Multi-Level Information Storage Using Engineered Electromechanical Resonances of Piezoelectric Wafers: A Concept Piezoelectric Quick Response (PQR) Code

A piezoelectric-based method for information storage is presented. It involves engineering the polarization profiles of multiple piezoelectric wafers to enhance/suppress specific electromechanical resonances. These enhanced/suppressed resonances can be used to represent multiple frequency-dependent bits, thus enabling multi-level information storage. This multi-level information storage is demonstrated by achieving three information states for a ternary encoding. Using the three information states, we present an approach to encode and decode information from a 2-by-3 array of piezoelectric wafers that we refer to as a concept Piezoelectric Quick Response (PQR) code. The scaling relation between the number of wafers used and the cumulative number of information states that can be achieved with the proposed methodology is briefly discussed. Potential applications of this methodology include tamper-evident devices, embedded product tags in manufacturing/inventory tracking, and additional layers of security with existing information storage technologies.

Download Full-text

Multi-State MRAM Cells For Hardware Neuromorphic Computing

10.21203/rs.3.rs-1047393/v1 ◽

2021 ◽

Author(s):

Piotr Rzeszut ◽

Jakub Chęciński ◽

Ireneusz Brzozowski ◽

Sławomir Ziętek ◽

Witold Skowroński ◽

...

Keyword(s):

Memory Cell ◽

Information Storage ◽

Sigmoid Function ◽

Digital Information ◽

Function Generator ◽

Random Number Generators ◽

Computing Device ◽

Summing Amplifier ◽

Potential Applications ◽

Storage Technologies

Abstract Magnetic tunnel junctions (MTJ) have been successfully applied in various sensing application and digital information storage technologies. Currently, a number of new potential applications of MTJs are being actively studied, including high-frequency electronics, energy harvesting or random number generators. Recently, MTJs have been also proposed in designs of new platforms for unconventional or bio-inspired computing. In the present work, it is shown that serially connected MTJs forming a multi-state memory cell can be used in a hardware implementation of a neural computing device. The main purpose of the multi-cell is the formation of quantized weights in the network, which can be programmed using the proposed electronic circuit. Multi-cells are connected to a CMOS-based summing amplifier and a sigmoid function generator, forming an artificial neuron. The operation of the designed network is tested using a recognition of hand-written digits in 20 × 20 pixels matrix and shows detection ratio comparable to the software algorithm, using weights stored in a multi-cell consisting of four MTJs or more.

Download Full-text

Multi-level 3D Densenets for False-positive Reduction in Lung Nodule Detection Based on Chest Computed Tomography

Current Medical Imaging Formerly Current Medical Imaging Reviews ◽

10.2174/1573405615666191113122840 ◽

2020 ◽

Vol 16 (8) ◽

pp. 1004-1021

Author(s):

Xiaoqi Lu ◽

Yu Gu ◽

Lidong Yang ◽

Baohua Zhang ◽

Ying Zhao ◽

...

Keyword(s):

False Positive ◽

Contextual Information ◽

Lung Nodule ◽

Positive Sample ◽

Lung Nodules ◽

Lung Nodule Detection ◽

Nodule Detection ◽

Level Information ◽

Multi Level ◽

Cade System

Objective: False-positive nodule reduction is a crucial part of a computer-aided detection (CADe) system, which assists radiologists in accurate lung nodule detection. In this research, a novel scheme using multi-level 3D DenseNet framework is proposed to implement false-positive nodule reduction task. Methods: Multi-level 3D DenseNet models were extended to differentiate lung nodules from falsepositive nodules. First, different models were fed with 3D cubes with different sizes for encoding multi-level contextual information to meet the challenges of the large variations of lung nodules. In addition, image rotation and flipping were utilized to upsample positive samples which consisted of a positive sample set. Furthermore, the 3D DenseNets were designed to keep low-level information of nodules, as densely connected structures in DenseNet can reuse features of lung nodules and then boost feature propagation. Finally, the optimal weighted linear combination of all model scores obtained the best classification result in this research. Results: The proposed method was evaluated with LUNA16 dataset which contained 888 thin-slice CT scans. The performance was validated via 10-fold cross-validation. Both the Free-response Receiver Operating Characteristic (FROC) curve and the Competition Performance Metric (CPM) score show that the proposed scheme can achieve a satisfactory detection performance in the falsepositive reduction track of the LUNA16 challenge. Conclusion: The result shows that the proposed scheme can be significant for false-positive nodule reduction task.

Download Full-text

Photo-induced variation of magnetism in coordination polymers with ligand-based electron transfer

Dalton Transactions ◽

10.1039/d1dt01963e ◽

2021 ◽

Author(s):

Yan-Lei Lu ◽

Wen-Long Lan ◽

Wei Shi ◽

Qionghua Jin ◽

Peng Cheng

Keyword(s):

Electron Transfer ◽

Coordination Polymers ◽

Optical Memory ◽

High Density ◽

Information Storage ◽

Memory Devices ◽

Storage Materials ◽

Potential Applications ◽

Induced Variation

Photo-induced variation of magnetism from ligand-based electron transfer have been extensively studied because of their potential applications in magneto-optical memory devices, light-responsive switches, and high-density information storage materials. In this...

Download Full-text

Self-assembly mode and supramolecular framework of cyclopentanocucurbit[6]uril and aromatic amines

Current Organic Chemistry ◽

10.2174/1385272825666211117153451 ◽

2021 ◽

Vol 25 ◽

Author(s):

Jun Zheng ◽

Yan Mei Jin ◽

Xi Nan Yang ◽

Lin Zhang ◽

Dao Fa Jiang ◽

...

Keyword(s):

Self Assembly ◽

Functional Materials ◽

Supramolecular Interactions ◽

X Ray Diffraction ◽

Guest Molecules ◽

Characterization Methods ◽

Potential Applications ◽

Non Covalent Interactions ◽

Multi Level ◽

Covalent Interactions

: Single-crystal X-ray diffraction analysis, nuclear magnetic resonance (NMR), and other characterization methods are used to characterize the complexes formed by cyclopentano-cucurbit[6]uril (abbreviated as CyP6Q[6]) as a host interacting with p-aminobenzenesulfonamide (G1), 4,4'-diaminobiphenyl (G2), and (E)-4,4'-diamino-1,2-diphenylethene (G3) as guests, respectively. The experimental results show that these three aromatic amine molecules have the same interaction mode with CyP6Q[6], interacting with its negatively electric potential portals. The supramolecular interactions include non-covalent interactions of hydrogen bonding and ion-dipole between host and guest molecules. CdCl2 acts as a structureinducing agent to form self-assemblies of multi-dimensional and multi-level supramolecular frameworks that may have potential applications in various functional materials.

Download Full-text

Dual Attention Multiscale Network for Vessel Segmentation in Fundus Photography

10.21203/rs.3.rs-1170254/v1 ◽

2021 ◽

Author(s):

Pengshuai Yin ◽

Yupeng Fang ◽

Qingyao Wu ◽

QiLin Wan

Keyword(s):

Disease Diagnosis ◽

Fundus Photography ◽

Essential Step ◽

Input Size ◽

Real World Applications ◽

Vessel Structure ◽

Level Information ◽

Multi Level ◽

Sensitivity Specificity ◽

To Receive

Abstract Background: Automatic vessel structure segmentation is an essential step towards an automatic disease diagnosis system. The task is challenging due to the variance shapes and sizes of vessels across populations.Methods: A multiscale network with dual attention is proposed to segment vessels in different sizes. The network injects spatial attention module and channel attention module on feature map which size is 1 8 of the input size. The network also uses multiscale input to receive multi-level information, and the network uses the multiscale output to gain more supervision. Results: The proposed method is tested on two publicly available datasets: DRIVE and CHASEDB1. The accuracy, AUC, sensitivity, specificity on DRIVE dataset is 0.9615, 0.9866, 0.7693, and 0.9851, respectively. On the CHASEDB1 dataset, the metrics are 0.9797, 0.9895, 0.8432, and 0.9863 respectively. The ablative study further shows effectiveness for each part of the network. Conclusions: Multiscale and dual attention mechanism both improves the performance. The proposed architecture is simple and effective. The inference time is 12ms on a GPU and has potential for real-world applications. The code will be made publicly available.

Download Full-text