Random Data Patterns Research Articles

Multilevel Operation of Ferroelectric FET Memory Arrays Considering Current Percolation Paths Impacting Switching Behavior

This paper reports multi-level-cell (MLC) operation of ferroelectric FETs (FeFET) arranged in AND-connected memory arrays with a bit-error rate (BER) of 4% when writing a random data pattern. The FeFETs with HfO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> have been embedded in GlobalFoundries 28 nm bulk high-k metal gate (HKMG) technology and are based on a metal-ferroelectric-isolator-semiconductor (MFIS) stack. Due to the direct field influence of the ferroelectric layer onto the Si-channel current percolation paths (CPPs) dominate the state transition behavior. This results in device-to-device variation and contributes to the asymmetry in programming and erasing progression. For array operation, write schemes and state-preserving inhibit schemes are evaluated to cope with the influence of the CPPs. Finally, this enables us to take advantage of the MLC capabilities of FeFETs on the array level, further enhancing the storage density of the 1T memory cells. Robust retention is proven for the MLC states at 85°C as well as reliable rewritability of the memory array with changing input patterns.

TCSE: A Target Cell States Elimination Coding Strategy for Highly Reliable Data Storage Based on 3-D nand Flash Memory

With the wide application of NAND flash storage systems in read-intensive memory, the corresponding reliability enhancement strategies for mitigating read disturb become the focus of investigations in recent years. The prior investigations have reported the strategies based on data modulation and verified their effectiveness in mitigating retention loss. The most significant advantage of these strategies is that they can often achieve significant reliability enhancement effect with great read performance, since they are usually based on asymmetric coding. This feature means that they have the potential to be ideal reliability enhancement strategies for read-intensive memory applications. To propose a universal and highly reliable data storage strategy, this article first observes the error modes at the cell-state level under the reliability stresses of retention loss and read disturb with floating-gate (FG) 3-D triple-level (TLC) NAND flash, and then proposes a target cell states elimination (TCSE) coding strategy for further restraining bit errors. In addition, this article for the first time reports the extra bit errors generated in the decoding process of the storage strategies based on data modulation, and defines the concept of transfer factor (TF) for evaluation. By using the proposed TCSE, the experimental results show that the overall bit error rate (BER) can be reduced by 80%–90% on average, compared with the raw random data pattern.

A time-series perspective on executive functioning: The benefits of a dynamic approach to random number generation.

Executive functioning (EF) is a key topic in neuropsychology. A multitude of underlying processes and constructs have been suggested to explain EF, which are measured by at least as many different neuropsychological tests. However, these tests often refer to summary statistics to quantify the construct under study, failing to capture the dynamic nature of EF. An alternative to these summary statistics is a time-series approach that quantifies all the available temporal information. We used recurrence quantification analysis (RQA) to quantify the characteristics of any temporal pattern in random number generation data and we compared RQA to the traditional and static analysis of random number sequences. The traditional measures yield inconsistent results with increasing sequences length, both for computer-generated and human-generated sequences, whereas the RQA measures do not. The results suggest that a time-series approach does a better job at modelling what is happening on different time-scales, and, therefore, is better at explaining how EF is changing in the course of the random number generation task. We argue that it is likely that these findings also apply to other neuropsychological EF tests, and that a time-series approach is an important addition to the study of EF.

Total Ionizing Dose Effects on Long-Term Data Retention Characteristics of Commercial 3-D NAND Memories

This article evaluates the data retention characteristics of irradiated multilevel-cell (MLC) 3-D NAND flash memories. We irradiated the memory chips by a Co-60 gamma-ray source for up to 50 krad(Si) and then wrote a random data pattern on the irradiated chips to find their retention characteristics. The experimental results show that the data retention property of the irradiated chips is significantly degraded when compared to the un-irradiated ones. We evaluated two independent strategies to improve the data retention characteristics of the irradiated chips. The first method involves high-temperature annealing of the irradiated chips, while the second method suggests preprogramming the memory modules before deploying them into radiation-prone environments.

A Hybrid Imputation Method for Multi-Pattern Missing Data: A Case Study on Type II Diabetes Diagnosis

Real medical datasets usually consist of missing data with different patterns which decrease the performance of classifiers used in intelligent healthcare and disease diagnosis systems. Many methods have been proposed to impute missing data, however, they do not fulfill the need for data quality especially in real datasets with different missing data patterns. In this paper, a four-layer model is introduced, and then a hybrid imputation (HIMP) method using this model is proposed to impute multi-pattern missing data including non-random, random, and completely random patterns. In HIMP, first, non-random missing data patterns are imputed, and then the obtained dataset is decomposed into two datasets containing random and completely random missing data patterns. Then, concerning the missing data patterns in each dataset, different single or multiple imputation methods are used. Finally, the best-imputed datasets gained from random and completely random patterns are merged to form the final dataset. The experimental evaluation was conducted by a real dataset named IRDia including all three missing data patterns. The proposed method and comparative methods were compared using different classifiers in terms of accuracy, precision, recall, and F1-score. The classifiers’ performances show that the HIMP can impute multi-pattern missing values more effectively than other comparative methods.

Performance Analysis of Subcarrier Multiplexed Optical Transmission System for QPSK Data

This paper focuses on the impact of different parameters on the performance of the Subcarrier Multiplexed Optical Transmission System for the application on radio link via optical fiber. Performance results are evaluated for QPSK data format for ODSB and OSSB modulation of Microwave subcarriers with digital NRZ coded random data patterns. The four subsystems of QPSK modulators are at 400, 500, 600, 700 MHz subcarrier frequencies with frequency spacing of 100 MHz. The power of subcarriers is decreasing with increasing the link distance due to dispersion and attenuation. By using dispersion compensation fiber, the link distance has been enhanced from 100 km to 240 km successfully. The impact of chromatic dispersion has been reduced in OSSB by using dual-electrode MZM. The constellation diagram also confirms that the phase of the signal after traveling through the link is changing due to dispersion. The phase is the same for subcarrier 600 MHz &amp; 700 MHz for ODSB and OSSB in QPSK SCM. The impact of linewidth and responsivity on SNR has also analyzed to evaluate the performance. It is concluded that the maximum SNR is decreasing with increase in the linewidth of laser source and increasing with the increase in responsivity of PIN diode for the same fiber length in SCM transmission.

Parameter Estimation of Autoregressive-Exogenous and Autoregressive Models Subject to Missing Data Using Expectation Maximization

Missing observations may present several problems for statistical analyses on datasets if they are not accounted for. This paper concerns a model-based missing data analysis procedure to estimate the parameters of regression models fit to datasets with missing observations. Both autoregressive-exogenous (ARX) and autoregressive (AR) models are considered. These models are both used to simulate datasets, and are fit to existing structural vibration data, after which observations are removed. A missing data analysis is performed using maximum-likelihood estimation, the expectation maximization (EM) algorithm, and the Kalman filter to fill in missing observations and regression parameters, and compare them to estimates for the complete datasets. Regression parameters from these fits to structural vibration data can thereby be used as damage-sensitive features. Favorable conditions for accurate parameter estimation are found to include lower percentages of missing data, parameters of similar magnitude with one another, and selected model orders similar to those true to the dataset. Favorable conditions for dataset reconstruction are found to include random and periodic missing data patterns, lower percentages of missing data, and proper model order selection. The algorithm is particularly robust to varied noise levels.

An Improved Low-Power Coding for Serial Network-On-Chip Links

In the fast nanosilicon revolution era, network-on-chip (NoC) architecture offers a significant research solution to on-chip multiprocessor-based real-time applications. As the number of cores increases, power consumption of the resources of NoC also increases. Links are the major power dissipator in NoC architecture owing to switching activity of data bits communicated through them. The performance of data communication links in NoC is strongly dependent on the factor of power dissipation. An efficient coding method is needed to reduce both coupling and self-switching activity of data bits of NoC links. In this work, an improved low-power encoding algorithm for serial links is proposed to reduce switching transition for any random data pattern making them more suited to real-time applications. The proposed encoding algorithm is coded, simulated and verified its area and power performance using Synopsys tools utilizing UMC 90 nm technology. Experimental results have shown that it provides on average 48.83% of switching activity reduction for any real-time applications.

Channel Cognitive Wireless Sensor System Based on Spectrum Sensing Technology

A channel cognitive wireless sensor system is proposed for low-power sensor network applications. Based on an RF spectrum sensing technology, the proposed receiver system is designed with channel-selective operation only for a valid input RF signal at a designated frequency channel. The new receiver architecture was implemented at the ISM band of 2.4-2.5 GHz. The system performance was evaluated by Bit-Error-Rate (BER) measurements with a data-rate of 64 kbps and a random data pattern of 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">9</sup> -1 for the sensitivity and selective receptions, respectively, in various multichannel environments. From the experimental results, the proposed system features excellent channel selectivity for the allocated channels.

Bayesian Latent-Class Mixed-Effect Hybrid Models for Dyadic Longitudinal Data with Non-Ignorable Dropouts

The analysis of longitudinal dyadic data is challenging due to the complicated correlations within and between dyads, as well as possibly non-ignorable dropouts. Based on a mixed-effects hybrid model, we propose an approach to analyze longitudinal dyadic data with non-ignorable dropouts. We factorize the joint distribution of the measurement and dropout processes into three components: the marginal distribution of random effects, the conditional distribution of the dropout process given the random effects, and the conditional distribution of the measurement process given the random effects and missing data patterns. We model the conditional dropout process using a discrete survival model, and the conditional measurement process using a latent-class pattern-mixture model. These models account for the dyadic interdependence using the "actor" and "partner" effects and dyad-specific random effects. We use the latent-dropout-class approach to address the problem of a large number of missing data patterns caused by the dyadic data structure. We evaluate the performance of the proposed method using a simulation study, and apply our method to a longitudinal dyadic data set that arose from a prostate cancer trial.

Detection and Analysis of the Shortest Bit Missing at High Data Rate Recording

Fast response in magnetic recording systems is a continuous demand as the data rate is increasing more rapidly than ever before. Recently, we observed the phenomena that several of the shortest bits are missing in random data patterns from patterns recorded on the magnetic media. This phenomenon is assumed to be caused by the shortest bits not being recorded properly because the write field rise time and transition energy are insufficient. To easily observe this phenomenon in experiments on our spinstand, we developed a method that is based on a comparison of different data patterns. In this paper, we analyze the shortest bits missing by using a spinstand and discuss the mechanism of these bits and a testing algorithm in order to contribute to designing a recording system for high data rate performance.

BCH 부호 식별 및 생성 파라미터 추정 기법

잡음이 존재하는 채널을 통하여 디지털 통신을 하는 경우 일반적으로 채널 부호를 사용한다. 만약 수신측에서 채널 부호의 생성 파라미터를 모르는 경우, 채널 부호의 복호는 매우 어렵다. 이러한 경우에 수신데이터의 정확한 복호를 위해서는 채널부호의 종류 및 생성 파라미터를 알아내는 방법이 필요하다. 본 논문에서는 BCH(Bose-Chaudhuri-Hocquenghem) 부호의 생성 파라미터인 생성다항식을 추정하는 기법을 소개한다. 이 방법은 생성다항식이 최소다항식으로 구성된다는 특징과 순회부호의 특성을 이용한 방법이다. 그리고 종래 방법에 비해 생성다항식 추정 성능을 향상 시킬 수 있는 결정 확률 변수 보상 기법을 제안한다. 제안한 기법은 랜덤데이터 패턴이 생성다항식을 구성하는 최소다항식으로 나누어지는 특성을 이용한 기법이다. 또한 컴퓨터 시뮬레이션을 통해 제안한 알고리즘의 우수성을 검증한다. The use of an error-correcting code is essential in communication systems where the channel is noisy. When channel coding parameters are unknown at a receiver side, decoding becomes difficult. To perform decoding without the channel coding information, we should estimate the parameters. In this paper, we introduce a method to reconstruct the generator polynomial of BCH(Bose-Chaudhuri-Hocquenghem) codes based on the idea that the generator polynomial is compose of minimal polynomials and BCH code is cyclic code. We present a probability compensation method to improve the reconstruction performance. This is based on the concept that a random data pattern can also be divisible by a minimal polynomial of the generator polynomial. And we confirm the performance improvement through an intensive computer simulation.

Time-based power control architecture for application processors in smartphones

Proposed is an architecture for reducing the power consumption of an application processor (AP) in a smartphone. The proposed architecture is designed for sharing the main memory between the AP and modem blocks. A power-saving algorithm is proposed that focuses on random and sparse data patterns in connected and idle modes. The algorithm automatically performs power/clock gating without the intervention of the CPU, unlike dynamic voltage and frequency scaling. To control power gating, a power consumption model is formulated to solve an optimisation problem. The proposed algorithm is verified with electronic system level simulation based on actual scenarios of a mobile terminal. The results show an improvement in power consumption.

Area-efficient programmable arbiter for inter-layer communications in 3-D network-on-chip

Abstract The Network-on-Chip (NoC) is an emerging communication technique for System-on-Chip (SoC) communications. The NoC uses multiple processors, usually targeted for embedded applications and other applications [3, 13]. Performance of the bus is degraded by the increasing number of processing elements and transaction oriented model [13]. This has attracted much attention for applying wireless network protocols as CDMA, TDMA, and dTDMA in SoC. The TDMA systems use a fixed number of timeslots. This protocol wastes bandwidth when some timeslots are allocated but not used. The dynamic TDMA (dTDMA) bus arbiter dynamically grows and shrinks the number of timeslots to match the number of active transmitters [14]. In this paper, we present a design of area-efficient switch for inter-layer communications in 3-D NoC. The arbitration logic in the switch is based on a programmable priority encoder. A 640-bit message with uniform random destination data pattern was injected per IP per machine clock cycle. We have obtained the maximum clock frequency of 2.09 GHz for 96(4 × 8 × 3) IP cores connected in a mesh topology. The presented architecture demonstrates their superior functionality in terms of speed, latency, area, and power consumption as compared with the existing implementation [14]. The maximum power consumption of the proposed area-efficient programmable arbiter is 0.625 mW. The design is synthesized using 180nm TSMC Technology.

High-speed dynamic TDMA arbiter for inter-layer communications in 3-D network-on-chip

The conventional two-dimensional 2-D integrated circuit IC has limited scope for floor planning and therefore limits the performance improvements resulting from the Network-on-Chip NoC paradigm. The arrangement of 3-D also offers opportunities for new circuit architecture based on the geometric capacity that provide greater numbers of interconnections among multi-layer active circuits. The emerging 3-D VLSI Integration and process technologies allow the new design opportunities in 3-D NoC. The 3-D NoC can reduce significant amount of wire length for local and global interconnects. The role of bus and arbitration logic is equally important in the design of NoC. The performance of the traditional bus is degraded by the increasing number of processing elements and transaction oriented model [On-Chip Communication Architectures, Morgan Kaufmann, 2008]. This has attracted much attention for applying wireless network protocols as CDMA, TDMA, dTDMA, etc. in SoC. The TDMA systems use a fixed number of timeslot. This protocol wastes bandwidth when some timeslots are allocated but not used. The dynamic TDMA dTDMA bus arbiter dynamically grows and shrinks the number of timeslots to match the number of active transmitters [in: VLSI Design, 2006. Held jointly with 5th International Conference on Embedded Systems and Design, 19th International Conference on, January 2006, p. 8]. In this paper, we present a design of high-speed switch and layer arbiter for inter-layer communications in 3-D Network-on-Chip. We have developed a Register Transfer Logic RTL level simulation model to evaluate the performance of proposed arbiter. A 640-bit message with uniform random destination data pattern was injected per IP per machine clock cycle. The worst case latency for the proposed design is 35 clock cycles. The presented architecture demonstrates their superior functionality in terms of speed and latency compared with existing implementation. The design is synthesized using 0.18 micron TSMC Technology [Taiwan Semiconductor Manufacturing Company, Ltd, 2011].

The Limits of the Wallace Approximation for PMR Recording at High Areal Density

This paper discusses the validity of the Triple Harmonic method and the Wallace readback model for the determination of head-disk spacing variations in today's perpendicular recording (PMR) systems at >; 500 Gb/in <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> areal density. For the recording system investigated here, and in the limit of high frequency, approximately beyond the frequency corresponding to the 2 T (0011) pattern, this method fails, as head and electronic noise start to interfere. In the low-frequency range of PMR systems, the Wallace equation does not apply any longer. Accurate magnetic spacing variations can be obtained by using the FFT of the intensity ratio of the readback signals, and fitting with the appropriate algebraic expression. Using this method, a repeatability of less than 0.1 nm was obtained, by using a random data pattern of only four 512 bytes data sector long.

Multi-Level Amplitude Modulation of a 16.8-GHz Class-E Power Amplifier With Negative Resistance Enhanced Power Gain for 400-Mbps Data Transmission

Multi-level modulation of a 16.8-GHz class-E power amplifier with negative resistance enhanced power gain is demonstrated in a 130-nm CMOS process. The circuit achieves power gain of ~ 30 dB, and power-added efficiency (PAE) of 16% for the highest output power level of 6.8 dBm. The average efficiency for a random data pattern is ~ 8%. The circuit also exhibits 10-dBm saturated output power and ~ 22% maximum PAE. By realigning the highest output power level to the 10-dBm saturated output power, the efficiency can be improved. For a random data pattern, this PA should achieve ~ 2X higher efficiency than Class A PAs. The circuit supports seven amplitude levels for 400 Megabits per second (Mbps) data transmission. The multi-level output signal levels follow a square-root relation. The circuit including an address decoder occupies ~ 1.4 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> .

Power analyses for longitudinal study designs with missing data

Existing methods for power analysis for longitudinal study designs are limited in that they do not adequately address random missing data patterns. Although the pattern of missing data can be assessed during data analysis, it is unknown during the design phase of a study. The random nature of the missing data pattern adds another layer of complexity in addressing missing data for power analysis. In this paper, we model the occurrence of missing data with a two-state, first-order Markov process and integrate the modelling information into the power function to account for random missing data patterns. The Markov model is easily specified to accommodate different anticipated missing data processes. We develop this approach for the two most popular longitudinal models: the generalized estimating equations (GEE) and the linear mixed-effects model under the missing completely at random (MCAR) assumption. For GEE, we also limit our consideration to the working independence correlation model. The proposed methodology is illustrated with numerous examples that are motivated by real study designs.

On the power of plotless density estimators for statistical comparisons of plant populations

Reliable density estimations of plant populations and their statistical comparison are fundamental in many ecological studies. Typical sampling methods are plotless density estimators (PDE) and plot counts. A simulation study was performed to examine the power of these sampling methods for the statistical comparison of the densitiesof two plant populations. The variable area transect estimator, the ordered distance estimator, and Byth’s T-square estimator were considered and compared with the quadrat count estimator. The relative root-mean-squared error and the relative bias were used to explore the quality of the estimators, and robust confidence intervals and standard deviation estimators were given. Tests were developed to compare the population densities of two independent populations, and the performance of the tests was examined. All simulations were run for spatially random and aggregate data patterns. For completely random data, all estimators and all tests behaved well if they were based on the same sampling intensity. For the aggregate pattern, all PDE’s were negatively biased, but the quadrat count estimator was unbiased. Nonparametric confidence intervals had the most robust performance for aggregate data. The results of the presented simulation study demonstrate that plant ecologists should avoid PDE for both density estimation and statistical testing except when the pattern of a population is known to follow a completely random spatial pattern. Instead, the use of the plot count estimator is recommended.

On Ultrafast Optical Switching Based on Quantum-Dot Semiconductor Optical Amplifiers in Nonlinear Interferometers

It is shown that interferometers containing quantum-dot semiconductor optical amplifiers can be effective for ultrafast cross-phase modulation and digital signal processing with low dependence on the specific random data pattern.