Typical functional diagrams and operation algorithms of tracking system of tracking radar of antiaircraft missile armament with digital processing of error signal are considered. It's showed that independent of th...
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advanced driver-assistance systems (ADASs) that provide machine support to avoid critical accidents are already in wide use in vehicles in today's market. SoCs for such systems have several requirements: 1) high c...
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advanced driver-assistance systems (ADASs) that provide machine support to avoid critical accidents are already in wide use in vehicles in today's market. SoCs for such systems have several requirements: 1) high computational performance to run several advancedalgorithms at low latency;2) low power consumption to permit running under the extreme heat and power conditions of real-world vehicles;and 3) high reliability to reduce the risk of serious accidents caused by faults. This article presents a novel SoC for ADAS applications, which resolves these difficult challenges. To achieve high performance with low power consumption, the SoC adopts the heterogeneous architecture, with ten processors, four DSPs, and eight types of accelerators, including the DNN accelerator and the image signal processor (ISP). To achieve higher reliability, the SoC implements several safety mechanisms, including system partitioning to prevent fault propagation, diagnostic features to detect faults, and a dedicated controller to operate runtime built-in self-test (BIST) of the ISP's diagnostic features during the vertical blanking interval (VBLANK). The SoC is implemented in a 16-nm process, and its size is 94.52 mm(2). Peak performance of 20.5 TOPS and low power consumption of 9.78 W are achieved.
This paper introduces the architecture of a convolutional autoencoder (CAE) for the task of peak-to-average power ratio (PAPR) reduction and waveform design, for orthogonal frequency division multiplexing (OFDM) syste...
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ISBN:
(纸本)9781728192901
This paper introduces the architecture of a convolutional autoencoder (CAE) for the task of peak-to-average power ratio (PAPR) reduction and waveform design, for orthogonal frequency division multiplexing (OFDM) systems. The proposed architecture integrates a PAPR reduction block and a nonlinear high power amplifier (HPA) model. We apply gradual loss learning for multi-objective optimization. We analyse the model's performance by examining the bit error rate (BER), the PAPR and the spectral response, and comparing them with common PAPR reduction algorithms.
The conventional high-resolution synthetic aperture radar(SAR) imagery based on Bayesian learning encounter the problems of static and inflexible of prior. In this paper, a novel Bayesian learning algorithm based on f...
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A method to detect the PD signals in the ring net cabinet when it is powered on based the pulse current method and 5G cloud computing is proposed in this work. During the experiment, a live indicator is used and the o...
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ISBN:
(纸本)9781665402651
A method to detect the PD signals in the ring net cabinet when it is powered on based the pulse current method and 5G cloud computing is proposed in this work. During the experiment, a live indicator is used and the operating condition of the ring network cabinet is considered. A better signal acquisition methods and signalprocessingalgorithms are applied to realize 5G wireless transmission. Phase resolved pulse sequence (PRPS) and phase resolved partial discharge (PRPD) can be intuitively displayed on the screen. Six insulation defects are selected and the effectiveness of the method is verified. This method is conducive to monitoring the PD signal of the ring network cabinet in the field.
This paper presents data fusion from multiple Global Navigation Satellite System (GNSS) constellations. GNSS brings more signals and more satellites to improve the accuracy of user's position. However, multiple fa...
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ISBN:
(纸本)9781728175133
This paper presents data fusion from multiple Global Navigation Satellite System (GNSS) constellations. GNSS brings more signals and more satellites to improve the accuracy of user's position. However, multiple failures in satellite's signals sometimes negatively impact the determination of the user's position and should be considered. For this purpose, the present paper provides robust Extended Kalman Filter (robust-EKF) to eliminate the outliers. The algorithms are tested by using GPS, Galileo and GLONASS data corresponding on data from base station GRAC in Grasse, France. Applying the robust-EKF method as well as the robust combination of GPS, Galileo, and GLONASS data improves the position accuracy by about 30.0%, 20.7%, and 90% compared to the use of GPS data only, Galileo data only, and GLONASS data only, respectively, and by about 67% compared to the non-robust combination of GPS, Galileo, and GLONASS data.
While the end-to-end speech recognition models show impressive performance on many domains, they have difficulties in decoding long-form utterances. The overlapped inference algorithm with tie-breaking between two par...
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While the end-to-end speech recognition models show impressive performance on many domains, they have difficulties in decoding long-form utterances. The overlapped inference algorithm with tie-breaking between two parallel hypotheses has been proposed for long-form speech recognition and shows dramatic performance improvements at the expense of double computational costs. In this paper, we propose a more effective way of overlapped inference by aligning partially matched hypotheses. Through the experiment on LibriSpeech dataset, the proposed algorithm showed improved performance with less computational cost compared to the conventional overlapped inference.
To improve the PLS for a MIMONOMA-based CRN, this paper proposes a transmit-zero-forcing beamforming technique to message alignment, using the ideal channel state data available at the earth station. The above investi...
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ISBN:
(纸本)9781665493970
To improve the PLS for a MIMONOMA-based CRN, this paper proposes a transmit-zero-forcing beamforming technique to message alignment, using the ideal channel state data available at the earth station. The above investigation makes use of a large constructability of several cells. Every group has a “primary user” and a “alternate user” who are responsible for implementing the CRN. Both the PU and SU of each cell are assigned locations at random. Finally, when only imperfect CSI is available at the ground station, we propose an eigen wideband technique to enhance the PLS for a CRN based on MIMO-NOMA. Optimal power allocation algorithms are created for both wideband methods to further improve the PLS. Finally, a closed-form expression for the probability of data lost due to Nakagami-m multi-path fading is presented. The following step involves contrasting the results of simulation studies and other methods currently in use with this statement.
The sophisticated Brain-Computer Interface (BCI) utilizes discrete or model-based control techniques to manage external devices. A continuous control strategy is vital for achieving optimal and smooth operation. Achie...
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ISBN:
(数字)9798350370942
ISBN:
(纸本)9798350370959
The sophisticated Brain-Computer Interface (BCI) utilizes discrete or model-based control techniques to manage external devices. A continuous control strategy is vital for achieving optimal and smooth operation. Achieving a continuous estimation of control parameters with minimal latency is crucial for enhancing the effectiveness and acceptance of mind-controlled prostheses, exoskeletons, and robotic arms by users. This research proposes a novel BCI model for controlling a robotic hand, incorporating Task Classification (TC) and Trajectory Estimation (TE) modules. The TC module interprets user intentions based on EEG signals, identifying tasks such as initiating, grasping, releasing, halting, or maintaining the current state. Upon detecting grasping (Task 2), the TE module estimates the 3D trajectory of hand movements, enabling precise control of the robotic hand. The control algorithm translates the estimated trajectory into commands for the robotic hand, facilitating object manipulation and task execution. Real-time feedback loops and error-handling mechanisms enhance user interaction and system robustness. Results demonstrate the efficacy of the integrated TC and TE modules in providing seamless control of the robotic hand, empowering individuals with motor disabilities to perform daily tasks independently. The implications of this work extend to the development of advanced BCIs, facilitating more natural and seamless communication between the human brain and technological devices.
Since recent years, denoising become one Of the most active area of research in image processing topic. Usually, MR images are affected by noise and artifacts during the acquisition process. Therefore, many denoising ...
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