Search

To address the issue of existing knowledge graph completion methods limited capability in capturing structural information within knowledge graphs, this paper proposed a novel model that leveraged bipartite graphs and an attention mechanism to acquire global structural insights and facilitate automatic knowledge graph completion. This model firstly constructed two subgraphs centered on entities and relationships to capture potential useful information about entity neighborhood and relationship structures, and inputted the information formed by the two subgraphs into the encoder to better update entity and relationship structure information. Then, it used attention mechanisms to adaptively learn important interaction features between updated entities and relationships. Finally, it inputted the feature vectors containing global structural information into the decoder, and it actively employed a scoring function to assess and predict scores for the input feature edges, ultimately utilizing the predicted outcomes to accomplish the task of knowledge graph completion. Comparing the performance of the proposed method with the baseline method on the FB15K-237 and NELL995 datasets, the MRR and hits @ 10 evaluation indicators achieved significant improvements of 5.1, 8.8, and 3.4, 2. 2 percentage points, respectively. At the same time, on the WN18RR dataset, these two indicators also were improved by 0.1 and 1.9 percentage points, respectively. The experimental results show that established model proactively adopts a structure that effectively captures the global structural information of the knowledge graph, thereby significantly enhancing the expression ability and predictive performance of the model. [ABSTRACT FROM AUTHOR]

A novel 4-to-2 photonic crystal encoder is proposed by modulating the intensity of four input optical signals, and four distinct output states are achieved. Nonlinear rods are employed to couple input waves into resonant cavities, directing the light to the desired output waveguides. The proposed design, with a footprint of 114 µm2, demonstrates efficient encoding operation at a wavelength of 1550 nm and is highly suitable for integrated photonics applications. A comprehensive comparative analysis revealed that the proposed 4-to-2 encoder exhibits a time response 176 fs faster than previously presented encoders. Furthermore, the contrast ratio of the designed structure is as high as 13.78 dB to distinguish between logic 0 and 1. These advancements hold significant potential for enhancing the performance of compact, high-speed digital circuits. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Zhengzhou University: Engineering Science is the property of Editorial Office of Journal of Zhengzhou University: Engineering Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Purpose: This study aims to propose a novel design for a 4 × 2 encoder and its corresponding 2 × 4 decoder using two-dimensional phononic crystal ring resonator (PnC-RR). Methods: The proposed encoder/decoder structure is based on acoustic waveguides and crystal ring resonator cavities, operating at a specific frequency of 45.1 kHz. The two-dimensional design employs a square lattice configuration comprising a rectangular mercury base and cylindrical water rods. The performance of the design is evaluated using key parameters such as Contrast Ratio, Extinction Ratio, and Insertion Loss. All simulations and analyses were conducted using COMSOL Multiphysics software. Results: The design demonstrates effective performance metrics, making it suitable for integration into phononic circuits. The encoder and decoder structures are optimized to enhance signal processing capabilities at the specified frequency. Applications: This design has potential applications in phononic integrated circuits, particularly in underwater acoustic systems and medical technologies. Conclusion: The proposed 4 × 2 encoder and 2 × 4 decoder based on PnC-RR offers a promising solution for efficient signal processing in specialized environments, supported by robust performance parameters and reliable simulation results. [ABSTRACT FROM AUTHOR]

Considerable variation in soil often occurs within and across production fields, which can significantly impact farming input management strategies. Optimizing resource utilization while enhancing crop productivity is critical for achieving Sustainable Development Goals (SDGs). This paper proposes a low-cost retrofittable Variable Rate Applicator Controller (VRAC) designed to leverage soil variability and facilitate the adoption of Variable Rate Technologies. The controller operates using a Raspberry Pi platform, RTK—Global Navigation Satellite System (GNSS), a stepper motor, and an anti-slip wheel encoder. The VRAC allows precise, on-the-fly control of the Variable Rate application of farming inputs utilizing an accurate GNSS to pinpoint geographic coordinates in real time. A wheel encoder measures accurate distance travel, providing a real-time calculation of speed with a slip-resistant wheel design for precise RPM readings. The Raspberry Pi platform processes the data, enabling dynamic adjustments of variability based on predefined maps, while the motor driver controls the motor's RPM. It is designed to be plug-and-play, user-friendly, and accessible for a broader range of farming practices, including seeding rates, dry fertilizer, and liquid fertilizer application. Data logging is performed from various field sensors. The controller exhibits an average of 0.864 s for rate changes from 267 to 45, 45 to 241, 241 to 128, 128 to 218, and 218 to 160 kg/ha at speeds of 8, 11, 16, 19, 24, and 32 km/h. It has an average coefficient of variation of 4.59, an accuracy of 97.17%, a root means square error (RMSE) of 4.57, an R square of 0.994, and an average standard deviation of 1.76 kg for seeding discharge. The cost-effectiveness and retrofitability of this technology offer an increase in precision agriculture adoption to a broader range of farmers and promote sustainable farming practices. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study comprehensively analyzes U-Net models for semantic segmentation in phytoplankton image recognition, leveraging encoders such as EfficientNet-B5, MobileNetV2, ResNet50, and ResNeXt50 and employing the Adam optimizer. The research highlights the U-Net MobileNetV2 model with optical distortion, which achieves notable test scores with 93.69% Dice, 88.14% intersection over union (IoU), 99.89% Precision, and 100% Recall, underscoring the efficacy of the applied augmentation strategies, including geometric and distortion transforms, and color and blur techniques. The U-Net ResNet50 model with mix transform consistently demonstrates high accuracy in critical metrics, outperforming others, while EfficientNet-B5 with blur suggests increased model sensitivity with improved recall. These results underscore the crucial role of encoderaugmentation synergy in model performance. Training and testing times across models have remained under 250 seconds, reflecting methodological efficiency. Overall, these results demonstrate the model's excellent performance for the semantic segmentation task. [ABSTRACT FROM AUTHOR]

The process of generating descriptive captions for images has witnessed significant advancements in last years, owing to the progress in deep learning techniques. Despite significant advancements, the task of thoroughly grasping image content and producing coherent, contextually relevant captions continues to pose a substantial challenge. In this paper, we introduce a novel multimodal method for image captioning by integrating three powerful deep learning architectures: YOLOv8 (You Only Look Once) for robust object detection, EfficientNetB7 for efficient feature extraction, and Transformers for effective sequence modeling. Our proposed model combines the strengths of YOLOv8 in detecting objects, the superior feature representation capabilities of EfficientNetB7, and the contextual understanding and sequential generation abilities of Transformers. We conduct extensive experiments on standard benchmark datasets to evaluate the effectiveness of our approach, demonstrating its ability to generate informative and semantically rich captions for diverse images. The experimental results showcase the synergistic benefits of integrating YOLOv8, EfficientNetB7, and Transformers in advancing the state-of-the-art in image captioning tasks. The proposed multimodal approach has yielded impressive outcomes, generating informative and semantically rich captions for a diverse range of images. By combining the strengths of YOLOv8, EfficientNetB7, and Transformers, the model has achieved state-of-the-art results in image captioning tasks. The significance of this approach lies in its ability to address the challenging task of generating coherent and contextually relevant captions while achieving a comprehensive understanding of image content. The integration of three powerful deep learning architectures demonstrates the synergistic benefits of multimodal fusion in advancing the state-of-the-art in image captioning. Furthermore, this approach has a profound impact on the field, opening up new avenues for research in multimodal deep learning and paving the way for more sophisticated and context-aware image captioning systems. These systems have the potential to make significant contributions to various fields, encompassing human-computer interaction, computer vision and natural language processing. [ABSTRACT FROM AUTHOR]

The propagation of wireless networks in e-business applications demands efficient and robust anomaly detection techniques to ensure data security and reliable communication. A conventional autoencoder is learned to efficiently compress data as an unsupervised neural network within an encoding process. The autoencoder can learn to rebuild the information from the compact model so that the dissimilarity of the reconstructed to the original data is on least. Conventional wireless communication techniques are developed to deliver consistent data transmit through an impaired channel of the transferred signals. This work presents an autoencoder model for an end-to-end information bits communication system over a wireless channel with reliable transmission. The Autoencoder for energy-efficient wireless networks is defined with a pair of (channel uses number, and input bits number). For both the transmitter (encoder) and receiver (decoder), the developed network architecture includes only two fully connected layers. The transmitter includes two fully connected layers and the input layer allows a vector (one-hot) with M length. Several normalized autoencoders are compared for the learned constellations to unit average power and unit energy. The AWGN channel layer is connected after the transmitter (encoder) layers. The training progress result demonstrated that the validation loss remains slowly declining, while the validation accuracy rapidly achieves a value larger than 92%. The block error rate (BLER) outcome demonstrated that the autoencoders could be trained and learned as a modulation scheme and joint coding in an unsupervised method. [ABSTRACT FROM AUTHOR]

The experimental object used is a three-wheeled omni-robot frame, where the wheel axes have an angle difference of 120 degrees from each other. The Omni wheels have a diameter of 48 mm connected to the DC motor axis through a gearbox, which has a ratio of 80 to 1. Each wheel has been controlled using a proportional plus integral plus derivative (PID) controller embedded in a microcontroller, which is an Arduino nano board. The motor axis is equipped with a two-phase optical encoder that definitively generates four cycles per revolution for wheel speed acquisition as the controller input. The wheel speed control signal is distributed to the wheel through the H bridge as the controller output. The controller constants have been directly tuned to the robot frame's physical omni-wheel speed control system. The controller is tuned to minimize steady-state error, achieve fast settling times, and minimize overshoot. The best constants obtained are 1.5 (proportional), 0.012 (integral), and 10 (derivative). Using a tolerance band of +/- 2.5%, the system achieved a settling time of 1.1 seconds and a steady-state error of 0.3%. The control system is unstable when the actuator is saturated, which produces oscillations. Controller optimization has been successful by using integrator wind-up reduction. The steady-state average error was reduced to 9.95% without oscillation after optimization, compared to 46.37% with oscillations before optimization. The controller has been validated with speed-tracking tests on all velocity vector regions. The robot frame has been tested with basic maneuvers such as rotation, concerning, forward, and sideways. [ABSTRACT FROM AUTHOR]

In this paper, the graph segmentation (GSeg) method has been proposed. This solution is a novel graph neural network framework for network embedding that leverages the inherent characteristics of nodes and the underlying local network topology. The key innovation of GSeg lies in its encoder-decoder architecture, which is specifically designed to preserve the network's structural properties. The key contributions of GSeg are: (1) a novel graph neural network architecture that effectively captures local and global network structures, and (2) a robust node representation learning approach that achieves superior performance in various network analysis tasks. The methodology employed in our study involves the utilization of a graph neural network framework for the acquisition of node representations. The design leverages the inherent characteristics of nodes and the underlying local network topology. To enhance the architectural framework of encoder- decoder networks, the GSeg model is specifically devised to exhibit a structural resemblance to the SegNet model. The obtained empirical results on multiple benchmark datasets demonstrate that the GSeg outperforms existing state-of-the-art methods in terms of network structure preservation and prediction accuracy for downstream tasks. The proposed technique has potential utility across a range of practical applications in the real world. [ABSTRACT FROM AUTHOR]

Understanding the intricacies of plant biology demands innovative analytical methods to elucidate genetic and molecular mechanisms underlying plant development and stress responses. Large language models (LLMs) can find patterns and correlations in noisy biological data, but LLMs have not yet been embraced by the plant community. We describe the different types of LLMs and cover how they can be used to study biological systems. The data, hardware, and software requirements are within reach to build powerful LLMs for the plant community. Large language models (LLMs), such as ChatGPT, have taken the world by storm. However, LLMs are not limited to human language and can be used to analyze sequential data, such as DNA, protein, and gene expression. The resulting foundation models can be repurposed to identify the complex patterns within the data, resulting in powerful, multipurpose prediction tools able to predict the state of cellular systems. This review outlines the different types of LLMs and showcases their recent uses in biology. Since LLMs have not yet been embraced by the plant community, we also cover how these models can be deployed for the plant kingdom. [ABSTRACT FROM AUTHOR]

Summary: Multivalued logic (MVL) offers higher information density as compared with binary logic systems for an equal number of digits. In a ternary microprocessor, memory access is the most time‐ and power‐consuming action. In order to design a ternary computer, the possibilities for designing ternary encoders and decoders must be examined. This paper presents the designs of 9:2 unbalanced ternary encoder (TENC) and 2:9 unbalanced ternary line address decoder (TDEC) based on novel designs of standard ternary inverter (STI), ternary NAND (TNAND), and ternary NOR (TNOR) logic gates using resistive random‐access memory (RRAM) and carbon nanotube field effect transistor (CNTFET) technology. The simulation results indicate an improvement in performance parameters such as power consumption, delay, power–delay product (PDP), and component count of the proposed circuits in comparison with the different existing counterparts. Moreover, a detailed analysis is carried out on the impact of different process, voltage, and temperature (PVT) variations on the performance metrics, including propagation delay, power consumption, and PDP of the 9:2 unbalanced ternary encoder and 2:9 unbalanced ternary line address decoder. The proposed ternary encoder circuit shows an improvement of 62% in delay, 71% in power consumption, 88.6% improvement in PDP, and 53% reduction in CNTFET count, whereas the ternary decoder circuit exhibits an improvement of 6.6% and 94% in delay and 43% and 94% improvement in power consumption, respectively. Moreover, the CNTFET count is reduced by 16.07% and 46%. [ABSTRACT FROM AUTHOR]

In this article we will approach the soft-decision decoding for the linear block codes, is a kind of decoding algorithms used to decode data to form better original estimated received message, it is considered as a NP-hard problem. In this article we present a new decoder using memetic algorithm such metaheuristic technic operates on the dual code rather than the code itself that aims to find the error caused when sending a codeword calculated from a message of k bits of information, the resulting codeword contains n bits, including the redundancy bits, the efficiency of an error-correcting code is equivalent to the ratio k/n, the rate is belong the interval [0,1]. Hence a good code is the one that ensures a certain error correcting capability at minimum ratio. The results proved that this approach using a combination of genetic algorithm and local search algorithm provides a sufficiently good solution to an optimization problem; the new decoder is applied on linear codes where the structure is given by a parity check matrix. [ABSTRACT FROM AUTHOR]

Currently, there is a significant amount of public code in the IT communities, programming forums and code repositories. Many of these codes lack classification labels, or have imprecise labels, which causes inconvenience to code management and retrieval. Some classification methods have been proposed to automatically assign labels to the code. However, these methods mainly rely on code comments or surrounding text, and the classification effect is limited by the quality of them. So far, there are a few methods that rely solely on the code itself to assign labels to the code. In this paper, an encoder-only method is proposed to assign multiple labels to the code of an algorithmic problem, in which UniXcoder is employed to encode the input code and the encoding results correspond to the output labels through the classification heads. The proposed method relies only on the code itself. We construct a dataset to evaluate the proposed method, which consists of source code in three programming languages (C + + , Java, Python) with a total size of approximately 120 K. The results of the comparative experiment show that the proposed method has better performance in multi-label classification task of pure code than encoder–decoder methods. [ABSTRACT FROM AUTHOR]

Today's news has the characteristics of short text, frequent release, timeliness, etc. A media account releases dozens of news in a day. Developing suitable and attractive headlines for large volumes of news has become a major part of the work of media workers. Media workers need a system that automatically generates short text headlines to relieve their stress. To solve this problem, this study proposes a short text news title generation model. The model adopts sequence-to-sequence structure, using pre-trained language model and layered self-attention decoder in encoder and decoder respectively. In order to make the generated headlines contain the key information of the original news, a staged training method based on LCSTS data set and Weibo4 data set is proposed, and the model learns to extract the key news information and construct a stylized expression from the two data sets respectively, so that the generated headlines can accurately express the core content of the news and attract readers. [ABSTRACT FROM AUTHOR]

To organize reliable control over emissions from chimneys of energy facilities, automatic monitoring systems are needed. Equipment is known for monitoring emissions from small-diameter chimneys, but for large-diameter pipes (15 m or more) available in our Republic, there are no corresponding technical solutions. The paper examines the problem of automating the sampling of flue gases based on an electric drive in large-diameter chimneys. To ensure the optimal trajectory of the sampler in the chimney section, it is necessary to use an asynchronous electric motor with a frequency converter and a position sensor. A functional diagram of the control system is proposed, which contains a programmable logic controller for generating the motion mode, as well as a method for calculating parameters and expressions for generating a task signal for continuous sampling mode. Since the range of speed control increases as the diameter of the chimney increases, depending on it, scalar or vector frequency control can be applied. An expression is proposed for calculating the optimal value of the parameter N of an incremental position and speed sensor (encoder), which contributes to a reasonable choice of sensor. The results of simulation modeling are presented, confirming the effectiveness of the proposed method for calculating the parameters of the sampler drive.

Due to the potential for airborne radar to capture incomplete observational information regarding unmanned aerial vehicle (UAV) trajectories, this study introduces a novel approach called Node-former, which integrates neural ordinary differential equations (NODEs) and the Informer framework. The proposed method exhibits high accuracy in trajectory prediction, even in scenarios with prolonged data interruptions. Initially, data outside the acceptable error range are discarded to mitigate the impact of interruptions on prediction accuracy. Subsequently, to address the irregular sampling caused by data elimination, NODEs are utilized to transform computational interpolation into an initial value problem (IPV), thus preserving informative features. Furthermore, this study enhances the Informer's encoder through the utilization of time-series prior knowledge and introduces an ODE solver as the decoder to mitigate fluctuations in the original decoder's output. This approach not only accelerates feature extraction for long sequence data, but also ensures smooth and robust output values. Experimental results demonstrate the superior performance of Node-former in trajectory prediction with interrupted data compared to traditional algorithms. [ABSTRACT FROM AUTHOR]

Image caption generation is becoming one of the hot research topics and attracts various researchers. It is a complex process because it utilizes both NLP (natural language processing) and computer vision approaches for generating the tasks. A range of strategies are available for image captioning that connect the visual material with everyday language, such as explaining images with textual descriptions. Pre-trained classification networks like CNN and RNN-based neural network models are used in the literature to encrypt visual data. Even though various literature works have analyzed outstanding image caption techniques, they still lack in providing better performance for diverse databases. To overcome such issues, this research work presents an automated optimization deep learning model for image caption generation. Initially, the input image is pre-processed, and then the encoder decoder-based structure is utilized for extracting the visual features and caption generation. On the encoder side, the pre-trained ResNet 101 (residual network) is used to extract the visual features, and the SA- Bi-LSTM (self-attention with bi-directional Long Short-Term Memory) is used to generate the caption on the decoder side. In addition, an optimization model CA (Chimp algorithm) is used to improve detection performance in caption generation. The proposed encoder-decoder model is tested on benchmark datasets like Flickr8k, Flickr30k and COCO. Further, this model attained better BLEU and ribes scores of 0.8595 and 0.3531 on the Flickr8k dataset. Thus, the proposed SA-BiLSTM model achieved a significant performance in image caption generation. [ABSTRACT FROM AUTHOR]

In communication systems, a proposed FPGA-based authentication setup utilizes an advanced version of the Golay alongside a Flexible Floating Point Processing Element (FFPPE) architecture to enhance fingerprint authentication accuracy. This study features a GOLAY code-integrated FFPPE design comprising various stages, including Timestamp (TS) output with a time to digital converter (TDC), a random number generator, and error correction components involving extended encoding and decoding processes, as well as fingerprint verification techniques. A novel compact integrated circuit (IC) is designed using FFPPE, which plays a key role in operations like multiplication, addition, and subtraction via an Arithmetic Logic Unit (ALU). The binary data structure is built on a Cyclic Redundancy Check (CRC) framework, using both encoders and decoders. This architecture enhances system security while optimizing circuit complexity. Within the error correction module, a polar code decoder tailored for extended Golay code is presented specifically for the fingerprint authentication system. The performance metrics are assessed in terms of look-up table (LUT) optimization, area, power, speed, circuit complexity, power consumption, slice count, clock frequency, false rejection rate (FRR), false acceptance rate (FAR), and total success rate (TSR), and these are compared against existing fingerprint authentication systems. A comparative evaluation between the proposed and existing methodologies is conducted based on multiple parameters to validate the efficacy of the proposed approach. Results indicate that the proposed approach surpasses traditional techniques in bolstering system security. Additionally, FPGA synthesis analysis is carried out across different FPGA families, including Virtex4, Virtex5, and Virtex7. [ABSTRACT FROM AUTHOR]

This paper presents a graphene–gold-metasurface sensor designed for rapid and precise detection of heavy metals, particularly Cu2+ and Mg2+ ions, in water samples. By employing COMSOL simulations, the sensor's performance across various cases was comprehensively analyzed. The sensor demonstrates an optimized sensitivity of 1140 GHz/RIU for Cu2+ detection and 1149 GHz/RIU for Mg2+ detection. Furthermore, this sensor achieves minimum figure of merit (FOM) of 2.781 RIU−1 and 2.855 RIU−1 for Cu2+and Mg2+ detection, respectively, showcasing its superior performance in sensing these metal ions. Also the highest quality factors (Q factors) of 11.244 for Cu2+ detection and Q factor 11.247 for Mg2+ detection are attained. The proposed sensor structure additionally demonstrates very low detection limits- which is 0.466 for Cu2+ and 0.485 for Mg2+ detection-and this implies it can detect trace amounts without any difficulty. The excellent promising features of this sensor position it as a self directed candidate for detection of Cu2+ and Mg2+ ions with high accuracy and sensitivity. These solid applications act as the gateway to the monitoring of environmental cleanliness among other applications. Additionally,the proposed sensor can also be used as a 2-bit encoder. [ABSTRACT FROM AUTHOR]

In order to solve the dynamic balancing problem of rigid rotor, a comprehensive experimental balancing method based on tension and pressure sensor was proposed. Firstly, based on the principle of dynamic balance, the experimental equivalent model of rotor unbalance was constructed, and the theoretical unbalance was solved to ensure the rigor and rationality of the experiment. Then, the structural design, instrument and equipment were selected, the rotor dynamic balance experiment platform was built to ensure the stability and repeatability of test data. Finally, the platform was used to collect mechanical performance data, the amplitude and phase of shafting unbalance were analyzed, and the rotor dynamic balancing experiment was carried out by adding balance mass, and the feasibility and effectiveness of the comprehensive experimental balance method based on tension and pressure sensor were verified. The experimental results show that the unbalance mass amplitudes of the left and right ends are respectively 2. 2 kg and - 3. 8 kg, and the unbalance mass amplitudes of the left and right ends are respectively 1. 2 kg and - 1. 4 kg; the unbalance of the rotor system is reduced by 1. 4 kg, and the balance rate is 87. 5%, which verifies the feasibility of the balancing method based on the cooperation of tension pressure sensor and encoder to eliminate unbalance. This experimental platform lies in the use of tension pressure sensor and encoder to accurately measure unbalance mass and phase, provides a new idea for solving the dynamic balance problem of rotating machinery, and is helpful to improve the operation efficiency and prolong the service life of rotating machinery. [ABSTRACT FROM AUTHOR]

Copyright of Revista Kronos is the property of Revista Kronos and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Signal Processing is the property of Journal of Signal Processing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In recent days, compressive video sensing combined both compression and video sensing into a single process, and has gained immense popularity in directly attaining compressed video data through arbitrary projections of each frame. However, it is a major issue in generating sophisticated videos. This paper devises a technique, namely a Shuffled sailfish optimizer (SSFO) for compressive video sensing using an encoder and decoder. The input video is divided into various Groups of Pictures and non-key frames. The video frames are divided into non-over-blocking blocks and every block is termed a vectorized column. The measurement vectors are quantized with Space Time Quantization and the bits linked with GOP are crowded in the packet and fed to the decoder once undergoing Huffman encoding. Once the decoder obtains the packet, it rebuilds GOP, and then the joint reconstruction is done with the proposed SSFO technique. Here, the proposed SSFO is obtained by combining the shuffled shepherd optimization algorithm, and the Sailfish optimizer. It utilizes a similar measurement matrix. The proposed SSFO outperformed with the highest Peak signal to noise ratio of 54.362 dB, Second derivative like measure of enhancement of 58.081 dB and structural similarity index measure of 0.927. [ABSTRACT FROM AUTHOR]

The bose-chaudhuri-hocquenghem (BCH) codes are a cyclic error correction codes (ECC) class. The BCH is constructed by using a polynomial over the Galois field. The BCH codes can detect and correct the multi-bits with an easy decoding mechanism. The BCH codes are used in most of the storage device's cryptography, disk drives, and satellite applications. This manuscript presents an efficient high-throughput BCH module with an encoding and decoding mechanism for multi-bit corrections. The BCH code of (15, k) is used to construct the encoder and decoder architectures. The BCH encoder decoder (ED) module with single error correction (SEC), double error correction (DEC), and triple-error correction (TEC) are discussed in detail. The BCH encoder module uses a linear feedback shift register (LFSR). The BCH decoder with SEC and DEC is constructed using the syndrome generator module (SGM) and chien search module (CSM). The BCH decoder with TEC is designed using SGM, inversion-based berlekamp-massey-algorithm (BMA), and CSMs. The BCH-ED module with SEC, DEC, and TEC utilizes <1 % chip area on Artix-7 FPGA. The BCH-ED with SEC, DEC, and TEC achieves a throughput of 7.13 Gbps, 1.2 Gbps, and 0.803 Gbps, respectively. Lastly, the BCH module is compared with existing BCH approaches with better improvement in chip area, frequency, and throughput parameters. [ABSTRACT FROM AUTHOR]

In this paper, the graph segmentation (GSeg) method has been proposed. This solution is a novel graph neural network framework for network embedding that leverages the inherent characteristics of nodes and the underlying local network topology. The key innovation of GSeg lies in its encoder-decoder architecture, which is specifically designed to preserve the network’s structural properties. The key contributions of GSeg are: (1) a novel graph neural network architecture that effectively captures local and global network structures, and (2) a robust node representation learning approach that achieves superior performance in various network analysis tasks. The methodology employed in our study involves the utilization of a graph neural network framework for the acquisition of node representations. The design leverages the inherent characteristics of nodes and the underlying local network topology. To enhance the architectural framework of encoder- decoder networks, the GSeg model is specifically devised to exhibit a structural resemblance to the SegNet model. The obtained empirical results on multiple benchmark datasets demonstrate that the GSeg outperforms existing state-of-the-art methods in terms of network structure preservation and prediction accuracy for downstream tasks. The proposed technique has potential utility across a range of practical applications in the real world.

Knowledge graph completion is a process of reasoning new triples based on existing entities and relations in knowledge graph. The existing methods usually use the encoder-decoder framework. Encoder uses graph convolutional neural network to get the embeddings of entities and relations. Decoder calculates the score of each tail entity according to the embeddings of the entities and relations. The tail entity with the highest score is the inference result. Decoder inferences triples independently, without consideration of graph information. Therefore, this paper proposes a graph completion algorithm based on contrastive learning. This paper adds a multi-view contrastive learning framework into the model to constrain the embedded information at graph level. The comparison of multiple views in the model constructs different distribution spaces for relations. Different distributions of relations fit each other, which is more suitable for completion tasks. Contrastive learning constraints the embedding vectors of entity and subgraph and enhahces peroformance of the task. Experiments are carried out on two datasets. The results show that MRR is improved by 12.6% over method A2N and 0.8% over InteractE on FB15k-237 dataset, and 7.3% over A2N and 4.3% over InteractE on WN18RR dataset. Experimental results demonstrate that this model outperforms other completion methods.

A novel 4-to-2 photonic crystal encoder is proposed by modulating the intensity of four input optical signals, and four distinct output states are achieved. Nonlinear rods are employed to couple input waves into resonant cavities, directing the light to the desired output waveguides. The proposed design, with a footprint of 114 µm2, demonstrates efficient encoding operation at a wavelength of 1550 nm and is highly suitable for integrated photonics applications. A comprehensive comparative analysis revealed that the proposed 4-to-2 encoder exhibits a time response 176 fs faster than previously presented encoders. Furthermore, the contrast ratio of the designed structure is as high as 13.78 dB to distinguish between logic 0 and 1. These advancements hold significant potential for enhancing the performance of compact, high-speed digital circuits.