Due to recent technology progress based on big-data processing, many applications present irregular or unpredictable communication patterns among compute nodes in high-performance computing (HPC) systems. Traditional communication infrastructures, e.g., torus or fat-tree interconnection networks, may not handle well their matchmaking problems with these newly emerging applications. There are already many communication-efficient application mapping algorithms for these typical non-random network topologies, which use nearby compute nodes to reduce the network distances. However, for the above unpredictable communication patterns, it is difficult to efficiently map their applications onto the non-random network topologies. In this context, we recommend using random network topologies as the communication infrastructures, which have drawn increasing attention for the use of HPC interconnects due to their small diameter and average shortest path length (ASPL). We make a comparative study to analyze the impact of application mapping performance on non-random and random network topologies. We propose using topology embedding metrics, i.e., diameter and ASPL, and list several diameter/ASPL-based application mapping algorithms to compare their job scheduling performances, assuming that the communication pattern of each application is unpredictable to the computing system. Evaluation with a large compound application workload shows that, when compared to non-random topologies, random topologies can reduce the average turnaround time up to 39.3% by a random connected mapping method and up to 72.1% by a diameter/ASPL-based mapping algorithm. Moreover, when compared to the baseline topology mapping method, the proposed diameter/ASPL-based topology mapping strategy can reduce up to 48.0% makespan and up to 78.1% average turnaround time, and improve up to 1.9x system utilization over random topologies.

Ontology describes concepts and relations in a specific domain-knowledge that are important for knowledge representation and knowledge sharing. In the past few years, several tools have been introduced for ontology modeling and editing. To design and develop an ontology is one of the challenge tasks and its challenges are quite similar to software development as it requires many collaborative activities from many stakeholders (e.g. domain experts, knowledge engineers, application users, etc.) through the development cycle. Most of the existing tools do not provide collaborative feature to support stakeholders to collaborate work more effectively. In addition, there are lacking of standard process adoption for ontology development task. Thus, in this work, we incorporated ontology development process into Scrum process as used for process standard in software engineering. Based on Scrum, we can perform standard agile development of ontology that can reduce the development cycle as well as it can be responding to any changes better and faster. To support this idea, we proposed a Scrum Ontology Development Framework, which is an online collaborative framework for agile ontology design and development. Each ontology development process based on Scrum model will be supported by different services in our framework, aiming to promote collaborative activities among different roles of stakeholders. In addition to services such as ontology visualized modeling and editing, we also provide three more important features such as 1) concept/relation misunderstanding diagnosis, 2) cross-domain concept detection and 3) concept classification. All these features allow stakeholders to share their understanding and collaboratively discuss to improve quality of domain ontologies through a community consensus.

Expectation propagation (EP) decoding is proposed for sparse superposition coding in orthogonal frequency division multiplexing (OFDM) systems. When a randomized discrete Fourier transform (DFT) dictionary matrix is used, the EP decoding has the same complexity as approximate message-passing (AMP) decoding, which is a low-complexity and powerful decoding algorithm for the additive white Gaussian noise (AWGN) channel. Numerical simulations show that the EP decoding achieves comparable performance to AMP decoding for the AWGN channel. For OFDM systems, on the other hand, the EP decoding is much superior to the AMP decoding while the AMP decoding has an error-floor in high signal-to-noise ratio regime.

In an internet of things (IoT) system using an energy harvesting device and a secondary (2nd) battery, regardless of the age of the 2nd battery, the power management shortens the lifespan of the entire system. In this paper, we propose a scheme that extends the lifetime of the energy harvesting-based IoT system equipped with a Lithium 2nd battery. The proposed scheme includes several policies of using a supercapacitor as a primary energy storage, limiting the charging level according to the predicted harvesting energy, swinging the energy level around the minimum stress state of charge (SOC) level, and delaying the charge start time. Experiments with natural solar energy measurements based on a battery aging approximation model show that the proposed method can extend the operation lifetime of an existing IoT system from less than one and a half year to more than four years.

Network throughput has become an important issue for big-data analysis on Warehouse-Scale Computing (WSC) systems. It has been reported that randomly-connected inter-switch networks can enlarge the network throughput. For irregular networks, a multi-path routing method called k-shortest path routing is conventionally utilized. However, it cannot efficiently exploit longer-than-shortest paths that would be detour paths to avoid bottlenecks. In this work, a novel routing method called k-optimized path routing to achieve high throughput is proposed for irregular networks. We introduce a heuristic to select detour paths that can avoid bottlenecks in the network to improve the average-case network throughput. Experimental results by network simulation show that the proposed k-optimized path routing can improve the saturation throughput by up to 18.2% compared to the conventional k-shortest path routing. Moreover, it can reduce the computation time required for optimization to 1/2760 at a minimum compared to our previously proposed method.

In this paper, we improve upon the automatic building extraction method, which uses a variational inference Gaussian mixture model for performing color clustering, by accelerating its computational speed. The improved method decreases the computational time using an image with reduced resolution upon applying color clustering. According to our experiment, in which we used 106 scenery images, the improved method could extract buildings at a rate 86.54% faster than that of the conventional methods. Furthermore, the improved method significantly increased the extraction accuracy by 1.8% or more by preventing over-clustering using the reduced image, which also had a reduced number of the colors.

Designing an optimum quantizer can be treated as the optimization problem of finding the quantization indices that minimize the quantization error. One solution to the optimization problem, DP quantization, is based on dynamic programming. Some applications, such as bit-depth scalable codec and tone mapping, require the construction of multiple quantizers with different quantization levels, for example, from 12bit/channel to 10bit/channel and 8bit/channel. Unfortunately, the above mentioned DP quantization optimizes the quantizer for just one quantization level. That is, it is unable to simultaneously optimize multiple quantizers. Therefore, when DP quantization is used to design multiple quantizers, there are many redundant computations in the optimization process. This paper proposes an extended DP quantization with a complexity reduction algorithm for the optimal design of multiple quantizers. Experiments show that the proposed algorithm reduces complexity by 20.8%, on average, compared to conventional DP quantization.

The nearest neighbor method is a simple and flexible scheme for the classification of data points in a vector space. It predicts a class label of an unseen data point using a majority rule for the labels of known data points inside a neighborhood of the unseen data point. Because it sometimes achieves good performance even for complicated problems, several derivatives of it have been studied. Among them, the discriminant adaptive nearest neighbor method is particularly worth revisiting to demonstrate its application. The main idea of this method is to adjust the neighbor metric of an unseen data point to the set of known data points before label prediction. It often improves the prediction, provided the neighbor metric is adjusted well. For statistical shape analysis, shape classification attracts attention because it is a vital topic in shape analysis. However, because a shape is generally expressed as a matrix, it is non-trivial to apply the discriminant adaptive nearest neighbor method to shape classification. Thus, in this study, we develop the discriminant adaptive nearest neighbor method to make it slightly more useful in shape classification. To achieve this development, a mixture model and optimization algorithm for shape clustering are incorporated into the method. Furthermore, we describe several helpful techniques for the initial guess of the model parameters in the optimization algorithm. Using several shape datasets, we demonstrated that our method is successful for shape classification.

Recently, the ECG-based diagnosis system based on wearable devices has attracted more and more attention of researchers. Existing studies have achieved high classification accuracy by using deep neural networks (DNNs), but there are still some problems, such as: imprecise heart beat segmentation, inadequate use of medical knowledge, the same treatment of features with different importance. To address these problems, this paper: 1) proposes an adaptive segmenting-reshaping method to acquire abundant useful samples; 2) builds a set of hand-crafted features and deep features on the inner-beat, beat and inter-beat scale by integrating enough medical knowledge. 3) introduced a modified channel attention module (CAM) to augment the significant channels in deep features. Following the Association for Advancement of Medical Instrumentation (AAMI) recommendation, we classified the dataset into four classes and validated our algorithm on the MIT-BIH database. Experiments show that the accuracy of our model reaches 96.94%, a 3.71% increase over that of a state-of-the-art alternative.

Air discharge immunity testing for electronic equipment is specified in the standard 61000-4-2 of the International Eelectrotechnical Commission (IEC) under the climatic conditions of temperature (T) from 15 to 35 degrees Celsius and relative humidity (RH) from 30 to 60%. This implies that the air discharge testing is likely to provide significantly different test results due to the wide climatic range. To clarify effects of the above climatic conditions on air discharge testing, we previously measured air discharge currents from an electrostatic discharge (ESD) generator with test voltages from 2kV to 15kV at an approach speed of 80mm/s under 6 combinations of T and RH in the IEC specified range and non-specified climatic range. The result showed that the same absolute humidity (AH), which is determined by T and RH, provides almost the identical waveforms of the discharge currents despite different T and RH, and also that the current peaks at higher test voltages decrease as the AH increases. In this study, we further examine the combined effects of air discharges on test voltages, T, RH and AH with respect to two different approach speeds of 20mm/s and 80mm/s. As a result, the approach speed of 80mm/s is confirmed to provide the same results as the previous ones under the identical climatic conditions, whereas at a test voltage of 15kV under the IEC specified climatic conditions over 30% RH, the 20mm/s approach speed yields current waveforms entirely different from those at 80mm/s despite the same AH, and the peaks are basically unaffected by the AH. Under the IEC non-specified climatic conditions with RH less than 20%, however, the peaks decrease at higher test voltages as the AH increases. These findings obtained imply that under the same AH condition, at 80mm/s the air discharge peak is not almost affected by the RH, while at 20mm/s the lower the RH is, the higher is the peak on air discharge current.

Training deep learning (DL) is a computationally intensive process; as a result, training time can become so long that it impedes the development of DL. High performance computing clusters, especially supercomputers, are equipped with a large amount of computing resources, storage resources, and efficient interconnection ability, which can train DL networks better and faster. In this paper, we propose a method to train DL networks distributed with high efficiency. First, we propose a hierarchical synchronous Stochastic Gradient Descent (SGD) strategy, which can make full use of hardware resources and greatly increase computational efficiency. Second, we present a two-level parameter synchronization scheme which can reduce communication overhead by transmitting parameters of the first layer models in shared memory. Third, we optimize the parallel I/O by making each reader read data as continuously as possible to avoid the high overhead of discontinuous data reading. At last, we integrate the LARS algorithm into our system. The experimental results demonstrate that our approach has tremendous performance advantages relative to unoptimized methods. Compared with the native distributed strategy, our hierarchical synchronous SGD strategy (HSGD) can increase computing efficiency by about 20 times.

RISC-V is a RISC based open and loyalty free instruction set architecture which has been developed since 2010, and can be used for cost-effective soft processors on FPGAs. The basic 32-bit integer instruction set in RISC-V is defined as RV32I, which is sufficient to support the operating system environment and suits for embedded systems. In this paper, we propose an optimized RV32I soft processor named RVCoreP adopting five-stage pipelining. Three effective methods are applied to the processor to improve the operating frequency. These methods are instruction fetch unit optimization, ALU optimization, and data memory optimization. We implement RVCoreP in Verilog HDL and verify the behavior using Verilog simulation and an actual Xilinx Atrix-7 FPGA board. We evaluate IPC (instructions per cycle), operating frequency, hardware resource utilization, and processor performance. From the evaluation results, we show that RVCoreP achieves 30.0% performance improvement compared with VexRiscv, which is a high-performance and open source RV32I processor selected from some related works.

The network load is increasing due to the spread of content distribution services. Caching is recognized as a technique to reduce the peak network load by storing popular content into memories of users. Coded caching is a new caching approach based on a carefully designed content placement to create coded multicasting opportunities. Coded caching schemes in single-rate networks are evaluated by the tradeoff between the size of memory and that of delivered data. For considering the network with multiple transmission rates, it is crucial how to operate multicast. In multicast delivery, a sender must communicate to intended receivers at a rate that is available to all receivers. Multicast scheduling method of determining rates to deliver are evaluated by throughput and delay in multi-rate wireless networks. In this paper, we discuss coded caching in the multi-rate wireless networks. We newly define a measure for evaluating the coded caching scheme as coded caching delay and propose a new coded caching scheme. Also, we compare the proposed coded caching scheme with conventional coded caching schemes and show that the proposed scheme is suitable for multi-rate wireless networks.

In emergency communication systems research, aerial wireless relay networks (AWRNs) using multicopter unmanned aerial vehicles (UAVs) have been proposed. The main issue of the AWRNs is how to minimize the delay time of packet transmissions since it is not easy to supply many multicopters to cover a wide area. In this paper, we review the flight schemes and their delay time for the AWRNs. Furthermore, the network has specific issues such as multicopters' drops due to their battery capacity depletion and inclination of moving multicopters. The inclination of multicopters affects the received power, and the communication range changes based on the inclination as well. Therefore, we clarify the effect of these issues on the delay time.

Currently, the top-k error ratio is one of the primary methods to measure the accuracy of multi-category classification. Top-k multiclass SVM was designed to minimize the empirical risk based on the top-k error ratio. Two SDCA-based algorithms exist for learning the top-k SVM, both of which have several desirable properties for achieving optimization. However, both algorithms suffer from a serious disadvantage, that is, they cannot attain the optimal convergence in most cases owing to their theoretical imperfections. As demonstrated through numerical simulations, if the modified SDCA algorithm is employed, optimal convergence is always achieved, in contrast to the failure of the two existing SDCA-based algorithms. Finally, our analytical results are presented to clarify the significance of these existing algorithms.

Massive multiple-input multiple-output (MIMO) is an enabling technology for next-generation wireless systems because it provides significant improvements in data rates compared to existing small-scale MIMO systems. However, the increased number of antennas results in high computational complexity for data detection, and requires more efficient detection algorithms. In this paper, we propose a new data detector based on a box-constrained complex-valued dichotomous coordinate descent (BCC-DCD) algorithm for large-scale MIMO systems. The proposed detector involves two steps. First, a transmitted data vector is detected using the BCC-DCD algorithm with a large number of iterations and high solution precision. Second, an improved soft output is generated by reapplying the BCC-DCD algorithm, but with a considerably smaller number of iterations and 1-bit solution precision. Numerical results demonstrate that the proposed method outperforms existing advanced detectors while possessing lower complexity. Specifically, the proposed method provides significantly better detection performance than a BCC-DCD algorithm with similar complexity. The performance advantage increases as the signal-to-noise ratio and the system size increase.

In this paper, we propose an accurate calibration method for glassless stereoscopic systems. The method uses a lenticular lens on a general display. Glassless stereoscopic displays are currently used in many fields; however, accurately adjusting their physical display position is difficult because an accuracy of several microns or one hundredth of a degree is required, particularly given their larger display area. The proposed method enables a dynamic adjustment of the positions of images on the display to match various physical conditions in three-dimensional (3D) displays. In particular, compared with existing approaches, this avoids degradation of the image quality due to the image location on the screen while improving the image quality by local mapping. Moreover, it is shown to decrease the calibration time by performing simultaneous processing for each local area. As a result of the calibration, the offset jitter representing the crosstalk reduces from 14.946 to 8.645 mm. It is shown that high-quality 3D videos can be generated. Finally, we construct a stereoscopic viewing system using a high-resolution display and lenticular lens and produce high-quality 3D images with automatic calibration.

This paper discusses a static analysis problem, called absolute consistency problem, for relational schema mappings. A given schema mapping is said to be absolutely consistent if every source instance has a corresponding target instance. Absolute consistency is an important property because it guarantees that data exchange never fails for any source instance. Originally, for XML schema mappings, the absolute consistency problem was defined and its complexity was investigated by Amano et al. However, as far as the authors know, there are no known results for relational schema mappings. In this paper, we focus on relational schema mappings such that both the source and the target schemas have functional dependencies, under the assumption that mapping rules are defined by constant-free tuple-generating dependencies. In this setting, we show that the absolute consistency problem is in coNP. We also show that it is solvable in polynomial time if the tuple-generating dependencies are full and the size of the left-hand side of each functional dependency is bounded by some constant. Finally, we show that the absolute consistency problem is coNP-hard even if the source schema has no functional dependency and the target schema has only one; or each of the source and the target schemas has only one functional dependency such that the size of the left-hand side of the functional dependency is at most two.

Recently, intelligent heating, next generation microwave ovens that achieve uniform heating and spot heating using solid-state devices, has been actively studied. There are two types of microwave generators using solid-state devices. Since compactness is indispensable to accommodate in a limited space, the miniaturized oscillator type was selected. The authors proposed an imbalanced coupling resonator, a resonator-less feedback circuit, a high power frequency variable resonator, and injection-locked phase control in order to achieve high performance of the oscillator type microwave generator. In addition, we confirmed that the oscillator type can be used as the microwave generator for intelligent heating using a Wilkinson combiner. As a result, it was demonstrated that the oscillator type microwave generator, realized the same high efficiency (67%) as the amplifier type, and found the possibility of variable frequency (2.4 to 2.5GHz) and variable phase, and can be used as the microwave generator for intelligent heating.

One of key technologies in the fifth generation mobile communications is a distributed antenna system (DAS). As DAS creates tightly packed antenna arrangements, inter-user interference degrades its spectrum efficiency. Round-robin (RR) scheduling is known as a scheme that achieves a good trade-off between computational complexity and spectrum efficiency. This paper proposes a user equipment (UE) allocation scheme for RR scheduling. The proposed scheme offers low complexity as the phase of UE allocation sequences are predetermined. Four different phase selection criteria are compared in this paper. Numerical results obtained through computer simulation show that maximum selection, which sequentially searches for the phase with the maximum tentative throughput realizes the best spectrum efficiency next to full search. There is an optimum number of UEs which obtains the largest throughput in single-user allocation while the system throughput improves as the number of UEs increases in 2-user RR scheduling.