ICT Express最新文献

The proposed idea is to give all the agricultural stakeholders secure storage. We must automate several processes utilizing brilliant codes to reduce risks and errors. The suggested schema applies Blockchain, source codes, and IoT on a farm network to enhance the analysis of agrarian datasets and tracking products to raise the productivity of agro-based supply chains. The application’s architecture will fix the faults found in earlier research. In the suggested method, sensors give us information about the environment. The Blockchain ledger stores our data in blocks. We create special agricultural automated codes in the treatment layer to automate task decisions.

In this paper, a bi-directional sliding window decoder is proposed for spatially coupled low-density parity-check (SC-LDPC) codes, which improves the decoding complexity and performance compared to the conventional sliding window decoding (SWD) by sharing messages at the overlapped part of forward and backward decoding windows. Moreover, by using proper scaling factors that determine the weight of each message at the overlapped part of two sliding windows, good local decoding effects can be efficiently spread out to both ends of SC-LDPC code during decoding process. Such effective message updates of the proposed bi-directional overlapped sliding window decoding (BO-SWD) improve error floor performance compared to the conventional SWD. The validity of BO-SWD is verified by simulation with various SC-LDPC ensembles.

This study introduces an advanced artificial model, grey wolf optimization (GWO)-nested complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN)-convolutional neural network (CNN)-bidirectional long short-term memory (BiLSTM), for wind speed forecasting. Initially, CEEMDAN with two nested layers decomposes the time series into intrinsic mode functions (IMFs) to enhance forecasting capabilities. Subsequently, CNN extracts features from IMFs, and BiLSTM captures temporal dependencies for precise predictions. GWO further enhances the accurac by selecting optimal hyperparameters based on decomposition results. Test results on diverse wind speed datasets demonstrate the model’s superiority, with a mean absolute percentage error (MAPE) of approximately 3%.

This paper proposes a channel response generative adversarial network (CRGAN)-based turbo code interleaver that estimates a channel response and interleaver indices at a transmitter by using a sound speed profile (SSP) and the ocean environments without feedback from a receiver. The interleaver indices are designed to allocate important bits from the turbo code to subcarriers with great channel gains, which reduces them from being affected by deep fading. Computer simulations and practical ocean experiments demonstrate that the proposed method estimates the channel response with low mean squared errors (MSEs) and improves bit error rate (BER) performances compared with the conventional method.

Photovoltaic power generation forecasting is crucial for energy management, smart grid construction, and energy markets. This study proposes a hybrid quantum–classical gated recurrent unit (HQGRU)-based framework for forecasting short-term photovoltaic power generation in a time-series manner. The HQGRU model uses a classical layer followed by a quantum embedding circuit to convert classical data into quantum data. Subsequently, variational quantum circuits are used for feature extraction. To demonstrate the performance of the proposed model, we used practical data on photovoltaic power generation and the weather in Busan, Republic of Korea. The results demonstrate the high accuracy of the proposed HQGRU model.

In this study, we present the Discriminative Temporal Shift Module (D-TSM), an enhancement of the Temporal Shift Module (TSM) for action recognition. TSM has limitations in capturing intricate temporal dynamics due to its simplistic feature shifting. D-TSM addresses this by introducing a subtraction operation before the shifting. This enables the extraction of discriminative features between adjacent frames, thereby allowing for effective action recognition where subtle motions serve as crucial cues. It preserves TSM’s foundational philosophy, prioritizing minimal computational overhead and no additional parameters. Our experiments demonstrate that D-TSM significantly improves performance of TSM and outperforms other leading 2D CNN-based methods.

Implementation of malware detection using Artificial Intelligence (AI) has emerged as a significant research theme to combat evolving various types of malwares. Researchers implement various detection mechanisms using shallow and deep learning models to counter new malware, and they continue to develop these mechanisms today. However, in the field of malware detection using AI, there are difficulties in collecting data, and it is difficult to compare research content and performance with related studies. Meanwhile, the number of well-organized papers is not sufficient to understand the overall research flow of these related studies. Before starting new research, researchers need to analyze the current state of research in the malware detection field they want to study. Therefore, based on these requirements, we present a summary of the general criteria related to malware detection and a classification table for detection mechanisms. Additionally, we have organized many studies in the field of various types of malware detection so that they can be viewed at a glance. We hope that the provided survey can help new researchers quickly understand the research flow in the field of AI-based malware detection and establish the direction for future research.

Recently, as data demand has increased owing to the rapidly increasing demand for wireless devices and the influence of data traffic, various technologies are being developed to support it. Among them, millimeter-wave (mmWave) frequencies with rich spectra and high data-transmission rates suffer from the problem of large path loss. Accordingly, there is a growing interest in unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RISs), which can be utilized advantageously to reconstruct wireless communication environments. Therefore, this work considers a large-scale system comprising a number of users and Flying RISs, combining UAVs and RISs to increase algorithm utilization. We propose a deep neural network-based algorithm that places Flying RISs in an appropriate location so that they can support as many users as possible. Simulation results confirmed that the proposed technique could place Flying RISs in an efficient location with higher accuracy and speed in large-scale systems compared to existing techniques.

With the rapid technological development, identifying the attackers behind cyber-attacks is getting more sophisticated. To cope with this phenomenon, the current process of cyber-threat attribution includes features like tactics techniques and procedures (TTP), tools, target country/ company and application. They do not include attacker context and motives; thus, they demand more refined traits. Adding behavioral features to this process is essential to better understand the attacker’s context, motivations and goals. This research study accentuates the impact of adding behavioral features with existing technical features in determining the actual actor. The behavioral features are extracted from Threat actor encyclopedia, a dataset published by Thai CERT. This research investigation also analyzes the impact of hybrid features (technical & and behavioral). For this procedure, the best features are chosen by implementing feature selection techniques. For empirical results, we use the threat actor encyclopedia, a data set published by Thai Cert, for extraction of behavioral attributes. With this augmentation, we achieve elevated results of 97%, 98.8%, 97%, and 97.2% in terms of accuracy, precision, recall and F1-measure using machine/deep learning algorithms.

In CRNs, it is crucial to develop an efficient and reliable spectrum detector that consistently provides accurate information about the channel state. In this work, we investigate a CSS in a fully-distributed environment where all secondary users (SUs) are equipped with directional antennas and make decisions based solely on their local knowledge without information sharing between SUs. First, we establish a stochastic sequential optimization problem, which is an NP-hard, that maximizes the SU’s detection accuracy by the dynamic and optimal control of the energy sensing/detection threshold. It can enable SUs to select an available channel and sector without causing interference to the primary network. To address it in a distributed environment, the problem is transformed into a decentralized partially observed Markov decision process (Dec-POMDP) problem. Second, in order to determine the best control for the Dec-POMDP in a practical environment without any prior knowledge of state–action transition probabilities, we develop a multi-agent deep deterministic policy gradient (MADDPG)-based algorithm, which is referred to as MA-DCSS. This algorithm adopts the centralized training and decentralized execution (CTDE) architecture. Third, we analyzed its computational complexity and showed the proposed approach’s scalability by the polynomial computational complexity, in terms of the number of channels, sectors, and SUs. Lastly, the simulation confirms that the proposed scheme provides enhanced performance in terms of convergence speed, accurate detection, and false alarm probabilities when it is compared to baseline algorithms.