experience with real-world users, UbiTap significantly improves usability and robustness, thus enabling the emergence of more exciting applications.

Progressive Web App (PWA) is a new generation of Web application designed to provide native app-like browsing experiences even when a browser is offline. PWAs make full use of new HTML5 features which include push notification, cache, and service worker to provide short-latency and rich Web browsing experiences. We conduct the first systematic study of the security and privacy aspects unique to PWAs. We identify security flaws in main browsers as well as design flaws in popular third-party push services, that exacerbate the phishing risk. We introduce a new side-channel attack that infers the victim's history of visited PWAs. The proposed attack exploits the offline browsing feature of PWAs using a cache. We demonstrate a cryptocurrency mining attack which abuses service workers. Defenses and recommendations to mitigate the identified security and privacy risks are suggested with in-depth understanding.

Sparse code multiple access (SCMA) is the most prominent non-orthogonal multiple access (NOMA) scheme considered as massive connectivity. Because SCMA users transmit information using the same time-frequency resources, properly estimating each user's channel information is a challenging issue. In this paper, we propose a channel estimator in the uplink SCMA system. We design a pilot structure based on a cyclically shifted Zadoff-Chu (ZC) sequence. The proposed algorithm using the autocorrelation property of the ZC sequence separates each user's channel information and estimates each user's channel frequency response. In addition, we calculate the number of required training blocks and prove that the number of training blocks in the proposed algorithm is lower than the number of needed in conventional channel estimation techniques. In simulation results, we compare the mean squared error (MSE) of the proposed algorithm with conventional approaches.

This paper proposes a method for improving a branch predictor for the extendable instruction set computer (EISC) processor. The original EISC branch predictor has several shortcomings: a small branch target buffer, absence of a global history, a one-bit local branch history, and unsupported prediction of branches following LERI, which is a special instruction to extend an immediate value. We adopt a G-share branch predictor and eliminate the existing shortcomings. We verified the new branch predictor on a field-programmable gate array with the Dhrystone benchmark. The newly proposed EISC branch predictor also accomplishes higher branch prediction accuracy than a conventional branch predictor.

The last decade has seen a rapid growth in the use of mobile devices all over the world. With an increasing use of mobile devices, mobile applications are becoming more diverse and complex, demanding more computational resources. However, mobile devices are typically resource-limited (i.e., a slower-speed CPU, a smaller memory) due to a variety of reasons. Mobile users will be capable of running applications with heavy computation if they can offload some of their computations to other places, such as a desktop or server machines. However, mobile users are typically subject to dynamically changing network environments, particularly, due to user mobility. This makes it hard to choose good offloading decisions in mobile environments. In general, users’ mobility can provide some hints for upcoming changes to network environments. Motivated by this, we propose a mobility model of each individual user taking advantage of the regularity of his/her mobility pattern, and develop an offloading decision-making technique based on the mobility model. We evaluate our technique through trace-based simulation with real log data traces from 14 Android users. Our evaluation results show that the proposed technique can help boost the performance of mobile devices in terms of response time and energy consumption, when users are highly mobile.

Interrupt handling is generally separated from process scheduling. This can lead to a scheduling anomaly and priority inversion. The processor can interrupt a higher priority process that is currently executing, in order to handle a network packet reception interruption on behalf of its intended lower priority receiver process. We propose a new network interrupt handling scheme that combines interrupt handling with process scheduling and the priority of the process. The proposed scheme employs techniques to identify the intended receiver process of an incoming packet at an earlier phase. We implement a prototype system of the proposed scheme on Linux 2.6, and our experiment results show that the prototype system supports the predictable real-time behavior of higher priority processes even when excessive traffic is sent to lower priority processes. Category: Embedded computing; Smart and intelligent computing

We introduce a multiprocessor scheduling parameter called quasi-deadline.We propose two scheduling algorithms, called EQDF and EQDZL.We derive new schedulability analysis tests for EQDF and EQDZL.We address the problem of priority assignment under EQDF and EQDZL scheduling.We demonstrate through simulation that EQDF and EQDZL can improve schedulability. Recent trends toward multi-core architectures in real-time embedded systems pose challenges in designing efficient real-time multiprocessor scheduling algorithms. We believe that it is important to take into consideration both timing constraints of tasks (urgency) and parallelism restrictions of multiprocessor platforms (parallelism) together when designing scheduling algorithms. Motivated by this, we define the quasi-deadline of a job as a weighted sum of its absolute deadline (capturing urgency) and its worst case execution time (capturing parallelism) with a system-level control knob to balance urgency and parallelism effectively. Using the quasi-deadline to prioritize jobs, we propose two new scheduling algorithms, called EQDF (earliest quasi-deadline first) and EQDZL (earliest quasi-deadline until zero laxity), that are categorized into job-level fixed-priority (JFP) scheduling and job-level dynamic-priority (JDP) scheduling, respectively. This paper provides a new schedulability analysis for EQDF/EQDZL scheduling and addresses the problem of priority assignment under EQDF/EQDZL by determining a right value of the system-level control knob. It presents optimal and heuristic solutions to the problem subject to our proposed EQDF and EQDZL analysis. Our simulation results show that EQDF and EQDZL can improve schedulability significantly compared to EDF and EDZL, respectively.

Although many cyber-physical systems are both mixed-criticality system and compositional system, there are little work on intersection of mixed-criticality system and compositional system. We propose novel concepts for task-level criticality-mode and reconsider temporal isolation in terms of compositional mixed-criticality scheduling.

The search for alternative memory technologies has attracted significant attention toward emerging non-volatile memories. Among them, STT-MRAM, PCM, RRAM have shown promising characteristic to gain a position inside the memory hierarchy of computing platforms, and even enable new computing paradigms. However like any other emerging technology these devices are affected by concerns to be resolved before they could become a mainstream. This paper reviews the main reliability and testability challenges of aforementioned emerging non-volatile memories and highlights the main future considerations toward them.

Recently, the orthogonal code-based block transmission (OCBT) systems have been proposed to fulfill the explosive growth of traffic demands. The multi-carrier-based structure of OCBT, however, requires accurate synchronization to combat inter-symbol interference (ISI) and inter-carrier interference (ICI). Fortunately, the repetitive pattern contained in the OCBT symbol enables this synchronization without preambles. In this paper, we propose a novel time-frequency offset estimation method based on the repetition characteristic. Simulation results demonstrate that the proposed estimator provides acceptable root-mean-square error (RMSE) performance. Moreover, we verify that the OCBT systems with the proposed method achieve remarkable throughput enhancement compared with conventional OFDM systems.

This paper investigates timing and frequency synchronization applicable for filter bank multicarrier (FBMC) systems. Filtering and symbol overlap make designing a training period that has a specific block pattern quite difficult. To address this, a receiver architecture that exploits synchronization structure in a simple way is proposed. Specifically, the receiver performs observation windowing and weighted correlation processes in order to overcome the problems from overlap and filtering, respectively. Simulation results show that the proposed method achieves accurate synchronization while utilizing a minimum of resources.

In recent years, the explosion of diverse smart devices such as mobile phones, TVs, watches, and even cars, has completely changed our lives. We communicate with friends through social network services (SNSs) whenever we want, buy stuff without visiting shops, and enjoy multimedia wherever we are, thanks to these devices. However, these smart devices cannot simply interact with each other even though they are right next to each other. For example, when you want to read a PDF stored on a smartphone on a larger TV screen, you need to do complicated work or plug in a bunch of cables. In this paper, we introduce M+, an extension of Android that supports cross-device functionality sharing in a transparent manner. As a platform-level solution, M+ enables unmodified Android applications to utilize not only application functionalities but also system functionalities across devices, as if they were to utilize them inside the same device. In addition to secure connection setup, M+ also allows performing of permission checks for remote applications in the same way as for local. Our experimental results show that M+ enables transparent cross-device sharing for various functionalities and achieves performance close to that of within-device sharing unless a large amount of data is transferred.

Owing to growing complexity and scale, safetycritical real-time systems are generally designed using the concept of mixed-criticality, wherein applications with different criticality or importance levels are hosted on the same hardware platform. To guarantee non-interference between these applications, the hardware resources, in particular the processor, are statically partitioned among them. To overcome the inefficiencies in resource utilization of such a static scheme, the concept of mixedcriticality real-time scheduling has emerged as a promising solution. Although there are several studies on such scheduling strategies for uniprocessor platforms, the problem of efficient scheduling for the multiprocessor case has largely remained open. In this work, we design a fluid-model based mixed-criticality scheduling algorithm for multiprocessors, in which multiple tasks are allowed to execute on the same processor simultaneously. We derive an exact schedulability test for this algorithm, and also present an optimal strategy for assigning the fractional execution rates to tasks. Since fluid-model based scheduling is not implementable on real hardware, we also present a transformation algorithm from fluid-schedule to a non-fluid one. We also show through experimental evaluation that the designed algorithms outperform existing scheduling algorithms in terms of their ability to schedule a variety of task systems. MOE (Min. of Education, S’pore) Accepted version

In real-time systems, schedulability analysis has been widely studied to provide offline guarantees on temporal correctness, producing many analysis methods. The demand-based schedulability analysis method has a great potential for high schedulability performance and broad applicability. However, such a potential is not yet fully realized for real-time multi-core scheduling mainly due to (i) the difficulty of calculating the resource demand under dynamic priority scheduling algorithms that are favorable to multi-cores, and (ii) the lack of understanding how to combine the analysis framework with deadline-miss conditions specialized for those scheduling algorithms. Addressing those two issues, to the best of our knowledge, this paper presents the first demand-based schedulability analysis for dynamic job-priority scheduling algorithms: EDZL (Earliest Deadline first until Zero-Laxity) and LLF (Least Laxity First), which are known to be effective for real-time multi-core scheduling. To this end, we first derive demand bound functions that compute the maximum possible amount of resource demand of jobs of each task while the priority of each job can change dynamically under EDZL and LLF. Then, we develop demand-based schedulability analyses for EDZL and LLF, by incorporating those new demand bound functions into the existing demand-based analysis framework. Finally, we combine the framework with additional deadline-miss conditions specialized for those two laxity-based dynamic job-priority scheduling algorithms, yielding tighter schedulability analyses. Via simulations, we demonstrate that the proposed schedulability analyses outperform the existing schedulability analyses for EDZL and LLF.