With the steady growth of the aging population, there has been increasing interest in the health of elderly people. In particular, a physical fall can be fatal for the elderly or result in complications that cause more serious physical and mental damage than the cause of the falls. For this reason, it is important to accurately predict the occurrence of abnormal behavior in advance. Although existing methods can be used to analyze general behavioral characteristics, personalized and customized behavioral prediction remains challenging. In this paper, we propose a customized pre-prediction method using sensor wearers’ biometric information as well as behavioral data reflecting the wearers’ unique behavioral characteristics. The technique is based on a temporal fusion transformers method [1] that can be effectively applied to multi-horizon forecasting. A performance evaluation was made to compare the effects of including and excluding biometric information to quantitatively evaluate the enhancement achieved by extending the prediction model. The use of additional biometric information enabled superior performance with 0.9702, 0.9702, 0.9702, 0.9883 in F-1 score, precision, recall respectively, which is all evaluation indicators. The code for the experiments can be found at the following GitHub address: https://github.com/IKKIM00/Fall_Detection_using_multihorizon_forecasting.

Address translation is increasingly a performance bottleneck in flash-based SSDs (solid state drives). We propose a hardware-accelerated flash map management unit called FMMU to speed up the address translation. The FMMU operates in a non-blocking manner without any involvement of software and uses in-cache linked list to enhance the performance of flush operations. Simulation study shows that the FMMU improves IOPS (I/O operations per second) by 135 % on average when compared against software-only approaches. The results also show that the performance gap widens as the number of flash chips/buses and the host interface speed increase.

We present a framework called Hierarchically Interacting Logs (HIL) for constructing Flash Translation Layers (FTLs). The main goal of the HIL framework is to heal the Achilles heel —the crash recovery—of FTLs (hence, its name). Nonetheless, the framework itself is general enough to encompass not only block-mapped and page-mapped FTLs but also many of their variants, including hybrid ones, because of its compositional nature. Crash recovery within the HIL framework proceeds in two phases: structural recovery and functional recovery. During the structural recovery, residual effects due to program operations ongoing at the time of the crash are eliminated in an atomic manner using shadow paging. During the functional recovery, operations that would have been performed if there had been no crash are replayed in a redo-only fashion. Both phases operate in an idempotent manner, preventing repeated crashes during recovery from causing any additional problems. We demonstrate the practicality of the proposed HIL framework by implementing a prototype and showing that its performance during normal execution and also during crash recovery is at least as good as those of state-of-the-art SSDs.

Flash memory is used as a main data storage medium in increasingly large areas of applications, rapidly replacing hard disk drives because of its low power consumption, fast random access, and high shock resistance. Such flash-based storage devices generally incorporate multiple flash memory chips to meet the ever growing capacity demands. Using multiple chips in a single storage device, at the same time, opens an opportunity to boost the performance based on multi-unit parallelism. However, parallel execution of multiple flash operations introduces complications when bad blocks occur, which is unavoidable due to flash memory’s physical characteristics. The situation gets even worse when bad block occurrences are accompanied by sudden power failures. We propose a bad block management scheme called P-BMS that can fully utilize flash-level parallelism, while guaranteeing provably correct block replacement. Experiments show that our P-BMS achieves a throughput that is more than 95% of the maximum bandwidth of the flash controller, even with bad block occurrences far heavier than in real flash memory.

Real-time embedded systems are typically constrained in terms of three system performance criteria: space, time, and energy. The performance requirements are directly translated into constraints imposed on the system's resources, such as code size, execution time, and energy consumption. These resource constraints often interact or even conflict with each other in a complex manner, making it difficult for a system developer to apply a well-defined design methodology in developing a real-time embedded system. Motivated by this observation, we propose a design framework that can flexibly balance the tradeoff involving the system's code size, execution time, and energy consumption. Given a system specification and an optimization criteria, the proposed technique generates a set of design parameters in such a way that a system cost function is minimized while the given resource constraints are satisfied. Specifically, the technique derives code generation decision for each task so that a specific version of code is selected among a number of different ones that have distinct characteristics in terms of code size and execution time. In addition, the design framework determines the voltage/frequency setting for a variable voltage processor whose supply voltage can be adjusted at runtime in order to minimize the energy consumption while execution performance is degraded accordingly. The proposed technique formulates this design process as a constrained optimization problem. We show that this optimization problem is NP-hard and then provide a heuristic solution to it. We show that these seemingly conflicting design goals can be pursued by using a simple optimization algorithm that works with a single optimization criteria. Moreover, the optimization is driven by an abstract system specification given by the system developer, so that the system development process can be automated. The results from our simulation show that the proposed algorithm finds a solution that is close to the optimal one with the average error smaller than 1.0%.

Embedded systems are often constrained in terms of both code size and execution time, because of a limited amount of available memory and real-time nature of applications. A dual instruction set processor, which supports a reduced instruction set (16 bits/instruction), in addition to a full instruction set (32 bits/instruction), allows an opportunity for a tradeoff between these two design criteria. Specifically, while the reduced instruction set can be used to reduce code size by providing smaller instructions, a program compiled into the reduced instruction set typically runs slower than the same program compiled into the full instruction set. Motivated by this observation, we propose a code generation technique that exploits this tradeoff relationship by selectively using the two instruction sets for different sections in the program. The proposed technique, called selective code transformation, not only provides a mechanism to enable a flexible tradeoff between a program's code size and its execution time, but also facilitates program optimization toward enhancing its worst case performance. The results from our experiments show that our proposed technique can be effectively used to fine-tune an application program on a spectrum of code size and execution performance, which, in turn, enables a system-wide optimization on memory space and execution speed involving multiple applications.