Ever-increasing demands for lower power loss, shorter transmission delay, and higher communication capacity have been a challenge in optical networks-on-chips (ONoCs). To address this, various multiplexing technologies have been proposed and applied to optical interconnection networks. Among these, Mode-Division Multiplexing (MDM) technology stands out for its ability to significantly enhance network throughput and reduce communication delays by simultaneously transmitting multiple-mode optical signals through a multi-mode waveguide, making it a compelling research area. In this paper, we propose a flexible and scalable multi-mode optical switching element (MOSE) that adjusts transmitted optical signals by modifying its structure. A multi-mode optical router (MOR) based on the proposed MOSE is introduced, followed by the design of MDM-based optical mesh networks-on-chip (MMONoCs) that support parallel transmission of multiple TE-polarization mode optical signals. Additionally, loss and crosstalk models for multi-mode optical devices, including MOSE and MOR, are systematically established. In conclusion, the loss, OSNR, and BER for three TE-polarization modes (TE0, TE1, and TE2) at different network scales are analyzed using MOR as a specific example. The findings indicate that the scalability of MMONoC and its communication quality are mainly influenced by mode crosstalk noise. Furthermore, network performance metrics, including End-to-End (ETE) delay and throughput, are discussed based on two-mode (TE0, TE1) and three-mode (TE0, TE1, and TE2) optical signals at 1550 nm in 4 × 4 and 5 × 5 optical mesh networks. Simulation results demonstrate that MMONoC exhibits significant improvements in ETE delay and throughput compared to traditional single-mode optical modes.

Reliable and energy-efficient wireless transmission is very important for the future success of Internet of Thing (IoT). Due to the sporadic nature of IoT transmissions, the energy consumption for individual transmission session varies dramatically with channel condition as well as the quality of service (QoS) requirements. In this article, we analyze and optimize the statistical energy consumption for wireless relaying communications in IoT. Particularly, we consider a dual-hop communication system with a wirelessly-powered decode-and-forward (DF) relay. In terms of time switching (TS) and power splitting (PS) modes, we analyze and minimize the statistical energy consumption for transmitting a certain amount of data. Through selected numerical results, we illustrate various design tradeoffs between statistical energy consumption, data rate, and latency. These results will provide some important guidelines for green IoT communications.

This study investigates covert communication for a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted system with non-orthogonal multiple access (NOMA). In particular, a source first transmits a NOMA signal consisting of both public and covert information to an unmanned aerial vehicle relay, which then forwards it to both the covert and the public users via an STAR-RIS in the presence of a warden, which attempts to detect whether the covert information is forwarded to the covert user or not. Under practical communication scenarios, the warden may locate at different positions in order to detect the covert transmission in the dual-hop systems, which may lead to several different detecting scenarios. Out of those possible scenarios, in this study, three typical ones are considered: 1) The warden can only detect the signal from the source in the first-hop transmission; 2) Both the signals from the source and the relay in the first-hop and second-hop transmissions could be detected; 3) The warden can detect the direct as well as the reflected transmission via the STAR-RIS from the relay in the second-hop transmission. Taking those three scenarios into consideration, the analytical expressions of detection error probability of the warden are derived and verified with Monte-Carlo simulations. In addition, the optimal detection threshold as well as the maximum effective covert rate is presented and discussed.

Due to some transmission delays and channel estimation errors, it is always difficult to acquire perfect channel state information (CSI) in practical communications. Therefore, in this study, taking the practical scenario of imperfect CSI into consideration, a simultaneously transmitting and reflecting-reconfigurable intelligent surface (STAR-RIS)-assisted satellite-autonomous aerial vehicle (AAV)-terrestrial non-orthogonal multiple access (NOMA) network is investigated, in which the AAV is assumed to be randomly located in a spherical cap space while the users are randomly located in an inner circular and an outer annular plane on the ground. Specifically, a satellite source first sends NOMA signals to a AAV relay, which then forwards them to both ground users via an STAR-RIS through reflection as well as transmission. In addition, considering that the satellite-AAV link is subject to the shadowed-Rician distribution while the other links are subject to the Nakagami-m distributions, the cumulative distribution functions and probability density functions of channel gains in the presence of imperfect CSI are derived. Furthermore, in practical application scenarios, the positions of the AAV and both users may be randomly located in some regions. Therefore, three randomly distributed scenarios are considered: 1) The users are randomly located while the AAV’s position is fixed; 2) The AAV is randomly located while both users are in fixed locations; and 3) Both are randomly located. Under those three scenarios, both analytical and asymptotic expressions of outage probability (OP) for two users as well as the system OP are derived using the stochastic geometry approach, and their accuracy is confirmed with Monte-Carlo simulations.

Reversible data hiding in encrypted image (RDHEI) is a powerful security technology that aims to hide data into the encrypted image without any distortions of data extraction and image recovery. Most existing RDHEI methods using vacated room-based data embedding algorithms face challenges in improving embedding capacity and security. In this paper, we develop a novel data hiding strategy via fusion based on reservoir computing (RC) system, upon which a new RDHEI scheme is further proposed. In the proposed scheme, the original image is first encrypted by the stream cipher-based encryption algorithm using the secret keys generated by an optical chaotic system. Then, by means of the RC system, the generated encrypted image can be fused with the secret data to produce the final masked image. Unlike the existing data embedding algorithms based on vacating rooms, the RC-based fusion strategy allows for hiding secret data comparable to the volume of the cover image into the encrypted image so that a higher embedding capacity can be greatly afforded. Moreover, the proposed strategy involves a chaotic transformation via the reservoir of RC system during data hiding, producing a masked image that is completely different from the encrypted image, thus the security is greatly enhanced. Experimental results show the contributions in improving the embedding capacity and security, and also demonstrate the superiority of the proposed scheme compared to some existing RDHEI methods.

Synthetic Aperture Radar (SAR) are fundamental tools for target classification and detection in the different applicative scenarios (military, agriculture, etc…). Extracting geometrical features of a target strongly help in its detection and classification. Indeed, the extraction of Attribute Scattering Center (ASC) characteristics is widely used from improving SAR target recognition. ASC extraction is a challenging task that requires the accurate estimation of tiny details (shape, orientation, etc…) from the SAR target backscattering. In this work, the aim is to exploit the potential of deep learning for ASC extraction. Considering a simulated environment, a deep-learning based classification solution is defined for extracting the target characteristics.We proposed a multi classification heads VGG solution, which can extract scattering parameters from complex images and also guarantee the estimation accuracy.

In this work, the physical-layer security for a maritime relay-assisted hybrid satellite and underwater wireless optical communication system consisting of a satellite source, a maritime relay (R), an underwater destination, and an eavesdropper (E) is investigated. In addition, both R and E are with multiple antennas and randomly located. In real application scenarios, E may be different carriers, i.e., an aircraft in the air or a ship on the sea, with various scopes of activity. Therefore, two different randomly located scenarios are considered, such as randomly distributed in a three dimensional space and a two dimensional plane. Furthermore, the beam-angle information of the source is assumed to be unavailable to E, and E may be located outside of the beam coverage area. Therefore, the beam coverage probability (BCP) for E is also presented. Employing the stochastic geometry theory and the maximum ratio combining scheme, the analytical and asymptotic expressions of secrecy outage probability (SOP) are obtained. In addition, the overall system SOP is defined and presented by taking both SOP and BCP into account. Finally, Monte-Carlo simulations verify the accuracy of our analysis.

SMART (Single-cycle Multi-hop Asynchronous Repeated Traversal) Network-on-Chip (NoC), a recently proposed dynamically reconfigurable NoC, enables single-cycle long-distance communication by building single-bypass paths directly between distant communication pairs. However, such a single-cycle single-bypass path will be readily broken when contention occurs. Thus, packets will be buffered at intermediate routers with blocking latency from other contending packets, and extra router-stage latency to rebuild the remaining path when available. In this article, we propose an effective contention-minimized routing algorithm to achieve maximal bypassing. Specifically, we identify two potential routes: direct route, with which packets can reach the destination in a single bypass; and indirect route, with which packets can reach the destination in multiple bypasses via an(multiple) intermediate router(s). The novel feature is that, contrary to an intuitive approach, not the routes with minimal distance but the indirect routes via the arbitrary intermediate routers (even if they may be non-minimal) that avoid contentions yield the minimized end-to-end latency. Evaluation on realistic benchmarks demonstrates the effectiveness of the proposed routing strategy, which achieves average performance improvement by 35.48 percent in communication latency, 28.31 percent in application schedule length, and 37.59 percent in network throughput, compared with the current routing in SMART NoCs.

For ultra-high resolution synthetic aperture radar (SAR), azimuth spectrum aliasing limits the application of frequency-domain autofocus algorithms. Therefore, time-domain autofocus algorithms are often used for ultra-high resolution SAR imaging. However, the azimuth deramping operation in current time-domain autofocus algorithms may introduce an additional azimuth-dependent phase. This phase can be regarded as a part of the phase error, which significantly reduces the estimation accuracy of the phase error. To address this issue, this article proposes a new time-domain autofocus algorithm based on azimuth scaling transformation for ultra-high resolution SAR. In this algorithm, we first adopt the azimuth scaling operation to avoid the azimuth-dependent phase so that the estimation accuracy of error can be greatly improved. Then, for the azimuth-dependent shifts caused by the azimuth scaling operation, we adopt the alignment processing to remove them in azimuth-time domain. Finally, we can estimate the error accurately from the aligned signal. The simulation and measured data were processed to verify the effectiveness of the algorithm.

The microwave photonic (MWP) radar has the capability of generating ultrawideband (UWB) signals. It is a challenge to realize accurate extraction of attributed scattering centers (ASCs) from MWP signals. This manuscript presents a scattering parameter estimation method in the image domain for UWB MWP signals. The polar-to-rectangular resampling is required for UWB MWP signals. Therefore, a range-azimuth decoupled representation based on the ASC model is formed. The model parameter estimation is converted into an optimization problem, where the statistics of the target signal and the features of interest are modeled to provide prior information. The distribution spread maximization (DSM) and peak magnitude maximization (PMM) principles in the optimization embody this prior information. The particle swarm optimization (PSO) is utilized to search for the parameters of each ASC in the image domain. Moreover, the orthogonal matching pursuit (OMP) algorithm is introduced to avoid repeated computation. Experimental results conducted on the simulated data, XPATCH data, and real data confirm the effectiveness of the proposed method. The proposed method takes into account the specific features of UWB MWP signals, which are neglected in the existing studies. Therefore, the proposed method performs better in extracting ASC parameters from UWB MWP signals in terms of accuracy and more complete sets.

The active recognition of interesting targets has been a vital issue for synthetic aperture radar (SAR) systems. The SAR recognition methods are mainly grouped as follows: extracting image features from the target amplitude image or matching the testing samples with the template ones according to the scattering centers extracted from the target complex data. For amplitude image-based methods, convolutional neural networks (CNNs) achieve nearly the highest accuracy for images acquired under standard operating conditions (SOCs), while scattering center feature-based methods achieve steady performance for images acquired under extended operating conditions (EOCs). To achieve target recognition with good performance under both SOCs and EOCs, a feature fusion framework (FEC) based on scattering center features and deep CNN features is proposed for the first time. For the scattering center features, we first extract the attributed scattering centers (ASCs) from the input SAR complex data, then we construct a bag of visual words from these scattering centers, and finally, we transform the extracted parameter sets into feature vectors with the k-means. For the CNN, we propose a modified VGGNet, which can not only extract powerful features from amplitude images but also achieve state-of-the-art recognition accuracy. For the feature fusion, discrimination correlation analysis (DCA) is introduced to the FEC framework, which not only maximizes the correlation between the CNN and ASCs but also decorrelates the features belonging to different categories within each feature set. Experiments on Moving and Stationary Target Acquisition and Recognition (MSTAR) database demonstrate that the proposed FEC achieves superior effectiveness and robustness under both SOCs and EOCs.

Although Convolutional Neural Networks (CNNs) have demonstrated the state-of-the-art inference accuracy in various intelligent applications, each CNN inference involves millions of expensive floating point multiply-accumulate (MAC) operations. To energy-efficiently process CNN inferences, prior work proposes an electro-optical accelerator to process power-of-2 quantized CNNs by electro-optical ripple-carry adders and optical binary shifters. The electro-optical accelerator also uses SRAM registers to store intermediate data. However, electro-optical ripple-carry adders and SRAMs seriously limit the operating frequency and inference throughput of the electro-optical accelerator, due to the long critical path of the adder and the long access latency of SRAMs. In this paper, we propose a photonic nonvolatile memory (NVM)-based accelerator, Light-Bulb, to process binarized CNNs by high frequency photonic XNOR gates and popcount units. LightBulb also adopts photonic racetrack memory to serve as input/output registers to achieve high operating frequency. Compared to prior electro-optical accelerators, on average, LightBulb improves the CNN inference throughput by 17× ~ 173× and the inference throughput per Watt by 17.5 × ~ 660×.

Continuous technology scaling in manycore systems leads to severe overheating issues. To guarantee system reliability, it is critical to accurately yet efficiently monitor runtime temperature distribution for effective chip thermal management. As an emerging communication architecture for new-generation manycore systems, optical network-on-chip (ONoC) satisfies the communication bandwidth and latency requirements with low power dissipation. Moreover, observation shows that it can be leveraged for runtime thermal sensing. In this article, we propose a brand-new on-chip thermal sensing approach for ONoC-based manycore systems by utilizing the intrinsic thermal sensitivity of optical devices and the inter-processor communications in ONoCs. It requires no extra hardware but utilizes existing optical devices in ONoCs and combines them with lightweight software computation in a hardware-software collaborative manner. The effectiveness of the our approach is validated both at the device level and the system level through professional photonic simulations. Evaluation results based on synthetic communication traces and realistic benchmarks show that our approach achieves an average temperature inaccuracy of only 0.6648 K compared to ground-truth values and is scalable to be applied for large-size ONoCs.

In this paper, we for the first time utilize the micro-ring resonators (MRs) in optical networks-on-chip (ONoCs) to implement thermal sensing without requiring additional hardware or chip area. The challenges in accuracy and reliability that arise from fabrication-induced process variations (PVs) and device-level wavelength tuning mechanism are resolved. We quantitatively model the intrinsic thermal sensitivity of MRs with finegrained consideration of wavelength tuning mechanism. Based on it, a novel PV-tolerant thermal sensor design is proposed. By exploiting the hidden ‘redundancy’ in wavelength division multiplexing (WDM) technique, our sensor achieves accurate and efficient temperature measurement with the capability of PV tolerance. Evaluation results based on professional photonic component and circuit simulations show an average of 86.49% improvement in measurement accuracy compared to the state-of-the-art on-chip thermal sensing approach using MRs. Our thermal sensor achieves stable performance in the ONoCs employing dense WDM with an inaccuracy of only 0.8650 K.

Convolutional Neural Networks (CNNs) are widely adopted in object recognition, speech processing and machine translation, due to their extremely high inference accuracy. However, it is challenging to compute massive computationally expensive convolutions of deep CNNs on traditional CPUs and GPUs. Emerging Nanophotonic technology has been employed for on-chip data communication, because of its CMOS compatibility, high bandwidth and low power consumption. In this paper, we propose a nanophotonic accelerator, HolyLight, to boost the CNN inference throughput in datacenters. Instead of an all-photonic design, HolyLight performs convolutions by photonic integrated circuits, and process the other operations in CNNs by CMOS circuits for high inference accuracy. We first build HolyLight-M by microdisk-based matrix-vector multipliers. We find analog-to-digital converters (ADCs) seriously limit its inference throughput per Watt. We further use microdisk-based adders and shifters to architect HolyLight-A without ADCs. Compared to the state-of-the-art ReRAM-based accelerator, HolyLight-A improves the CNN inference throughput per Watt by 13× with trivial accuracy degradation.

Internet of Multimedia Things (IoMT) faces the challenge of how to realize low-cost data acquisition while still preserve data confidentiality. In this paper, we present a low-cost and confidentiality-preserving data acquisition framework for IoMT. First, we harness chaotic convolution and random subsampling to capture multiple image signals. The measurement matrix is under the control of chaos, ensuring the security of the sampling process. Next, we assemble these sampled images into a big master image, and then encrypt this master image based on Arnold transform and single value diffusion. The computation of these two transforms only requires some low-complexity operations. Finally, the encrypted image is delivered to cloud servers for storage and decryption service. Experimental results demonstrate the security and effectiveness of the proposed framework.

Optical networks-on-chip (ONoCs) is a new promising communication paradigm that upgrades the traditional on-chip networks (NoCs) with the ultra-high communication bandwidth and low latency. Silicon microring resonators (MRRs), as a critical component of ONoCs used to implement the selection and redirection of optical signals, are inherently sensitive to the environmental temperature. The applicability of the ONoCs is essentially restricted by the performance of these optical devices that relies on the thermal conditions of the chip. In this paper, we study the thermo-optic effects of the MRRs quantitatively, build and verify the models of the MRRs based on the finite-difference time-domain (FDTD) method. We present formal relationship models between the temperature of a MRR and its optical losses and resonance wavelength. For the first time, the variation between the two types of MRRs, the parallel microring resonators (PMRs) and the crossing microring resonators (CMRs), are systematically addressed, which greatly improves the accuracy and applicability of the models. The results presented in this paper are systematically verified using professional optics methodology, and can be widely applied for accurate and efficient analysis of the thermo-optic effects in different domains of the ONoC community.

We consider a cooperative system consisting of a source node, a destination node, N ( N >1) wireless-powered relays, and an eavesdropper. Each relay is assumed to be with a nonlinear energy harvester, in which there exists a saturation threshold, limiting the level of the harvested power. For decode-and-forward and power splitting protocols, the K th best relay is selected to assist the source-relay-destination transmission. An analytical expression for the secrecy outage probability is derived, and also verified by simulation.

Based on a metal-insulator-metal (MIM) waveguide with a side-coupled nanodisk cavity, the sensor using the surface plasmon polaritons (SPPs) refractive index is investigated and studied numerically. The finite-difference time-domain (FDTD) method is used to simulate the performance of the sensor. The numerical simulation result indicates that all the resonance wavelengths in the transmission characteristic of the structure have a linear relationship with the refractive index of the cavity. Furthermore, the sensitivities of the sensor in this paper for the refractive index can be achieved as high as 1320 nm RIU for the mode1, 812.5 nm RIU for the mode2, 600 nm RIU for the mode3, respectively. Besides, the influences of the structural parameters on the transmission characteristic and the sensing characteristic are also studied in detail by the FDTD method. The sensor with compact and simple structure not only can be used to measure the temperature based on the linear relation between the refractive index and temperature, but also has many potential applications in optical networks on chip and On-chip sensor networks.

In this paper, the insertion loss and crosstalk noise of M × N Torus-based optical networks-on-chip (ONoCs) is systematically analyzed, which caused performance degradation. The proposed analysis model can be applied to arbitrary 5×5 routers and Torus-based ONoCs. When traditional non-blocking five-port optical routers used in the original Torus structure, it's suffered lager Bit Error Rate (BER) in a small scale. The router optimization and angle optimization method is used for achieving a better quality of network communication and performance improvement. The numerical results show the signal-to-noise ratio (SNR) of the worst-case transmission link in Torus-based ONoCs with certain size. When the network scale of Torus-based ONoC is 6×6 and the input power is 0 dB, the SNR of Torus-based ONoC using Crux router is 21.66 dB, which is 7.06 dB higher than optimized Crossbar router. With angle optimization further used in router level and network level, the SNR can reach to 23.87 dB. Moreover, we also find that a better SNR can be got with M gradually close to N .

In the optical communication system, crosstalk noise is always the critical factor affecting the optical signal transmission, especially in networks-on-chip (ONoCs). Based on the model at device, router and network level, this paper proposes the Optimized Crux (OC) router which uses the crossing angle of 60° or 120° instead of the conventional 90° to optimize the Crux optical router. The SNR of the Optimized Crux (OC) router is improved by 2.1dB compared with the Crux on the premise that the size of mesh-based ONoCs is 7×7. By comparing analysis, the results show that to achieve the bit error rate (BER) of 10−9for reliable transmissions, the maximum mesh-based ONoCs size has expanded from 4×4 when using the Crossbar optical router to 8×8 when using the OC router.

Crosstalk noise is an intrinsic characteristic as well as a potential issue of photonic devices. In large scale optical networks-on-chips (ONoCs), crosstalk noise could cause severe performance degradation and prevent ONoC from communicating properly. The novel contribution of this paper is the systematical modeling and analysis of the crosstalk noise and the signal-to-noise ratio (SNR) of optical routers and mesh-based ONoCs using a formal method. Formal analytical models for the worst-case crosstalk noise and minimum SNR in mesh-based ONoCs are presented. The crosstalk analysis is performed at device, router, and network levels. A general 5$\,\times\,$5 optical router model is proposed for router level analysis. The minimum SNR optical link candidates, which constrain the scalability of mesh-based ONoCs, are identified. It is also shown that symmetric mesh-based ONoCs have the best SNR performance. The presented formal analyses can be easily applied to other optical routers and mesh-based ONoCs. Finally, we present case studies of mesh-based ONoCs using the optimized crossbar and Crux optical routers to evaluate the proposed formal method. We find that crosstalk noise can significantly limit the scalability of mesh-based ONoCs. For example, when the mesh-based ONoC size, using optimized crossbar, is larger than 8$\,\times\,$8, the optical signal power is smaller than the crosstalk noise power; when the network size is 16$\,\times\,$16 and the input power is 0 dBm, in the worst-case, the signal power is${-}{\rm 24.9}~{\rm dBm}$and the crosstalk noise power is${-}{\rm 11}~{\rm dBm}$.

Crosstalk noise is an intrinsic characteristic of photonic devices used by optical networks-on-chip (ONoCs) as well as a potential issue. For the first time, this paper analyzed and modeled the crosstalk noise, signal-to-noise ratio (SNR), and bit error rate (BER) of optical routers and ONoCs. The analytical models for crosstalk noise, minimum SNR, and maximum BER in meshbased ONoCs are presented. An automated crosstalk analyzer for optical routers is developed. We find that crosstalk noise significantly limits the scalability of ONoCs. For example, due to crosstalk noise, the maximum BER is 10-3 on the 8x8 mesh-based ONoC using an optimized crossbar-based optical router. To achieve the BER of 10-9 for reliable transmissions, the maximum ONoC size is 6x6. A novel compact high-SNR optical router is proposed to improve the maximum ONoC size to 8x8.