Robust Information Hiding Based on Neural Style Transfer with Artificial Intelligence

This paper proposes an artificial intelligence-based robust information hiding algorithm to address the issue of confidential information being susceptible to noise attacks during transmission.The algorithm we designed aims to mitigate the impact of various noise attacks on the integrity of secret information during transmission.The method we propose involves encoding secret images into stylized encrypted images and applies adversarial transfer to both the style and content features of the original and embedded data.This process effectively enhances the concealment and imperceptibility of confidential information,thereby improving the security of such information during transmission and reducing security risks.Furthermore,we have designed a specialized attack layer to simulate real-world attacks and common noise scenarios encountered in practical environments.Through adversarial training,the algorithm is strengthened to enhance its resilience against attacks and overall robustness,ensuring better protection against potential threats.Experimental results demonstrate that our proposed algorithm successfully enhances the concealment and unknowability of secret information while maintaining embedding capacity.Additionally,it ensures the quality and fidelity of the stego image.The method we propose not only improves the security and robustness of information hiding technology but also holds practical application value in protecting sensitive data and ensuring the invisibility of confidential information.

Wireless Information and Power Transfer in Underwater Acoustic Sensor Networks

Wireless information and power transfer(WIPT) enables simultaneously communications and sustainable power supplement without the erection of power supply lines and the replacement operation of the batteries for the terminals. The application of WIPT to the underwater acoustic sensor networks(UWASNs) not only retains the long range communication capabilities, but also provides an auxiliary and convenient energy supplement way for the terminal sensors, and thus is a promising scheme to solve the energy-limited problem for the UWASNs. In this paper, we propose the integration of WIPT into the UWASNs and provide an overview on various enabling techniques for the WIPT based UWASNs(WIPT-UWASNs) as well as pointing out future research challenges and opportunities for WIPT-UWASNs.

Constructive Robust Steganography Algorithm Based on Style Transfer

Traditional information hiding techniques achieve information hiding by modifying carrier data,which can easily leave detectable traces that may be detected by steganalysis tools.Especially in image transmission,both geometric and non-geometric attacks can cause subtle changes in the pixels of the image during transmission.To overcome these challenges,we propose a constructive robust image steganography technique based on style transformation.Unlike traditional steganography,our algorithm does not involve any direct modifications to the carrier data.In this study,we constructed a mapping dictionary by setting the correspondence between binary codes and image categories and then used the mapping dictionary to map secret information to secret images.Through image semantic segmentation and style transfer techniques,we combined the style of secret images with the content of public images to generate stego images.This type of stego image can resist interference during public channel transmission,ensuring the secure transmission of information.At the receiving end,we input the stego image into a trained secret image reconstruction network,which can effectively reconstruct the original secret image and further recover the secret information through a mapping dictionary to ensure the security,accuracy,and efficient decoding of the information.The experimental results show that this constructive information hiding method based on style transfer improves the security of information hiding,enhances the robustness of the algorithm to various attacks,and ensures information security.

A Practical Regular LDPC Coded Scheme for Physical-Layer Information Security

In this paper,we aim to design a practical low complexity low-density parity-check(LDPC)coded scheme to build a secure open channel and protect information from eavesdropping.To this end,we first propose a punctured LDPC coded scheme,where the information bits in a codeword are punctured and only the parity check bits are transmitted to the receiver.We further propose a notion of check node type distribution and derive multi-edge type extrinsic information transfer functions to estimate the security performance,instead of the well-known weak metric bit error rate.We optimize the check node type distribution in terms of the signal-to-noise ratio(SNR)gap and modify the progressive edge growth algorithm to design finite-length codes.Numerical results show that our proposed scheme can achieve a lower computational complexity and a smaller security gap,compared to the existing scrambling and puncturing schemes.

Cultivated land information extraction in UAV imagery based on deep convolutional neural network and transfer learning

The development of precision agriculture demands high accuracy and efficiency of cultivated land information extraction. As a new means of monitoring the ground in recent years, unmanned aerial vehicle （UAV） low-height remote sensing technique, which is flexible, efficient with low cost and with high resolution, is widely applied to investing various resources. Based on this, a novel extraction method for cultivated land information based on Deep Convolutional Neural Network and Transfer Learning （DTCLE） was proposed. First, linear features （roads and ridges etc.） were excluded based on Deep Convolutional Neural Network （DCNN）. Next, feature extraction method learned from DCNN was used to cultivated land information extraction by introducing transfer learning mechanism. Last, cultivated land information extraction results were completed by the DTCLE and eCognifion for cultivated land information extraction （ECLE）. The location of the Pengzhou County and Guanghan County, Sichuan Province were selected for the experimental purpose. The experimental results showed that the overall precision for the experimental image 1, 2 and 3 （of extracting cultivated land） with the DTCLE method was 91.7%, 88.1% and 88.2% respectively, and the overall precision of ECLE is 9o.7%, 90.5% and 87.0%, respectively. Accuracy of DTCLE was equivalent to that of ECLE, and also outperformed ECLE in terms of integrity and continuity.

MINDTL： Multiple Incomplete Domains Transfer Learning for Information Recommendation

Collaborative filtering is the most popular and successful information recommendation technique. However, it can suffer from data sparsity issue in cases where the systems do not have sufficient domain information. Transfer learning, which enables information to be transferred from source domains to target domain, presents an unprecedented opportunity to alleviate this issue. A few recent works focus on transferring user-item rating information from a dense domain to a sparse target domain, while almost all methods need that each rating matrix in source domain to be extracted should be complete. To address this issue, in this paper we propose a novel multiple incomplete domains transfer learning model for cross-domain collaborative filtering. The transfer learning process consists of two steps. First, the user-item ratings information in incomplete source domains are compressed into multiple informative compact cluster-level matrixes, which are referred as codebooks. Second, we reconstruct the target matrix based on the codebooks. Specifically, for the purpose of maximizing the knowledge transfer, we design a new algorithm to learn the rating knowledge efficiently from multiple incomplete domains. Extensive experiments on real datasets demonstrate that our proposed approach significantly outperforms existing methods.

Secure Transmission Scheme Based on Dual-Thresholds for Simultaneous Wireless Information and Power Transfer

This paper studies a simultaneous wireless information and power transfer system with multiple external eavesdroppers and internal curious users.We model the random network by Poisson cluster process in consideration of the case where eavesdroppers hide around certain targets.Focusing on the users that work in harvesting-transmitting mode with time switching receivers,we establish communication model via time division multiple access.On this basis,we propose a lightweight secure transmission scheme based on dual-thresholds for physical-layer security enhancement,which consists of two protocols applied to the downlink(DL) and uplink(UL) transmission respectively.In the DL,we design a dynamic information-power switching transmission protocol based on signal-to-noise ratio threshold,which provides an opportunistic approach to reform the fixed period allocation of information and power transfer;in the UL,an energy threshold is proposed for users to control the transmission,which is called a user-led on-off transmission protocol.Furthermore,we give a comprehensive performance analysis for the proposed scheme in terms of delay,reliability,security and secrecy throughput.Based on the analysis results,we optimize the two thresholds and the DL-UL allocationcoefficient to maximize the secrecy throughput.Simulation results show the proposed scheme can bring about a substantial secrecy gain.

High-Power Simultaneous Wireless Information and Power Transfer: Injection-Locked Magnetron Technology

Applications using simultaneous wireless information and power transfer(SWIPT)have increased significantly.Wireless communication technologies can be combined with the Internet of Things to develop many innovative applications using SWIPT,which is mainly based on wireless energy harvesting from electromagnetic waves used in communications.Wireless power transfer that uses magnetrons has been developed for communication technologies.Injection-locked magnetrons that can be used to facilitate high-power SWIPT for several devices are reviewed in this paper.This new technology is expected to pave the way for promoting the application of SWIPT in a wide range of fields.

Multi-Sinusoidal Waveform Shaping for Integrated Data and Energy Transfer in Aging Channels

Integrated data and energy transfer(IDET)is capable of simultaneously delivering on-demand data and energy to low-power Internet of Everything(Io E)devices.We propose a multi-carrier IDET transceiver relying on superposition waveforms consisting of multi-sinusoidal signals for wireless energy transfer(WET)and orthogonal-frequency-divisionmultiplexing(OFDM)signals for wireless data transfer(WDT).The outdated channel state information(CSI)in aging channels is employed by the transmitter to shape IDET waveforms.With the constraints of transmission power and WDT requirement,the amplitudes and phases of the IDET waveform at the transmitter and the power splitter at the receiver are jointly optimised for maximising the average directcurrent(DC)among a limited number of transmission frames with the existence of carrier-frequencyoffset(CFO).For the amplitude optimisation,the original non-convex problem can be transformed into a reversed geometric programming problem,then it can be effectively solved with existing tools.As for the phase optimisation,the artificial bee colony(ABC)algorithm is invoked in order to deal with the nonconvexity.Iteration between the amplitude optimisation and phase optimisation yields our joint design.Numerical results demonstrate the advantage of our joint design for the IDET waveform shaping with the existence of the CFO and the outdated CSI.

Efficient scheme for entangled states and quantum information transfer with trapped atoms in a resonator

A protocol is proposed to generate atomic entangled states and implement quantum information transfer in a cavity quantum electrodynamics system. It utilizes Raman transitions or stimulated Raman adiabatic passages between two systems to entangle the ground states of two three-state A-type atoms trapped in a single mode cavity. It does not need the measurements on cavity field nor atomic detection and can be implemented in a deterministic fashion. Since the present protocol is insensitive to both cavity decay and atomic spontaneous emission, it may have some interesting applications in quantum information processing.

Scheme for realizing quantum computation and quantum information transfer with superconducting qubits coupling to a 1D transmission line resonator

This paper proposes a simple scheme for realizing one-qubit and two-qubit quantum gates as well as multiqubit entanglement based on de-SQUID charge qubits through the control of their coupling to a 1D transmission line resonator （TLR）. The TLR behaves effectively as a quantum data-bus mode of a harmonic oscillator, which has several practical advantages including strong coupling strength, reproducibility, immunity to 1/f noise, and suppressed spontaneous emission. In this protocol, the data-bus does not need to stay adiabatically in its ground state, which results in not only fast quantum operation, hut also high-fidelity quantum information processing. Also, it elaborates the transfer process with the 1D transmission line.

Bidirectional transfer of quantum information for unknown photons via cross-Kerr nonlinearity and photon-number-resolving measurement

We propose an arbitrary controlled-unitary （CU） gate and a bidirectional transfer scheme of quantum information （BTQI） for unknown photons. The proposed CU gate utilizes quantum non-demolition photon-number-resolving measure- ment based on the weak cross-Kerr nonlinearities （XKNLs） and two quantum bus beams; the proposed CU gate consists of consecutive operations of a controlled-path gate and a gathering-path gate. It is almost deterministic and is feasible with current technology when a strong amplitude of the coherent state and weak XKNLs are employed. Compared with the existing optical multi-qubit or controlled gates, which utilize XKNLs and homodyne detectors, the proposed CU gate can increase experimental realization feasibility and enhance robustness against decoherence. According to the CU gate, we present a BTQI scheme in which the two unknown states of photons between two parties （Alice and Bob） are mutually swapped by transferring only a single photon. Consequently, by using the proposed CU gate, it is possible to experimentally implement the BTQI scheme with a certain probability of success.

Reconstruction and implementation of information management system of library based on MARC

The limitations of MARC and the advantages of applying XML in the library are analyzed. We also have made partial reconstruction to the existing Information Management System of the HIT library and implementation of the prototype system. This research work will significantly influence the coordination of international library resource databases and the achievement of global information sharing.

The Image Property in an EIT Information Transfer System

We focus on the study of the transferred image property in an electromagnetically induced transparency （EIT）system. In our experiment, a triple-peak image is effectively transferred trom a coupling beam to a signal beam based on the FIT effect. It is found that the transferred image intensity profile of the signal beam is the same as that of the coupling beam while not in phase. Furthermore, the propagation property of the transferred image is studied. Due to the narrowing effect, the transferred image keeps narrowing and maintains the shape well within a certain distance outside of the medium. Our experimental results are in excellent agreement with the theoretical analysis.

Information Detection of Seismic Debris Flow by UAV High-resolution Image Based on Transfer Learning

A large number of debris flow disasters(called Seismic debris flows) would occur after an earthquake, which can cause a great amount of damage. UAV low-altitude remote sensing technology has become a means of quickly obtaining disaster information as it has the advantage of convenience and timeliness, but the spectral information of the image is so scarce, making it difficult to accurately detect the information of earthquake debris flow disasters. Based on the above problems, a seismic debris flow detection method based on transfer learning(TL) mechanism is proposed. On the basis of the constructed seismic debris flow disaster database, the features acquired from the training of the convolutional neural network(CNN) are transferred to the disaster information detection of the seismic debris flow. The automatic detection of earthquake debris flow disaster information is then completed, and the results of object-oriented seismic debris flow disaster information detection are compared and analyzed with the detection results supported by transfer learning.

Quantum Information Transfer from a Double Quantum Dot System to a Cavity Field

In this paper, we propose a scheme to realize quantum information transfer from a double quantum dot （DQD） system to a quantized cavity field. The DQD and the cavity field are treated as a two-state charge qubit and a continuous-variable system, respectively. It is shown that quantum information encoded in the two-state DQD system can be transferred to quantum states of the cavity field with a continuous-variable basis through appropriate projective measurements with respect to the DQD.

Scalable Quantum Information Transfer between Individual Nitrogen-Vacancy Centers by a Hybrid Quantum Interface

We develop a design of a hybrid quantum interface for quantum information transfer （QIT）, adopting a nanome- chanical resonator as the intermedium, which is magnetically coupled with individual nitrogen-vacancy centers as the solid qubits, while eapacitively coupled with a coplanar waveguide resonator as the quantum data bus. We describe the Hamiltonian of the model, and analytically demonstrate the QIT for both the resonant interaction and large detuning cases. The hybrid quantum interface allows for QIT between arbitrarily selected individual nitrogen-vacancy centers, and has advantages of the sealability and controllability. Our methods open an alter- native perspective for implementing QIT, which is important during quantum storing or processing procedures in quantum computing.

Information Transfer Index-A Promising Measure of the Corticomusclar Interaction

It is generally believed that a major cause of motor dysfunction is the impairment in neural network that controls movement. But little is known about the underlying mechanisms of the impairment in cortical control or in the neural connections between cortex and muscle that lead to the loss of motor ability. So understanding the functional connection between motor cortex and effector muscle is of utmost importance. Previous study mostly relied on cross-correlation, coherence functions or model based approaches such as Granger causality or dynamic causal modeling. In this work the information transfer index (ITI) was introduced to describe the information flows between motor cortex and muscle. Based on the information entropy the ITI can detect both linear and nonlinear interaction between two signals and thus represent a very comprehensive way to define the causality strength. The applicability of ITI is investigated based on simulations and electroencephalogram (EEG), surface electromyography (sEMG) recordings in a simple motor task.

Simultaneous wireless information and power transfer with fixed and adaptive modulation

Activating Wireless Power Transfer (WPT) in Radio-Frequency (RF) to provide on-demand energy supply to widely deployed Internet of Everything devices is a key to the next-generation energy self-sustainable 6G network. However, Simultaneous Wireless Information and Power Transfer (SWIPT) in the same RF bands is challenging. The majority of previous studies compared SWIPT performance to Gaussian signaling with an infinite alphabet, which is impossible to implement in any realistic communication system. In contrast, we study the SWIPT system in a well-known Nakagami-m wireless fading channel using practical modulation techniques with finite alphabet. The attainable rate-energy-reliability tradeoff and the corresponding rationale are revealed for fixed modulation schemes. Furthermore, an adaptive modulation-based transceiver is provided for further expanding the attainable rate-energy-reliability region based on various SWIPT performances of different modulation schemes. The modulation switching thresholds and transmit power allocation at the SWIPT transmitter and the power splitting ratios at the SWIPT receiver are jointly optimized to maximize the attainable spectrum efficiency of wireless information transfer while satisfying the WPT requirement and the instantaneous and average BER constraints. Numerical results demonstrate the SWIPT performance of various fixed modulation schemes in different fading conditions. The advantage of the adaptive modulation-based SWIPT transceiver is validated.

Crowd Counting Based on WiFi Channel State Information and Transfer Learning

With the popularity and development of indoor WiFi equipment, they have more sensing capability and can be used as a human monitoring device. We can collect the channel state information (CSI) from WiFi device and acquire the human state based on the measurements. These studies have attracted wide attention and become a hot research topic. This paper concentrated on the crowd counting based on CSI and transfer learning. We utilized the CSI signal fluctuations caused by human motion in WiFi coverage to identify the person count because different person counts would lead to unique signal propagation characteristics. First, this paper presented recent studies of crowd counting based on CSI. Then, we introduced the basic concept of CSI, and described the fundamental principle of CSI-based crowd counting. We also presented the system framework, experiment scenario, and neural network structure transferred from the ResNet. Next, we presented the experiment results and compared the accuracy using different neural network models. The system achieved recognition accuracy of this 100 percent for seven participants using the transfer learning technique. Finally, we concluded the paper by discussing the current problems and future work.