This paper proposes two VLSI implementation approaches for periods estimation hardware of periodic signals. Digital signal processing is one of the important technologies, and to estimate periods of signals are widely used in many areas such as IoT, predictive maintenance, anomaly detection, health monitoring, and so on. This paper focuses on accumulation for real-time serial-to-parallel converter (ARS) which is a simple parameter estimation method for periodic signals. ARS is simple algorithm to estimate periods of periodic signals without complex instructions such as multiplier and division. However, this algorithm is implemented only on software, suitable hardware implementation methods are not clear. Therefore, this paper proposes two VLSI implementation methods called ARS-DFF and ARS-MEM. ARS-DFF is simple and fast implementation method, but hardware scale is large. ARS-MEM reduces hardware scale by introducing an SRAM macro cell. This paper also designs both approaches using SystemVerilog and evaluates VLSI implementation. According to our evaluation results, both proposed methods can reduce the power consumption to less than 1/1000 compared to the implementation on a microprocessor.

Wafer-level performance prediction techniques have been increasingly gaining attention in production LSI testing due to their ability to reduce measurement costs without compromising test quality. Despite the availability of several efficient methods, the site-to-site variation commonly observed in multi-site testing for radio frequency circuits remains inadequately addressed. In this manuscript, we propose a wafer-level performance prediction approach for multi-site testing that takes into account the site-to-site variation. Our proposed method is built on the Gaussian process, a widely utilized wafer-level spatial correlation modeling technique, and enhances prediction accuracy by extending hierarchical modeling to leverage the test site information test engineers provide. Additionally, we propose a test-site sampling method that maximizes cost reduction while maintaining sufficient estimation accuracy. Our experimental results, which employ industrial production test data, demonstrate that our proposed method can decrease the estimation error to 1/19 of that a conventional method achieves. Furthermore, our sampling method can reduce the required measurements by 97% while ensuring satisfactory estimation accuracy.

In this study, a Coanda-drone with length, width, and height of 121.6, 121.6, and 191[mm] was designed, and its total mass was 1166.7[g]. Using four propulsion devices, it could produce a maximum of 5428[g] thrust. Its structure is very different from conventional drones because in this study it combines the design of the jet engine of a jet fixed-wing drone with the fuselage structure layout of a rotary-wing drone. The advantage of jet drone's high propulsion is kept so that it can output greater thrust under the same variation of PWM waveform output. In this study, the propulsion device performs high-speed jetting, and the airflow around the propulsion device will also be jetted downward along the direction of the airflow.

This paper mainly proposes a line segment detection method based on pseudo peak suppression and local Hough transform, which has good noise resistance and can solve the problems of short line segment missing detection, false detection, and oversegmentation. In addition, in response to the phenomenon of uneven development in nuclear emulsion tomographic images, this paper proposes an image preprocessing process that uses the “Difference of Gaussian” method to reduce noise and then uses the standard deviation of the gray value of each pixel to bundle and unify the gray value of each pixel, which can robustly obtain the linear features in these images. The tests on the actual dataset of nuclear emulsion tomographic images and the public YorkUrban dataset show that the proposed method can effectively improve the accuracy of convolutional neural network or vision in transformer-based event classification for alpha-decay events in nuclear emulsion. In particular, the line segment detection method in the proposed method achieves optimal results in both accuracy and processing speed, which also has strong generalization ability in high quality natural images.

We propose an open source cell library characterizer. Recently, free and open-sourced silicon design communities are attracted by hobby designers, academies and industries. These open-sourced silicon designs are supported by free and open sourced EDAs, however, in our knowledge, tool-chain lacks cell library characterizer to use original standard cells into digital circuit design. This paper proposes an open source cell library characterizer which can generate timing models and power models of standard cell library.

Double modular redundancy (DMR) is to execute an operation twice and detect a soft error by comparing the duplicated operation results. The soft error is corrected by re-executing necessary operations. The re-execution requires error-free input data and registers are needed to store such necessary error-free data. In this paper, a method to minimize the required number of registers is proposed where an appropriate subgraph partitioning of operation nodes are searched. In addition, using the proposed register minimization method, a minimization of the area of functional units and registers required to implement the DMR design is proposed.

Hardware accelerators are designed to support a specialized processing dataflow for everchanging deep neural networks (DNNs) under various processing environments. This paper introduces two hardware properties to describe the cost of data movement in each memory hierarchy. Based on the hardware properties, this paper proposes a set of evaluation metrics that are able to evaluate the number of memory accesses and the required memory capacity according to the specialized processing dataflow. Proposed metrics are able to analytically predict energy, throughput, and area of a hardware design without detailed implementation. Once a processing dataflow and constraints of hardware resources are determined, the proposed evaluation metrics quickly quantify the expected hardware benefits, thereby reducing design time.

The frequency of uterine peristalsis is closely related to the success rate of pregnancy. An ultrasonic imaging is almost always employed for the measure of the frequency. The physician subjectively evaluates the frequency from the ultrasound image by the naked eyes. This paper aims to measure the frequency of uterine peristalsis from the ultrasound image. The ultrasound image consists of relative amounts in the brightness, and the contour of the uterine is not clear. It was not possible to measure the frequency by using the inter-frame difference and optical flow, which are the representative methods of motion detection, since uterine peristaltic movement is too small to apply them. This paper proposes a measurement method of the frequency of the uterine peristalsis from the ultrasound image in the implantation phase. First, traces of uterine peristalsis are semi-automatically done from the images with location-axis and time-axis. Second, frequency analysis of the uterine peristalsis is done by Fourier transform for 3 minutes. As a result, the frequency of uterine peristalsis was known as the frequency with the dominant frequency ingredient with maximum value among the frequency spectrums. Thereby, we evaluate the number of the frequency of uterine peristalsis quantitatively from the ultrasound image. Finally, the success rate of pregnancy is calculated from the frequency based on Fuzzy logic. This enabled us to evaluate the success rate of pregnancy by measuring the uterine peristalsis from the ultrasound image.

Likelihood ratios (LRs), which are commonly used for probabilistic data processing, are often estimated based on the frequency counts of individual elements obtained from samples. In natural language processing, an element can be a continuous sequence of N items, called an N-gram, in which each item is a word, letter, etc. In this paper, we attempt to estimate LRs based on N-gram frequency information. A naive estimation approach that uses only N-gram frequencies is sensitive to low-frequency (rare) N-grams and not applicable to zero-frequency (unobserved) N-grams; these are known as the low- and zero-frequency problems, respectively. To address these problems, we propose a method for decomposing N-grams into item units and then applying their frequencies along with the original N-gram frequencies. Our method can obtain the estimates of unobserved N-grams by using the unit frequencies. Although using only unit frequencies ignores dependencies between items, our method takes advantage of the fact that certain items often co-occur in practice and therefore maintains their dependencies by using the relevant N-gram frequencies. We also introduce a regularization to achieve robust estimation for rare N-grams. Our experimental results demonstrate that our method is effective at solving both problems and can effectively control dependencies.

Impulsive noise (IN) is the most dominant factor degrading the performance of communication systems over powerlines. In order to improve performance of high-speed power line communication (PLC), this work focuses on mitigating burst IN effects based on compressive sensing (CS), and an adaptive burst IN mitigation method, namely combination of adaptive interleaver and permutation of null carriers is designed. First, the long burst IN is dispersed by an interleaver at the receiver and the characteristic of noise is estimated by the method of moment estimation, finally, the generated sparse noise is reconstructed by changing the number of null carriers(NNC) adaptively according to noise environment. In our simulations, the results show that the proposed IN mitigation technique is simple and effective for mitigating burst IN in PLC system, it shows the advantages to reduce the burst IN and to improve the overall system throughput. In addition, the performance of the proposed technique outpeformences other known nonlinear noise mitigation methods and CS methods.

In this paper, we aim to develop technologies for the circuit fault diagnosis and propose a formulation of a measure of a test pattern for the circuit fault diagnosis. Given a faulty circuit, the fault diagnosis is to deduce locations of faults that had occurred in the circuit. The fault diagnosis is executed in software before the failure analysis by which engineers inspect physical defects, and helps to improve the manufacturing process which yielded faulty circuits. The heart of the fault diagnosis is to distinguish between candidate faults by using test patterns, which are applied to the circuit-under-diagnosis (CUD), and thus test patterns that can distinguish as many faults as possible need to be generated. This fact motivates us to consider the test pattern measure based on the number of fault-pairs that become distinguished by a test pattern. To the best of the authors' knowledge, that measure requires the computational time of complexity order O(NF2), where NF denotes the number of candidate faults. Since NF is generally large for real industrial circuits, the computational time of the measure is long even when a high-performance computer is used. The formulation proposed in this paper makes it possible to calculate the measure in the computational complexity of O(NF log NF), and thus that measure is useful for the test pattern selection in the fault diagnosis. In computational experiments, the effectiveness of the formulation is demonstrated as samples of computational times of the measure calculated by the traditional and the proposed formulae and thorough comparisons between several greedy heuristics which are based on the measure.

Speech enhancement has been proposed to reduce the impulsive noise whose frequency characteristic is wideband. On the other hand, it is challenging to reduce the ringing sound, which is narrowband in impulsive noise. Therefore, we propose the modeling of the ringing sound and its estimation by a linear predictor (LP). However, it is difficult to estimate the ringing sound only in noisy speech due to the auto-correlation property of speech. The proposed system adopts the 4th order moment-based adaptive algorithm by noticing the difference between the 4th order statistics of speech and impulsive noise. The brief analysis and simulation results show that the proposed system has the potential to reduce ringing sound while keeping the quality of enhanced speech.

A method of determining emission limits was studied by using the amplitude probability distribution (APD) for low-probability pulsed electromagnetic disturbances due to discharge. The features of this method are 1) without using the previously reported relationship between APD and bit error rate, the limits are derived using the measured impact of a pulsed disturbance on various wireless communication systems having different bandwidths, and 2) disturbances caused by discharge with poor reproducibility are simulated by regularly repeated pulse-modulated sine waves to enable stable evaluation of the communication quality. APD-based limits are determined from the pulse repetition frequency of the simulated disturbance such that the block error rate (BLER) is less than a certain limit in wireless systems that are most sensitive to the pulsed disturbance. In the international standard CISPR 32 regulating electromagnetic disturbance, radiated disturbance due to discharge is excluded from the application of peak detection limits because of its low occurrence probability. In this paper we quantitatively determine appropriate criteria of the probability for the exclusion. Using the method, we measured the impact of low-probability pulsed interference on major wireless systems and found that GSM and Wi-Fi systems were the most sensitive. New APD-based limits were derived on the basis of these findings. The APD-based limits determined by the proposed method enable a valid evaluation of low-occurrence-probability pulsed disturbances without unconditionally excluding the measurement.

This work reports a new technique to suppress the undesirable multiple-triggering effect in the typical diode triggered silicon controlled rectifier (DTSCR), which is frequently used as an ESD protection element in the advanced CMOS technologies. The technique is featured by inserting additional N-Well areas under the N+ region of intrinsic SCR, which helps to improve the substrate resistance. As a consequence, the delay of intrinsic SCR is reduced as the required triggering current is largely decreased and multiple-triggering related higher trigger voltage is removed. The novel DTSCR structures can alter the stacked diodes to achieve the precise trigger voltage to meet different ESD protection requirements. All explored DTSCR structures are fabricated in a 65-nm CMOS process. Transmission-line-pulsing (TLP) and Very-Fast-Transmission-line-pulsing (VF-TLP) test systems are adopted to confirm the validity of this technique and the test results accord well with our analysis.

In this paper, we propose an enhanced selected mapping (e-SLM) technique to improve the performance of OFDM-PLC systems under impulsive noise. At the transmitter, the best transmit sequence is selected from among possible candidates so as to minimize the weighted sum of transmit signal peak power and the estimated receive one, where the received signal peak power is estimated at the transmitter using channel state information (CSI). At the receiver, a nonlinear blanking is applied to hold the impulsive noise under a given threshold, where impulsive noise detection accuracy is improved by the proposed e-SLM. We evaluate the probability of false alarms raised by impulsive noise detection and bit error rate (BER) of OFDM-PLC system using the proposed e-SLM. The results show the effectiveness of the proposed method in OFDM-PLC system compared with the conventional blanking technique.

High Efficiency Video Coding (HEVC) standard is now becoming one of the most widespread video coding standards in the world. As a successor of H.264 standard, it aims to provide a much superior encoding performance. To fulfill this goal, several new notations along with the corresponding computation processes are introduced by this standard. Among those computation processes, the integer motion estimation (IME) is one of bottlenecks due to the complex partitions of the inter prediction units (PU) and the large search window commonly adopted. Many algorithms have been proposed to address this issue and usually put emphasis on a large search window and great computation amount. However, the coding efforts should be related to the scenes. To be more specific, for relatively static videos, a small search window along with a simple search scheme should be adopted to reduce the time cost and power consumption. In view of this, a micro-code-based IME engine is proposed in this paper, which could be applied with search schemes of different complexity. To test the performance, three different search schemes based on this engine are designed and evaluated under HEVC test model (HM) 16.9, achieving a B-D rate increase of 0.55/-0.07/-0.14%. Compared with our previous work, the hardware implementation is optimized to reduce 64.2% of the SRAMs bits and 32.8% of the logic gate count. The final design could support 4K×2K @139/85/37fps videos @500MHz.

Spectrum sensing is a fundamental requirement for cognitive radio, and it is a challenging problem in impulsive noise modeled by symmetric alpha-stable (SαS) distributions. The Gaussian kernelized energy detector (GKED) performs better than the conventional detectors in SαS distributed noise. However, it fails to detect the DC signal and has high computational complexity. To solve these problems, this paper proposes a more efficient and robust detector based on a Gaussian function (GF). The analytical expressions of the detection and false alarm probabilities are derived and the best parameter for the statistic is calculated. Theoretical analysis and simulation results show that the proposed GF detector has much lower computational complexity than the GKED method, and it can successfully detect the DC signal. In addition, the GF detector performs better than the conventional counterparts including the GKED detector in SαS distributed noise with different characteristic exponents. Finally, we discuss the reason why the GF detector outperforms the conventional counterparts.

Data rate requirements, from consumer application to automotive and aerospace grew rapidly in the last years. This led to the development of a series of communication protocols (i.e. Ethernet, PCI-Express, RapidIO and SpaceFibre), which use more than one communication lane, both to speed up data rate and to increase link reliability. Some of these protocols, such as SpaceFibre, are able to detect real-time changes in the number of active lanes and to adapt the data flow appropriately, providing a flexible solution, robust to lane failures. This results in a real time varying data path in the lower layers of the data handling system. The aim of this paper is to propose the architecture of a hardware block capable of reading a fixed number of words from a host FIFO and shaping them on a real time variable number of words equal to the number of active lanes.

Affine projection sign algorithm (APSA) is an important adaptive filtering method to combat the impulsive noisy environment. However, the performance of APSA is poor, if its regularization parameter is not well chosen. We propose a variable regularization APSA (VR-APSA) approach, which adopts a gradient-based method to recursively reduce the norm of the a priori error vector. The resulting VR-APSA leverages the time correlation of both the input signal matrix and error vector to adjust the value of the regularization parameter. Simulation results confirm that our algorithm exhibits both fast convergence and small misadjustment properties.

Copper (Cu) electroless plating was conducted on planar and microstructured polydimethylsiloxane (PDMS) substrates. In this study, organic thin films terminated with nitrogen (N)-containing groups, e.g. poly (dimethylaminoethyl methacrylate) brush (PDMAEMA), aminopropyl trimethoxysilane monolayer (APTES), and polydopamine (PDA) were used to anchor palladium (Pd) catalyst. While electroless plating was successfully promoted on all sample surfaces, PDMAEMA was found to achieve the best adhesion strength to the PDMS surfaces, compared to APTES- and PDA-covered PDMS substrates, due to covalent bonding, anchoring effects of polymer chains as well as high affinity of N atoms to Pd species. Our process was also successfully applied to the electroless plating of microstructured PDMS substrates.