Modeling spatio-temporal read voltages with complex distortions arising from the write and read mechanisms in flash memory devices is essential for the design of signal processing and coding algorithms. In this work, we propose Flash-Gen, a data-driven approach to generating flash memory read voltages in both space and time using conditional generative networks. This generative modeling method reconstructs read voltages from an individual memory cell based on the program levels of the cell and its surrounding cells, as well as the time stamp, in a time-efficient, resource-saving, and function-comprehensive manner. We evaluate the model over a range of time stamps using the read voltage distributions, the cell level error rates, and the relative frequency of errors for patterns most susceptible to inter-cell interference (ICI) effects. We propose a flash system optimization procedure, referred to as the Flash-Gen coding workflow, that leverages reconstructed read voltages for the development of error correction codes (ECCs) and constrained codes. Experimental results demonstrate that the model accurately captures the complex spatial and temporal features of the flash memory channel. Flash-Gen coding workflow can effectively address a range of important tasks, including threshold determination, coding performance estimation, and pattern characterization.

The advent of byte-addressable persistent memory (PM) has led to a resurgence of interest in adapting existing dynamic hashing schemes to PM. Compared with its two well-known peers (extendible hashing and linear hashing), spiral storage has received little attention due to its limitations. After an in-depth analysis, however, we discover that it has a good potential for PM. To show its strength, we develop a persistent spiral storage called PASS (Persistence-Aware Spiral Storage), which is facilitated by a group of new/existing techniques. Further, we conduct a comprehensive evaluation of PASS on a server equipped with Intel Optane DC Persistent Memory Modules (DCPMM). Experimental results demonstrate that compared with two state-of-the-art schemes it exhibits better performance.

The goal of functional error correction is to preserve neural network performance when stored network weights are corrupted by noise. To achieve this goal, a selective protection (SP) scheme was proposed to optimally protect the functionally important bits in binary weight representations in a layer-dependent manner. Although it showed its effectiveness in image classification tasks on some relatively simple networks such as ResNet-18 and VGG-16, it becomes inadequate for emerging complex machine learning tasks generated from natural language processing and vision-language association domains. To solve this problem, we extend the SP scheme in three directions: task complexity, model complexity, and storage complexity. Extensions to complex natural language and vision-language tasks include text categorization and “zero-shot” textual classification of images. Extensions to more complex models with deeper block structures and attention mechanisms consist of Very Deep Convolutional Neural Network (VDCNN) and Contrastive Language-Image Pre-Training (CLIP) networks. Extensions to more complex storage configurations focus on distributed storage architectures to support model parallelism. Experimental results show that the optimized SP scheme preserves network performance in all of these settings. The results also provide insights into redundancy-performance tradeoffs, generalizability of SP across datasets and tasks, and robustness of partitioned network architectures.

A triboelectric nanogenerator (TENG) is a kinetic energy transducer with small and time-varying internal capacitance, which increases the difficulties of extracting harvested energy. In this paper, an efficient rectifier, hybridizing synchronized electric charge extraction (SECE) and bias-flipping techniques, is proposed. The two techniques alternatively operate at opposite voltage polarities of the TENG. By taking advantage of the varying capacitance, the proposed synchronized extraction and flipping (SEF) rectifier shows significantly improved energy extraction performance. The design is implemented in a 180-nm high-voltage BCD technology, and the results show a 7.4X energy extraction enhancement, 65-V voltage tolerance, and 35-nA quiescent current.

Modeling spatio-temporal read voltages with complex distortions arising from the write and read mechanisms in flash memory devices is essential for the design of signal processing and coding algorithms. In this work, we propose a data-driven approach to modeling NAND flash memory read voltages in both space and time using conditional generative networks. This generative flash modeling (GFM) method reconstructs read voltages from an individual memory cell based on the program levels of the cell and its surrounding cells, as well as the time stamp. We evaluate the model over a range of time stamps using the cell read voltage distributions, the cell level error rates, and the relative frequency of errors for patterns most susceptible to inter-cell interference (ICI) effects. Experimental results demonstrate that the model accurately captures the complex spatial and temporal features of the flash memory channel.

A triboelectric nanogenerator (TENG) is a new transducer utilizing contact electrification and electrostatic induction to transform mechanical energy into electric energy. Due to its high energy density and flexibility, it can be employed to make electronic devices self-powered by harvesting ambient mechanical energy in many application scenarios, such as biomedical devices, wearable electronics, and Internet-of-Things (IoT) sensors. However, due to the time-varying and low internal capacitance of a TENG, it is challenging to extract electrical energy from it. Hence, good power conversion techniques are crucial in TENG energy harvesting systems. Currently, studies on dedicated integrated power conversion techniques are very limited. Due to the exponentially increasing research interests in TENG, a comprehensive study on the TENG energy harvesting system, emphasizing integrated-circuit (IC) power conversion techniques, is urgently needed. This paper summarizes and compares the state-of-the-art triboelectric energy harvesting systems, focusing on different power conversion techniques for output power enhancement. Some techniques, which have been widely used in other relevant energy harvesting systems, are also mentioned to inspire innovative design strategies for TENG systems.

Abstract Copy–move forgery poses a significant threat to social life and has aroused much attention in recent years. Although many copy‐move forgery detection (CMFD) methods have been proposed, the most existing CMFD methods are short of adaptability in detecting images, which leads to the limitation on detection effects. To solve this problem, the paper proposes a novel keypoint‐based CMFD method: second‐keypoint matching and double adaptive filtering (SMDAF). Motivated by image matching based on keypoint, the second‐keypoint matching method is designed to match keypoints extracted from copy–move forgery images, which can be used for both the single‐CMFD and the multiple‐CMFD. Then, a double adaptive filter (DAF) based on the AdaLAM algorithm and the KANN‐DBSCAN clustering algorithm to filter wrong keypoint matches adaptively are proposed, according to the distinct distribution of keypoints in each image. Finally, the forgery regions are presented by finding their convex hulls and padding them. Compared with existing methods, extensive experiments show that the SMDAF method significantly provides more efficiency in detecting images under simulated real‐world conditions, has better robustness when facing images with different post‐treatment attacks, and is more effective in distinguishing images that look copy–move forged but are real.

To alleviate the dependence on sensor quality and to reduce the accumulated error in traditional inertial navigation systems, this paper proposes RUPT: a millimeter-wave radar aided pedestrian dead reckoning system with dual foot-mounted inertial measurement units (IMU). RUPT in this paper is a comprehensive data processing procedure which pre-processes both inertial data and millimeter-wave data and fuses them in a complementary way. Extensive experiments have demonstrated that the accuracy of RUPT has been improved by up to 65% over the conventional dual-foot mounted pedestrian tracking system.

Process mining aims to extract useful process knowledge and provide valuable insights to better understand, monitor, and improve current business processes. The most critical learning task in process mining is process discovery. Process discovery takes an event log as an input and generates a process model as an output. In the last two decades, processing mining communities have proposed several process discovery algorithms. Many of these algorithms are based on or are extensions of three commonly used process mining algorithms. These algorithms are known as the α algorithm, the Heuristic algorithm and the Inductive algorithm. This study provides an evaluation of these three algorithms using both artificial event logs and real-life event logs. We study the impact of dependency patterns, noise, and complexity. Our work aims to provide clear guidelines for academics or business organizations that are interested in using process mining algorithms to discover their hidden process models and choose the most appropriate process discovery algorithm.

The efficiency of bandwidth scheduling in high-performance networks (HPNs) is critical to the utilization of network resources and the satisfaction of user requests. We consider a periodic bandwidth scheduling problem to maximize the number of satisfied fixed-slot bandwidth reservation requests, referred to as multiple fixed-slot bandwidth scheduling (MFSBS), which is shown to be NP-complete. We first design a minimum resource occupation algorithm for a special type of M-FSBS with identical slots, referred to as MinRO-IS, and further propose a generalized version of MinRO for M-FSBS with arbitrary slots. We also design four greedy algorithms for performance comparison. Extensive simulation results illustrate that both MinRO-IS and MinRO have a superior performance over the existing algorithms in the literature and the other four greedy algorithms in comparison. Considering the popularity of the FSBS-based service model and the rapid expansion of HPNs in both speed and scope, the proposed scheduling algorithms have great potential to improve the network performance of big-data applications that require the FSBS service in HPNs.