BLS-identification:A device fingerprint classification mechanism based on broad learning for Internet of Things

The popularity of the Internet of Things(IoT)has enabled a large number of vulnerable devices to connect to the Internet,bringing huge security risks.As a network-level security authentication method,device fingerprint based on machine learning has attracted considerable attention because it can detect vulnerable devices in complex and heterogeneous access phases.However,flexible and diversified IoT devices with limited resources increase dif-ficulty of the device fingerprint authentication method executed in IoT,because it needs to retrain the model network to deal with incremental features or types.To address this problem,a device fingerprinting mechanism based on a Broad Learning System(BLS)is proposed in this paper.The mechanism firstly characterizes IoT devices by traffic analysis based on the identifiable differences of the traffic data of IoT devices,and extracts feature parameters of the traffic packets.A hierarchical hybrid sampling method is designed at the preprocessing phase to improve the imbalanced data distribution and reconstruct the fingerprint dataset.The complexity of the dataset is reduced using Principal Component Analysis(PCA)and the device type is identified by training weights using BLS.The experimental results show that the proposed method can achieve state-of-the-art accuracy and spend less training time than other existing methods.

3D reconstruction and defect pattern recognition of bonding wire based on stereo vision

Non-destructive detection of wire bonding defects in integrated circuits(IC)is critical for ensuring product quality after packaging.Image-processing-based methods do not provide a detailed evaluation of the three-dimensional defects of the bonding wire.Therefore,a method of 3D reconstruction and pattern recognition of wire defects based on stereo vision,which can achieve non-destructive detection of bonding wire defects is proposed.The contour features of bonding wires and other electronic components in the depth image is analysed to complete the 3D reconstruction of the bonding wires.Especially to filter the noisy point cloud and obtain an accurate point cloud of the bonding wire surface,a point cloud segmentation method based on spatial surface feature detection(SFD)was proposed.SFD can extract more distinct features from the bonding wire surface during the point cloud segmentation process.Furthermore,in the defect detection process,a directional discretisation descriptor with multiple local normal vectors is designed for defect pattern recognition of bonding wires.The descriptor combines local and global features of wire and can describe the spatial variation trends and structural features of wires.The experimental results show that the method can complete the 3D reconstruction and defect pattern recognition of bonding wires,and the average accuracy of defect recognition is 96.47%,which meets the production requirements of bonding wire defect detection.

Exploring the effect of fingertip aero-haptic feedforward cues in directing eyes-free target acquisition in VR

Background The sense of touch plays a crucial role in interactive behavior within virtual spaces,particularly when visual attention is absent.Although haptic feedback has been widely used to compensate for the lack of visual cues,the use of tactile information as a predictive feedforward cue to guide hand movements remains unexplored and lacks theoretical understanding.Methods This study introduces a fingertip aero-haptic rendering method to investigate its effectiveness in directing hand movements during eyes-free spatial interactions.The wearable device incorporates a multichannel micro-airflow chamber to deliver adjustable tactile effects on the fingertips.Results The first study verified that tactile directional feedforward cues significantly improve user capabilities in eyes-free target acquisition and that users rely heavily on haptic indications rather than spatial memory to control their hands.A subsequent study examined the impact of enriched tactile feedforward cues on assisting users in determining precise target positions during eyes-free interactions,and assessed the required learning efforts.Conclusions The haptic feedforward effect holds great practical promise in eyeless design for virtual reality.We aim to integrate cognitive models and tactile feedforward cues in the future,and apply richer tactile feedforward information to alleviate users'perceptual deficiencies.

The Impact of Domain Name Server(DNS)over Hypertext Transfer Protocol Secure(HTTPS)on Cyber Security:Limitations,Challenges,and Detection Techniques

The DNS over HTTPS(Hypertext Transfer Protocol Secure)(DoH)is a new technology that encrypts DNS traffic,enhancing the privacy and security of end-users.However,the adoption of DoH is still facing several research challenges,such as ensuring security,compatibility,standardization,performance,privacy,and increasing user awareness.DoH significantly impacts network security,including better end-user privacy and security,challenges for network security professionals,increasing usage of encrypted malware communication,and difficulty adapting DNS-based security measures.Therefore,it is important to understand the impact of DoH on network security and develop newprivacy-preserving techniques to allowthe analysis of DoH traffic without compromising user privacy.This paper provides an in-depth analysis of the effects of DoH on cybersecurity.We discuss various techniques for detecting DoH tunneling and identify essential research challenges that need to be addressed in future security studies.Overall,this paper highlights the need for continued research and development to ensure the effectiveness of DoH as a tool for improving privacy and security.

Model Prediction and Optimal Control of Gas Oxygen Content for A Municipal Solid Waste Incineration Process

In the municipal solid waste incineration process,it is difficult to effectively control the gas oxygen content by setting the air flow according to artificial experience.To address this problem,this paper proposes an optimization control method of gas oxygen content based on model predictive control.First,a stochastic configuration network is utilized to establish a prediction model of gas oxygen content.Second,an improved differential evolution algorithm that is based on parameter adaptive and t-distribution strategy is employed to address the set value of air flow.Finally,model predictive control is combined with the event triggering strategy to reduce the amount of computation and the controller's frequent actions.The experimental results show that the optimization control method proposed in this paper obtains a smaller degree of fluctuation in the air flow set value,which can ensure the tracking control performance of the gas oxygen content while reducing the amount of calculation.

Data cleaning method for the process of acid production with flue gas based on improved random forest

Acid production with flue gas is a complex nonlinear process with multiple variables and strong coupling.The operation data is an important basis for state monitoring,optimal control,and fault diagnosis.However,the operating environment of acid production with flue gas is complex and there is much equipment.The data obtained by the detection equipment is seriously polluted and prone to abnormal phenomena such as data loss and outliers.Therefore,to solve the problem of abnormal data in the process of acid production with flue gas,a data cleaning method based on improved random forest is proposed.Firstly,an outlier data recognition model based on isolation forest is designed to identify and eliminate the outliers in the dataset.Secondly,an improved random forest regression model is established.Genetic algorithm is used to optimize the hyperparameters of the random forest regression model.Then the optimal parameter combination is found in the search space and the trend of data is predicted.Finally,the improved random forest data cleaning method is used to compensate for the missing data after eliminating abnormal data and the data cleaning is realized.Results show that the proposed method can accurately eliminate and compensate for the abnormal data in the process of acid production with flue gas.The method improves the accuracy of compensation for missing data.With the data after cleaning,a more accurate model can be established,which is significant to the subsequent temperature control.The conversion rate of SO_(2) can be further improved,thereby improving the yield of sulfuric acid and economic benefits.

Prediction of NO_(x)concentration using modular long short-term memory neural network for municipal solid waste incineration

Air pollution control poses a major problem in the implementation of municipal solid waste incineration(MSWI).Accurate prediction of nitrogen oxides(NO_(x))concentration plays an important role in efficient NO_(x)emission controlling.In this study,a modular long short-term memory(M-LSTM)network is developed to design an efficient prediction model for NO_(x)concentration.First,the fuzzy C means(FCM)algorithm is utilized to divide the task into several sub-tasks,aiming to realize the divide-and-conquer ability for complex task.Second,long short-term memory(LSTM)neural networks are applied to tackle corresponding sub-tasks,which can improve the prediction accuracy of the sub-networks.Third,a cooperative decision strategy is designed to guarantee the generalization performance during the testing or application stage.Finally,after being evaluated by a benchmark simulation,the proposed method is applied to a real MSWI process.And the experimental results demonstrate the considerable prediction ability of the M-LSTM network.

Optimizing Fully Convolutional Encoder-Decoder Network for Segmentation of Diabetic Eye Disease

In the emerging field of image segmentation,Fully Convolutional Networks(FCNs)have recently become prominent.However,their effectiveness is intimately linked with the correct selection and fine-tuning of hyperparameters,which can often be a cumbersome manual task.The main aim of this study is to propose a more efficient,less labour-intensive approach to hyperparameter optimization in FCNs for segmenting fundus images.To this end,our research introduces a hyperparameter-optimized Fully Convolutional Encoder-Decoder Network(FCEDN).The optimization is handled by a novel Genetic Grey Wolf Optimization(G-GWO)algorithm.This algorithm employs the Genetic Algorithm(GA)to generate a diverse set of initial positions.It leverages Grey Wolf Optimization(GWO)to fine-tune these positions within the discrete search space.Testing on the Indian Diabetic Retinopathy Image Dataset(IDRiD),Diabetic Retinopathy,Hypertension,Age-related macular degeneration and Glacuoma ImageS(DR-HAGIS),and Ocular Disease Intelligent Recognition(ODIR)datasets showed that the G-GWO method outperformed four other variants of GWO,GA,and PSO-based hyperparameter optimization techniques.The proposed model achieved impressive segmentation results,with accuracy rates of 98.5%for IDRiD,98.7%for DR-HAGIS,and 98.4%,98.8%,and 98.5%for different sub-datasets within ODIR.These results suggest that the proposed hyperparameter-optimized FCEDN model,driven by the G-GWO algorithm,is more efficient than recent deep-learning models for image segmentation tasks.It thereby presents the potential for increased automation and accuracy in the segmentation of fundus images,mitigating the need for extensive manual hyperparameter adjustments.

Analysis and Optimization of Validation Procedure in Blockchain-Enhanced Wireless Resource Sharing and Transactions

To ensure the security of resource and intelligence sharing in 6G,blockchain has been widely adopted in wireless communications and applications.Although blockchain can ensure the traceability and non-tamperability of data in the concatenated blocks,it cannot guarantee the honest behaviors of users in the application before the generation of transactions.Thus,additional technologies are required to ensure that the source of blockchain data is reliable.In this paper,the detailed procedure is designed for the application-oriented task validation in the blockchainenhanced computing resource sharing and transactions in ultra dense networks(UDN).The corresponding queuing model is built and analyzed with the consideration of the wireless re-transmission and the probability of malicious deception by users.Based on the analysis results,the UDN deployment is optimized to save network cost while ensuring latency performance.Numerical results verify our analysis,and the optimized system deployment including the number and service capacities of both base stations and mobile edge computing(MEC)servers are also given with various system settings.

Multivariable identification of membrane fouling based on compacted cascade neural network

The membrane fouling phenomenon,reflected with various fouling characterization in the membrane bioreactor(MBR)process,is so complicated to distinguish.This paper proposes a multivariable identification model(MIM)based on a compacted cascade neural network to identify membrane fouling accurately.Firstly,a multivariable model is proposed to calculate multiple indicators of membrane fouling using a cascade neural network,which could avoid the interference of the overlap inputs.Secondly,an unsupervised pretraining algorithm was developed with periodic information of membrane fouling to obtain the compact structure of MIM.Thirdly,a hierarchical learning algorithm was proposed to update the parameters of MIM for improving the identification accuracy online.Finally,the proposed model was tested in real plants to evaluate its efficiency and effectiveness.Experimental results have verified the benefits of the proposed method.

SBFT:A BFT Consensus Mechanism Based on DQN Algorithm for Industrial Internet of Thing

With the development and widespread use of blockchain in recent years,many projects have introduced blockchain technology to solve the growing security issues of the Industrial Internet of Things(IIoT).However,due to the conflict between the operational performance and security of the blockchain system and the compatibility issues with a large number of IIoT devices running together,the mainstream blockchain system cannot be applied to IIoT scenarios.In order to solve these problems,this paper proposes SBFT(Speculative Byzantine Consensus Protocol),a flexible and scalable blockchain consensus mechanism for the Industrial Internet of Things.SBFT has a consensus process based on speculation,improving the throughput and consensus speed of blockchain systems and reducing communication overhead.In order to improve the compatibility and scalability of the blockchain system,we select some nodes to participate in the consensus,and these nodes have better performance in the network.Since multiple properties determine node performance,we abstract the node selection problem as a joint optimization problem and use Dueling Deep Q Learning(DQL)to solve it.Finally,we evaluate the performance of the scheme through simulation,and the simulation results prove the superiority of our scheme.

Exploration of high-speed 3.0 THz imaging with a 65 nm CMOS process

This paper describes a promising route for the exploration and development of 3.0 THz sensing and imaging with FET-based power detectors in a standard 65 nm CMOS process.Based on the plasma-wave theory proposed by Dyakonov and Shur,we designed high-responsivity and low-noise multiple detectors for monitoring a pulse-mode 3.0 THz quantum cascade laser(QCL).Furthermore,we present a fully integrated high-speed 32×32-pixel 3.0 THz CMOS image sensor(CIS).The full CIS measures 2.81×5.39 mm^(2) and achieves a 423 V/W responsivity(Rv)and a 5.3 nW integral noise equivalent power(NEP)at room temperature.In experiments,we demonstrate a testing speed reaching 319 fps under continuous-wave(CW)illumina-tion of a 3.0 THz QCL.The results indicate that our terahertz CIS has excellent potential in cost-effective and commercial THz imaging and material detection.

Joint optimization of serving node selection and wireless resources allocation for transactions data in mobile blockchain enhanced Internet of Things

With the increased emphasis on data security in the Internet of Things(IoT), blockchain has received more and more attention.Due to the computing consuming characteristics of blockchain, mobile edge computing(MEC) is integrated into IoT.However, how to efficiently use edge computing resources to process the computing tasks of blockchain from IoT devices has not been fully studied.In this paper, the MEC and blockchain-enhanced IoT is considered.The transactions recording the data or other application information are generated by the IoT devices, and they are offloaded to the MEC servers to join the blockchain.The practical Byzantine fault tolerance(PBFT) consensus mechanism is used among all the MEC servers which are also the blockchain nodes, and the latency of the consensus process is modeled with the consideration of characteristics of the wireless network.The joint optimization problem of serving base station(BS) selection and wireless transmission resources allocation is modeled as a Markov decision process(MDP), and the long-term system utility is defined based on task reward, credit value, the latency of infrastructure layer and blockchain layer, and computing cost.A double deep Q learning(DQN) based transactions offloading algorithm(DDQN-TOA) is proposed, and simulation results show the advantages of the proposed algorithm in comparison to other methods.

Deep reinforcement learning based task offloading in blockchain enabled smart city

With the expansion of cities and emerging complicated application,smart city has become an in-telligent management mechanism.In order to guarantee the information security and quality of service(QoS)of the Internet of Thing(IoT)devices in the smart city,a mobile edge computing(MEC)en-abled blockchain system is considered as the smart city scenario where the offloading process of com-puting tasks is a key aspect infecting the system performance in terms of service profit and latency.The task offloading process is formulated as a Markov decision process(MDP)and the optimal goal is the cumulative profit for the offloading nodes considering task profit and service latency cost,under the restriction of system timeout as well as processing resource.Then,a policy gradient based task of-floading(PG-TO)algorithm is proposed to solve the optimization problem.Finally,the numerical re-sult shows that the proposed PG-TO has better performance than the comparison algorithm,and the system performance as well as QoS is analyzed respectively.The testing result indicates that the pro-posed method has good generalization.

A Cross-Domain Trust Model of Smart City IoT Based on Self-Certification

Smart city refers to the information system with Intemet of things and cloud computing as the core tec hnology and government management and industrial development as the core content,forming a large scale,heterogeneous and dynamic distributed Internet of things environment between different Internet of things.There is a wide demand for cooperation between equipment and management institutions in the smart city.Therefore,it is necessary to establish a trust mechanism to promote cooperation,and based on this,prevent data disorder caused by the interaction between honest terminals and malicious temminals.However,most of the existing research on trust mechanism is divorced from the Internet of things environment,and does not consider the characteristics of limited computing and storage capacity and large differences of Internet of hings devices,resuling in the fact that the research on abstract trust trust mechanism cannot be directly applied to the Internet of things;On the other hand,various threats to the Internet of things caused by security vulnerabilities such as collision attacks are not considered.Aiming at the security problems of cross domain trusted authentication of Intelligent City Internet of things terminals,a cross domain trust model(CDTM)based on self-authentication is proposed.Unlike most trust models,this model uses self-certified trust.The cross-domain process of internet of things(IoT)terminal can quickly establish a trust relationship with the current domain by providing its trust certificate stored in the previous domain interaction.At the same time,in order to alleviate the collision attack and improve the accuracy of trust evaluation,the overall trust value is calculated by comprehensively considering the quantity weight,time attenuation weight and similarity weight.Finally,the simulation results show that CDTM has good anti collusion attack ability.The success rate of malicious interaction will not increase significantly.Compared with other models,the resource consumption of our proposed model is significantly reduced.

Multisource localization based on angle distribution of time-frequency points using an FOA microphone

Multisource localization occupies an important position in the field of acoustic signal processing and is widely applied in scenarios,such as human‐machine interaction and spatial acoustic parameter acquisition.The direction‐of‐arrival(DOA)of a sound source is convenient to render spatial sound in the audio metaverse.A multisource localization method in a reverberation environment is proposed based on the angle distribution of time-frequency(TF)points using a first‐order ambisonics(FOA)microphone.The method is implemented in three steps.1)By exploring the angle distribution of TF points,a single‐source zone(SSZ)detection method is proposed by using a standard deviation‐based measure,which reveals the degree of convergence of TF point angles in a zone.2)To reduce the effect of outliers on localization,an outlier removal method is designed to remove the TF points whose angles are far from the real DOAs,where the median angle of each detected zone is adopted to construct the outlier set.3)DOA estimates of multiple sources are obtained by postprocessing of the angle histogram.Experimental results in both the simulated and real scenarios verify the effectiveness of the proposed method in a reverberation environment,which also show that the proposed method outperforms reference methods.

Thermal resistance matrix representation of thermal effects and thermal design of microwave power HBTs with two-dimensional array layout

Based on the thermal network of the two-dimensional heterojunction bipolar transistors(HBTs) array, the thermal resistance matrix is presented, including the self-heating thermal resistance and thermal coupling resistance to describe the self-heating and thermal coupling effects, respectively.For HBT cells along the emitter length direction, the thermal coupling resistance is far smaller than the self-heating thermal resistance, and the peak junction temperature is mainly determined by the self-heating thermal resistance.However, the thermal coupling resistance is in the same order with the self-heating thermal resistance for HBT cells along the emitter width direction.Furthermore, the dependence of the thermal resistance matrix on cell spacing along the emitter length direction and cell spacing along the emitter width direction is also investigated, respectively.It is shown that the moderate increase of cell spacings along the emitter length direction and the emitter width direction could effectively lower the self-heating thermal resistance and thermal coupling resistance,and hence the peak junction temperature is decreased, which sheds light on adopting a two-dimensional non-uniform cell spacing layout to improve the uneven temperature distribution.By taking a 2 × 6 HBTs array for example, a twodimensional non-uniform cell spacing layout is designed, which can effectively lower the peak junction temperature and reduce the non-uniformity of the dissipated power.For the HBTs array with optimized layout, the high power-handling capability and thermal dissipation capability are kept when the bias voltage increases.

A Trusted Attestation Mechanism for the Sensing Nodes of Internet of Things Based on Dynamic Trusted Measurement

Internet of things has been widely applied to industrial control, smart city and environmental protection, in these applica- tion scenarios, sensing node needs to make real-time response to the feedback control of the application layer. Therefore, it is nec- essary to monitor whether or not awareness nodes are trusted in real time, but the existing mechanisms for trusted certification lack the real-time measurement and tracking of the sensing node. To solve the above problems, this paper proposes a dynamic metric based authentication mechanism for sensing nodes of Internet of things. Firstly, the dynamic trustworthiness measure of the sensing nodes is carried out by introducing the computational function such as the trust function, the trust- worthiness risk assessment function, the feed- back control function and the active function of the sensing node. The dynamic trustworthi- ness measure of sensing nodes from multiple dimensions can effectively describe the change of trusted value of sensing nodes. Then, on the basis of this, a trusted attestation based on node trusted measure is realized by using the revocable group signature mechanism of local verifier. The mechanism has anonymity, un- forgeability and traceability, which is proved the security in the standard model. Simulationexperiments show that the proposed trusted attestation mechanism is flexible, practical and ef｜Scient and has better attack resistance. It can effectively guarantee the reliable data transmission of nodes and realize the dynamic tracking of node reliability, which has a lower impact on system performance.

Impact of variations of threshold voltage and hold voltage of threshold switching selectors in 1S1R crossbar array

The impact of the variations of threshold voltage(V_(th))and hold voltage(V_(hold))of threshold switching(TS)selector in1 S1 R crossbar array is investigated.Based on ON/OFF state I–V curves measurements from a large number of Ag-filament TS selectors,V_(th)and V_(hold)are extracted and their variations distribution expressions are obtained,which are then employed to evaluate the impact on read process and write process in 32×321 S1 R crossbar array under different bias schemes.The results indicate that V_(th)and V_(hold)variations of TS selector can lead to degradation of 1 S1 R array performance parameters,such as minimum read/write voltage,bit error rate(BER),and power consumption.For the read process,a small V_(hold)variation not only results in the minimum read voltage increasing but it also leads to serious degradation of BER.As the standard deviation of V_(hold)and V_(th)increases,the BER and the power consumption of 1 S1 R crossbar array under 1/2 bias,1/3 bias,and floating scheme degrade,and the case under 1/2 bias tends to be more serious compared with other two schemes.For the write process,the minimum write voltage also increases with the variation of V_(hold)from small to large value.A slight increase of V_(th)standard deviation not only decreases write power efficiency markedly but also increases write power consumption.These results have reference significance to understand the voltage variation impacts and design of selector properly.

Improvement of tunnel compensated quantum well infrared detector

To reduce the difficulty of the epitaxy caused by multiple quantum well infrared photodetector(QWIP)with tunnel compensation structure,an improved structure is proposed.In the new structure,the superlattices are located between the tunnel junction and the barrier as the infrared absorption region,eliminating the effect of doping concentration on the well width in the original structure.Theoretical analysis and experimental verification of the new structure are carried out.The experimental sample is a two-cycle device,each cycle contains a tunnel junction,a superlattice infrared absorption region and a thick barrier.The photosurface of the detector is 200×200μm^2 and the light is optically coupled by 45°oblique incidence.The results show that the optimal operating voltage of the sample is-1.1 V,the dark current is 2.99×10^-8A,and the blackbody detectivity is1.352×10^8 cm·Hz^1/2·W^-1at 77 K.Our experiments show that the new structure can work normally.