Frontiers of Information Technology & Electronic Engineering最新文献

A compact coupled-fed dual-antenna building block has been constructed in this study. The building block is simple in structure and easy to process, and has a high degree of isolation. The dual-antenna building block is composed of a coupled-fed loop antenna and a coupled-fed slot antenna that completely overlap. Based on this dual-antenna module, an eight-element MIMO system is designed, and the fabricated eight-element MIMO array is measured. The measured isolation of the designed eight-element MIMO system is >18.5 dB without any decoupling element. In addition, the MIMO array has good measured efficiencies, with a measured efficiency variation range of 43%–54% in the entire working frequency band. The measured ECC of the MIMO system is <0.02. Therefore, the designed MIMO array has great potential in 5G metal-rimmed mobile phone applications.

This paper presents a novel topology to control the baseband impedance of a power amplifier (PA) to avoid performance deterioration in concurrent dual-band mode. This topology can avoid pure resonance of capacitors and inductors LC, which leads to a high impedance at some frequency points. Consequently, it can be applied to transmitters that are excited by broadband signals. In particular, by adjusting the circuit parameters and increasing stages, the impedance of the key frequency bands can be flexibly controlled. A PA is designed to support this design idea. Its saturated output power is around 46.7 dBm, and the drain efficiency is >68.2% (1.8–2.3 GHz). Under concurrent two-tone excitation, the drain efficiency reaches around 40% even under 5.5 dB back-off power with the tone spacing from 10 MHz to 500 MHz. These results demonstrate that the proposed topology is capable of controlling wideband baseband impedance.

Magnetically driven microrobots hold great potential to perform specific tasks more locally and less invasively in the human body. To reach the lesion area in vivo, microrobots should usually be navigated in flowing blood, which is much more complex than static liquid. Therefore, it is more challenging to design a corresponding precise control scheme. A considerable amount of work has been done regarding control of magnetic microrobots in a flow and the corresponding theories. In this paper, we review and summarize the state-of-the-art research progress concerning magnetic microrobots in blood flow, including the establishment of flow systems, dynamics modeling of motion, and control methods. In addition, current challenges and limitations are discussed. We hope this work can shed light on the efficient control of microrobots in complex flow environments and accelerate the study of microrobots for clinical use.

Technological advancements continue to expand the communications industry’s potential. Images, which are an important component in strengthening communication, are widely available. Therefore, image quality assessment (IQA) is critical in improving content delivered to end users. Convolutional neural networks (CNNs) used in IQA face two common challenges. One issue is that these methods fail to provide the best representation of the image. The other issue is that the models have a large number of parameters, which easily leads to overfitting. To address these issues, the dense convolution network (DSC-Net), a deep learning model with fewer parameters, is proposed for no-reference image quality assessment (NR-IQA). Moreover, it is obvious that the use of multimodal data for deep learning has improved the performance of applications. As a result, multimodal dense convolution network (MDSC-Net) fuses the texture features extracted using the gray-level co-occurrence matrix (GLCM) method and spatial features extracted using DSC-Net and predicts the image quality. The performance of the proposed framework on the benchmark synthetic datasets LIVE, TID2013, and KADID-10k demonstrates that the MDSC-Net approach achieves good performance over state-of-the-art methods for the NR-IQA task.

Unsupervised domain adaptation enables neural networks to transfer from a labeled source domain to an unlabeled target domain by learning domain-invariant representations. Recent approaches achieve this by directly matching the marginal distributions of these two domains. Most of them, however, ignore exploration of the dynamic trade-off between domain alignment and semantic discrimination learning, thus rendering them susceptible to the problems of negative transfer and outlier samples. To address these issues, we introduce the dynamic parameterized learning framework. First, by exploring domain-level semantic knowledge, the dynamic alignment parameter is proposed, to adaptively adjust the optimization steps of domain alignment and semantic discrimination learning. Besides, for obtaining semantic-discriminative and domain-invariant representations, we propose to align training trajectories on both source and target domains. Comprehensive experiments are conducted to validate the effectiveness of the proposed methods, and extensive comparisons are conducted on seven datasets of three visual tasks to demonstrate their practicability.

Harmony search (HS) is a form of stochastic meta-heuristic inspired by the improvisation process of musicians. In this study, a modified HS with a hybrid cuckoo search (CS) operator, HS-CS, is proposed to enhance global search ability while avoiding falling into local optima. First, the randomness of the HS pitch disturbance adjusting method is analyzed to generate an adaptive inertia weight according to the quality of solutions in the harmony memory and to reconstruct the fine-tuning bandwidth optimization. This is to improve the efficiency and accuracy of HS algorithm optimization. Second, the CS operator is introduced to expand the scope of the solution space and improve the density of the population, which can quickly jump out of the local optimum in the randomly generated harmony and update stage. Finally, a dynamic parameter adjustment mechanism is set to improve the efficiency of optimization. Three theorems are proved to reveal HS-CS as a global convergence meta-heuristic algorithm. In addition, 12 benchmark functions are selected for the optimization solution to verify the performance of HS-CS. The analysis shows that HS-CS is significantly better than other algorithms in optimizing high-dimensional problems with strong robustness, high convergence speed, and high convergence accuracy. For further verification, HS-CS is used to optimize the back propagation neural network (BPNN) to extract weighted fuzzy production rules. Simulation results show that the BPNN optimized by HS-CS can obtain higher classification accuracy of weighted fuzzy production rules. Therefore, the proposed HS-CS is proved to be effective.

As one of the most fundamental topics in reinforcement learning (RL), sample efficiency is essential to the deployment of deep RL algorithms. Unlike most existing exploration methods that sample an action from different types of posterior distributions, we focus on the policy sampling process and propose an efficient selective sampling approach to improve sample efficiency by modeling the internal hierarchy of the environment. Specifically, we first employ clustering methods in the policy sampling process to generate an action candidate set. Then we introduce a clustering buffer for modeling the internal hierarchy, which consists of on-policy data, off-policy data, and expert data to evaluate actions from the clusters in the action candidate set in the exploration stage. In this way, our approach is able to take advantage of the supervision information in the expert demonstration data. Experiments on six different continuous locomotion environments demonstrate superior reinforcement learning performance and faster convergence of selective sampling. In particular, on the LGSVL task, our method can reduce the number of convergence steps by 46.7% and the convergence time by 28.5%. Furthermore, our code is open-source for reproducibility. The code is available at https://github.com/Shihwin/SelectiveSampling.

Embracing LLMs is definitely a correct and even necessary direction for software enterprises to improve quality and efficiency. However, achieving systematic and comprehensive intelligent software development still requires careful consideration and there is much fundamental work to do. For enterprises, solidifying the digitization and knowledge accumulation of software development, as well as the fundamental capabilities of software engineering such as requirement analysis, design, and validation, remains crucial and is also a basic condition for achieving higher levels of intelligent development. For academic research, there is still much work to do in the direction of systematic and comprehensive intelligent software development. This also requires us have a deeper understanding of the complexity of software systems and software requirements and design, based on understanding the capabilities of LLMs.

Heavy-duty diesel vehicles are important sources of urban nitrogen oxides (NO_x) in actual applications for environmental compliance, emitting more than 80% of NO_x and more than 90% of particulate matter (PM) in total vehicle emissions. The detection and control of heavy-duty diesel emissions are critical for protecting public health. Currently, vehicles on the road must be regularly tested, every six months or once a year, to filter out high-emission mobile sources at vehicle inspection stations. However, it is difficult to effectively screen high-emission vehicles in time with a long interval between annual inspections, and the fixed threshold cannot adapt to the dynamic changes of vehicle driving conditions. An on-board diagnostic device (OBD) is installed inside the vehicle and can record the vehicle’s emission data in real time. In this paper, we propose a temporal optimization long short-term memory (LSTM) and adaptive dynamic threshold approach to identify heavy-duty high-emitters by using OBD data, which can continuously track and record the emission status in real time. First, a temporal optimization LSTM emission prediction model is established to solve the attention bias discrepancy problem on time steps that is caused by the large number of OBD data streams in practice. Then, the concentration prediction error sequence is detected and distinguished from the anomalous emission contexts using flexible criteria, calculated by an adaptive dynamic threshold with changing driving conditions. Finally, a similarity metric strategy for the time series is introduced to correct some pseudo anomalous results. Experiments on three real OBD time-series emission datasets demonstrate that our method can achieve high accuracy anomalous emission identification.

How to collaboratively offload tasks between user devices, edge networks (ENs), and cloud data centers is an interesting and challenging research topic. In this paper, we investigate the offloading decision, analytical modeling, and system parameter optimization problem in a collaborative cloud–edge–device environment, aiming to trade off different performance measures. According to the differentiated delay requirements of tasks, we classify the tasks into delay-sensitive and delay-tolerant tasks. To meet the delay requirements of delay-sensitive tasks and process as many delay-tolerant tasks as possible, we propose a cloud–edge–device collaborative task offloading scheme, in which delay-sensitive and delay-tolerant tasks follow the access threshold policy and the loss policy, respectively. We establish a four-dimensional continuous-time Markov chain as the system model. By using the Gauss–Seidel method, we derive the stationary probability distribution of the system model. Accordingly, we present the blocking rate of delay-sensitive tasks and the average delay of these two types of tasks. Numerical experiments are conducted and analyzed to evaluate the system performance, and numerical simulations are presented to evaluate and validate the effectiveness of the proposed task offloading scheme. Finally, we optimize the access threshold in the EN buffer to obtain the minimum system cost with different proportions of delay-sensitive tasks.