In this paper, the random numbers generated by a true random number generator, using the oscillator sampling method, are formulated using a renewal process, and this formulation is used to demonstrate the uniformity of the random numbers and the independence between different bits. Using our results, a lower bound for the speed of random number generation could easily be identified, according to the required statistical quality.

This paper presents examination result of possibility for automatic unclear region detection in the CAD system for colorectal tumor with real time endoscopic video image. We confirmed that it is possible to realize the CAD system with navigation function of clear region which consists of unclear region detection by YOLO2 and classification by AlexNet and SVMs on customizable embedded DSP cores. Moreover, we confirmed the real time CAD system can be constructed by a low power ASIC using customizable embedded DSP cores.

Active network monitoring based on Boolean network tomography is a promising technique to localize link failures instantly in transport networks. However, the required set of monitoring trails must be recomputed after each link failure has occurred to handle succeeding link failures. Existing heuristic methods cannot compute the required monitoring trails in a sufficiently short time when multiple-link failures must be localized in the whole of large-scale managed networks. This paper proposes an approach for computing the required monitoring trails within an allowable expected period specified beforehand. A random walk-based analysis estimates the number of monitoring trails to be computed in the proposed approach. The estimated number of monitoring trails are computed by a lightweight method that only guarantees partial localization within restricted areas. The lightweight method is repeatedly executed until a successful set of monitoring trails achieving unambiguous localization in the entire managed networks can be obtained. This paper demonstrates that the proposed approach can compute a small number of monitoring trails for localizing all independent dual-link failures in managed networks made up of thousands of links within a given expected short period.

In this paper, we present a classification method for a Computer-Aided Diagnosis (CAD) system in a colorectal magnified Narrow Band Imaging (NBI) endoscopy. In an endoscopic video image, color shift, blurring or reflection of light occurs in a lesion area, which affects the discrimination result by a computer. Therefore, in order to identify lesions with high robustness and stable classification to these images specific to video frame, we implement a CAD system for colorectal endoscopic images with the Convolutional Neural Network (CNN) feature and Support Vector Machine (SVM) classification on the embedded DSP core. To improve the robustness of CAD system, we construct the SVM learned by multiple image sizes data sets so as to adapt to the noise peculiar to the video image. We confirmed that the proposed method achieves higher robustness, stable, and high classification accuracy in the endoscopic video image. The proposed method also can cope with differences in resolution by old and new endoscopes and perform stably with respect to the input endoscopic video image.

The solution of the ordinary kernel ridge regression, based on the squared loss function and the squared norm-based regularizer, can be easily interpreted as a stochastic linear estimator by considering the autocorrelation prior for an unknown true function. As is well known, a stochastic affine estimator is one of the simplest extensions of the stochastic linear estimator. However, its corresponding kernel regression problem is not revealed so far. In this paper, we give a formulation of the kernel regression problem, whose solution is reduced to a stochastic affine estimator, and also give interpretations of the formulation.

The present paper proposes orthogonal variable spreading factor codes over finite fields for multi-rate communications. The proposed codes have layered structures that combine sequences generated by discrete Fourier transforms over finite fields, and have various code lengths. The design method for the proposed codes and examples of the codes are shown.

Observed results of arc discharges generated between the brush and commutator are reported. The motion of the arc discharges was observed by a high-speed camera. The brush and commutator were installed to an experimental device that simulated the rotational motion of a real DC motor. The aim of this paper is to investigate the occurring position, dimensions, and moving characteristics of the arc discharges by means of high-speed imaging. Time evolutions of the arc voltage and current were measured, simultaneously. The arc discharges were generated when an inductive circuit was interrupted. Circuit current before interruption was 4A. The metal graphite or graphite brush and a copper commutator were used. Following results were obtained. The arc discharge was dragged on the brush surface and the arc discharge was sticking to the side surface of the commutator. The positions of the arc spots were on the end of the commutator and the center of the brush in rotational direction. The dimensions of the arc discharge were about 0.2 mm in length and about 0.3 mm in width. The averaged arc voltage during arc duration became higher and the light emission from the arc discharge became brighter, as the copper content of the cathode decreased.

Convolutional neural networks (CNNs) are widely used for image processing tasks in both embedded systems and data centers. In data centers, high accuracy and low latency are desired for various tasks such as image processing of streaming videos. We propose an FPGA-based low-latency CNN inference for randomly wired convolutional neural networks (RWCNNs), whose layer structures are based on random graph models. Because RWCNNs have several convolution layers that have no direct dependencies between them, our architecture can process them efficiently using a pipeline method. At each layer, we need to use the calculation results of multiple layers as the input. We use an FPGA with HBM2 to enable parallel access to the input data with multiple HBM2 channels. We schedule the order of execution of the layers to improve the pipeline efficiency. We build a conflict graph using the scheduling results. Then, we allocate the calculation results of each layer to the HBM2 channels by coloring the graph. Because the pipeline execution needs to be properly controlled, we developed an automatic generation tool for hardware functions. We implemented the proposed architecture on the Alveo U50 FPGA. We investigated a trade-off between latency and recognition accuracy for the ImageNet classification task by comparing the inference performances for different input image sizes. We compared our accelerator with a conventional accelerator for ResNet-50. The results show that our accelerator reduces the latency by 2.21 times. We also obtained 12.6 and 4.93 times better efficiency than CPU and GPU, respectively. Thus, our accelerator for RWCNNs is suitable for low-latency inference.

With the development of IoT devices and sensors, edge computing is leading towards new services like autonomous cars and smart cities. Low-latency data access is an essential requirement for such services, and a large-capacity cache server is needed on the edge side. However, it is not realistic to build a large capacity cache server using only DRAM because DRAM is expensive and consumes substantially large power. A hybrid main memory system is promising to address this issue, in which main memory consists of DRAM and non-volatile memory. It achieves a large capacity of main memory within the power supply capabilities of current servers. In this paper, we propose Fogcached, that is, the extension of a widely-used KVS (Key-Value Store) server program (i.e., Memcached) to exploit both DRAM and non-volatile main memory (NVMM). We used Intel Optane DCPM as NVMM for its prototype. Fogcached implements a Dual-LRU (Least Recently Used) mechanism that seamlessly extends the memory management of Memcached to hybrid main memory. Fogcached reuses the segmented LRU of Memcached to manage cached objects in DRAM, adds another segmented LRU for those in DCPM and bridges the LRUs by a mechanism to automatically replace cached objects between DRAM and DCPM. Cached objects are autonomously moved between the two memory devices according to their access frequencies. Through experiments, we confirmed that Fogcached improved the peak value of a latency distribution by about 40% compared to Memcached.

Hybrid storage techniques are useful methods to improve the cost performance for input-output (IO) intensive workloads. These techniques choose areas of concentrated IO accesses and migrate them to an upper tier to extract as much performance as possible through greater use of upper tier areas. Automated tiered storage with fast memory and slow flash storage (ATSMF) is a hybrid storage system situated between non-volatile memories (NVMs) and solid-state drives (SSDs). ATSMF aims to reduce the average response time for IO accesses by migrating areas of concentrated IO access from an SSD to an NVM. When a concentrated IO access finishes, the system migrates these areas from the NVM back to the SSD. Unfortunately, the published ATSMF implementation temporarily consumes much NVM capacity upon migrating concentrated IO access areas to NVM, because its algorithm executes NVM migration with high priority. As a result, it often delays evicting areas in which IO concentrations have ended to the SSD. Therefore, to reduce the consumption of NVM while maintaining the average response time, we developed new techniques for making ATSMF more practical. The first is a queue handling technique based on the number of IO accesses for NVM migration and eviction. The second is an eviction method that selects only write-accessed partial regions in finished areas. The third is a technique for variable eviction timing to balance the NVM consumption and average response time. Experimental results indicate that the average response times of the proposed ATSMF are almost the same as those of the published ATSMF, while the NVM consumption is three times lower in best case.

Computing the Lempel-Ziv Factorization (LZ77) of a string is one of the most important problems in computer science. Nowadays, it has been widely used in many applications such as data compression, text indexing and pattern discovery, and already become the heart of many file compressors like gzip and 7zip. In this paper, we show a linear time algorithm called Xone for computing the LZ77, which has the same space requirement with the previous best space requirement for linear time LZ77 factorization called BGone. Xone greatly improves the efficiency of BGone. Experiments show that the two versions of Xone: XoneT and XoneSA are about 27% and 31% faster than BGoneT and BGoneSA, respectively.

A hard-type oscillator is defined as an oscillator having stable fixed points within a stable limit cycle. For resonant tunneling diode (RTD) oscillators, using hard-type configuration has a significant advantage that it can suppress spurious oscillations in a bias line. We have fabricated hard-type oscillators using an InGaAs-based RTD, and demonstrated a proper operation. Furthermore, the oscillating properties have been compared with a soft-type oscillator having a same parameters. It has been demonstrated that the same level of the phase noise can be obtained with a much smaller power consumption of approximately 1/20.

DoS (Denial of Service) attacks are becoming one of the most serious security threats to global networks. We analyze the existing DoS detection methods and defense mechanisms in depth. In recent years, K-Means and improved variants have been widely examined for security intrusion detection, but the detection accuracy to data is not satisfactory. In this paper we propose a multi-dimensional space feature vector expansion K-Means model to detect threats in the network environment. The model uses a genetic algorithm to optimize the weight of K-Means multi-dimensional space feature vector, which greatly improves the detection rate against 6 typical Dos attacks. Furthermore, in order to verify the correctness of the model, this paper conducts a simulation on the NSL-KDD data set. The results show that the algorithm of multi-dimensional space feature vectors expansion K-Means improves the recognition accuracy to 96.88%. Furthermore, 41 kinds of feature vectors in NSL-KDD are analyzed in detail according to a large number of experimental training. The feature vector of the probability positive return of security attack detection is accurately extracted, and a comparison chart is formed to support subsequent research. A theoretical analysis and experimental results show that the multi-dimensional space feature vector expansion K-Means algorithm has a good application in the detection of DDos attacks.

Non-linear behavioral models play a key role in designing digital pre-distorters (DPDs) for non-linear power amplifiers (NLPAs). In general, more complex behavioral models have better capability, but they should be converted into simpler versions to assist implementation. In this paper, a conversion from a complex fifth order inverse of a parallel Wiener (PRW) model to a simpler memory polynomial (MP) model is developed by using frequency domain expressions. In the developed conversion, parameters of the converted MP model are calculated from those of original fifth order inverse and frequency domain statistics of the transmit signal. Since the frequency domain statistics of the transmit signal can be precalculated, the developed conversion is deterministic, unlike the conventional conversion that identifies a converted model from lengthy input and output data. Computer simulations are conducted to confirm that conversion error is sufficiently small and the converted MP model offers equivalent pre-distortion to the original fifth order inverse.

Herein, a novel self-oscillating active integrated array antenna (AIAA) is proposed for beam switching X-band applications. The proposed AIAA comprises four linearly polarized microstrip antenna elements, a Gunn oscillator, two planar magic-Ts, and two single-pole single-throw (SPST) switches. The in/anti-phase signal combination approach employing planar magic-Ts is adopted to attain bidirectional radiation patterns in the φ =90° plane with a simple structure. The proposed antenna can switch its beam using the SPST switches. The antenna is analyzed through simulations, and a prototype of the antenna is fabricated and tested to validate the concept. The proposed concept is found to be feasible; the prototype has an effective isotropic radiated power of +15.98dBm, radiated power level of +4.28dBm, and cross-polarization suppression of better than 15dB. The measured radiation patterns are in good agreement with the simulation results.

Focusing on the planar slab structure of OLEDs, it is found the threshold value of the in-plane wave number at which the spectrum component of the electromagnetic field at the outermost boundary is divided into a radiation mode and a guided (confined) mode. This is equivalent to the total reflection condition in the ray optics. The spectral integral of the Poynting power was calculated from the boundary values of the electromagnetic fields in each. Both become average power and reactive power respectively, and the sum of them becomes the total volt-amperes from the light emitting dipole. Therefore, the ratio of average power to this total is the power factor that can be a quantitative index of light extraction.

In this paper, we present an analysis of local variability of bias temperature instability (BTI) by measuring Ring-Oscillators (RO) on various processes and its impact on logic circuit and SRAM. The evaluation results based on measuring ROs of a test elementary group (TEG) fabricated in 7nm Fin Field Effect Transistor (FinFET) process, 16/14nm generation FinFET processes and a 28nm planer process show that the standard deviations of Negative BTI (NBTI) Vth degradation (σ(ΔVthp)) are proportional to the square root of the mean value (µ(ΔVthp)) at any stress time, Vth flavors and various recovery conditions. While the amount of local BTI variation depends on the gate length, width and number of fins, the amount of local BTI variation at the 7nm FinFET process is slightly larger than other processes. Based on these measurement results, we present an analysis result of its impact on logic circuit considering measured Vth dependency on global NBTI in the 7nm FinFET process. We also analyse its impact on SRAM minimum operation voltage (Vmin) of static noise margin (SNM) based on sensitivity analysis and shows non-negligible Vmin degradation caused by local NBTI.

In [31], Shin et al. proposed a Leakage-Resilient and Proactive Authenticated Key Exchange (LRP-AKE) protocol for credential services which provides not only a higher level of security against leakage of stored secrets but also secrecy of private key with respect to the involving server. In this paper, we discuss a problem in the security proof of the LRP-AKE protocol, and then propose a modified LRP-AKE protocol that has a simple and effective measure to the problem. Also, we formally prove its AKE security and mutual authentication for the entire modified LRP-AKE protocol. In addition, we describe several extensions of the (modified) LRP-AKE protocol including 1) synchronization issue between the client and server's stored secrets; 2) randomized ID for the provision of client's privacy; and 3) a solution to preventing server compromise-impersonation attacks. Finally, we evaluate the performance overhead of the LRP-AKE protocol and show its test vectors. From the performance evaluation, we can confirm that the LRP-AKE protocol has almost the same efficiency as the (plain) Diffie-Hellman protocol that does not provide authentication at all.

The Smith method has been used to control physical plants with dead time components, where plant states after the dead time is elapsed are predicted and a control input is determined based on the predicted states. We extend the method to the symbolic control and design a symbolic Smith controller to deal with a nondeterministic embedded system. Due to the nondeterministic transitions, the proposed controller computes all reachable plant states after the dead time is elapsed and determines a control input that is suitable for all of them in terms of a given control specification. The essence of the Smith method is that the effects of the dead time are suppressed by the prediction, however, which is not always guaranteed for nondeterministic systems because there may exist no control input that is suitable for all predicted states. Thus, in this paper, we discuss the existence of a deadlock-free symbolic Smith controller. If it exists, it is guaranteed that the effects of the dead time can be suppressed and that the controller can always issue the control input for any reachable state of the plant. If it does not exist, it is proved that the deviation from the control specification is essentially inevitable.

Since HEVC intra rate control has no prior information to rely on for coding, it is a difficult work to obtain the optimal λ for every coding tree unit (CTU). In this paper, a convolutional neural network (CNN) based intra rate control is proposed. Firstly, a CNN with two last output channels is used to predict the key parameters of the CTU R-λ curve. For well training the CNN, a combining loss function is built and the balance factor γ is explored to achieve the minimum loss result. Secondly, the initial CTU λ can be calculated by the predicted results of the CNN and the allocated bit per pixel (bpp). According to the rate distortion optimization (RDO) of a frame, a spatial equation is derived between the CTU λ and the frame λ. Lastly, The CTU clipping function is used to obtain the optimal CTU λ for the intra rate control. The experimental results show that the proposed algorithm improves the intra rate control performance significantly with a good rate control accuracy.