The traditional styles of signalprocessing are passing constraints in their capability to handle the different and dynamic character of ultramodern data transfers. This is because communication systems are getting in...
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ISBN:
(数字)9798331543624
ISBN:
(纸本)9798331543631
The traditional styles of signalprocessing are passing constraints in their capability to handle the different and dynamic character of ultramodern data transfers. This is because communication systems are getting increasingly complicated. The operation of machine literacy presents a potentially useful answer since it enables data-driven, adaptive approaches that have the eventuality to ameliorate signalprocessing performance. This composition provides a comprehensive examination of a variety of machine literacy ways and their operations in important disciplines such as modulation recognition, noise reduction, channel estimation, and signal discovery. The study demonstrates how machine literacy ways may vastly increase the effectiveness, delicacy, and robustness of signal-processing tasks in communication systems. This is fulfilled through detailed analysis and comparison. With the help of machine literacy, unborn communication systems will be suitable to attain advanced situations of robotization and intelligence, which will pave the way for further developments in areas similar to wireless dispatches, the Internet of Effects(IoT), and other areas. In light of these findings, it's clear that machine literacy can fully transfigure assiduity by furnishing new shoes and approaches to the optimization of signalprocessing in the unborn generation of communication networks.
Google’s Tensor processing Unit (TPU) verifies the power of the systolic array architecture in accelerating specific computational tasks. Since the present Neural processing Unit (NPU) design faces challenges in mult...
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ISBN:
(数字)9798350360882
ISBN:
(纸本)9798350360899
Google’s Tensor processing Unit (TPU) verifies the power of the systolic array architecture in accelerating specific computational tasks. Since the present Neural processing Unit (NPU) design faces challenges in multitasking, this paper proposes a multitasking NPU architecture based on the systolic architecture. Performance improvement and latency optimization are achieved by using modules for an efficient scheduler and a mobile adder. The resultant NPU architecture facilitates parallel computation and task prioritization. Further to a random forest method to simulate hardware resource consumption, a genetic algorithms is used to achieve multi-task weight optimization, allocating resources to minimize consumption. Simulation results show a 26.2% improvement on processing element utilization, and a reduction on hardware logic resources by 5.1%, memory usage by 48.46% and simulation time by 60.38%.
Fault diagnosis is crucial for ensuring the stable operation of key components such as rotating machinery and rolling bearings in industrial equipment maintenance. To enhance the accuracy of fault diagnosis, this pape...
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ISBN:
(数字)9798331535087
ISBN:
(纸本)9798331535094
Fault diagnosis is crucial for ensuring the stable operation of key components such as rotating machinery and rolling bearings in industrial equipment maintenance. To enhance the accuracy of fault diagnosis, this paper proposes a novel method that combines Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Composite Multiscale Dispersion Entropy (CMDE). The method analyzes the vibration signal characteristics under different bearing conditions and performs classification using a Particle Swarm Optimization-based Extreme Learning Machine (PSO-ELM). First, CEEMDAN is employed to decompose the vibration signals and extract a series of Intrinsic Mode Functions (IMFs). Then, CMDE is used to extract feature vectors from these IMFs. Finally, PSO-ELM is utilized for fault classification. This study evaluates the impact of the optimized approach on diagnostic performance to demonstrate its effectiveness. Experiments were conducted using the Case Western Reserve University (CWRU) bearing dataset, and the results showed that the proposed method achieved a classification accuracy of 97.5%, demonstrating superior diagnostic performance compared to previous methods.
This research explores the latest advancements in channel estimation, precoding, and detection techniques within massive multiple-input multiple-output (MIMO) systems, which are crucial for the evolution of fifth-gene...
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ISBN:
(数字)9798350368697
ISBN:
(纸本)9798350368703
This research explores the latest advancements in channel estimation, precoding, and detection techniques within massive multiple-input multiple-output (MIMO) systems, which are crucial for the evolution of fifth-generation (5G) and beyond. As global data traffic surges, traditional methodologies face significant limitations, necessitating innovative approaches to enhance performance. This paper critically examines how these advanced techniques effectively address challenges such as increased spectral efficiency and reduced latency while significantly improving overall signalprocessing efficiency. This work presents practical applications of these methodologies, showcasing a detailed analysis of novel signal detection algorithms designed to maximize system performance in real-world scenarios. By leveraging state-of-the-art signalprocessing frameworks, it is demonstrated how these techniques enhance detection accuracy and optimize resource allocation, ensuring robust communication in dense user environments. Ultimately, this study underscores the transformative potential of massive MIMO in revolutionizing wireless communications. The findings offer critical insights and practical guidelines that contribute to advancing telecommunications infrastructure, equipping stakeholders to meet the dynamic demands of next-generation wireless networks. This research aims to inspire further exploration and development in this rapidly evolving field, establishing massive MIMO as a cornerstone of future connectivity solutions.
The article discusses a general approach to using the Karhunen-Loève transform to construct an optimal functional basis for coherent signalprocessing tasks. The potential gain is demonstrated by applying the syn...
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ISBN:
(数字)9798331520564
ISBN:
(纸本)9798331520571
The article discusses a general approach to using the Karhunen-Loève transform to construct an optimal functional basis for coherent signalprocessing tasks. The potential gain is demonstrated by applying the synthesized matched basis in filtering narrowband signals against a quasi-white noise background. An example of a PSK signal fragment illustrates the general approach to the synthesis and use of a synthesized linear basis space in matched filtering.
As spectrum congestion intensifies in wireless communication, efficient spectrum utilization through advanced sensing techniques has become increasingly important. This paper proposes a joint carrier frequency and two...
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ISBN:
(数字)9798350368741
ISBN:
(纸本)9798350368758
As spectrum congestion intensifies in wireless communication, efficient spectrum utilization through advanced sensing techniques has become increasingly important. This paper proposes a joint carrier frequency and two-dimensional (2-D) Direction of Arrival (DOA) estimation algorithm with signal recovery, utilizing identical-delay channels and sub-Nyquist sampling rates with a Uniform Rectangular Array (URA). Compared to multi-coset structures, the proposed method places identical-delay channels only along the edges of the URA, eliminating the need for additional ADCs and reducing hardware cost. Moreover, by leveraging tensor techniques, the spatial structure of the array is preserved, and the parameter pairing problem is avoided, leading to higher precision in estimation. Simulation results demonstrate the superior performance of the proposed method.
The paper presents a family of novel light blob shape descriptors for use in selected active safety algorithms used in advanced Driver Assistance Systems (ADAS). One of the motivations was to obtain a descriptor that ...
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Deep reinforcement learning (DRL) has demonstrated advanced ability to deal with complex decision making problems in autonomous driving. However, the driving demands (preferences) of passengers are hardly considered. ...
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ISBN:
(数字)9798331516550
ISBN:
(纸本)9798331516567
Deep reinforcement learning (DRL) has demonstrated advanced ability to deal with complex decision making problems in autonomous driving. However, the driving demands (preferences) of passengers are hardly considered. In addition, the preferences usually change over time, how to balance these preferences still remain challenge for traditional DRL. In this paper, we present a multi-objective DRL approach considering dynamic preferences. Specifically, we model the sequential decision making problem with dynamic preferences for lane merging as markov decision process. Additionally, the random sampling with prior knowledge is advanced to reduce the sampling space and promote convergence rate. Finally, the homotopy optimization is introduced to avoid overfitting and balance the multiple preferences. We test the effectiveness of our algorithm in a lane-merging scenario. The results show that our method out performs the baseline method.
Artificial Intelligence (AI) is revolutionising the telecommunications industry by enhancing signalprocessing, network management, and overall system performance. With the rise of 5G and IoT, AI has become a critical...
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ISBN:
(数字)9798331518523
ISBN:
(纸本)9798331518530
Artificial Intelligence (AI) is revolutionising the telecommunications industry by enhancing signalprocessing, network management, and overall system performance. With the rise of 5G and IoT, AI has become a critical technology driving innovation and efficiency. AI algorithms have improved error correction, data compression techniques, and bandwidth allocation, ensuring networks can handle growing data traffic volumes. AI's ability to predict, analyse, and adapt to real-time data has improved network management capabilities, allowing more intelligent resource allocation, reduced latency, and increased network reliability. AI-driven network automation has enabled providers to shift from reactive to proactive management, minimising downtime and enhancing user experience. This shift boosts operational efficiency and reduces costs by enabling predictive maintenance and optimising energy consumption in data centres and network infrastructure. The future of telecommunications will be shaped by AI technologies like machine learning, deep learning, and natural language processing, leading to advanced services like self-healing networks, real-time traffic management, and enhanced cybersecurity measures. As the telecommunication industry embraces AI-driven innovations, it will unlock new opportunities for growth and transform the everyday communication experience for billions of people worldwide.
With the continuous development of drone-related technologies, the application of UAV (Unmanned Aerial Vehicle) in urban low-altitude scenes is gradually increasing. Autonomous localization and perception of the surro...
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