A Low-Cost Improved Method of Raw Bit Error Rate Estimation for NAND Flash Memory of High Storage Density

Cells wear fast in NAND flash memory of high storage density (HSD), so it is very necessary to have a long-term frequent in-time monitoring on its raw bit error rate (RBER) changes through a fast RBER estimation method. As the flash of HSD already has relatively lower reading speed, the method should not further degrade its read performance. This paper proposes an improved estimation method utilizing known data comparison, includes interleaving to balance the uneven error distribution in the flash of HSD, a fast RBER estimation module to make the estimated RBER highly linearly correlated with the actual RBER, and enhancement strategies to accelerate the decoding convergence of low-density parity-check (LDPC) codes and thereby make up the rate penalty caused by the known data. Experimental results show that when RBER is close to the upper bound of LDPC code, the reading efficiency can be increased by 35.8% compared to the case of no rate penalty. The proposed method only occupies 0.039mm2 at 40nm process condition. Hence, the fast, read-performance-improving, and low-cost method is of great application potential on RBER monitoring in the flash of HSD

Neural Network-Enabled Flexible Pressure and Temperature Sensor with Honeycomb-like Architecture for Voice Recognition

Flexible pressure sensors have been studied as wearable voice-recognition devices to be utilized in human-machine interaction. However, the development of highly sensitive, skin-attachable, and comfortable sensing devices to achieve clear voice detection remains a considerable challenge. Herein, we present a wearable and flexible pressure and temperature sensor with a sensitive response to vibration, which can accurately recognize the human voice by combing with the artificial neural network. The device consists of a polyethylene terephthalate (PET) printed with a silver electrode, a filament-microstructured polydimethylsiloxane (PDMS) film embedded with single-walled carbon nanotubes and a polyimide (PI) film sputtered with a patterned Ti/Pt thermistor strip. The developed pressure sensor exhibited a pressure sensitivity of 0.398 kPa−1 in the low-pressure regime, and the fabricated temperature sensor shows a desirable temperature coefficient of resistance of 0.13% ∘C in the range of 25 ∘C to 105 ∘C. Through training and testing the neural network model with the waveform data of the sensor obtained from human pronunciation, the vocal fold vibrations of different words can be successfully recognized, and the total recognition accuracy rate can reach 93.4%. Our results suggest that the fabricated sensor has substantial potential for application in the human-computer interface fields, such as voice control, vocal healthcare monitoring, and voice authentication

Highly Compressible and Sensitive Flexible Piezoresistive Pressure Sensor Based on MWCNTs/Ti3C2Tx MXene @ Melamine Foam for Human Gesture Monitoring and Recognition

Flexible sensing devices provide a convenient and effective solution for real-time human motion monitoring, but achieving efficient and low-cost assembly of pressure sensors with high performance remains a considerable challenge. Herein, a highly compressible and sensitive flexible foam-shaped piezoresistive pressure sensor was prepared by sequential fixing multiwalled carbon nanotubes and Ti3C2Tx MXene on the skeleton of melamine foam. Due to the porous skeleton of the melamine foam and the extraordinary electrical properties of the conductive fillers, the obtained MWCNTs/Ti3C2Tx MXene @ melamine foam device features high sensitivity of 0.339 kPa−1, a wide working range up to 180 kPa, a desirable response time and excellent cyclic stability. The sensing mechanism of the composite foam device is attributed to the change in the conductive pathways between adjacent porous skeletons. The proposed sensor can be used successfully to monitor human gestures in real-time, such as finger bending and tilting, scrolling the mouse and stretching fingers. By combining with the decision tree algorithm, the sensor can unambiguously classify different Arabic numeral gestures with an average recognition accuracy of 98.9%. Therefore, our fabricated foam-shaped sensor may have great potential as next-generation wearable electronics to accurately acquire and recognize human gesture signals in various practical applications

Edge-Guided Cell Segmentation on Small Datasets Using an Attention-Enhanced U-Net Architecture

Over the past several decades, deep neural networks have been extensively applied to medical image segmentation tasks, achieving significant success. However, the effectiveness of traditional deep segmentation networks is substantially limited by the small scale of medical datasets, a limitation directly stemming from current medical data acquisition capabilities. To this end, we introduce AttEUnet, a medical cell segmentation network enhanced by edge attention, based on the Attention U-Net architecture. It incorporates a detection branch enhanced with edge attention and a learnable fusion gate unit to improve segmentation accuracy and convergence speed on small medical datasets. The AttEUnet allows for the integration of various types of prior information into the backbone network according to different tasks, offering notable flexibility and generalization ability. This method was trained and validated on two public datasets, MoNuSeg and PanNuke. The results show that AttEUnet significantly improves segmentation performance on small medical datasets, especially in capturing edge details, with F1 scores of 0.859 and 0.888 and Intersection over Union (IoU) scores of 0.758 and 0.794 on the respective datasets, outperforming both convolutional neural networks (CNNs) and transformer-based baseline networks. Furthermore, the proposed method demonstrated a convergence speed over 10.6 times faster than that of the baseline networks. The edge attention branch proposed in this study can also be added as an independent module to other classic network structures and can integrate more attention priors based on the task at hand, offering considerable scalability

Lightweight End-to-End Neural Network Model for Automatic Heart Sound Classification

Heart sounds play an important role in the initial screening of heart diseases. However, the accurate diagnosis with heart sound signals requires doctors to have many years of clinical experience and relevant professional knowledge. In this study, we proposed an end-to-end lightweight neural network model that does not require heart sound segmentation and has very few parameters. We segmented the original heart sound signal and performed a short-time Fourier transform (STFT) to obtain the frequency domain features. These features were sent to the improved two-dimensional convolutional neural network (CNN) model for features learning and classification. Considering the imbalance of positive and negative samples, we introduced FocalLoss as the loss function, verified our network model with multiple random verifications, and, hence, obtained a better classification result. Our main purpose is to design a lightweight network structure that is easy for hardware implementation. Compared with the results of the latest literature, our model only uses 4.29 K parameters, which is 1/10 of the size of the state-of-the-art work

A Personalized Compression Method for Steady-State Visual Evoked Potential EEG Signals

As an informative electroencephalogram (EEG) signal, steady-state visual evoked potential (SSVEP) stands out from many paradigms for application in wireless wearable devices. However, its data are usually enormous, occupy too many bandwidth sources and require immense power when transmitted in the raw data form, so it is necessary to compress the signal. This paper proposes a personalized EEG compression and reconstruction algorithm for the SSVEP application. In the algorithm, to realize personalization, a primary artificial neural network (ANN) model is first pre-trained with the open benchmark database towards BCI application (BETA). Then, an adaptive ANN model is generated with incremental learning for each subject to compress their individual data. Additionally, a personalized, non-uniform quantization method is proposed to reduce the errors caused by compression. The recognition accuracy only decreases by 3.79% when the compression rate is 12.7 times, and is tested on BETA. The proposed algorithm can reduce signal loss by from 50.43% to 81.08% in the accuracy test compared to the case without ANN and uniform quantization

Lightweight End-to-End Neural Network Model for Automatic Heart Sound Classification

Heart sounds play an important role in the initial screening of heart diseases. However, the accurate diagnosis with heart sound signals requires doctors to have many years of clinical experience and relevant professional knowledge. In this study, we proposed an end-to-end lightweight neural network model that does not require heart sound segmentation and has very few parameters. We segmented the original heart sound signal and performed a short-time Fourier transform (STFT) to obtain the frequency domain features. These features were sent to the improved two-dimensional convolutional neural network (CNN) model for features learning and classification. Considering the imbalance of positive and negative samples, we introduced FocalLoss as the loss function, verified our network model with multiple random verifications, and, hence, obtained a better classification result. Our main purpose is to design a lightweight network structure that is easy for hardware implementation. Compared with the results of the latest literature, our model only uses 4.29 K parameters, which is 1/10 of the size of the state-of-the-art work

A Personalized Compression Method for Steady-State Visual Evoked Potential EEG Signals

As an informative electroencephalogram (EEG) signal, steady-state visual evoked potential (SSVEP) stands out from many paradigms for application in wireless wearable devices. However, its data are usually enormous, occupy too many bandwidth sources and require immense power when transmitted in the raw data form, so it is necessary to compress the signal. This paper proposes a personalized EEG compression and reconstruction algorithm for the SSVEP application. In the algorithm, to realize personalization, a primary artificial neural network (ANN) model is first pre-trained with the open benchmark database towards BCI application (BETA). Then, an adaptive ANN model is generated with incremental learning for each subject to compress their individual data. Additionally, a personalized, non-uniform quantization method is proposed to reduce the errors caused by compression. The recognition accuracy only decreases by 3.79% when the compression rate is 12.7 times, and is tested on BETA. The proposed algorithm can reduce signal loss by from 50.43% to 81.08% in the accuracy test compared to the case without ANN and uniform quantization

A Low-Cost Improved Method of Raw Bit Error Rate Estimation for NAND Flash Memory of High Storage Density

Cells wear fast in NAND flash memory of high storage density (HSD), so it is very necessary to have a long-term frequent in-time monitoring on its raw bit error rate (RBER) changes through a fast RBER estimation method. As the flash of HSD already has relatively lower reading speed, the method should not further degrade its read performance. This paper proposes an improved estimation method utilizing known data comparison, includes interleaving to balance the uneven error distribution in the flash of HSD, a fast RBER estimation module to make the estimated RBER highly linearly correlated with the actual RBER, and enhancement strategies to accelerate the decoding convergence of low-density parity-check (LDPC) codes and thereby make up the rate penalty caused by the known data. Experimental results show that when RBER is close to the upper bound of LDPC code, the reading efficiency can be increased by 35.8% compared to the case of no rate penalty. The proposed method only occupies 0.039mm2 at 40nm process condition. Hence, the fast, read-performance-improving, and low-cost method is of great application potential on RBER monitoring in the flash of HSD