Search Results (15,360)

In this paper, we investigate a long-term power minimization problem of cell-free massive multiple-input multiple-output (MIMO) systems. To address this issue and to ensure the system queue stability, we formulate a dynamic optimization problem aiming to minimize the average total power cost in a time-varying system under imperfect channel conditions. The problem is then converted into a real-time weighted sum rate maximization problem for each time slot using the Lyapunov optimization technique. We employ approximation techniques to design robust sparse beamforming, which enables energy savings of the network and mitigates channel uncertainty. By applying direct fractional programming (DFP) and alternating optimization, we can obtain a locally optimal solution. Our DFP-based algorithm minimizes the average total power consumption of the network while satisfying the quality of service requirements for each user. Simulation results demonstrate the rapid convergence of the proposed algorithm and illustrate the tradeoff between average network power consumption and queue latency. Full article

With the development of low-orbit mega-constellations, low-orbit navigation augmentation systems, and other emerging LEO projects, the tracking accuracy requirement for low-orbit satellites is constantly increasing. However, existing methods have obvious shortcomings, and a new tracking and measurement method for LEO satellites is thus urgently needed. Given this, in this paper, a Connected Element Interferometry (CEI)-based “near-field” measurement model for low-orbit satellites is proposed. On this basis, the goniometric error formula of the model is derived, and the factors included in each error source are briefly discussed, followed by the simplification of the error formula. Furthermore, for the feasibility analysis of the proposed method, the common view time of CEI array on LEO satellites is analyzed in different regions and different baseline lengths. Finally, this paper simulates the effects of satellite–station distance, baseline length, and goniometric angle on the error coefficients in the goniometric error formula, and provides the theoretical goniometric accuracy of this model for different baseline lengths and goniometric angles. Under a baseline length of 240 km, the accuracy can reach 10 nrad. The research results of this paper could play the role of theoretical a priori in accuracy prediction in future low-orbit satellite tracking measurements. Full article

Stochastic Computing (SC) is an alternative way of computing with binary weighted words that can significantly reduce hardware resources. This technique relies on transforming information from a conventional binary system to the probability domain in order to perform mathematical operations based on probability theory, where smaller amounts of binary logic elements are required. Despite the advantage of computing with reduced circuitry, SC has a well known issue; the input interface known as stochastic number generator (SNG), is a hardware consuming module, which is disadvantageous for small digital circuits or circuits with several input data. Hence, in this work, efforts are dedicated to improving a classic weighted binary SNG (WBSNG). For this, one of the internal modules (weight generator) of the SNG was redesigned by detecting a pattern in the involved signals that helped to pose the problem in a different way, yielding equivalent results. This greatly reduced the number of logical elements used in its implementation. This pattern is interpreted with Boolean equations and transferred to a digital circuit that achieves the same behavior of a WBSNG but with less resources. Full article

Detecting anomalies, intrusions, and security threats in the network (including Internet of Things) traffic necessitates the processing of large volumes of sensitive data, which raises concerns about privacy and security. Federated learning, a distributed machine learning approach, enables multiple parties to collaboratively train a shared model while preserving data decentralization and privacy. In a federated learning environment, instead of training and evaluating the model on a single machine, each client learns a local model with the same structure but is trained on different local datasets. These local models are then communicated to an aggregation server that employs federated averaging to aggregate them and produce an optimized global model. This approach offers significant benefits for developing efficient and effective intrusion detection system (IDS) solutions. In this research, we investigated the effectiveness of federated learning for IDSs and compared it with that of traditional deep learning models. Our findings demonstrate that federated learning, by utilizing random client selection, achieved higher accuracy and lower loss compared to deep learning, particularly in scenarios emphasizing data privacy and security. Our experiments highlight the capability of federated learning to create global models without sharing sensitive data, thereby mitigating the risks associated with data breaches or leakage. The results suggest that federated averaging in federated learning has the potential to revolutionize the development of IDS solutions, thus making them more secure, efficient, and effective. Full article

Oil refineries have high operating expenses and are often exposed to increased asset integrity risks and functional failure. Real-time monitoring of their operations has always been critical to ensuring safety and efficiency. We proposed a novel Industrial Internet of Things (IIoT) design that employs a text-to-speech synthesizer (TTS) based on neural networks to build an intelligent extension control system. We enhanced a TTS model to achieve high inference speed by employing HiFi-GAN V3 vocoder in the acoustic model FastSpeech 2. We experimented with our system on a low resources-embedded system in a real-time environment. Moreover, we customized the TTS model to generate two target speakers (female and male) using a small dataset. We performed an ablation analysis by conducting experiments to evaluate the performance of our design (IoT connectivity, memory usage, inference speed, and output speech quality). The results demonstrated that our system Real-Time Factor (RTF) is 6.4 (without deploying the cache mechanism, which is a technique to call the previously synthesized speech sentences in our system memory). Using the cache mechanism, our proposed model successfully runs on a low-resource computational device with real-time speed (RTF equals 0.16, 0.19, and 0.29 when the memory has 250, 500, and 1000 WAV files, respectively). Additionally, applying the cache mechanism has reduced memory usage percentage from 16.3% (for synthesizing a sentence of ten seconds) to 6.3%. Furthermore, according to the objective speech quality evaluation, our TTS model is superior to the baseline TTS model. Full article

The knowledge of signal demodulation processes using superheterodyne receivers is of great importance in the field of telecommunications. Superheterodyne receivers receive the current focus of many scientists in a wide variety of applications. This topic is part of the syllabus of the Communication Theory course at the School of Telecommunications of the Polytechnic University of Cartagena, Spain. The authors found that the academic performance of the students had not been entirely satisfactory in recent years. This situation was aggravated during the COVID-19 lockdown. Students had to reinforce their knowledge independently at home, despite the support provided by teachers. To the best of the authors’ knowledge, there is a noticeable lack of educational tools in this area; of those that are available, they exhibit a mismatch with the specific needs of this subject. This manuscript shows how the design of the educational software tool DOSHER, tailored to enhance the understanding of superheterodyne receivers, successfully alleviated the aforementioned drawbacks. DOSHER was designed, developed, and applied during the 2020–2021 academic year (during the COVID-19 lockdown). The results show that students were not only very satisfied with its use, but they also improved their marks. Analysis of students’ academic performance in the year of DOSHER implementation showed an average improvement in their marks of between 9–12% compared with previous years. In 2021–2022, when DOSHER was fully operational from the start, the improvement in terms of pass rate (31%) at the first mid-term was significant compared to previous years (<20%). Full article

This paper investigates the problem of trajectory tracking control in the presence of bounded model uncertainty and external disturbance. To cope with this problem, we propose a novel intelligent operator-based sliding mode control scheme for stability guarantee and control performance improvement in the closed-loop system. Firstly, robust stability is guaranteed by using the operator-based robust right coprime factorization method. Secondly, in order to further achieve the asymptotic tracking and enhance the responsiveness to disturbance, a finite-time integral sliding mode control law is designed for fast convergence and non-zero steady-state error in accordance with Lyapunov stability analysis. Lastly, the controller’s parameters are automatically adjusted by the proved stabilizing particle swarm optimization with the linear time-varying inertia weight, which significantly saves tuning time with a remarkable performance guarantee. The effectiveness and efficiency of the proposed method are verified on a highly nonlinear ionic polymer metal composite application. The extensive numerical simulations are conducted and the results show that the proposed method is superior to the state-of-the-art methods in terms of tracking accuracy and high robustness against disturbances. Full article

The study of the multi-agent task allocation problem with multiple depots is crucial for investigating multi-agent collaboration. Although many traditional heuristic algorithms can be adopted to handle the concerned task allocation problem, they are not able to efficiently obtain optimal or suboptimal solutions. To this end, a graph attention pointer network is built in this paper to deal with the multi-agent task allocation problem. Specifically, the multi-head attention mechanism is employed for the feature extraction of nodes, and a pointer network with parallel two-way selection and parallel output is introduced to further improve the performance of multi-agent cooperation and the efficiency of task allocation. Experimental results are provided to show that the presented graph attention pointer network outperforms the traditional heuristic algorithms. Full article

In this work, a tunable pseudo-resistor was designed, simulated, and tested using a 0.35 µm CMOS technology. The proposal used a compact voltage bias circuit free of body-effect, allowing a constant resistance value over the pseudo-resistor’s dynamic range, improving its linearity. A fabricated cell was characterized providing a resistance value from 300 kΩ to 10 GΩ with a THD from <2.5% to 1 GΩ. Additionally, the pseudo-resistor was incorporated into a high-pass OTA filter showing a THD below 0.2% for input voltages in the range ≤ 0.3 V_p. The simulations were compared with experimental measurements in a CMOS-fabricated cell, which verified the proposal’s feasibility. Full article

As smart transportation systems evolve, secure and efficient V2X communication between vehicles and infrastructure becomes crucial. This paper introduces a Vehicle-to-Blockchain (V2B) communication architecture, leveraging blockchain technology for transparent and decentralized interactions. Our work contributes to the integration of blockchain into V2X and IoT for next-generation transportation systems. We propose several novel blockchain use cases, including a blockchain-based vehicle ownership system based on the multi-token standard, a vehicle scoring system, blockchain–IoT integration, and a decentralized ticket management system for transportation services. The architecture addresses key aspects, such as data integration, validity, and secure messaging, and introduces a decentralized payment system and marketplace for transportation in smart cities. We specifically emphasize the technical implementation of smart contracts for these use cases, underscoring their role in ensuring robust and reliable interactions. Through our decentralized approach, we pave the way for a transformative transportation ecosystem that is adaptable, resilient, and capable of meeting the evolving needs of smart cities. Full article

In this work, an IoT system with edge computing capability is proposed, facilitating the postoperative surveillance of patients who have undergone knee surgery. The main objective is to reliably identify whether a set of orthopedic rehabilitation exercises is executed correctly, which is critical since it is often necessary to supervise patients during the rehabilitation period so as to avoid injuries or long recovery periods. The proposed system leverages the Internet of Things (IoT) paradigm in combination with deep learning and edge computing to classify the extension–flexion movement of one’s knee via embedded machine learning (ML) classification algorithms. The contribution of the proposed work is multilayered, as this paper proposes a system tackling the challenges at the embedded system level, algorithmic level, and user-friendliness level considering a performance evaluation, including the metrics at the power consumption level, delay level, and throughput requirement level, as well as its accuracy and reliability. Furthermore, as an outcome of this work, a dataset of labeled knee movements is freely available to the research community with no limitations. It also provides real-time movement detection with an accuracy reaching 100%, which is achieved with an ML model trained to fit a low-cost off-the-shelf Bluetooth Low Energy platform. The proposed edge computing approach allows predictions to be performed on device rather than solely relying on a Cloud service. This yields critical benefits in terms of wireless bandwidth and power conservation, drastically enhancing device autonomy while delivering reduced event detection latency. In particular, the “on device” implementation is able to yield a drastic 99.9% wireless data transfer reduction, a critical 39% prediction delay reduction, and a valuable 17% increase in the event prediction rate considering a reference period of 60 s. Finally, enhanced privacy comprises another significant benefit from the implemented edge computing ML model, as sensitive data can be processed on site and only events or predictions are shared with medical personnel. Full article

Parking space recognition is an important part in the process of automatic parking, and it is also a key issue in the research field of automatic parking technology. The parking space recognition process was studied based on vision and the YOLOv5 target detection algorithm. Firstly, the fisheye camera around the body was calibrated using the Zhang Zhengyou calibration method, and then the corrected images captured by the camera were top-view transformed; then, the projected transformed images were stitched and fused in a unified coordinate system, and an improved image equalization processing fusion algorithm was used in order to improve the uneven image brightness in the parking space recognition process; after that, the fused images were input to the YOLOv5 target detection model for training and validation, and the results were compared with those of two other algorithms. Finally, the contours of the parking space were extracted based on OpenCV. The simulations and experiments proved that the brightness and sharpness of the fused images meet the requirements after image equalization, and the effectiveness of the parking space recognition method was also verified. Full article

Smart warehouses are revolutionizing traditional logistics operations by incorporating advanced technologies such as Internet of Things, robotics, and artificial intelligence. In these complex and dynamic environments, control and operation instructions need to be transmitted through wireless networks. Therefore, wireless communication plays a crucial role in enabling efficient and reliable operations. Meanwhile, channel measurements and modeling in smart warehouse scenarios are essential for understanding and optimizing wireless communication performance. By accurately characterizing radio channels, communication systems can be better designed and deployed to meet unique challenges in smart warehouse scenarios. In this paper, we present an overview of smart warehouse scenarios and explore channel characteristics in smart warehouse scenarios. We conducted a measurement campaign for millimeter wave radio channels in smart warehouse scenarios. A vector network analyzer-based channel sounder was exploited to record channel characteristics at 28 GHz. Based on the measurements, large-scale channel parameters, including path loss, root-mean-square (RMS) delay spread, and Rician K factor were investigated. The unique channel characteristics in smart warehouse scenarios were explored. Full article

The coronavirus epidemic has imposed a devastating impact on humans around the globe, causing profound anxiety, fear, and complex emotions and feelings. Vaccination against the new coronavirus has started, and people’s feelings are becoming more diverse and complicated. In the presented work, our goal is to use the deep learning (DL) technique to understand and elucidate their feelings. Due to the advancement of IT and internet facilities, people are socially connected to explain their emotions and sentiments. Among all social sites, Twitter is the most used platform among consumers and can assist scientists to comprehend people’s opinions related to anything. The major goal of this work is to understand the audience’s varying sentiments about the vaccination process by using data from Twitter. We have employed both the historic (All COVID-19 Vaccines Tweets Kaggle dataset) and real (tweets) data to analyze the people’s sentiments. Initially, a preprocessing step is applied to the input samples. Then, we use the FastText approach for computing semantically aware features. In the next step, we apply the Valence Aware Dictionary for sentiment Reasoner (VADER) method to assign the labels to the collected features as being positive, negative, or neutral. After this, a feature reduction step using the Non-Negative Matrix Factorization (NMF) approach is utilized to minimize the feature space. Finally, we have used the Random Multimodal Deep Learning (RMDL) classifier for sentiment prediction. We have confirmed through experimentation that our work is effective in examining the emotions of people toward the COVID-19 vaccines. The presented work has acquired an accuracy result of 94.81% which is showing the efficacy of our strategy. Other standard measures like precision, recall, F1-score, AUC, and confusion matrix are also reported to show the significance of our work. The work is aimed to improve public understanding of coronavirus vaccines which can help the health departments to stop the anti-vaccination leagues and motivate people to a booster dose of coronavirus. Full article

Measuring the radar cross-section (RCS) of a far-field (FF) target in engineering can be challenging, especially when remote measurement is difficult. To overcome this challenge, an FF RCS can be predicted by near-field (NF)-extrapolated transformation. However, due to the relative error between the theoretical and measured electric field (E-field) values in a NF, the extrapolation calculation of a FF can be carried out by correcting the NF amplitude. This paper proposes the use of the state space method (SSM) to estimate the amplitude-only of NF E-fields for improving the prediction accuracy of FFs. The simulation results demonstrate that the SSM can estimate NF amplitude, which can be transformed into a FF, and which can lead to improved prediction accuracy when compared to reference-FF-calculated and to circular-NF-to-FF-transform-(CNFFFT)-calculated RCSs. Full article