Logical OR Operation Research Articles

Method of bed exit intention based on the internal pressure features in array air spring mattress

With the population ages, many patients are unable to receive comprehensive care, leading to an increase in hazardous incidents, particularly falls occurring after getting out of bed. To address this issue, this paper proposes a method for recognizing bed-exit intentions using an array air spring mattress. The method integrates convolutional neural networks with feature point matching techniques to identify both global and local features of the array air spring. For global features, a one-dimensional focal loss convolutional neural network (1D-FLCNN) model is employed to classify eight internal pressure time series and determine bed-exit status based on global features. For local features, the distribution matrix and feature point matrix of the internal pressure features are extracted to represent the spatial distribution of bed-exit postures. Euclidean distance is utilized to measure the similarity between these matrices and match bed-exit postures. Finally, the recognition results from both feature types are combined using a logical OR operation to produce the final result. Experimental validation confirms that the proposed method greatly improves the anti-interference capability and effectively avoids the problem of non-recognition due to body position and external environment.

Dual-Mode Reconfigurable Split-Gate Logic Transistor through Van der Waals Integration.

As silicon-based transistors approach their physical size limitations, two-dimensional material-based reconfigurable functional electronic devices are considered the most promising novel device architectures beyond Moore strategies. While these devices have garnered significant attention, they often require complex device fabrication processes and extra electric fields. Additionally, the device performance is usually limited by the metal-semiconductor interface properties. In this Letter, we have constructed a reconfigurable logic device based on a WSe2 transistor with a nanofloating gate and split-gates through van der Waals integration. This device achieves a small Schottky barrier height due to the van der Waals contacts. By varying the split-gate biases, we can realize volatile reconfigurable homojunctions as well as AND, OR, NOR, and NAND logic operations with just a single device. Furthermore, with the charge trapping effect of nanofloating gate, we can also achieve nonvolatile reconfigurable homojunctions, as well as AND and OR logic operations. The volatile and nonvolatile logic operations are similar to the short-term plasticity and long-term plasticity, respectively, of synapses in the human brain. This work offers a potential approach for creating novel reconfigurable functional electronic devices with a simple fabrication process and low cost.

Artificial synapse based on low-voltage Ni-doped CuI thin-film transistors for neuromorphic application

Inspired by the human brain's capacity as a powerful biological computer capable of simultaneously processing a vast array of cognitive tasks, many emerging artificial synapse devices have been developed in recent years. Electric-double-layer (EDL) transistors based on interfacial ion-modulation have attracted widespread attention for simulating synaptic plasticity and neural functions. Here, low-voltage EDL p-type thin-film transistors (TFTs) are fabricated on glass substrates, with Ni-doped cuprous iodide (Ni0.06Cu0.94I) as the channel and chitosan as the dielectric. The electrical performance of the Ni0.06Cu0.94I TFTs is investigated: current on/off ratio of 6.4 × 104, subthreshold swing of 33 mV/dec, threshold voltage of 1.38 V, operating voltage of 2 V, and saturation field-effect mobility of 15.75 cm2 V−1 s−1. A dual in-plane gate OR logic operation is demonstrated. Importantly, by applying single voltage pulses, dual voltage pulses, and multiple voltage pulses to the gate, the Ni0.06Cu0.94I transistors exhibited typical synaptic characteristics, including short-term potentiation, short-term depression, long-term potentiation, long-term depression, paired-pulse facilitation, and spiking-rate-dependent plasticity. Furthermore, the synaptic transistor can also simulate the learning–forgetting–relearning process of the human brain. These remarkable behaviors of voltage-stimulated synaptic transistors have potential for neuromorphic applications in future artificial systems.

Design and analysis of ultralow-power all-optical EX-OR and OR logic operations with chiral Mach-Zehnder interferometer

Design and analysis of ultralow-power all-optical EX-OR and OR logic operations with chiral Mach-Zehnder interferometer

Utilizing topological invariants for encoding and manipulating chiral phonon devices

As a fundamental degree of freedom, phonon chirality is expected to promote the development of quantum information technology just like electron spin. Currently, central to this area is the realization of efficient transmission and control of chiral information. In this paper, we propose an approach by integrating topological theory, leveraging topologically invariant Chern numbers, to encode hexagonal lattice systems. Our investigation reveals the presence of topologically protected chiral interface states within the shared band gaps of both trivial and non-trivial system units. By precisely modulating the magnetic field distribution within the encoding system, we can effectively manipulate the topological pathways. Building upon this framework, we design and implement a chiral phonon three-port device. Through dynamic calculations, we demonstrate the transmission process of chiral information, showcasing the chiral phonon switching effect and logical OR operation. Our findings not only establish a fundamental mechanism for the manipulation and control of phonon chiral information but also provide a promising direction for research in harnessing chirality degrees of freedom in practical applications.

Optimisation Challenge for a Superconducting Adiabatic Neural Network That Implements XOR and OR Boolean Functions.

In this article, we consider designs of simple analog artificial neural networks based on adiabatic Josephson cells with a sigmoid activation function. A new approach based on the gradient descent method is developed to adjust the circuit parameters, allowing efficient signal transmission between the network layers. The proposed solution is demonstrated on the example of a system that implements XOR and OR logical operations.

Abstract 1065: Two-step method for early detection of ovarian cancer with high specificity via platelet RNA

Abstract Owing to lack of disease-specific symptoms and screening methods, most of patients with ovarian cancer are diagnosed at an advanced stage. Recent technology based on platelet RNA aids in detection of tumors at the early stages. Also, for population screening of low-prevalence diseases like cancer, accurate detection with high specificity is essential to improve diagnostic methods. Thus, we proposed platelet RNA-based early ovarian cancer detection with great specificity. It is a two-step method for detecting ovarian tumors with more than 99% specificity using exon-exon junction features and sampling invariant normalization and finding their malignancy with more than 99% negative predictive value using exon-exon junction and hematology parameters. First, we downloaded public platelet transcriptome data of gynecological cancer including ovarian cancer (n=151), benign tumor (n=30), and healthy counterparts (n=218) from GEO (GSE158508, 89843, and 183635). Second, we prospectively enrolled patients with gynecological cancer (n=31), benign tumors (n=20), and healthy women (n=18) at Seoul National University Hospital (SNUH) and Boaz Medical Center and obtained platelet transcriptome data (FMH data). Feature selection and model development were conducted using public training set (n=243), public validation set (n=156), and FMH training/validation set (n=47). The batch-invariant normalization was performed, and 198 splice junctions were selected as features from training/validation set for the bootstrap aggregation of the SVM model diagnosing the existence of an ovarian tumor. The model performance was assessed from a separate test set only composed of FMH data (n=22). Our model that demonstrated 96.8% sensitivity, 100.0% specificity, with a predetermined cut-off value. Next, FMH training/validation set was used for model development of following malignancy test. 10 splice junctions and 19 hematology parameters were selected and SVM models were separately developed for splice junctions and hematology parameters using 5-fold cross validation. The predictions were combined using a logical OR operation to obtain the final prediction result. The result showed its diagnostic performance with 100% sensitivity or NPV, and 50.0% specificity in the test data set with a predetermined cut-off value. We proposed a two-step method to operationally improve the existing protocol for ovarian cancer screening. Our model initially excludes most healthy individuals who do not have ovarian tumors and excludes patients with benign ovarian tumors who do not necessitate medical testing. To conclude, our model allows physicians to identify a subset of ovarian tumor patients with a higher risk of malignancy even at early stages; it exhibits potential in enhancing early diagnosis of ovarian cancer, leading to increase of treatment effectiveness and ultimately augmenting the survival of patients with ovarian cancer. Citation Format: Eunyong Ahn, Se Ik Kim, Sungmin Park, Sarah Kim, Yeochan Kim, Eunchong Huang, Suyeon Lee, Dong Won Hwang, Heeyeon Kim, HyunA Jo, Untack Cho, Juwon Lee, TaeJin Ahn, Yong-Sang Song. Two-step method for early detection of ovarian cancer with high specificity via platelet RNA [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1065.

All-Optical XOR, AND, OR, NOT, NOR, NAND, and XNOR Logic Operations Based on M-Shaped Silicon Waveguides at 1.55 μm.

Silicon waveguides are essential to integrated photonics, which is where optical and electronic components are coupled together on a single silicon chip. These waveguides allow for the integration of signal processing and optical transmission, which advances data centers, telecommunications, and other optical applications. Thus, our study involves the simulation of essential all-optical logic operations, namely XOR, AND, OR, NOT, NOR, NAND, and XNOR, and utilizes M-shaped silicon optical waveguides at a wavelength of 1.55 μm. This simulation is conducted through Lumerical FDTD solutions. The suggested waveguide comprises four identical slots, configured in the shape of the letter 'M', and all of which are formed of core silicon and silica cladding. These logic operations work based on constructive and destructive interferences that are caused by phase changes in the input optical beams. The contrast ratio (CR) is employed to quantitatively and comparatively assess the degree to which the target logic operations are efficiently executed. The simulation results indicate that, compared to other reported designs, the considered logic functions constructed using the proposed waveguide can be implemented with higher CRs. The outcomes of this paper can be utilized regarding the implementation of optoelectronic combinational logic circuits of enhanced functionality.

Dynamically encircling exceptional points for robust eigenstate generation and all-optical logic operations in a three-dimensional photonic chip

Dynamically encircling exceptional points (EEPs) in non-Hermitian systems have been utilized to realize robust on-chip mode conversions by taking the advantage that the output states are usually fixed and independent of the input states. This highly protects one of the specific eigenstates, but restricts their practical applications requiring superposition states. Here, we realize an on-chip robust eigenstate generator using a depth-varying 3D waveguide to overpass the EEP system. It not only preserves the robustness of the EEP system to generate one determinate eigenstate, but also generates another two-level eigenstate and arbitrary superposition states. Furthermore, by ingeniously combining two identical generators with a symmetric Y combiner, both the all-optical XOR and OR logic operations insensitive to the variations in the input light are realized by the destructive or constructive interference between the two generators' near-neighboring output signals. This paper paves the way for applications of EEP systems to highly robust state manipulations and information processing in integrated photonic and quantum devices. Published by the American Physical Society 2024

Harnessing Multistability: A Novel Approach to Optical Logic Gate Construction Using Erbium-Doped Fiber Lasers

We present an innovative method harnessing multistability within a diode-pumped erbium-doped fiber laser to construct logic gates. Our approach involves manipulating the intensity of external noise to regulate the probability of transitioning among four concurrent attractors. In this manner, we facilitate the realization of OR, AND, NOR, and NAND logic operations, aligning with the coexisting period-1, period-3, period-4, and period-5 orbits. Employing detrended fluctuation analysis, we establish equilibrium in the probability distributions of these states. The obtained results denote a substantial advancement in the field of optical logic gate development, representing a pivotal stride toward the seamless integration of an all-optical logic gate within laser oscillator-based systems.

MIFAM-DTI: a drug-target interactions predicting model based on multi-source information fusion and attention mechanism.

Accurate identification of potential drug-target pairs is a crucial step in drug development and drug repositioning, which is characterized by the ability of the drug to bind to and modulate the activity of the target molecule, resulting in the desired therapeutic effect. As machine learning and deep learning technologies advance, an increasing number of models are being engaged for the prediction of drug-target interactions. However, there is still a great challenge to improve the accuracy and efficiency of predicting. In this study, we proposed a deep learning method called Multi-source Information Fusion and Attention Mechanism for Drug-Target Interaction (MIFAM-DTI) to predict drug-target interactions. Firstly, the physicochemical property feature vector and the Molecular ACCess System molecular fingerprint feature vector of a drug were extracted based on its SMILES sequence. The dipeptide composition feature vector and the Evolutionary Scale Modeling -1b feature vector of a target were constructed based on its amino acid sequence information. Secondly, the PCA method was employed to reduce the dimensionality of the four feature vectors, and the adjacency matrices were constructed by calculating the cosine similarity. Thirdly, the two feature vectors of each drug were concatenated and the two adjacency matrices were subjected to a logical OR operation. And then they were fed into a model composed of graph attention network and multi-head self-attention to obtain the final drug feature vectors. With the same method, the final target feature vectors were obtained. Finally, these final feature vectors were concatenated, which served as the input to a fully connected layer, resulting in the prediction output. MIFAM-DTI not only integrated multi-source information to capture the drug and target features more comprehensively, but also utilized the graph attention network and multi-head self-attention to autonomously learn attention weights and more comprehensively capture information in sequence data. Experimental results demonstrated that MIFAM-DTI outperformed state-of-the-art methods in terms of AUC and AUPR. Case study results of coenzymes involved in cellular energy metabolism also demonstrated the effectiveness and practicality of MIFAM-DTI. The source code and experimental data for MIFAM-DTI are available at https://github.com/Search-AB/MIFAM-DTI.

Skyrmion motion under temperature gradient and application in logic devices

Under the presence of temperature gradient (TG) on a nanotrack, it is necessary to investigate the skyrmion dynamics in various magnetic systems under the combined effect of forces due to magnonic spin transfer torque thermal STT (), entropic difference as well as thermal induced dipolar field (DF). Hence, in this work, the dynamics of skyrmions in ferromagnets (FM), synthetic antiferromagnets (SAF), and antiferromagnets (AFM) have been studied under different TGs and damping constants (). It is observed that plays a major role in deciding the direction of skyrmion motion either towards the hotter or colder side in different magnetic structures. Later, FM skyrmion based logic device is proposed that consists of a cross-coupled nanotrack, where the skyrmions on horizontal and vertical nanotrack are controlled by exploiting TG and electrical STT (eSTT), respectively by taking the advantages of thermal induced skyrmion Hall effect (SkHE). The proposed device performs AND and OR logic functionalities simultaneously, when the applied current density is Moreover, the proposed device is also able to exhibit the half adder functionality by tuning the applied current density to The total energy consumption for AND and OR logic operation and half adder are 33.63 fJ and 25.06 fJ, respectively. This paves the way for the development of energy-efficient logic devices with ultra-high storage density.

The Amplified DNA Logic Gates Based on Aptamer-Receptor Recognition for Cell Detection and Bioimaging.

A powerful and accurate method for identifying and isolating cells would be of great importance due to its sensitivity, gentleness and effectiveness. Here, we designed a receptor-based DNA logic device that allows Boolean logic analysis of multiple cells. For ease of expression, the molecules on the cell surface that can bind to the aptamer are referred to as "receptors". This DNA logic device sends signals based on cell surface sgc8c and sgc4f receptor expression by performing NOT, NOR, AND and OR logic operations, and amplifies and evaluates the signals using HCR. Meanwhile, the release of ICG from the endopore of HMSNs is controlled by affecting structural changes in the DNA logic device. This approach can accurately identify and treat multiple cells on demand based on the presence or absence of cell-specific receptors, facilitating the development of personalized medicine.

Arithmetic logic unit based on the metastructure with coherent absorption.

An arithmetic logic unit (ALU) based on the metastructure (MS) with coherent absorption (CA) is proposed in this Letter. The ALU can perform AND and exclusive OR logical operations at the same frequency point. So it can be regarded as an optical half-adder. By controlling the chemical potential of graphene and the phase difference of coherent electromagnetic waves (EWs), two different binary numbers are input into the ALU. The dynamic absorption peaks, which are generated based on the CA, output the outcomes of the carry-digit bit and non-carry sum bit. This ALU can be used in the field of optical processing and encryption, such as processing hamming code. This given ALU based on the MS with CA has major implications for advancing the study and investigation into the application of CA. Furthermore, it also provides a new, to the best of our knowledge, idea for the study of MS with logical operation.

A Novel Hybrid Edge Detection and LBP Code-Based Robust Image Steganography Method

In digital image processing and steganography, images are often described using edges and local binary pattern (LBP) codes. By combining these two properties, a novel hybrid image steganography method of secret embedding is proposed in this paper. This method only employs edge pixels that influence how well the novel approach embeds data. To increase the quantity of computed edge pixels, several edge detectors are applied and hybridized using a logical OR operation. A morphological dilation procedure in the hybridized edge image is employed to this purpose. The least significant bits (LSB) and all LBP codes are calculated for edge pixels. Afterward, these LBP codes, LSBs, and secret bits using an exclusive-OR operation are merged. These resulting implanted bits are delivered to edge pixels’ LSBs. The experimental results show that the suggested approach outperforms current strategies in terms of measuring perceptual transparency, such as peak signal-to-noise ratio (PSNR) and structural similarity index (SSI). The embedding capacity per tempered pixel in the proposed approach is also substantial. Its embedding guidelines protect the privacy of implanted data. The entropy, correlation coefficient, cosine similarity, and pixel difference histogram data show that our proposed method is more resistant to various types of cyber-attacks.

Developing a Novel Methodology by Integrating Deep Learning and HMM for Segmentation of Retinal Blood Vessels in Fundus Images.

Accurate segregation of retinal blood vessels network plays a crucial role in clinical assessments, treatments, and rehabilitation process. Owing to the presence of acquisition and instrumentation anomalies, precise tracking of vessels network is challenging. For this, a new fundus image segmentation framework is proposed by combining deep neural networks, and hidden Markov model. It has three main modules: the Atrous spatial pyramid pooling-based encoder, the decoder, and hidden Markov model vessel tracker. The encoder utilized modified ResNet18 deep neural networks model for low-and-high-levels features extraction. These features are concatenated in module-II by the decoder to perform convolution operations to obtain the initial segmentation. Previous modules detected the main vessel structure and overlooked some small capillaries. For improved segmentation, hidden Markov model vessel tracker is integrated with module-I and-II to detect overlooked small capillaries of the vessels network. In last module, final segmentation is obtained by combining multi-oriented sub-images using logical OR operation. This novel framework is validated experimentally using two standard DRIVE and STARE datasets. The developed model offers high average values of accuracy, area under the curve, and sensitivity of 99.8, 99.0, and 98.2%, respectively. Analysis of the results revealed that the developed approach offered enhanced performance in terms of sensitivity 18%, accuracy 3%, and specificity 1% over the state-of-the-art approaches. Owing to better learning and generalization capability, the developed approach tracked blood vessels network efficiently and automatically compared to other approaches. The proposed approach can be helpful for human eye assessment, disease diagnosis, and rehabilitation process.

Logic gating of low-abundance molecules using polyelectrolyte-based diodes.

Bioinspired artificial ionic components are extensively utilized to mimic biological systems, as the vast majority of biological signaling is mediated by ions and molecules. Particular attention is given to nanoscale fluidic components where the ion transport can be regulated by the induced ion permselectivity. As a step from fundamentals toward ion-controlled devices, this study presents the use of ionic diodes made of oppositely charged polyelectrolytes, as a gate for low-abundance molecules. The use of ionic diodes that exhibited nonlinear current-voltage responses enabled realization of a basic Boolean operation of an ionic OR logic gate. Aside from the electrical response, the asymmetric ion transport through the diode was shown to affect the transport of low-abundance molecules across the diode, only allowing crossing when the diode was forward-biased. Integration of multiple diodes enabled implementation of an OR logic operation on both the voltage and the molecule transport, while obtaining electrical and optical output readouts that were associated with low and high logic levels. Similarly to electronics, implementation of logic gates opens up new functionalities of on-chip ionic computation via integrated circuits consisting of multiple basic logic gates.

Design of 1T2M integration of storage and calculation based on threshold memristor

Memristor is a new type of electronic component with small size and non-volatile, and it is the most promising new electronic component to replace MOS tube. The computing and storage parts of the computing electronic machines we use today are separate. With the development of technology and new materials, the indicators of CPU and memory have undergone qualitative changes, but the data transmission of CPU and memory still needs to rely on the bus, which greatly reduces the performance of computing and storage. This paper studies the integration of storage and computing. The ultimate purpose of this experiment is to integrate computing and storage into one chip. This paper proposes a 1T2M memory-computing integrated circuit design based on threshold-type memristors. This circuit is a new circuit structure. This circuit consists of an NMOS transistor and two threshold-type memristors. This structure can realize “OR logic operation” and “storage”. The circuit structure is simple, the performance is better, the number of transistors is less, the integration level is higher, and the reading and writing speed is faster. The simulation verification is carried out by LTSpice.

Dual‐Gate Anti‐Ambipolar Transistor with Van der Waals ReS2/WSe2 Heterojunction for Reconfigurable Logic Operations

AbstractA dual‐gate anti‐ambipolar transistor (AAT) with a two‐dimensional ReS2 and WSe2 heterojunction is developed. The characteristic Λ‐shaped transfer curves yielded by the bottom‐gate voltage are effectively controlled by the top‐gate voltage. This feature is applied to logic operations, with the bottom‐ and top‐gate voltages acting as two input signals and the drain current (Id) monitored as an output signal. Importantly, a single dual‐gate AAT exhibits all the two‐input logic operations (AND, OR, XOR, NAND, NOR, and XNOR) under optimized input voltages. Additionally, drain voltage (Vd)‐induced switching between AND and OR logic operations is achieved. These features are advantageous for simplifying circuit design.

Realization of optical logic gates using on-chip diffractive optical neural networks

Optical computing is highly desired as a potential strategy for circumventing the performance limitations of semiconductor-based electronic devices and circuits. Optical logic gates are considered as fundamental building blocks for optical computation and they enable logic functions to be performed extremely quickly without the generation of heat and crosstalk. Here, we discuss the design of a multi-functional optical logic gate based on an on-chip diffractive optical neural network that can perform AND, NOT and OR logic operations at the wavelength of 1.55 µm. The wavelength-independent operation of the multi-functional logic gate at seven wavelengths (over a bandwidth of 60 nm) is also studied which paves the way for wavelength division multiplexed parallel computation. This simple, highly-integrable, low-loss, energy-efficient and broadband optical logic gate provides a path for the development of high-speed on-chip nanophotonic processors for future optical computing applications.