Non-coherent detection for ultraviolet communications with inter-symbol interference

Ultraviolet communication (UVC) serves as a promising supplement to share the responsibility for the overloads in conventional wireless communication systems. One challenge for UVC lies in inter-symbol-interference (ISI), which combined with the ambient noise, contaminates the received signals and thereby deteriorates the communication accuracy. Existing coherent signal detection schemes (e.g. maximum likelihood sequence detection, MLSD) require channel state information (CSI) to compensate the channel ISI effect, thereby falling into either a long overhead and large computational complexity, or poor CSI acquisition that further hinders the detection performance. Non-coherent schemes for UVC, although capable of reducing the complexity, cannot provide high detection accuracy in the face of ISI. In this work, we propose a novel non-coherent paradigm via the exploration of the UV signal features that are insensitive to the ISI. By optimally weighting and combining the extracted features to minimize the bit error rate (BER), the optimally-weighted non-coherent detection (OWNCD) is proposed, which converts the signal detection with ISI into a binary detection framework with a heuristic decision threshold. As such, the proposed OWNCD avoids the complex CSI estimation and guarantees the detection accuracy. Compared to the state-of-the-art MLSD in the cases of static and time-varying CSI, the proposed OWNCD can gain ∼1 dB and 8 dB in signal-to-noise-ratio (SNR) at the 7% overhead FEC limit (BER of 4.5×10 −3 , respectively, and can also reduce the computational complexity by 4 order of magnitud

Optimising signal detection techniques in wireless ultraviolet communication systems

Wireless ultraviolet (UV) communication is regarded as a promising supplement to conventional wireless communications. The challenges lie in the inter-symbol-interference (ISI) and the time-varying channel impulse response (CIR), which deteriorate the detection/estimation of transmitted symbols from the received signals. The existing coherent detection schemes that leverage CIR estimation for ISI compensation, fall into two camps. They present either an overhead burden of pilot sequences and computational complexity or poor CIR acquisition that further hinders the detection performance. The aim of this thesis is to design non-coherent detection schemes that can transform the ISI contaminated sequential detection process into discrete binary or multiple detection framework. This is achieved by extracting the UV communication signal-related geometrical features that are inherently resistant to ISI. Then, one-dimensional and high-dimensional non-coherent detection schemes are proposed, by designing optimal linear and high-dimensional combinations of these features that minimize the theoretical bit error rate (BER). Both theoretical and simulation verification are performed to validate the proposed scheme, showing the comparable detection accuracy of the state-of-the-art coherent schemes but at the expense of lower computational complexity. To further expand the scope of the ISI-resistant features, machine learning is employed to discover nonlinear features that can express hidden relations from received signals to transmitted symbols. This is done by (i) a supervised neural network (NN) based detector, and (ii) a more explainable Parzen window technique based NN to approximate the detection likelihoods. For future work, deep reinforcement learning will be utilized to explore better ISI-resistant features for detection purposes. As such, by casting the complex sequential detection into the concise discrete detection framework, and combining the manual and machine learning-based ISI-resistant feature construction, this work provides a novel idea, not only for UV communications but can also be applied to other communication and signal detection scenarios suffering from ISI

Look, Listen and Learn - A Multimodal LSTM for Speaker Identification

Speaker identification refers to the task of localizing the face of a person who has the same identity as the ongoing voice in a video. This task not only requires collective perception over both visual and auditory signals, the robustness to handle severe quality degradations and unconstrained content variations are also indispensable. In this paper, we describe a novel multimodal Long Short-Term Memory (LSTM) architecture which seamlessly unifies both visual and auditory modalities from the beginning of each sequence input. The key idea is to extend the conventional LSTM by not only sharing weights across time steps, but also sharing weights across modalities. We show that modeling the temporal dependency across face and voice can significantly improve the robustness to content quality degradations and variations. We also found that our multimodal LSTM is robustness to distractors, namely the non-speaking identities. We applied our multimodal LSTM to The Big Bang Theory dataset and showed that our system outperforms the state-of-the-art systems in speaker identification with lower false alarm rate and higher recognition accuracy.Comment: The 30th AAAI Conference on Artificial Intelligence (AAAI-16

Sequential Bayesian Detection of Spike Activities from Fluorescence Observations

Extracting and detecting spike activities from the fluorescence observations is an important step in understanding how neuron systems work. The main challenge lies in that the combination of the ambient noise with dynamic baseline fluctuation, often contaminates the observations, thereby deteriorating the reliability of spike detection. This may be even worse in the face of the nonlinear biological process, the coupling interactions between spikes and baseline, and the unknown critical parameters of an underlying physiological model, in which erroneous estimations of parameters will affect the detection of spikes causing further error propagation. In this paper, we propose a random finite set (RFS) based Bayesian approach. The dynamic behaviors of spike sequence, fluctuated baseline and unknown parameters are formulated as one RFS. This RFS state is capable of distinguishing the hidden active/silent states induced by spike and non-spike activities respectively, thereby \emph{negating the interaction role} played by spikes and other factors. Then, premised on the RFS states, a Bayesian inference scheme is designed to simultaneously estimate the model parameters, baseline, and crucial spike activities. Our results demonstrate that the proposed scheme can gain an extra $12\%$ detection accuracy in comparison with the state-of-the-art MLSpike method

U-shaped relationship between managerial herd behavior and corporate financialization with the moderating effect of corporate governance: evidence from China

Based on behavioral finance theory, we discuss the influence of managers’ herd behavior on corporate financialization from the perspective of managers’ behavioral preferences. Empirical testing was conducted using data from nonfinancial listed firms on the Shanghai and Shenzhen  A-shares from 2007 to 2021 and a U-shaped relationship was found between managerial herd behavior and corporate financialization. When managerial herd behavior is within an appropriate range, the increase in managerial herd behavior has a negative influence on corporate financialization. In contrast, excessive managerial herd behavior leads to excessive corporate financialization. Additionally, corporate governance has a weakening effect on this relationship. Heterogeneity analyses indicate significant disparities in the effect of managerial herd behavior on corporate financialization among enterprises with diverse ownership structures. Finally, corporate financialization and innovation investments have an inverted U-shaped relationship, and their relationship is moderated positively by management herd behavior. Our results have strong practical significance for fostering the balanced growth of the financial sector and the real economy. First published online 05 January 202

Eavesdropping against bidirectional physical layer secret key generation in fiber communications

Physical layer secret key exploits the random but reciprocal channel features between legitimate users to encrypt their data against fiber-tapping. We propose a novel tapping-based eavesdropper scheme, leveraging its tapped signals from legitimate users to reconstruct their common features and the secret key.EU Horizon 2020: Grant No. 10100828

An extended framework of privacy-preserving computation with flexible access control

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