237 research outputs found
The Misappropriation Theory Under the Chinese Securities Law - A Comparative Study With Its U.S. Counterpart
The first stock exchange in China, the Shanghai Stock Exchange, opened n December 1990. Since then, China’s securities market has been a journey of unprecedented development. However, the fledgling securities market is troubled by rampant securities fraud, evidence by Chinese officials’ open admission that investment in China’s securities market is very risky because of fraud and corruption. After a tortuous six-year drafting process, on December 29, 1998, the Chinese parliament passed the country’s first national Securities Law (“the Chinese Securities Law”), hoping to regulate the overwhelming fraud and corruption in China’s securities market. The Chinese Securities Law devoted one entire section of the Chapter “Securities Trade,” entitled “Prohibited Trading Activities,” to regulate securities fraud
The Chinese have transitioned directly to a mobile-only era
A successful example of China's mobile business models is how they now read digital novels in instalments, writes Winston Wenyan M
Movable Antennas for Wireless Communication: Opportunities and Challenges
Movable antenna (MA) technology is a recent development that fully exploits
the wireless channel spatial variation in a confined region by enabling local
movement of the antenna. Specifically, the positions of antennas at the
transmitter and/or receiver can be dynamically changed to obtain better channel
conditions for improving the communication performance. In this article, we
first provide an overview of the promising applications for MA-aided wireless
communication. Then, we present the hardware architecture and channel
characterization for MA systems, based on which the variation of the channel
gain with respect to the MA's position is illustrated. Furthermore, we analyze
the performance advantages of MAs over conventional fixed-position antennas, in
terms of signal power improvement, interference mitigation, flexible
beamforming, and spatial multiplexing. Finally, we discuss the main design
challenges and their potential solutions for MA-aided communication systems
Movable-Antenna Array Enhanced Beamforming: Achieving Full Array Gain with Null Steering
Conventional beamforming with fixed-position antenna (FPA) arrays has a
fundamental trade-off between maximizing the signal power (array gain) over a
desired direction and simultaneously minimizing the interference power over
undesired directions. To overcome this limitation, this letter investigates the
movable antenna (MA) array enhanced beamforming by exploiting the new degree of
freedom (DoF) via antenna position optimization, in addition to the design of
antenna weights. We show that by jointly optimizing the antenna positions
vector (APV) and antenna weights vector (AWV) of a linear MA array, the full
array gain can be achieved over the desired direction while null steering can
be realized over all undesired directions, under certain numbers of MAs and
null-steering directions. The optimal solutions for AWV and APV are derived in
closed form, which reveal that the optimal AWV for MA arrays requires only the
signal phase adjustment with a fixed amplitude. Numerical results validate our
analytical solutions for MA array beamforming and show their superior
performance to the conventional beamforming techniques with FPA arrays.Comment: Submitted to IEEE Communications Letter
Movable-Antenna Enhanced Multiuser Communication via Antenna Position Optimization
Movable antenna (MA) is a promising technology to improve wireless
communication performance by varying the antenna position in a given finite
area at the transceivers to create more favorable channel conditions. In this
paper, we investigate the MA-enhanced multiple-access channel (MAC) for the
uplink transmission from multiple users each equipped with a single MA to a
base station (BS) with a fixed-position antenna (FPA) array. A field-response
based channel model is used to characterize the multi-path channel between the
antenna array of the BS and each user's MA with a flexible position. To
evaluate the MAC performance gain provided by MAs, we formulate an optimization
problem for minimizing the total transmit power of users, subject to a
minimum-achievable-rate requirement for each user, where the positions of MAs
and the transmit powers of users, as well as the receive combining matrix at
the BS are jointly optimized. To solve this non-convex optimization problem
involving intricately coupled variables, we develop two algorithms based on
zero-forcing (ZF) and minimum mean square error (MMSE) combining methods,
respectively. Specifically, for each algorithm, the combining matrix of the BS
and the total transmit power of users are expressed as a function of the MAs'
position vectors, which are then optimized by using the gradient descent method
in an iterative manner. It is shown that the proposed ZF-based and MMSE-based
algorithms can converge to high-quality suboptimal solutions with low
computational complexities. Simulation results demonstrate that the proposed
solutions for MA-enhanced multiple access systems can significantly decrease
the total transmit power of users as compared to conventional FPA systems under
both perfect and imperfect field-response information.Comment: Submitted to IEEE Transactions on Wireless Communication
A temporal Convolutional Network for EMG compressed sensing reconstruction
Electromyography (EMG) plays a vital role in detecting medical abnormalities and analyzing the biomechanics of human or animal movements. However, long-term EMG signal monitoring will increase the bandwidth requirements and transmission system burden. Compressed sensing (CS) is attractive for resource-limited EMG signal monitoring. However, traditional CS reconstruction algorithms require prior knowledge of the signal, and the reconstruction process is inefficient. To solve this problem, this paper proposed a reconstruction algorithm based on deep learning, which combines the Temporal Convolutional Network (TCN) and the fully connected layer to learn the mapping relationship between the compressed measurement value and the original signal, and it has been verified in the Ninapro database. The results show that, for the same subject, compared with the traditional reconstruction algorithms orthogonal matching pursuit (OMP), basis pursuit (BP), and Modified Compressive Sampling Matching Pursuit (MCo), the reconstruction quality and efficiency of the proposed method is significantly improved under various compression ratios (CR)
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