The secure transmission of images in critical applications like smart healthcare and autonomous driving demands encryption schemes that are both highly secure and efficient. While chaos-based systems are promising, their security is fundamentally limited by the complexity of the underlying chaotic generator. This paper introduces a novel complex-valued discrete-time Hopfield neural network (CVDHNN) to address this challenge. We demonstrate that the CVDHNN exhibits rich hyperchaotic dynamics through various numerical analyses. The network is successfully implemented on an FPGA, verifying its capability for chaotic sequence generation. Leveraging this complex chaos, we design a robust image encryption algorithm that integrates multi-stage confusion and diffusion. Security analysis confirms the cipher’s excellence, achieving favorable statistical properties, high key sensitivity, and strong resistance to various attacks.

Memristors with nonlinearity and memory characteristics can effectively enhance chaotic dynamics complexity for chaotic maps. In this work, we present a novel discrete memristor model and couple it with sine maps and iterative chaotic maps with infinite collapse (ICMIC) to construct a class of discrete memristive hyperchaotic maps with multicavity multistructure attractors. This class of multicavity multistructure memristive sine ICMIC modulation maps (MCMS-MSIMMs) possesses an infinite variety of configurations, where the quantity and position of coupled discrete memristors can be arbitrarily combined. Numerical simulation results demonstrate that the sample map can exhibit hyperchaos, nondegeneracy, large-scale parameter control, multicavity attractors, multistructure attractors, and multicavity multistructure attractors. The complexity and initial values of the system are explored, revealing the high permutation entropy and initial offset-boosting behavior. In addition, the field programmable gate array (FPGA)-based MCMS-MSIMM hardware circuit is designed, and the experimental results are consistent with the numerical results. Finally, MCMS-MSIMM is applied in secure communication, and the experimental results indicate that the proposed map has better noise resistance performance compared to existing maps.

This paper presents a solid state power amplifier (PA) at 24 - 28 GHz in a 0.15-μm GaN on SiC technology. The PA adopts single-ended architecture containing power and a driver stages based on common source topology to get an output power and a gain greater than 32 dBm, and 20 dB, respectively. Harmonic balance(HB) simulations are performed to optimize the single-ended PA for wideband characteristics. Optimum stability and matching networks are introduced to meet the desired characteristics. The performance of the PA is experimentally characterized and a good co-relation between simulation and measurement is found. The PA shows a peak small-signal gain of 21.5 dB at 26 GHz. In terms of large-signal excitation, the PA delivers a maximum output power greater 32 dBm at 26 GHz with peak PAE of at least 36 %. The PA demonstrates high output power without power combining and it occupies an area of 4 mm2. The PA is suitable for various applications targeting frequency band of 24-28 GHz.

Traffic flow prediction is crucial for intelligent transportation and aids in route planning and navigation. However, existing studies often focus on prediction accuracy improvement, while neglecting external influences and practical issues like resource constraints and data sparsity on edge devices. We propose an online transfer learning (OTL) framework with a multi-layer perceptron (MLP)-assisted graph convolutional network (GCN), termed OTL-GM, which consists of two parts: transferring source-domain features to edge devices and using online learning to bridge domain gaps. Experiments on four data sets demonstrate OTL’s effectiveness; in a comparison with models not using OTL, the reduction in the convergence time of the OTL models ranges from 24.77% to 95.32%.

Most existing visual object tracking (VOT) approaches are implemented based on the von Neumann computation systems, which inevitably have the problems of high latency. Additionally, remote server processing of video resources requires a large amount of data transmission over the Internet, which limits real-time tracking performance. The integration of VOT technology into electronic devices has become a new trend. However, current VOT approaches have high algorithm complexity, making it difficult to design the circuits to implement the corresponding functions. In this article, a memristor-based attention network (MAN) and its corresponding algorithm are proposed to achieve online real-time tracking under parallel computing. Memristors are used to construct the attention encoding circuits to record changes of the target in historical frames, and adjust attention signals to the target online and in real-time during the tracking process, avoiding the latency problem of the von Neumann architecture. Inspired by the working process of$\gamma $-GABAergic interneuron and tripartite synapse, we propose an attention allocation module to selectively allocate attention values. Combining the winner-take-all principle, we design a target localization circuit and an optimal attention zone selection circuit for the parallel computation to track the location of the target. Finally, the experiments and analyses on the OTB-100, NFS, and VOT-RTb2022 benchmark datasets verify that the proposed MAN has promising tracking performance and achieves a tracking speed of 1000 frames per second, demonstrating superior real-time performance.

The brain emotional learning network offers several advantages when compared to traditional neural networks. It features a simpler structure, low computational complexity, and fast training speed. These characteristics make it ideal for applications like pattern recognition, data classification, and intelligent control. However, current brain emotional learning networks, including their modified networks, are not capable of recognizing or classifying data in complex environments. To address this issue, this paper proposes a brain emotional learning network with an attention mechanism that strengthens the processing of key information while suppressing interfering information, thereby enabling the network to recognize data within complex environments. Furthermore, software implementation of neural networks often experiences slow computing speeds due to the separation of storage and computation in traditional von Neumann computers. To combat this issue, the paper presents a hardware circuit implementation of the attention mechanism-based brain emotional learning network using memristors. Finally, the designed in-memory computing neural network has been successfully applied to the recognition of traffic signs within complex environments, and has achieved accurate and rapid recognition.

Memristors are often used to emulate neural synapses or to describe electromagnetic induction effects in neural networks. However, when these two things occur in one neuron concurrently, what dynamical behaviors could be generated in the neural network? Up to now, it has not been comprehensively studied in the literature. To this end, this article constructs a new memristive Hopfield neural network (HNN) by simultaneously introducing two memristors into one Hopfield-type neuron, in which one memristor is employed to mimic an autapse of the neuron and the other memristor is utilized to describe the electromagnetic induction effect. Dynamical behaviors related to the two memristive systems are investigated. Research results show that the constructed memristive HNN can generate the Lorenz-like double-wing and four-wing butterfly attractors by changing the parameters of the first memristive system. Under the simultaneous influence of the two memristive systems, the memristive HNN can generate complex multibutterfly chaotic attractors, including multidouble-wing-butterfly attractors and multifour-wing-butterfly attractors, and the number of butterflies contained in an attractor can be freely controlled by adjusting the control parameter of the second memristive system. Moreover, by switching the initial state of the second memristive system, the multibutterfly memristive HNN exhibits initial-boosted coexisting double-wing and four-wing butterfly attractors. Undoubtedly, such diversified butterfly attractors make the proposed memristive HNN more suitable for the chaos-based engineering applications. Finally, based on the multibutterfly memristive HNN, a novel privacy protection scheme in the Internet of Medical Things is designed. Its effectiveness is demonstrated through the encryption tests and hardware experiments.

Memristors, with their unique nonlinear characteristics, are highly suitable for construction novel neural models with rich dynamic behaviors. In this paper, a memristor with piecewise nonlinear state function is introduced into the tabu learning neuron model, resulting in a novel memristive tabu learning neuron model capable of generating a double-wing chaotic butterfly. By modulating the state function of the memristor, we can effectively and easily alter the number of wings of the chaotic butterfly. Equilibrium points analysis further elucidates the mechanism behind the generation of multi-wing chaos. Various numerical simulation techniques, including phase portraits, bifurcation diagrams, Lyapunov exponent spectra, and local attraction basins, are employed to illustrate the dynamical behaviors of the proposed model. Moreover, the newly constructed neuron model is validated using FPGA hardware, with the results aligning with numerical simulations, thereby offering a dependable foundation for a memristor digital circuit-based brain-like neuron model. Lastly, an image encryption application based on the multi-wing chaotic butterfly is developed to demonstrate the potential application of the model.

The delay of state variable plays a crucial role in chaotic systems. However, it has not received sufficient attention in discrete memristor-based maps. This paper presents a study on the effects of delay feedback in the discrete memristive system, proposing a generalized delay difference feedback memristive map. The dynamical behaviors influenced by control parameters, delay length and initial conditions, are explored through four discrete memristive maps. The Kaplan-Yorke dimension is utilized as an indicator to investigate the chaotic dynamic variations induced by the delay length within memristive maps. Furthermore, digital circuits for the proposed systems are designed and implemented, with hardware experimental results that are consistent with numerical simulations, thereby verifying the effectiveness of the digital circuit-based system and providing a foundation for hardware-based delay difference system design. Additionally, the chaotic series are integrated into the particle swarm optimization for tackling obstacle avoidance path planning. The superiority of the designed delay difference feedback memristive maps is highlighted through comparisons with several classical chaotic maps, showcasing their enhanced performance in terms of the speed and cost efficiency in solving the path planning task.

Plasticity is key to the trainability of neural networks and has long been a focus in the field of brain-inspired research. Currently, neuromorphic networks primarily achieve plasticity through synaptic and myelin structures. However, these two are often studied separately, limiting further enhancement of neuronal node plasticity. This paper proposes a neuron model that incorporates both synapses and myelin, designs the corresponding neuronal circuit, and introduces a method for quantifying its discharge characteristics. Through theoretical analysis, simulations, and physical experiments, we validate the effectiveness of this quantification method. Furthermore, we summarize the formation mechanisms of synaptic and myelin plasticity, clarify the differences in their respective plasticity effects, and use the quantification method to compute the response speed, power consumption, and spike firing frequency of neuronal circuits. We also analyze the impact of synaptic and myelin plasticity and their synergistic effects on these three factors. Results demonstrate that the plasticity of synapses and myelin, as well as their synergistic interaction, can significantly optimize the performance of neuron nodes: the response duration is reduced to 2.9% of its initial value, the energy consumption per spike decreases to 38.4%, and the spike firing frequency increases to 1982.6% of the baseline level. This synergy contributes to improving the computational efficiency and energy management capabilities of neuromorphic networks.

The randomness of chaotic systems are crucial for their application in secure communication. Conservative systems exhibit enhanced ergodicity and randomness in comparison to dissipative chaotic systems. However, the memristor-based conservative chaotic maps remain unreported. This article presents a study of volume-preserving chaotic maps based on discrete memristor (DM). We propose and analyze a generic conservative map that incorporates DM. The conservative characteristics of the proposed iterative map are confirmed through the determinant of its Jacobian matrix. Furthermore, four distinct DM models are introduced and their memristive characteristics are verified through numerical simulations of hysteresis loops. To investigate the dynamical properties of the discrete memristive conservative map (DMCM), we incorporate the proposed DM models into the generic conservative map model using numerical methods, including phase portraits, Lyapunov exponents, and bifurcation diagrams. Additionally, the hardware implementation of the DMCM on an FPGA platform demonstrates the reliability of the model. Finally, secure communication experiments based on the DMCM show that it outperforms some classical dissipative chaotic maps in terms of bit error rate performance.

This paper presents the design and evaluation of an RFID-based laboratory equipment tracking system, aimed at enhancing research laboratory inventory management, security, and operations. The process of designing and deploying the RFID infrastructure involves careful selection of RFID components, a microcontroller, and inventory management software. This includes database construction, cloud connection, and graphical user interface development, with a focus on prioritizing real-time data tracking, ensuring data accuracy, and creating user-friendly interfaces. The RFID-based Lab Inventory Management System simplifies laboratory resource management by employing RFID Reader units and tags at specific locations for accurate monitoring. The strategic placement of RFID readers in crucial areas enhances real-time tracking capabilities. Integration with Hostinger connectivity offers an intuitive GUI and centralized database, streamlining operations. The use of resource-efficient RFID tags and reader kits, along with a wide range of hardware solutions, helps reduce costs. Overall, the proposed system’s effectiveness lies in its ability to improve laboratory accountability and resource management through real-time tracking, access to historical data, and user-friendly control interfaces.

Nowadays, the Internet of Things (IoT) technology has been widely applied in the police security system. However, with more and more image data that concerns crime scenes being transmitted through the police IoT, there are some new security and privacy issues. Therefore, how to design a safe and efficient secret image sharing solution suitable for police IoT has become a very urgent task. In this work, a grid multibutterfly memristive Hopfield neural network (HNN) with three memristive systems is constructed and its complex dynamics are deeply analyzed. Among them, the first memristive system is modeled by emulating a self-connection synapse, the second memristive system is modeled by coupling two neurons, and the third memristive system is modeled by describing external electromagnetic radiation. Dynamic analyses show that the proposed memristive HNN can not only generate two kinds of 1-directional (1-D) multibutterfly chaotic attractors but also produce complex grid (2-D) multibutterfly chaotic attractors. More importantly, by switching the initial states of the second and third memristive systems, the grid multibutterfly memristive HNN exhibits initial-boosted plane coexisting multibutterfly attractors. Moreover, the number of butterflies contained in a multibutterfly attractor and coexisting attractors can be easily adjusted by changing memristive parameters. Based on these complex dynamics, an image security solution is designed to show the application of the newly constructed grid multibutterfly memristive HNN to police IoT security. Security performances indicate the designed scheme can resist various attacks and has high robustness. Finally, the test results are further demonstrated through Raspberry Pi-based hardware experiments.

Memristive neuromorphic networks have great potential and advantage in both technology and computational protocols for artificial intelligence. Efficient hardware design of biological neuron models forms the core of research problems in neuromorphic networks. However, most of the existing research has been based on logic or integrated circuit principles, limited to replicating simple integrate-and-fire behaviors, while more complex firing characteristics have relied on the inherent properties of the devices themselves, without support from biological principles. This paper proposes a memristor-based neuron circuit system (MNCS) according to the microdynamics of neurons and complex neural cell structures. It leverages the nonlinearity and non-volatile characteristics of memristors to simulate the biological functions of various ion channels. It is designed based on the Hodgkin-Huxley (HH) model circuit, and the parameters are adjusted according to each neuronal firing mechanism. Both PSpice simulations and practical experiments have demonstrated that MNCS can replicate 24 types of repeating biological neuronal behaviors. Furthermore, the results from the Joint Inter-spike Interval(JISI) experiment indicate that as the background noise increases, MNCS exhibits pulse emission characteristics similar to those of biological neurons.

The high cost, low yield, and low stability of nanomaterials significantly hinder the application and development of memristors. To promote the application of memristors, researchers proposed a variety of memristor emulators to simulate memristor functions and apply them in various fields. However, these emulators lack nonvolatile characteristics, limiting their scope of application. This article proposes an innovative nonvolatile memristor circuit based on complementary metal–oxide–semiconductor (CMOS) technology, expanding the horizons of memristor emulators. The proposed memristor is fabricated in a reconfigurable array architecture using the standard CMOS process, allowing the connection between memristors to be altered by configuring theon–offstate of switches. Compared to nanomaterial memristors, the CMOS nonvolatile memristor circuit proposed in this article offers advantages of low manufacturing cost and easy mass production, which can promote the application of memristors. The application of the reconfigurable array is further studied by constructing an echo state network for short-term load forecasting in the power system.

This paper studies the secrecy energy efficiency (SEE) of a simultaneous wireless information and power transfer (SWIPT) network aided by multiple reconfigurable intelligent surfaces (RIS). The SWIPT network comprises several information decoding receivers (IDRs) and energy harvesting receivers (EHR) served by an access point (AP) supported by several distributed RIS. To effectively define the trade-off between the secrecy rate and energy efficiency of the multi-RIS SWIPT system, an optimization problem is formulated to maximize the SEE by optimizing the transmit beamforming at the AP and the phase shift at each RIS while dynamically controlling each RIS's ON/OFF status. The resultant non-convex optimization problem is solved using a deep reinforcement learning (DRL) framework to design the beamforming policy and a control mechanism for the RISs. Simulation results show that the proposed algorithm enhances the SEE compared to other benchmark schemes.

Spiking neural networks (SNNs) are biologically plausible and computationally powerful. The current computing systems based on the von Neumann architecture are almost the hardware basis for the implementation of SNNs. However, performance bottlenecks in computing speed, cost, and energy consumption hinder the hardware development of SNNs. Therefore, efficient non von Neumann hardware computing systems for SNNs remain to be explored. In this article, a selective supervised algorithm for spiking neurons (SNs) inspired by the selective attention mechanism is proposed, and a memristive SN circuit as well as a memristive SNN circuit based on the proposed algorithm are designed. The memristor realizes the learning and memory of the synaptic weight. The proposed algorithm includes a top-down (TD) selective supervision method and a bottom-up (BU) selective supervision method. Compared with other supervised algorithms, the proposed algorithm has excellent performance on sequence learning. Moreover, TD and BU attention encoding circuits are designed to provide the hardware foundation for encoding external stimuli into TD and BU attention spikes, respectively. The proposed memristive SNN circuit can perform classification on the MNIST dataset and the Fashion-MNIST dataset with superior accuracy after learning a small number of labeled samples, which greatly reduces the cost of manual annotation and improves the supervised learning efficiency of the memristive SNN circuit.

Memristors have recently demonstrated great promise in constructing memristive neural networks with complex dynamics. This article proposes a memristive Hopfield neural network with three memristive coupling synaptic weights. The complex dynamical behaviors of the triple-memristor Hopfield neural network (TM-HNN), which have never been observed in previous Hopfield-type neural networks, include space multistructure chaotic attractors and space initial-offset coexisting behaviors. Bifurcation diagrams, Lyapunov exponents, phase portraits, Poincaré maps, and basins of attraction are used to reveal and examine the specific dynamics. Theoretical analysis and numerical simulation show that the number of space multistructure attractors can be adjusted by changing the control parameters of the memristors, and the position of space coexisting attractors can be changed by switching the initial states of the memristors. Extreme multistability emerges as a result of the TM-HNN’s unique dynamical behaviors, making it more suitable for applications based on chaos. Moreover, a digital hardware platform is developed and the space multistructure attractors as well as the space coexisting attractors are experimentally demonstrated. Finally, we design a pseudorandom number generator to explore the potential application of the proposed TM-HNN.

In this work, a ten-way power-combined power amplifier is designed using a load modulated balanced amplifier (LMBA)-based architecture. To provide the required magnitude and phase controls between the main and control-signal paths of the LMBA, an unequal power splitter and a phase compensation network are proposed. As proof of concept, the designed power amplifier is implemented in a 45-nm SOI CMOS process. At 40 GHz, it delivers a 25.1 dBm$\text{P}_{\text {sat}}$with a peak power-added efficiency (PAE) of 27.9%. At 6-dB power back-off level, it achieves 1.39 times drain efficiency enhancement over an ideal Class-B power amplifier. Using a 200-MHz single-carrier 64-QAM signal, the designed amplifier delivers an average output power of 16.5 dBm with a PAE of 13.1% at an EVMrmsof −23.9 dB and ACPR of −25.3 dBc. The die size, including all testing pads, is only 1.92 mm2. To the best of the authors’ knowledge, compared with the other recently published silicon-based LMBAs, this design achieves the highest$\text{P}_{\text {sat}}$.

With the development of intelligent transportation system (ITS), the vital technology of ITS, short-term traffic forecasting, gains increasing attention. However, the existing prediction models ignore the impact of urban functional zones (FZs) on traffic data, resulting in inaccurate extractions of dynamic spatial relationships from network. Furthermore, how to calculate the influence of external factors, such as weather and holidays on traffic is an unsolved problem. This article proposes a spatio-temporal hierarchical mapping and interactive attention network (HMIAN), which extracts the spatial features from traffic network by constructing FZs, and designs an effective external factors fusion method. HMIAN uses the hierarchical mapping structure to aggregate the roads into FZs, calculate the interaction between FZs and feed this information back to the spatial features. And the interactive attention mechanism is utilized to fuse the traffic data with external factors effectively, and extracts temporal features. In addition, some experiments were carried out on three real traffic data sets. First, experiment results show the better prediction performance of the proposed model compared with other existing methods in a complex traffic network. Second, the longitudinal comparison experiment verifies that the hierarchical mapping structure is effective in extracting spatial features in a complex road network. Finally, the influence of different external factors and fusion methods on traffic prediction are compared, which provides a consult for subsequent research on the influence of external factors.

Neural networks have been widely and deeply studied in the field of computational neurodynamics. However, coupled neural networks and their brain-like chaotic dynamics have not been noticed yet. In this article, we focus on the coupled neural network-based brain-like initial boosting coexisting hyperchaos and its application in biomedical image encryption. We first construct a memristive-coupled neural network (MCNN) model based on two subneural networks and one multistable memristor synapse. Then we investigate its coupling strength-related dynamical behaviors, initial states-related dynamical behaviors, and initial-boosted coexisting hyperchaos using bifurcation diagrams, phase portraits, Lyapunov exponents, and attraction basins. The numerical results demonstrate that the proposed MCNN not only can generate hyperchaotic attractors with high complexity but also can boost the attractor positions by switching their initial states. This makes the MCNN more suitable for many chaos-based engineering applications. Moreover, we design a biomedical image encryption scheme to explore the application of the MCNN. Performance evaluations show that the designed cryptosystem has several advantages in the keyspace, information entropy, and key sensitivity. Finally, we develop a field-programmable gate array test platform to verify the practicability of the presented MCNN and the designed medical image cryptosystem.

Currently, some works on studying complete synchronization of dynamical systems are usually restricted to its two special cases: 1) power-rate synchronization and 2) exponential synchronization. Therefore, how to give a generalization of these types of complete synchronization by the mathematical expression is an open question that needs to be urgently solved. To begin with, this article proposes multimode function synchronization by the mathematical expression for the first time, which is a generalization of exponential synchronization, power-rate synchronization, logarithmical synchronization, and so on. Moreover, two adaptive controllers are designed to achieve robust multimode function synchronization of memristive neural networks (MNNs) with mismatched parameters and uncertain parameters. Each adaptive controller includes function$r(t)$and update gain$\sigma $. By choosing different types of$r(t)$, multiple types of complete synchronization, including power-rate synchronization and exponential synchronization can be obtained. And update gain$\sigma $can be used to adjust the speed of synchronization. Therefore, our results enlarge and strengthen the existing results. Two examples are put forward to verify the validity of our results.

In this paper a step-down ZVS power converter with a self-driven synchronous rectifier (SDSR) for a low-voltage high-current applications is proposed. A transformer leakage inductance, a resonant capacitor and a diode make up the active resonant network. To improve the performance of the converter, a SDSR with a center-tapped transformer is used at the secondary side of the converter. Consequently, due to transformer leakage inductance in secondary side, the output section requires no additional inductor, leading to a major size reduction of the circuit. For verification purposes, a laboratory prototype of the proposed converter is manufactured. Experimental results are presented for waveforms to validate the theoretical outcomes. Additionally, to substantiate the design of the proposed converter, a laboratory prototype is manufactured.

Passive UHF Radio Frequency Identification (RFID) is a potential technology for Automatic Vehicle Identification (AVI) and Electronic Toll Collection (ETC) systems. However, the identification performance is often seriously influenced by the RF radiation zone and anti-collision protocol simultaneously. The impacts of scene geometry and vehicle speed on the identification rate are analyzed and modeled for a typical AVI/ETC application scenario. A calculation method of identification zone is firstly proposed based on the ray-tracing theory. Then the communication procedure is divided into three processes, which are also modeled using individual probability methods. Numerical simulations show that there are strong influences on the tag identification rate caused by the tag speed and antenna inclination angles, and we can obtain a higher identification rate through optimizing them.

The millimeter-wave (mm-wave) single-pole double-throw (SPDT) switch designed in bulk CMOS technology has limited power-handling capability in terms of 1-dB compression point (P1dB) inherently. This is mainly due to the low threshold voltage of the switching transistors used for shunt-connected configuration. To solve this issue, an innovative approach is presented in this work, which utilizes a unique passive ring structure. It allows a relatively strong RF signal passing through the TX branch, while the switching transistors are turned on. Thus, the fundamental limitation for P1dB due to reduced threshold voltage is overcome. To prove the presented approach is feasible in practice, a 90-GHz asymmetrical SPDT switch is designed in a standard 55-nm bulk CMOS technology. The design has achieved an insertion loss of 3.2 dB and 3.6 dB in TX and RX mode, respectively. Moreover, more than 20 dB isolation is obtained in both modes. Because of using the proposed passive ring structure, a remarkable P1dB is achieved. No gain compression is observed at all, while a 19.5 dBm input power is injected into the TX branch of the designed SPDT switch. The die area of this design is only 0.26 mm2.

Nowadays, research, modeling, simulation and realization of brain-like systems to reproduce brain behaviors have become urgent requirements. In this paper, neural bursting and synchronization are imitated by modeling two neural network models based on the Hopfield neural network (HNN). The first neural network model consists of four neurons, which correspond to realizing neural bursting firings. Theoretical analysis and numerical simulation show that the simple neural network can generate abundant bursting dynamics including multiple periodic bursting firings with different spikes per burst, multiple coexisting bursting firings, as well as multiple chaotic bursting firings with different amplitudes. The second neural network model simulates neural synchronization using a coupling neural network composed of two above small neural networks. The synchronization dynamics of the coupling neural network is theoretically proved based on the Lyapunov stability theory. Extensive simulation results show that the coupling neural network can produce different types of synchronous behaviors dependent on synaptic coupling strength, such as anti-phase bursting synchronization, anti-phase spiking synchronization, and complete bursting synchronization. Finally, two neural network circuits are designed and implemented to show the effectiveness and potential of the constructed neural networks.

Adaptive observer based grid synchronization technique has received wide attention recently. This technique has fast convergence property. However, adaptive observer is sensitive to unmodeled dynamics e.g. harmonics. Moreover, no small-signal models are available which can be useful for gain tuning purpose. To solve these issues, in this work, a harmonic robust adaptive observer is presented using the concept of in-loop filter. This improves the existing literature on adaptive observer based grid synchronization technique, which is the main novelty of this paper. To analyze the stability of the proposed technique, small-signal model of the adaptive observer with and without in-loop filter are presented. Finally, simulation study is presented to show the effectiveness of the proposed technique over two other advanced techniques from the literature.

The application of graph convolutional network in short-term traffic flow forecasting of road network has effectively improved the prediction accuracy. The key point of this method is to construct the Laplacian matrix through extracting spatial features among nodes of the road network. However, most available methods mainly rely on the spatial distance among nodes to construct Laplacian matrix, then optimized the Laplacian matrix by other methods, which limits the wide application of the model. In this paper, we propose a dynamic spatial-temporal graph attention graph convolutional network (GAGCN) method to improve the generality of the model. The Laplacian matrix in this model is constructed directly by the dependencies among the nodes hidden in the traffic data which are identified by the graph attention networks, and can be dynamic adjust over time, the information of spatial distance among nodes and human intervention are not required in the process. Experimental results of two real-world datasets show that both the generality and prediction accuracy of the proposed model had been significantly improved.

In this paper, we propose a distance based energy efficient multiple relay selection algorithm for cooperative virtual Multiple-Input-Multiple-Output (MIMO). The fundamentals of this method is to forward the source signal using the node which minimizes the end-to-end total path distance, such that the total energy cost per bit is reduced at the relay and at the source. An energy efficient multiple relay selection algorithm is proposed to minimize the energy cost per bit while achieving a target system performance in terms of BER at the destination. The core of the proposed relay selection method is selecting the node set that minimize the overall path lengths. We present the impact of the relay location and the constellation size for different MIMO configuration, and prove numerically that minimizing the sum of all path link length leads to lower energy consumption under the same performance requirement for MIMO, Multiple-Input-Single-Output (MISO) and Single-Input-Multiple-Output (SIMO) configuration. We compare the performance of MIMO, SIMO and MISO in terms of energy consumption and we present the results in terms of energy cost per bit against transmission distance. The results presented show that the proposed algorithm outperforms non optimized MIMO and traditional virtual MIMO communication in terms of energy consumption per bit for fixed rate and variable rate systems.

Accurately obtaining channel state information (CSI) in wireless systems is significant but challenging. This paper focuses the technique of machine-learning-based channel estimation. In particular, a jointly optimized echo state network (JOESN) is proposed to form a concept of the CSI prediction which is made up of two interacting aspects of output weight regularization and initial parameter optimization. First, in order to enhance noise robustness, a sparse regression based on L2 regularization is employed to finely learn the output weights of ESN. Second, vital reservoir parameters (i.e., global scaling factor, reservoir size, scaling coefficient and sparsity degree) are learned by a linear-weighted particle swarm optimization (LW-PSO) for further improve the prediction accuracy and reliability. The experiments about computational complexity and three evaluating metrics are carried out on two chaotic benchmarks and one real-world dataset. The analyzed results indicate that the JOESN performs promisingly on multivariate chaotic time series prediction.

Cognitive radio networks (CRNs), an assemble of smart schemes intended for permitting secondary users (SUs) to opportunistically access spectral bands vacant by primary user (PU), has been deliberated as a solution to improve spectrum utilization. Cooperative spectrum sensing (CSS) is a vital technology of CRN systems used to enhance the PU detection performance by exploiting SUs' spatial diversity, however CSS leads to spectrum sensing data falsification (SSDF), a new security threat in CR system. The SSDF by malicious users can lead to a decrease in CSS performance. In this work, we propose a CSS scheme in which the presence and absence hypotheses distribution of PU signal is estimated based on past sensing received energy data incorporating robust statistics, and the data fusion are performed according to an evidence based approach. Simulation results show that the proposed scheme can achieve a significant malicious user reduction due to the abnormality of the distribution of malicious users compared with that of other legitimate users. Furthermore, the performance of our data fusion scheme is improved by supplemented nodes' credibility weight.

It is well known that weighted sum of node states plays an essential role in function implementation of neural networks. Therefore, this paper proposes a new weighted sum synchronization model for memristive neural networks. Unlike the existing synchronization models of memristive neural networks which control each network node to reach synchronization, the proposed model treats the networks as dynamic entireties by weighted sum of node states and makes the entireties instead of each node reach expected synchronization. In this paper, weighted sum complete synchronization and quasi-synchronization are both investigated by designing feedback controller and aperiodically intermittent controller, respectively. Meanwhile, a flexible control scheme is designed for the proposed model by utilizing some switching parameters and can improve anti-interference ability of control system. By applying Lyapunov method and some differential inequalities, some effective criteria are derived to ensure the synchronizations of memristive neural networks. Moreover, the error level of the quasi-synchronization is given. Finally, numerical simulation examples are used to certify the effectiveness of the derived results.

Cognitive radio networks (CRNs) are found to be, without difficulty wide-open to external malicious threats. Secure communication is an important prerequisite for forthcoming fifth-generation (5G) systems, and CRs are not exempt. A framework for developing the accomplishable benefits of physical layer security (PLS) in an amplify-and-forward cooperative spectrum sensing (AF-CSS) in a cognitive radio network (CRN) using a stochastic geometry is proposed. In the CRN the spectrum sensing data from secondary users (SU) are collected by a fusion center (FC) with the assistance of access points (AP) as cognitive relays, and when malicious eavesdropping SU are listening. In this paper we focus on the secure transmission of active APs relaying their spectrum sensing data to the FC. Closed expressions for the average secrecy rate are presented. Analytical formulations and results substantiate our analysis and demonstrate that multiple antennas at the APs is capable of improving the security of an AF-CSSCRN. The obtained numerical results also show that increasing the number of FCs, leads to an increase in the secrecy rate between the AP and its correlated FC.

A design methodology using quasi-lumped elements for compact millimeter-wave on-chip bandpass filter (BPF) is presented in this work. To implement BPF using this approach, a novel inductor cell is presented first and then using this cell along with metal-insulator-metal (MIM) capacitors, two BPFs are designed. For the purpose of proof-of-concept, all three designs are implemented and fabricated in a standard 0.13-μm (Bi)-CMOS technology. The measurements show that the inductor cell generates a notch at 47 GHz with a chip size of 0.096 × 0.294 mm2without pads. Moreover, the 1st BPF has the center frequency at 27 GHz with an insertion loss of 2.5 dB and it has one transmission zero at 58 GHz with a peak attenuation of 23 dB. Unlike the 1st design, the 2nd design has two transmission zeros. The center frequency of this BPF is located at 29 GHz with a minimum insertion loss of 3.5 dB. Without the measurement pads, the chip sizes of the two BPFs are 0.076 × 0.296 mm2and 0.096 × 0.296 mm2, respectively.

In this work, an on-chip bandpass filter (BPF) with ultra-wideband, low insertion loss, sharp selectivity and excellent in-band flatness is achieved using a novel design approach based on a quasi-lumped-element method. This approach simply utilizes folded metal strip lines with metal-insulator-metal (MIM) capacitors. To understand the principle of the presented design approach, theoretical analysis is given by means of a simplified equivalent LC-circuit model. Using the analyzed results with a full-wave electromagnetic (EM) simulator to guide the design, a BPF is implemented and fabricated in a standard 0.13-μm (Bi)-CMOS technology. The measurements show that a return loss of better than 10 dB is obtained from 13.5 to 32 GHz. Furthermore, the insertion loss of less than 2.3 dB is achieved with less than 0.1 dB in-band magnitude ripple. The BPF size without measurement pads is only 0.148 mm2(0.37 × 0.4 mm2).

In this work, a new type of miniaturized on-chip resonator using an inductively-coupled structure is presented. The resonator is constructed by two spiral conductors that are implemented using two different metal layers. Since the two conductors are identical but placed in different rotating pattern, a kind of inductive coupling called inverting coupling will be introduced in addition to the broadside capacitive coupling. To fully understand the working mechanism of the resonator, simplified LC equivalent-circuit models and thorough analysis are provided. To further demonstrate the feasibility of the proposed miniaturized resonator in practice, two bandpass filters, namely a 1st-order and 2nd-order, are designed and fabricated in a standard 0.13-μm (Bi)-CMOS technology. Good agreements between simulation and measurement have obtained, which verify that the presented design approach is suitable for miniaturized on-chip passive design.

In this paper, a framework for capitalizing on the potential benefits of physical layer security in an amplify-and-forward cooperative spectrum sensing (AF-CSS) in a cognitive radio network (CRN) using a stochastic geometry is proposed. In the CRN network the sensing data from secondary users (SUs) are collected by a fusion center (FC) with the help of access points (AP) as relays, and when malicious eavesdropping secondary users (SUs) are listening. We focus on the secure transmission of active SUs transmitting their sensing data to the AP. Closed expressions for the average secrecy rate are presented. Numerical results corroborate our analysis and show that multiple antennas at the APs can enhance the security of the AF-CSS-CRN. The obtained numerical results show that average secrecy rate between the AP and its correlated FC decreases when the number of AP is increased. Nevertheless, we find that an increase in the number of AP initially increases the overall average secrecy rate, with a perilous value at which the overall average secrecy rate then decreases. While increasing the number of active SUs, there is a decrease in the secrecy rate between the sensor and its correlated AP.

Except for the advantages of supporting arbitrary kernels, probabilistic predictions and automatic estimation of hyper-parameters, relevance vector machine (RVM) also encounters some of training time increase and classification accuracy recession, compared with SVM. In order to suppress such `nuisance' imperfections, this paper proposed a sparsity-aware RVM model for multi-class classification (denoted as Sa-MRVM) by developing a configurable singular entropy decision mechanism. Multiple driven data sets captured from both emulational and actual circuits under test (CUTs) are involved to further improve the model's generalization ability and judging confidence. Experimental results carried out on two CUTs indicate that our proposed learning methodology is speedy and accurate enough for real world fault diagnosis tasks of analog and mixed-signal circuits.

Cognitive radio is a promising technology that answers the spectrum scarcity problem arising from the proliferation of wireless networks and mobile services. In this paper, spectrum sensing of digital video broadcasting-second generation terrestrial (DVB-T2) signals in AWGN, WRAN and COST207 multipath fading environment are considered. ED is known to achieve an increased performance among low computational complexity detectors, but it is susceptible to noise uncertainty. Taking into consideration the edge pilot and scattered pilot periodicity in DVB-T2 signals, a low computational noise power estimator is proposed. Analytical forms for the detector are derived. Simulation results show that with the noise power estimator, ED significantly outperforms the pilot correlation-based detectors. Simulation also show that the proposed scheme enables ED to obtain increased detection performance in multi-path fading environments. Moreover, based on this algorithm a practical sensing scheme for cognitive radio networks is proposed.

This paper focuses on the issues of cooperative spectrum sensing (CSS) in a large cognitive radio network (CRN) where cognitive radio (CR) nodes can cooperative with neighboring nodes using spatial cooperation. A novel optimal global primary user (PU) detection framework with geographical cooperation using a deflection coefficient metric measure to characterize detection performance is proposed. It is assumed that only a small fraction of CR nodes communicate with the fusion center (FC). Optimal cooperative techniques which are global for class deterministic PU signals are proposed. By establishing the relationship between the CSS technique design issues and Kosambi-Karhunen-Loève transform (KLT) the problem is solved efficiently and the impact on detection performance is evaluated using simulation.

© 2017 by SCITEPRESS. Published under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International licence (CC BY-NC-ND 4.0: https://creativecommons.org/licenses/by-nc-nd/4.0/)

Design of high-performance continuous- time filter (CTF) for analogue signal processing is presented in this paper. To demonstrate of using a novel voltage-mode multiple-loop feedback (MLF) approach for CTF design, a 5th-order elliptic lowpass filter (LPF) is implemented in a standard 0.18-μm CMOS technology. The LPF is based on an inverse-follow-the-leader feedback structure with an input distribution network to generate the required transmission zeros. The LPF consumes 35 mA from a single 1.8 V power supply and it has a cut-off frequency of 30 MHz with less than 0.7 dB passband ripple and more than 60 dB stopband attenuation. In addition, a 65 dB dynamic range is achieved.

In this paper a novel simple all digital frequency locking circuit design is presented together with its performance results. The proposed method can be used to lock DDSs to a reference clock such as a data clock. It allows for efficient fully multi-rate modem design (with a resolution bound by the DDS) within a low cost FPGA. In fact, using the new scheme the baseband modem can be completely constructed within the FPGA without external complicated and expensive VCOs and their associated locking loops.

For a multiuser multiple-input-multiple-output (MIMO) overlay cognitive radio (CR) network, a spacetime opportunistic interference alignment (ST-OIA) technique has been proposed that allows spectrum sharing between primary users (PU) and secondary users (SU) while ensuring zero interference to the PU. The CR system consists of one primary user (PU) and K secondary users (SU) where local channel state information is available at both the transmitters and receivers of SUs. The PU uses space-time water-filling (ST-WF) algorithm to optimize the PUs transmission and in the process, frees up unused eigenmodes that can be exploited by the SU. Because ST-WF achieves higher capacity per antenna than other methods, at low to moderate SNR regimes, it makes it ideal for implementation in CR networks. The SUs align their transmitted signals in such a way their interference impairs only the PUs unused eigenmodes. For this solution with multiple SUs exploiting the benefits of cooperative spectrum sensing to work, there are three separate conditions which must be met. For single user MIMO PU and SU link, the first condition requires there should be zero interference at the PU receiver and secondly, there should be zero interference to both the PU and SU receivers. The third condition caters for the multiple SU scenario which requires limited cooperation between the PU receiver and the multiple SUs to ensure interference from multiple SUs are aligned along unused eigenmodes. Finally, the SU system is assumed to be a time division duplex (TDD) system such that principle of Reciprocity is employed towards optimizing the SUs transmission rates.

A coefficient vector technique implemented on a differential distributed space time block coding (DDSTBC) scheme is presented in this paper to improve on the computation complexity of existing DDSTBC schemes. The full mapping scheme and differential technique for utilizing the co-efficient vectors in a two-relay cooperative network is presented and comparison is made between the proposed technique and the traditional unitary matrices based technique. Results obtained from the numerical and simulation analysis conducted, showed that the proposed method presents an improvement in terms of computation complexity and BER performance. The proposed scheme was extended to accommodate networks with four and eight relay nodes utilizing square-real orthogonal codes.

In this paper, a new dynamic string tautening algorithm is proposed to generate the most energy-efficient off-line schedule for delay-limited traffic of transmitters with non-negligible circuit power. The algorithm is based on two key findings that we derive through judicious convex formulation and resultant optimality conditions, specifies a set of simple but optimal rules, and generates the optimal schedule with a low complexity of O(N2) in the worst case. The proposed algorithm is also extended to on-line scenarios, where the transmit schedule is generated on-the-fly. Simulation shows that the proposed algorithm requires substantially lower average complexity by almost two orders of magnitude to retain optimality than general convex solvers. The effective transmit region, specified by the tradeoff of the data arrival rate and the energy harvesting rate, is substantially larger using our algorithm than using other existing alternatives. Significantly more data or less energy can be supported in the proposed algorithm.

A 0.5-30GHz wideband differential CMOS T/R switch is proposed with low insertion loss (IL), high power handling capacity and high TX-RX isolation. The independent bias technique is proposed to keep the transistors in ideal on/off mode to improve IL and power handling capacity. The leakage cancellation technique is introduced to cancel leakage from TX port to RX port with two match paths. The proposed T/R switch has been implemented in 65nm CMOS, and simulation results show that it achieves 1.2/1.9dB IL and 43/31dBm 1-dB compression point (P1dB) in TX/RX mode and 60dB TX-RX isolation over 0.5-30GHz.

Cooperative Spectrum Sensing has been investigated in Rayleigh-fading environments over non-ideal reporting channels, where the simulation results have shown that its performance is limited by the probability of reporting errors. This paper proposes a transmit diversity scheme using Differential Space-Time coding where channel state information is not required. By regarding multiple pairs of Cognitive Radios as virtual antenna arrays in multiple clusters, Differential space-time coding is applied for the purpose of decision reporting over Rayleigh channels. Hard combination schemes are employed at the fusion center due to their minimal bandwidth requirements. Simulations results show that this method also achieves full transmit diversity, albeit with slight performance degradation in terms of power. The results also show improvements in sensing performance when compared to conventional cooperative spectrum sensing over non-ideal reporting channels.

Present day requirements of high quality audio and video surveillance has instigated research interests in wireless multimedia sensor networks (WMSN). In order for the WMSN to achieve trademark performance in audio and video surveillance applications, certain design requirements must be met. In this work, we identify vital design issues affecting diversity gain in conditions especially, where channel fading characteristics fluctuate rapidly. We apply the cooperative communication technique in WMSN to create a framework that optimizes diversity gain in an environment where channel state information (CSI) is unknown. We then discuss promising research directions for optimizing diversity gain and noncoherent communication efficiency in cooperative WMSN.

Energy harvesting has been widely considered in many wireless applications, especially the wireless sensor networks. This paper develops a novel approach to energy-harvesting powered transmissions under arbitrary packet arrival process and strict deadline constraints over time-varying channels. It is shown that the problem can be formulated as a convex program. Relying on the specific structure of the optimality conditions, we put forth an efficient algorithm with a low computational complexity to find the optimal rate control strategy. An insightful visualization is also provided to depict the construction of the optimal policy. Numerical results are presented to demonstrate the merit of the proposed scheme.

An iterative pilot‐aided channel estimation technique for space–frequency block coded (SFBC) multiple‐input multiple‐output orthogonal frequency division multiplexing systems is proposed. Traditionally, when channel estimation techniques are utilised, the SFBC information signals are decoded one block at a time. In the proposed algorithm, multiple blocks of SFBC information signals are decoded simultaneously. The proposed channel estimation method can thus significantly reduce the amount of time required to decode information signals compared to similar channel estimation methods proposed in the literature. The proposed method is based on the maximum likelihood approach that offers linearity and simplicity of implementation. An expression for the pairwise error probability (PEP) is derived based on the estimated channel. The derived PEP is then used to determine the optimal power allocation for the pilot sequence. The performance of the proposed algorithm is demonstrated in high frequency selective channels, for different number of pilot symbols, using different modulation schemes. The algorithm is also tested under different levels of Doppler shift and for different number of transmit and receive antennas. The results show that the proposed scheme minimises the error margin between slow and high speed receivers compared to similar channel estimation methods in the literature.

So far, complex valued orthogonal codes have been used differentially in cooperative broadband networks. These codes however achieve less than unitary code rate when utilized in cooperative networks with more than two relays. Therefore, the main challenge is how to construct unitary rate codes for non-coherent cooperative broadband networks with more than two relays while exploiting the achievable spatial and frequency diversity. In this paper, we extend full rate quasi-orthogonal codes to differential cooperative broadband networks where channel information is unavailable. From this, we propose a generalized differential distributed quasi-orthogonal space-frequency coding (DQSFC) protocol for cooperative broadband networks. Our proposed scheme is able to achieve full rate, and full spatial and frequency diversity in cooperative networks with any number of relays. Through pairwise error probability analysis we show that the diversity gain of our scheme can be improved by appropriate code construction and sub-carrier allocation. Based on this, we derive sufficient conditions for the proposed code structure at the source node and relay nodes to achieve full spatial and frequency diversity.

This paper outlines the popular circuit tuning strategies reported for the implementation of reconfigurable low-noise amplifiers (LNAs). It presents a continuously-tuned LNA intended for multi-standard applications as well as enhancing the yield of conventional narrowband LNAs. The presented LNA is designed and implemented in a 0.25μm silicon-on-sapphire (SOS) CMOS process. It uses MOS-varactors at the output to continuously tune its load resonance frequency and input matching frequency without the need of a tunable input network, achieving optimized power consumption and noise figure (NF). The post-layout simulations show that the designed LNA can be continuously tuned from 2.6 GHz to 3.5 GHz. Over this frequency range, an input IP3 of of -12 dB, gain of 17 dB and a NF of less than 2 dB have been achieved with 3.4 mW of power consumption at 1.8V.

In this paper, a source-assisting differential distributed space time block coding (SA-DDSTBC) scheme is proposed for cooperative networks. Firstly, in most existing works on distributed space time block coding (DSTBC), the destination node is assumed to have perfect channel state information (CSI), thus, signal recovery is straight forward. In practice however, some scenarios exist whereby the destination node is unable to acquire CSI. Consequently, this work incorporates differential concepts with DSTBC to facilitate signal recovery in cooperative networks operating in environments where CSI acquisition is impractical. Secondly, different from most works on DSTBC which assume that the source-destination link is unavailable, the proposed scheme employs a source-assisting (SA) strategy that exploits the additional diversity path provided by the source-destination link. The numerical and simulation results obtained illustrate that compared to the conventional DSTBC schemes, the proposed SA-DDSTBC scheme achieves non-coherent signal recovery and improved BER and diversity performance with negligible increase in decoding complexity.

We propose co-efficient vector based differential distributed quasi-orthogonal space time block codes (DQSTBC) for cooperative networks utilizing the decode-and-forward protocol. In our work, we employ rotated constellation quasiorthogonal codes which guarantee full code-rate and full diversity in cooperative networks with more than two relay nodes.

Combination of STBC with OFDM has gained considerable interest and has become a promising technique for future wireless communications. However, such systems require the knowledge of the Channel State Information (CSI) at the receiver. In this paper, a new channel estimation approach is proposed using dedicated pilot subcarriers defined at fixed intervals to estimate the channel parameters. Once channel parameters at the pilot subcarriers have been estimated using known pilot symbols at the receiver, the iterative channel estimation process is initiated, and data symbols positioned at adjacent data subcarriers are recovered. Subsequently, the recovered data symbols become the new set of pilots which are then used to re-estimate the channel parameters and recover the next adjacent STBC block. Another major novel contribution of the paper is the proposal of a new group decoding method that reduces the processing time significantly via the use of subcarrier grouping for transmitted data recovery. The OFDM symbols are divided into groups to which a set of pilot subcarriers are assigned and used to initiate the channel estimation process. Designated data symbols contained within each group of the OFDM symbols are decoded simultaneously in order to improve the decoding time duration.

The Maximum Likelihood (ML) detector is a detection criteria which yields an optimal solution to Multiple-Input Multiple-Output (MIMO) systems but however, at the expense of its NP-hard complexity. Instead, the Sphere Decoder (SD) was proposed as an efficient algorithm for finding the solution to the ML detection problem in MIMO digital communication systems. Unlike the ML detector whose complexity rises exponentially with the number of transmit and receive antennas, the complexity of the SD is polynomial for both finite and infinite lattices which makes real-time implementation of the ML detector practical. The choice of the initial radius for the SD has a significant impact on the complexity and the performance of the SD. However, the problem of selecting the initial radius is NP-hard itself. In this paper, we propose a simple Schnorr-Euchner SD (SE-SD) with a novel radius based on the received signal, noise statistics, number of transmit antennas, the energy of the transmitted symbols and on the channel matrix. The proposed method does not only reduce the complexity of the SD, but it also improves the bit error rate performance of the SD, particularly at low signal-to-noise ratios (SNR). To demonstrate the feasibility of our proposed method, we compare our method with the conventional SD radius and with other methods proposed in the literature.

This paper introduces a computationally efficient pilot aided channel estimation method for space-frequency block coding (SFBC) Orthogonal Frequency Division Multiplexing (OFDM) systems under frequency selective channels. The proposed method, simulated under WiMax requirements, is based on the use of eight pilots defined in the standard to estimate the channel parameters at constant interval. The pilots are also coded in the same SFBC format to simplify the estimation computations, but can be modulated by different modulation scheme to reduce the estimation error. The method offers tradeoff between accurate channel estimation and efficient bandwidth usage as more pilots would allow the algorithm to perform a more accurate estimation but at the cost of less transmitted data. Performances are evaluated for high mobility applications and with pilots modulated using different modulation schemes. Simulation results are presented for different number of antenna at the receiver, different values of Doppler shifts and different modulations for pilot and data subcarriers.

Algebraic Space-Time Codes (ASTC) for MIMO systems are based on quaternion algebras. Thanks to their algebraic construction, the ASTC codes are full-rank, full-rate and have the non-vanishing determinant property. These codes have been proposed for MIMO flat fading channels in order to increase the spectral efficiency and to maximize the coding gain. The purpose of this work is to analyze the performance of the ASTC in a frequency selective Rayleigh channel. To deal with the frequency selectivity, we use the OFDM modulation. The capacity performances of an ASTC-MIMO-OFDM system, under correlated Rayleigh frequency-selective channel, have been evaluated. Index Terms-

This paper presents a method of designing wavelet filters for high-frequency real-time applications, in which the Gm-C technique and current-mode follow-the-leader multiple loop feedback structure are employed. The Marr wavelet is utilized as an example to elaborate the design procedure. Using TSMC 0.18µm CMOS process, the center frequency of the Marr wavelet filter can be tuned from 61.1MHz to 127MHz. The total power consumption at 100MHz is 60mW. Simulation results indicate that the proposed approach is feasible for high-quality high-frequency operation.

This paper describes a channel estimation scheme for Multiple Input Multiple Output (MIMO)-Orthogonal Frequency Division Multiplexing (OFDM) systems based on training sequence. We first develop an approach to channel estimation which is crucial for the decoding of the transmitted data. We then discuss the implementation of the proposed method for WiMax systems under various channel conditions. The efficiency of the new algorithm is demonstrated through the simulation of the MIMO-OFDM system for two and four transmit antennas and different number of receive antennas. The Space-Time Coding with 192 information subcarriers per codeword is used as defined in the WiMax standard. Through simulations, it is shown that the proposed method has between 1.5 dB and 2dB loss compared to the ideal case where the channel coefficients are known at the receiver. In summary, with the proposed channel estimation technique, combining diversity using Space-Time Codes with OFDM is proved to be a promising technique for the present and future wireless communications.

The design and implementation of a CMOS continuous-time multiple loop feedback (MLF) leap frog (LF) filter is described. The filter is implemented using a fully.differential linear, low voltage operational transconductance amplifier (OTA). PSpice simulations using a standard TSMC 0.18μm CMOS process with 1.8V power supply have shown that the cut-off frequency of the filter ranges from 455MHz to 780MHz and dynamic range is about 58dB. The group delay is less than 5% over the whole tuning range; the maximum power consumption of the filter with gain boost is only 240mW.

We propose an easily implemented and low-cost design-for-testability scheme for OTA-C filters based on an oscillation-based test (OBT) methodology. The OBT method is a vectorless output test strategy easily applicable to built-in self-test. During test mode, the filter under test is converted into an oscillator by establishing the oscillation condition in its transfer function. The oscillator frequency can be measured using digital circuitry and deviations from the cut-off frequency indicate faulty behaviour of the filter. The proposed method is suitable for both catastrophic and parametric fault diagnosis and is effective in detecting single and multiple faults. The validity of the proposed method has been verified using comparison between faulty and fault-free simulation results of two-integrator loop, Tow-Thomas and KHN OTA-C filters. Simulation results for 2ndorder filters using a 0.25μm CMOS technology show that the proposed oscillation-based test strategy has more than 96% fault coverage and, with a minimum number of extra components, requires a negligible area overhead.

A 300MHz CMOS seventh-order linear phase gm-C filter based on a current-mode multiple loop feedback (MLF) leap-frog (LF) structure is realized. The filter is implemented using a fully-differential linear operational transconductance amplifier (OTA) based on a source degeneration topology. PSpice simulations using a standard TSMC 0.18μm CMOS process with 2.5V power supply have shown that the cut-off frequency of the filter can be tuned from 260MHz to 320MHz and dynamic range is about 66dB. Group delay ripple is approximately 4.5% over the whole tuning range and maximum power consumption is 210mW.

A 100MHz centre frequency, 10MHz bandwidth 6/sup th/ order Butterworth bandpass filter using a leap-frog gm-C structure is described. Fully differential Nauta OTAs are used in the design, which include compensation of parasitic output resistance and feed-forward effects to obtain high Q. A mixed-signal, on-chip tuning system based on Dishal's method is described. The filter and tuning system have been simulated using Mosis 0.18mm BSIM 3v3 models and PSpice.

The Q-based method of /spl Pi/ impedance matching network design is studied systematically. A more practical definition of the loaded quality factor Q than that used hitherto is adopted. Designable conditions and design formulas based on the loaded Q are analytically demonstrated. Accurate explicit expressions of network frequency responses and harmonic rejection in term of the loaded Q are established and a method of determining the loaded Q for the required harmonic attenuation is developed. We also formulate all tolerance and parasitic sensitivities and their relations to the loaded Q.>