Adaptive DLL Update Scheme for Power Fluctuation Immunity Using Phase Error Detector

In most of existing multiobjective estimation of distribution algorithms (MEDAs), there exist drawbacks: incorrect treatment of population outliers; the loss of population diversity; and too much computational effort being spent on finding an optimal population model. To ease the drawbacks, this paper designs a novel clustering-based multivariate Gaussian sampling strategy and proposes an adaptive MEDA called AMEDA. A clustering analysis approach is utilized in AMEDA to discover the distribution structure of the population. Based on the distribution information, with a certain probability, a local or a global multivariate Gaussian model (MGM) is built for each solution to sample a new solution. A covariance sharing strategy is designed in AMEDA to reduce the complexity of building MGMs, and an adaptive update strategy of the probability that controls the contributions of the two types of MGMs is developed to dynamically balance exploration and exploitation. AMEDA is compared with four representative MOEAs on a number of test instances with complex Pareto fronts and variable linkages. Experimental results suggest that AMEDA outperforms the comparison algorithms on dealing with the test instances. The effectiveness of the clustering-based multivariate Gaussian sampling strategy and the adaptive probability update strategy is also experimentally verified.

A digital tanlock loop (DTL) is a nonuniform sampling digital phase-locked loop (DPLL) whose phase error detector, using the tan/sup -1/[/spl middot/] function with in-phase and quadrature samples of incoming signal, has a linear characteristic with a period of 2/spl pi/. In this paper, we study the application of an N-phase DTL in digital cellular radio with /spl pi//4-DQPSK modulation. The performance degradation due to fading effect is investigated in terms of bit error rate and steady state phase error variance by computer simulation. It is shown that, in the digital cellular radio system, the performances on both bit error rate and phase error variance using DTL are far better than that using the traditional digital N-phase I-Q loop.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Model update is an important module in target trackers. It plays an important role in adaptive tracking. Many researches have proven that different model update strategies should be adopted, when tracking in different scenes, especially in occlusion and deformation. Though many strategies have been proposed in recent years, few of them make high improvement and good combination on trackers. In this paper, we first proved there is a close relationship between the tracking scenes and the response maps. Then, we proposed an adaptive model update strategy for calculating model update rate based on the response map. Many experiments have been done to compare the proposed model update strategy with some state-of-the-art strategies, and the results have shown that the proposed model update strategy outperforms the best model update strategy by 7% on the test of Kernel Correlation Filter tracker. Furthermore, the proposed model update strategy was evaluated on some state-of-the-art correlation filter trackers. Results have shown the proposed strategy was well integrated into many trackers, and improved the tracking accuracy effectively.

In geographic routing, nodes need to maintain up-to-date positions of their immediate neighbors for making effective forwarding decisions. Periodic broadcasting of beacon packets that contain the geographic location coordinates of the nodes is a popular method used by most geographic routing protocols to maintain neighbor positions. We contend and demonstrate that periodic beaconing regardless of the node mobility and traffic patterns in the network is not attractive from both update cost and routing performance points of view. We propose the Adaptive Position Update (APU) strategy for geographic routing, which dynamically adjusts the frequency of position updates based on the mobility dynamics of the nodes and the forwarding patterns in the network. APU is based on two simple principles: 1) nodes whose movements are harder to predict update their positions more frequently (and vice versa), and (ii) nodes closer to forwarding paths update their positions more frequently (and vice versa). Our theoretical analysis, which is validated by NS2 simulations of a well-known geographic routing protocol, Greedy Perimeter Stateless Routing Protocol (GPSR), shows that APU can significantly reduce the update cost and improve the routing performance in terms of packet delivery ratio and average end-to-end delay in comparison with periodic beaconing and other recently proposed updating schemes. The benefits of APU are further confirmed by undertaking evaluations in realistic network scenarios, which account for localization error, realistic radio propagation, and sparse network.

Class-incremental learning (CIL) aims to continuously introduce novel categories into a classification system without forgetting previously learned ones, thus adapting to evolving data distributions. Researchers are currently focusing on leveraging the rich semantic information of pre-trained models (PTMs) in CIL tasks. Prompt learning has been adopted in CIL for its ability to adjust data distribution to better align with pre-trained knowledge. This paper critically examines the limitations of existing methods from the perspective of prompt learning, which heavily rely on input information. To address this issue, we propose a novel PTM-based CIL method called Input-Agnostic Prompt Enhancement with NegAtive Feedback ReguLation (PEARL). In PEARL, we implement an input-agnostic global prompt coupled with an adaptive momentum update strategy to reduce the model's dependency on data distribution, thereby effectively mitigating catastrophic forgetting. Guided by negative feedback regulation, this adaptive momentum update addresses the parameter sensitivity inherent in fixed-weight momentum updates. Furthermore, it fosters the continuous enhancement of the prompt for new tasks by harnessing correlations between different tasks in CIL. Experiments on six benchmarks demonstrate that our method achieves state-of-the-art performance.

Modulation and Demodulation are the most critical blocks in a communication system. Demodulators used in coherent communication receivers not only get back the message signal, but also perform phase error detection and correction. Phase error detection and correction involves tuning or adjusting the phase of the reference signal to that of the carrier signal received. This is an iterative process. One of the popular techniques employed for phase error detection and correction is the Costas Loop. There has been research about Numerically Controlled Oscillators (NCO) based on CORDIC algorithm being used in a Costas Loop [1]. The other simple yet memory expensive alternative to the CORDIC algorithm is to use a LookUp Table (LUT) based technique. This paper tries to model a Costas Loop based on each of the methods separately and then compare the results. There has been notable work involving the use of symmetric FIR filters in a Costas Loop [2] which saves on memory. However there has been no attempt to compare the results of folded filter (uses one half of the filter coefficients only) architecture versus that obtained from unfolded filter (uses the entire coefficient sequence) architecture implementations. This paper compares the results obtained from folded filter implementation versus that obtained from unfolded filter implementation. The system was simulated using MATLAB and Simulink. The Verilog implementation was programmed on Spartan 3e FPGA board.

We present a subsampling polar transmitter (SSPTX) in 28-nm CMOS that consists of a low-noise phase modulating (PM) digital subsampling phase-locked loop (PLL) and a harmonic rejection mixed (HRM) inverse-class-D ( $\mathrm {D}^{-1}$ ) digital power amplifier (DPA) for amplitude modulation (AM). The DPA is, unlike in a typical polar transmitter (TX), placed within the PLL and the phase-error detection happens directly at the DPA output. The subsampling polar TX thus becomes sensitive not only to phase errors but also to modulation amplitude. That feature enables AM–AM and PM–PM distortion to be detected and cancelled digitally in the background, while the transmitter operates normally. Moreover, the AM–PM cross distortion is filtered by the loop itself. The chip operates from a 0.9-V supply at 5.5 GHz with 2.5 MHz BW (1024 QAM) with average 1.1-dBm output power and total power consumption of 50 mW.

Accurately predicting trends in NOx emission is essential for effectively controlling pollution in municipal solid waste incineration (MSWI) power plants. However, the MSWI process exhibits notable dynamic nonlinearity, time series characteristics, and fluctuations that are distinct from those present in fossil fuel combustion processes. Therefore, the model must possess excellent capabilities in handling time series and nonlinear features while achieving adaptive updates to account for complex working conditions. To address these issues, we have developed a robust prediction model for NOx emission trends using the bi‐directional long short‐term memory (Bi‐LSTM) deep learning algorithm. This model encompasses maximum information coefficient and expert experience for input variables selection, parameter optimization using the linear inertial weight particle swarm algorithm (LDWPSO), and an adaptive update strategy based on probabilistic statistics. The prediction performance of this model was compared to that of the traditional and widely used backpropagation neural network (BPNN), extreme learning machine (ELM), and LSTM. Furthermore, we verified the adaptive update effect of the proposed model using additional data. The results demonstrate that the proposed model exhibits robust prediction and adaptive capabilities. This study's originality is presenting a satisfactory trend prediction for NOx emission from the MSWI process using an adaptive LDWPSO‐(Bi‐LSTM) model. It will be essential for the optimization and control of NOx emissions from the MSWI process.

The timing skew between signals reduces the timing margin of the receiver and limits the data rate of the parallel link. This issue becomes more critical for applications with many IO pins, such as a high bandwidth memory (HBM). The inter-signal skew compensation scheme for many IO pins requires not only de-skew performance but also the minimization of area and power overheads. In this brief, we propose an inter-pin skew compensation scheme using bypass-controlled all digital delay locked loops (ADDLL). The adoption of the proposed bypass control register that operates with a binary search algorithm, such as the successive approximation register (SAR), allows the digital control delay line (DCDL) controller to be embedded in the delay line. This can alleviate the limitation of bandwidth, which is a disadvantage of SAR and occupies smaller area than SAR whereas maintaining the fast lock time. The circuit is fabricated using a 28 nm CMOS technology with a 1 V supply voltage and an area of 0.0009 mm2 for one de-skew module. The measured result shows that inter-signal skew is reduced to less than 3 ps for 2 Gb/s/pin $\times8$ parallel signals.

In hostile environments, GNSS is a potentially unreliable solution for self-localization and navigation. Many systems only use an IMU as a backup system, resulting in integration errors which can dramatically increase during mission execution. We suggest using a fighter radar to illuminate satellites with known trajectories to enhance the self-localization information. This technique is time-consuming and resource-demanding but necessary as other tasks depend on the self-localization accuracy. Therefore an adaption of classical resource management frameworks is required. We propose a quality of service based resource manager with capabilities to account for inter-task dependencies to optimize the self-localization update strategy. Our results show that this leads to adaptive navigation update strategies, mastering the trade-off between self-localization and the requirements of other tasks.

The external environment of the real world is often filled with various types of multimodal information that are highly dynamic and unpredictable. Therefore, intelligent systems need to continuously learn and retain knowledge. Continual learning (CL) provides a foundation for vision-and-language (VaL) models to adapt to the changes in the real world. Existing research on CL for VaL models mainly focus on preserving memory stability to overcome catastrophic forgetting. However, it is difficult for existing methods to flexibly adapt to the dynamic growth of external information. In this paper, we propose a generic approach that appropriately attenuates and employs old memories in parameter distributions to improve learning plasticity. We propose a new parameter-sharing CL method that combines adaptive parameter update strategies with network pruning to enhance the plasticity of VaL models during the continual learning process. When learning a new task, we utilize the pruned model for learning new tasks. And then we employ the sub-network parameter initialization updating strategy to transfer the most similar important knowledge from learned tasks. Subsequently, to strength the stability of the model, we further update the model parameters, adjusting old memories to better adapt to new task learning. Experiments on a series of VaL tasks have shown that our proposed method outperforms the compared methods in improving model plasticity, and is more stable than existing parameter sharing methods.

Location management is one of the most important issues in the design of future cellular systems. It consists of two basic operations: location update and paging. The paging operation is dependent on the corresponding location update operation. The time based, movement based, and distance based location update schemes are three of the most popular dynamic location update schemes. It has been shown that the distance based scheme is more efficient than the movement based. However the distance between two cells is difficult to obtain. The author proposes a coordinate system which can be used to systematically assign IDs to the cells in a cellular system. With this cell ID assignment scheme, the distance between any two cells can be computed easily. Based on our distance computation scheme, a speed adaptive location update scheme is presented. It is shown that the speed adaptive location update scheme is a generalization of both time based and distance based schemes.

Object tracking is still an intriguing task as the target undergoes significant appearance changes due to illumination, fast motion, occlusion and shape deformation. Background clutter and numerous other environmental factors are other major constraints which remain a riveting challenge to develop a robust and effective tracking algorithm. In the present study, an adaptive Spatio-temporal context (STC)-based algorithm for online tracking is proposed by combining the context-aware formulation, Kalman filter, and adaptive model learning rate. For the enhancement of seminal STC-based tracking performance, different contributions were made in the proposed study. Firstly, a context-aware formulation was incorporated in the STC framework to make it computationally less expensive while achieving better performance. Afterwards, accurate tracking was made by employing the Kalman filter when the target undergoes occlusion. Finally, an adaptive update scheme was incorporated in the model to make it more robust by coping with the changes of the environment. The state of an object in the tracking process depends on the maximum value of the response map between consecutive frames. Then, Kalman filter prediction can be updated as an object position in the next frame. The average difference between consecutive frames is used to update the target model adaptively. Experimental results on image sequences taken from Template Color (TC)-128, OTB2013, and OTB2015 datasets indicate that the proposed algorithm performs better than various algorithms, both qualitatively and quantitatively.

In mobile broadband wireless access networks such as Mobile WiMAX, idle mode is defined to save radio resources and reduce power consumption when a mobile station is not involved in packet transmission and reception. In order to support idle mode, location update and paging should be supported, where location update and paging overheads are affected by paging group size. In this paper, we propose a hierarchical paging group ID structure, an adaptive location update algorithm based on the paging group structure, and a predictive paging scheme for large size paging groups. The performance of the proposed scheme is compared with conventional scheme from the aspect of location update and paging signaling overheads. Numerical results show that the proposed scheme performs well for varying the velocity of mobile stations.

Fine-grained power monitoring, which refers to power monitoring at the server level, is critical to the efficient operation and energy saving of datacenters. Fined-grained power monitoring, however, is extremely challenging in legacy datacenters that host server systems not equipped with power monitoring sensors. Installing power monitoring hardware at the server level not only incurs high costs but also complicates the maintenance of high-density server clusters and enclosures. In this paper, we present a zero-cost, purely software-based solution to this challenging problem. We use a novel technique of non-intrusive power disaggregation (NIPD) that establishes power mapping functions (PMFs) between the states of servers and their power consumption, and infer the power consumption of each server with the aggregated power of the entire datacenter. The PMFs that we have developed can support both linear and nonlinear power models via the state feature transformation. To reduce the training overhead, we further develop adaptive PMFs update strategies and ensure that the training data and state features are appropriately selected. We implement and evaluate NIPD over a real-world datacenter with $326$ nodes. The results show that our solution can provide high precision power estimation at both rack level and server level. In specific, with PMFs including only two nonlinear terms, our power estimation i) at rack level has mean relative error of $2.18$ percent, and ii) at server level has mean relative errors of $9.61$ and $7.53$ percent corresponding to the idle and peak power, respectively.