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Digital microfluidic, as an emerging and potential technology, diversifies the biochemical applications platform, such as protein dilution sewage detection. At present, a vast majority of universal cyberphysical digital microfluidic biochips (DMFBs) transmit data through wires via personal computers and microcontrollers (like Arduino), consequently, susceptible to various security threats and with the popularity of wireless devices, losing competitiveness gradually. On the premise that security be ensured first and foremost, calls for wireless portable, safe, and economical DMFBs are imperative to expand their application fields, engage more users, and cater to the trend of future wireless communication. To this end, a new cyber-physical DMFB called PortableLab is proposed in this paper, which guarantees data security through wireless sensors at low cost. After considering the security, computing consumption, and cost, a mobile module is added. In addition, the improved Advanced Encryption Standard (AES) and Cyclic Redundancy Check (CRC) algorithms are utilized to ensure the integrity and confidentiality of data transmission. Ultimately, all the security analysis, cost analysis, and experimental results on multiple protocols demonstrate the feasibility of the proposed PortableLab DMFB in time and space.

In this research, we present a cooperative communication network based on two energy-harvesting (EH) decode-and-forward (DF) relays that contain the harvest-store-use (HSU) architecture and may harvest energy from the surrounding environment by utilizing energy buffers. To improve the performance of this network, an opportunistic routing (OR) algorithm is presented that takes into account channel status information, relay position, and energy buffer status, as well as using the maximal ratio combining (MRC) at the destination to combine the signals received from the source and relays. The theoretical expressions for limiting distribution of energy stored in infinite-size buffers are derived from the discrete-time continuous-state space Markov chain model (DCSMC). In addition, the theoretical expressions for outage probability, throughput, and per-packet time slot cost in the network are obtained utilizing both the limiting distributions of energy buffers and the probabilities of transmitter candidates set. Through numerous simulation results, it is demonstrated that simulation results match with corresponding theoretical results.

As modern vehicles become more intelligent and connected, the number of ECUs and communication interfaces with external networks (such as 3G/4G and Bluetooth) has increased significantly, which raises potential network security risks. Due to the absence of effective security measures, there is a frequent occurrence of cybersecurity incidents targeting vehicles, particularly the in-vehicle CAN bus network. As the main bus in the vehicle, the CAN bus faces important challenges in its safety due to the lack of security mechanisms. Therefore, we propose a CAN bus intrusion detection system based on multiple observation HMM for in-vehicle networks to enhance the security of the vehicle. Specifically, the proposed algorithm builds a multiple observation HMM-based on the ID and data fields of normal CAN bus traffic. According to the established HMMs, we calculate the existence probability of the frame under the defined time window as the detection threshold. When the existence probability of the frame to be detected exceeds the normal threshold range, it is considered abnormal. Furthermore, we establish four common attack models based on the collected real vehicle data and evaluate the performance of the proposed algorithm in these attack scenarios. The experimental results show that the proposed method has better detection performance than other frame-by-frame anomaly detection methods in four attack scenarios.

With the booming development of the cyber-physical system, human society is much more dependent on information technology. Unfortunately, like software, hardware is not trusted at all, due to so many third parties involved in the separated integrated circuit's (IC) design and manufacturing stages for the high profit. The malicious circuits (named Hardware Trojans) can be implanted during any stage of the ICs' design and manufacturing process. However, the existing pre-silicon approaches based on machine learning theory have good performance, they all belong to supervised learning methods, which have a key prerequisite that is numerous already known information. Meanwhile, hardware Trojans are even more unimaginable because today's ICs are becoming more complicated. The known information is even harder to gain. Furthermore, the training process for supervised learning methods tends to be time-consuming and generally requires a huge amount of balanced training data. Therefore, this paper proposes an unsupervised hardware Trojans detection approach by combined the principal component analysis (PCA) and local outlier factor (LOF) algorithm, called PL-HTD. We firstly visualize the distribution features of normal nets and Trojan nets, and then reveal the differences between the two types of nets to reduce the dimension of the feature set. According to the outliers of each net, the abnormal nets are selected and verified by professionals later to confirm whether it is a true Trojan relative to the host circuit to realize the detection. The experiments show that the proposed method can detect hardware Trojans effectively and reduce the cost of manual secondary detection. For the Trust-HUB benchmarks, the PL-HTD achieves up to 73.08% TPR and 97.52% average TNR, moreover, it achieves average 96.00% accuracy, which shows the feasibility and efficiency of hardware Trojans detecting by employing a method without the guidance of class label information.

Piezoresistive acceleration sensors are widely used in various fields of the industrial Internet of Things because of their lightweight, fast response, and small size. The structural sensitivity of the sensor affects the accuracy of the measurement. And the sensitivity that the traditional method designs are only a feasible solution, not an optimal solution. Due to the differences in factory processes, the optimization of structural sensitivity is an NP-hard problem. To solve the design problem of structural sensitivity, we adopt the swarm intelligence algorithm in this paper, and we design a model for the structural sensitivity of the piezoresistive acceleration sensor. In addition, an improved grasshopper optimization algorithm (CC-GOA) that combines chaos strategy and Cauchy mutation is proposed to optimize the structural sensitivity of the piezoresistive acceleration sensor, and the structure of the sensor is composed of four beams and mass block. The experiments are compared with six well-known algorithms on 16 benchmark functions to verify the algorithm performance of CC-GOA, and then, the structural sensitivity of the piezoresistive acceleration sensor is optimized by CC-GOA. The results indicate that the piezoresistive acceleration sensor is designed with high sensitivity and superiority.

Since integrated circuits are performed by several untrusted manufacturers, malicious circuits (hardware Trojans) can be implanted in any stage of the Internet-of-Things (IoT) devices. With the globalization of the IoT device manufacturing technologies, protecting the system-on-chip (SoC) security is always the keys issue for scientists and IC manufacturers. The existing SoC high-level synthesis approaches cannot guarantee both register-transfer-level and gate-level security, such as some formal verification and circuit characteristic analysis technologies. Based on the structural characteristics of hardware Trojans, we propose a multi-layer hardware Trojan protection framework for the Internet-of-Things perception layer called RG-Secure, which combines the third-party intellectual property trusted design strategy with the scan-chain netlist feature analysis technology. Especially at the gate level of chip design, our RG-Secure is equipped with a distributed, lightweight gradient lifting algorithm called lightGBM. The algorithm can quickly process high-dimensional circuit feature information and effectively improve the detection efficiency of hardware Trojans. In the meanwhile, a common evaluation index F-measure is used to prove the effectiveness of our method. The experiments show that RG-Secure framework can simultaneously detect register-transfer-level and gate-level hardware Trojans. For the trust-HUB benchmarks, the optimized lightGBM classifier achieves up to 100% true positive rate and 94% true negative rate; furthermore, it achieves 99.8% average F-measure and 99% accuracy, which shows a promising approach to ensure security during the design stage.

With the development of science and technology, the accuracy requirements for solving engineering problems are getting stricter than before. Most structural design optimization problems in civil and mechanical engineering have proven to be the non-deterministic polynomial hard problems. The artificial bee colony (ABC) algorithm has been proven to be an effective method of design optimization problems. This paper proposes an improved ABC algorithm (DSM-ABC) combined with dual-search mechanism containing Lévy flight and differential self-perturbation and applies it to three classical structural design problems, including cantilever beam design, gear train design, and three-bar truss design. The experimental results of benchmark functions from CEC2005 reveal that the proposed DSM-ABC algorithm accelerates the convergence and improves the performance. Eventually, the obtained results of optimization structural design problems prove that the DSM-ABC algorithm has a strong superiority compared with the state-of-the-art algorithms in solving optimization engineering design problems.

Salp swarm algorithm is a new meta-heuristic algorithm which has excellent advantages for solving the multidimensional optimization problem. In this paper, a hybrid model assisted evolutionary algorithm for solving engineering design optimization problems is proposed and investigated. The purpose of the optimizer is to improve the potential shortcomings of the basic salp swarm optimization, including trapping in local or deceptive optima easily. On the one hand, quantum behavior can increase an individual's searchability, which can promote the overall optimization trend. On the other hand, the elite opposition-based learning strategy is used to enhance the diversity of the population. Besides, mutation mechanism is introduced to prevent the individuals of the population from being in stagnation behavior. The proposed quantum-behaved and wavelet mutation salp swarm algorithm (QSSA) is applied on twenty-three benchmark functions and three basic constrained engineering problems. Experimental results demonstrate that the algorithm has excellent solution quality, and it can overcome the defect of the low convergence rate.

Recently, there are various methods for detecting the hardware trojans (HTs) in the integrated circuits (ICs). The circuit's logic representations of different types, structures, and functional characteristics should be different. Each type of circuit has its' own performance characteristics according to its' purpose. However, the traditional HTs detection methods adopt the same approach to deal with the multi-purpose hardware trojan. At the same time, as the scale of integrated circuits growing, the structures are more complex, and the functions are more refined. The current situation makes the traditional HTs detection methods weaker and even unfeasible. Therefore, we propose an HTs classified locating method based on machine learning, named ML-HTCL, which belongs to the static detection and locating method. In ML-HTCL, different purpose HTs were represented by different features. Due to the different features, the ML-HTCL employs the multi-layer BP neural network for the control signal type HTs and the one-class SVM for the information leakage HTs, respectively. To deal with the HTs completely, the boundary nets are considered for all data set, while those were ignored by the most existing methods due to the high detection error rate. After detection, the HTs' precise locations were achieved. To evaluate the ML-HTCL, 17 gate-level netlist benchmarks are used for training and testing by leave-one-out cross-validation. From the results, the ML-HTCL reaches 85.05% of TPR and 73.91% of TNR for all kinds of HTs, which performs better than the most existing methods.

The precoding-aided spatial modulation (PSM) is first characterized with respect to its secrecy performance, which shows that the PSM is a secrecy embedded communication scheme when operated in the time-division duplex mode, but experiences security risk under frequency-division duplex mode. Therefore, we extend the PSM to a secret PSM (SPSM) that is suitable for operation in any communications scenarios experiencing passive eavesdropping. In the proposed SPSM, the secrecy performance is enhanced by using a part of transmit power to interfere the eavesdropper's reception. In this paper, the secrecy performance of SPSM with discrete inputs is comprehensively investigated in terms of both bit error rate and secrecy rate and by both analysis and simulations. Relying on the asymptotic analysis, we also derive the upper and lower bounds for the error performance and secrecy rate of SPSM. Furthermore, we study the power allocation between information and interference transmission in order to maximize SPSM's secrecy performance. Our studies result in a range of expressions, which are validated by Monte Carlo simulations. Furthermore, our studies demonstrate that the analytical formulas are beneficial to the optimization of SPSM systems for maximizing its security performance.

In this paper, a general function expansion bilinear (FEB) filter with a 3-D diagonal structure is proposed to deal with the problem of saturation nonlinearity in active noise control (ANC) systems. In an ANC system, the reference microphone and/or loudspeaker may be saturated when the acoustic noise and/or the controller output exceeds the dynamic limits of electronic devices. Such saturation nonlinearity degrades the control performance of the linear and some nonlinear control filters equipped with a filtered-x least mean square (FXLMS) algorithm. In order to tackle the problem of signal saturation, we use a new nonlinear saturation ANC (NSANC) model and derive a function expansion diagonal-structure bilinear FXLMS (FEDBFXLMS) algorithm. The performance of the proposed filters equipped with the associated FEDBFXLMS algorithm is validated through the analysis of computational complexity and simulations of various nonlinearities for NSANC systems. Computer simulation results demonstrate that the proposed FEB filter can achieve significant performance improvement in reducing saturation effects in comparison with the diagonal-channel bilinear filter and the recursive second-order Volterra filter based on the FXLMS algorithm, which often outperform the conventional nonlinear Volterra and functional link artificial neural network (FLANN) filters.

Colour-shift keying (CSK) constitutes an important modulation scheme conceived for the visible light communications (VLC). The signal constellation of CSK relies on three different-color light sources invoked for information transmission. The CSK constellation has been optimized for minimizing the bit error rate, but no effort has been invested in investigating the feasibility of CSK aided unequal error protection (UEP) schemes conceived for video sources. Hence, in this treatise, we conceive a hierarchical CSK (HCSK) modulation scheme based on the traditional CSK, which is capable of generating interdependent layers of signals having different error probability, which can be readily reconfigured by changing its parameters. Furthermore, we conceived an HCSK design example for transmitting scalable video sources with the aid of a recursive systematic convolutional (RSC) code. An optimization method is conceived for enhancing the UEP and for improving the quality of the received video. Our simulation results show that the proposed optimized-UEP 16-HCSK-RSC system outperforms the traditional equal error protection scheme by ~ 1.7 dB of optical SNR at a peak signal-to-noise ratio of 37 dB, while optical SNR savings of up to 6.5 dB are attained at a lower PSNR of 36 dB.

The cognitive radio (CR) paradigm has the potential of improving the exploitation of the electromagnetic spectrum by detecting instantaneously unoccupied spectrum slots allocated to primary users (PUs). In order to support the process of spectrum reuse, we consider a CR scheme, which senses and opportunistically accesses a PU's spectrum for communication between a pair of nodes relying on the stop-and-wait hybrid automatic repeat request (SW-HARQ) protocol. This arrangement is represented by the cognitive SW-HARQ (CSW-HARQ), where the availability/unavailability of the PU's channel is modeled as a two-state Markov chain having OFF and ON states, respectively. Once the cognitive user (CU) finds that the PU's channel is available (i.e., in the OFF state), the CU transmits data over the PU channel's spectrum, while relying on the principles of SW-HARQ. We investigate both the throughput and the delay of CSW-HARQ, with a special emphasis on the impact of the various system parameters involved in the scenarios of both perfect and imperfect spectrum sensing. Furthermore, we analyze both the throughput as well as the average packet delay and end-to-end packet delay of the CSW-HARQ system. We propose a pair of analytical approaches: 1) the probability-based and 2) the discrete time Markov chain-based. Closed-form expressions are derived for both the throughput and the delay under the perfect and imperfect sensing environments that are validated by simulation. We demonstrate that the activity of PUs, the transmission reliability of the CU, and the sensing environment have a significant impact on both the throughput and the delay of the CR system.

Visible light communications (VLCs) relying on commercially available light-emitting diode (LED) transmitters offer a huge data rate potential in this license-free spectral domain, while simultaneously satisfying energy-efficient conventional illumination demands. In an LED-based VLC system, the achievable data rate may be severely degraded owing to the interference, when the VLC system employs the unity frequency reuse (FR) approach. In order to mitigate the effects of interference, we propose a pair of interference avoidance approaches, namely, FR-based transmission and vectored transmission (VT). Furthermore, the resource allocation (RA) problems of indoor mobile terminals (MTs) are investigated based on different transmission strategies. Inspired by the concept of effective capacity (EC), we formulate our RA optimization problems applying proportional fairness, while satisfying specific statistical delay constraints. Our optimization procedure solves the RA problem of indoor MTs with the aid of a decentralized algorithm. Simulation results are also presented for quantifying the performance of the proposed algorithm. It is shown that both of our interference avoidance approaches are capable of reducing the interference, hence improving the overall performance. Furthermore, it is also observed that our VT transmission is capable of achieving a higher EC than the FR approach, when the statistical delay requirements are loose. By contrast, the FR-based transmission attains the best performance, when we tighten the delay requirements.

Cognitive radio (CR) techniques have been proposed for improving the spectral efficiency by exploiting the temporarily unoccupied segments of the licensed spectrum, provided that the transmission of primary users (PUs) is not hampered. In this paper, we propose a cognitive Go-Back-N Hybrid Automatic Repeat reQuest (CGBN-HARQ) scheme that enables the cognitive user (CU) to opportunistically transmit data over a primary radio (PR) channel. Based on the sensing decisions by the CU, it decides about the availability of the PU's channel for its own transmission using the proposed CGBN-HARQ scheme. In addition, it enables the CR transmitter to receive feedback concerning the success/failure of its prior transmissions during the sensing and transmission phases of the time-slot (TS). A discrete time Markov chain model is invoked for the theoretical analysis of the proposed system, where we conceive an algorithm to generate all possible states of the CR transmitter. Both the throughput and delay of the CGBN-HARQ scheme is analyzed by deriving a range of closed-form formulas, which are validated by simulation results. The occupation of the channel by the PU and the reliability of the CU's channel significantly affect both the achievable throughput and the delay of the CGBN-HARQ scheme. Finally, our studies show that the number of packets transmitted within a TS should be adapted according to the communication channel for attaining the maximum throughput and the lowest average transmission delay.

Hierarchical modulation (HM), which is also known as layered modulation, has been widely adopted across the telecommunication industry. Its strict backward compatibility with single-layer modems and its low complexity facilitate the seamless upgrading of wireless communication services. The specific features of HM may be conveniently exploited for improving the throughput/information-rate of the system without requiring any extra bandwidth, while its complexity may even be lower than that of the equivalent system relying on conventional modulation schemes. As a recent research trend, the potential employment of HM in the context of cooperative communications has also attracted substantial research interests. Motivated by the lower complexity and higher flexibility of HM, we provide a comprehensive survey and conclude with a range of promising future research directions. Our contribution is the conception of a new cooperative communication paradigm relying on turbo trellis-coded modulation-aided twin-layer HM-16QAM and the analytical performance investigation of a four-node cooperative communication network employing a novel opportunistic routing algorithm. The specific performance characteristics evaluated include the distribution of delay, the outage probability, the transmit power of each node, the average packet power consumption, and the system throughput. The simulation results have demonstrated that when transmitting the packets formed by layered modulated symbol streams, our opportunistic routing algorithm is capable of reducing the transmit power required for each node in the network compared with that of the system using the traditional opportunistic routing algorithm. We have also illustrated that the minimum packet power consumption of our system using our opportunistic routing algorithm is also lower than that of the system using the traditional opportunistic routing algorithm.

Convolutional neural networks (CNNs) have been widely used with its powerful discriminative ability in change detection (CD), but most CNN-based methods are still exploring ways to capture relatively long-range context in spatial-temporal domain. The recent vision transformer (ViT), which is based on the self-attention mechanism, has been applied in CD to model long-range dependencies. However, such transformer-based architectures do not fully consider the potential of interdependencies among the high-level semantic feature maps and easily overlook local detail features, resulting in a noncompact interior of the large-scale change area and missing small changes. Therefore, we propose a new transformer-based hybrid network called interact-feature transformer network with spatial detail enhancement module (IFTSDNet), which takes advantage of transformers to capture long-range context, and of CNNs to extract local information. We design an interact-feature transformer (IFT), which can not only obtain the global contextual information, but also achieve the interactions of high-level semantic feature maps. The spatial detail enhancement module (SDEM) with a group of various receptive fields is built to refine spatial features, which incorporates more discriminative feature representations. Comparative experiments prove the effectiveness of the proposed method, which shows better performance than four recent transformer-based methods. The code will be available at https://github.com/wanglinlin0219/IFTSDNet. [ABSTRACT FROM AUTHOR]

Deep learning (DL) based methods, such as the representative vision transformer (ViT) and convolutional neural network (CNN) structures, can characterize spatial-spectral features of hyperspectral images (HSIs) well and achieve outstanding classification performance. Nevertheless, when land cover is complex, the intraclass spectral consistency may be weak and difficult to express effectively in the original data space, leading to potential bias regarding the validity of spatial-spectral information utilization. We propose a new method GSPFormer that first constructs a global spectral projection space (GSPS) to generate land cover more robust representations and enhance the spectral consistency in local neighborhoods. After that, a space aggregation idea is introduced to obtain the central pixel’s more abundant spectral feature expression for better classification by fusing all spectral features in the local neighborhood. Extensive experiments are conducted on various HSI datasets for evaluating the classification performance of GSPFormer and other state-of-the-art networks. Comparison results indicate the superiority of the proposed method not only in classification accuracy but also in the number of parameters and convergence. The code of GSPFormer will be found at https://github.com/Preston-Dong/ GSPFormer. [ABSTRACT FROM AUTHOR]