This paper proposes a layer-wise tunable retraining method for edge FeFET Computation-in-Memory (CiM) to compensate the accuracy degradation of neural network (NN) by FeFET device errors. The proposed retraining can tune the number of layers to be retrained to reduce inference accuracy degradation by errors that occur after retraining. Weights of the original NN model, accurately trained in cloud data center, are written into edge FeFET CiM. The written weights are changed by FeFET device errors in the field. By partially retraining the written NN model, the proposed method combines the error-affected layers of NN model with the retrained layers. The inference accuracy is thus recovered. After retraining, the retrained layers are re-written to CiM and affected by device errors again. In the evaluation, at first, the recovery capability of NN model by partial retraining is analyzed. Then the inference accuracy after re-writing is evaluated. Recovery capability is evaluated with non-volatile memory (NVM) typical errors: normal distribution, uniform shift, and bit-inversion. For all types of errors, more than 50% of the degraded percentage of inference accuracy is recovered by retraining only the final fully-connected (FC) layer of Resnet-32. To simulate FeFET Local-Multiply and Global-accumulate (LM-GA) CiM, recovery capability is also evaluated with FeFET errors modeled based on FeFET measurements. Retraining only FC layer achieves recovery rate of up to 53%, 66%, and 72% for FeFET write variation, read-disturb, and data-retention, respectively. In addition, just adding two more retraining layers improves recovery rate by 20-30%. In order to tune the number of retraining layers, inference accuracy after re-writing is evaluated by simulating the errors that occur after retraining. When NVM typical errors are injected, it is optimal to retrain FC layer and 3-6 convolution layers of Resnet-32. The optimal number of layers can be increased or decreased depending on the balance between the size of errors before retraining and errors after retraining.

The phaseless antenna measurement technique is advantageous for high-frequency near-field measurements in which the uncertainty of the measured phase is a problem. In the phaseless measurement, which is expected to be used in the frequency band with a short wavelength, a slight positional deviation error of the probe greatly deteriorates the measurement result. This paper proposes a phase retrieval method that can compensate the measurement errors caused by misalignment of a probe and its jig. And this paper proposes a far-field estimation method by phase resurrection that incorporated the compensation techniques. We find that the positioning errors are due to the random errors occurring at each measurement point because of minute vibrations of the probe; in addition, we determine that the stationary depth errors occurring at each measurement surface as errors caused by improper setting of the probe jig. The random positioning error is eliminated by adding a low-pass filter in wavenumber space, and the depth positioning error is iteratively compensated on the basis of the relative residual obtained in each plane. The validity of the proposed method is demonstrated by estimating the far-field patterns using the results from numerical simulations, and is also demonstrated using measurement data with probe-positioning error. The proposed method can reduce the probe-positioning error and improve the far-field estimation accuracy by more over than 10 dB.

This study presents a method for improving the heartbeat interval accuracy of photoplethysmographic (PPG) sensors at ultra-low sampling rates. Although sampling rate reduction can extend battery life, it increases the sampling error and degrades the accuracy of the extracted heartbeat interval. To overcome these drawbacks, a sampling-error compensation method is proposed in this study. The sampling error is reduced by using linear interpolation and autocorrelation based on the waveform similarity of heartbeats in PPG. Furthermore, this study introduces two-line approximation and first derivative PPG (FDPPG) to improve the waveform similarity at ultra-low sampling rates. The proposed method was evaluated using measured PPG and reference electrocardiogram (ECG) of seven subjects. The results reveal that the mean absolute error (MAE) of 4.11ms was achieved for the heartbeat intervals at a sampling rate of 10Hz, compared with 1-kHz ECG sampling. The heartbeat interval error was also evaluated based on a heart rate variability (HRV) analysis. Furthermore, the mean absolute percentage error (MAPE) of the low-frequency/high-frequency (LF/HF) components obtained from the 10-Hz PPG is shown to decrease from 38.3% to 3.3%. This error is small enough for practical HRV analysis.

With the increasing demand of Internet-of-Things applicability in various devices and location-based services (LBSs) with positioning capabilities, we proposed simple and effective post-processing techniques to reduce positioning error and provide more precise navigation to users in a pedestrian environment in this letter. The proposed positioning error reduction techniques (Technique 1-minimum range securement and bounce elimination, Technique 2-direction vector-based error correction) were studied considering low complexity and wide applicability to various types of positioning systems, e.g., global positioning system (GPS). Through the real field tests in urban areas, we have verified that an average positioning error of the proposed techniques is significantly decreased compared to that of a GPS-only environment.

In this paper a nonlinearity compensation algorithm based on the extended Kalman filter is proposed to improve the measurement accuracy of a heterodyne laser interferometer. The heterodyne laser interferometer is used for ultra-precision measurements such as those used in semiconductor manufacturing. However the periodical nonlinearity property caused by frequency-mixing restricts the accuracy of the nanometric measurements. In order to minimize the effect of the nonlinearity, the measurement process of the laser interferometer is modeled as a state equation and the extended Kalman filtering approach is applied to the process. The effectiveness of our proposed algorithm is demonstrated by comparing the results of the algorithm with experimental results for the laser system.

This paper analyzes the spurious sources in DDS synthesizers and deduces the simple model of DDS output signal. The method of feeding pseudo-random noise into the phase accumulator for spurious reduction is discussed. A new method for spurious reduction by compensating for DAC integer nonlinearity is proposed with two DACs and a power combiner. One DAC generates the error signal to compensate for the other DAC INL. The factor how the amplitude error and the phase error between the two combined signals affect the spurious level is also analyzed. The experiment shows that the spurious reduction can be improved by at least 18 dB, which proves the validity of the DAC INL compensation method for the spurious reduction.

In this paper, we present a design method for fixed-width squarer that receives an n-bit input and produces an n-bit squared product. To efficiently compensate for the truncation error, modified Booth-folding encoder signals are used for the generation of error compensation bias. The truncated bits are divided into two groups (major and minor) depending upon their effects on the truncation error. Then, different error compensation methods are applied to each group. By simulations, it is shown that the proposed fixed-width squarers have lower error than other fixed-width squarers and are cost-effective.

Automatic repeat request (ARQ) for wireless ATM (WATM) operating at 20 Mbit/s or higher is required to achieve high throughput performance as well as high transmission quality, i.e., low CLR (cell loss ratio). Selective Repeat (SR) and Go-Back-N (GBN) are typical ARQ schemes. Though SR-ARQ is superior to GBN-ARQ in throughput performance, the implementation complexity of SR-ARQ's control procedures is disadvantageous to its application to high-speed wireless systems. In addition, when PDU (protocol data unit) length on wireless link is short, the capacity for ARQ control messages can be significantly large. GBN-ARQ, on the other hand, cannot avoid serious throughput degradation due to fairly high BER caused by multipath fading and shadowing, though its implementation is simple. To solve the above-mentioned problems, this paper proposes a novel ARQ scheme named PRIME-ARQ (Partial selective Repeat superIMposEd on gbn ARQ). PRIME-ARQ achieves high throughput performance, almost equal to selective repeat ARQ, with a simple algorithm resulting in reduced implementation complexity for high speed operation. This paper describes the design, implementation, and performance of the proposed PRIME-ARQ. In addition, it shows the experimental results using an experimental PRIME-ARQ hardware processor and proto-type AWA equipment.