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Cognitive training with adaptive algorithm improves cognitive ability in older people with MCI

AGING CLINICAL AND EXPERIMENTAL RESEARCH 2025年第1期37卷 1-8页

作者： Li, Chenxi Li, Meiyun Shang, Yunfeng Yueyang Vocat Tech Coll Sch Nursing Yueyang 414000 Peoples R China Guangzhou Xinhua Univ Sch Management Guangzhou 510006 Peoples R China Yueyang Cent Hosp Rehabil Dept Yueyang 414000 Peoples R China

Recent discoveries indicating that the brain retains its ability to adapt and change throughout life have sparked interest in cognitive training (CT) as a possible means to postpone the development of dementia. Despite this, most research has focused on confirming the efficacy of training outcomes, with few studies examining the correlation between performance and results across various stages of training. In particular, the relationship between initial performance and the extent of improvement, the rate of learning, and the asymptotic performance level throughout the learning curve remains ambiguous. In this study, older adults underwent ten days of selective attention training using an adaptive algorithm, which enabled a detailed analysis of the learning curve's progression. Cognitive abilities were assessed before and after CT using the Mini-mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA). The findings indicated that: (1) Initial performance is positively correlated with Learning amount and asymptotic performance level, and negatively correlated with learning speed;(2) Age is negatively correlated with learning speed, while it is positively correlated with the other three parameters. (3) Higher pre-training MMSE scores predicted higher post-training MMSE scores but less improvement;(4) Higher pre-training MoCA scores predicted higher post-training MoCA scores and less improvement;(5) The parameters of the learning curve did not correlate with performance on the MMSE or MoCA. These results indicate that: (1)Selective attention training using adaptive algorithms is an effective tool for cognitive intervention;(2) Older individuals with poor baseline cognitive abilities require more diversified cognitive training;(3) The study supports the compensation hypothesis.

关键词： CT adaptive algorithm Cognitive ability Learning curve Compensation assumption

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Anomaly Detection in Sports Training Data: An Improved adaptive algorithm 24

Anomaly Detection in Sports Training Data: An Improved Adapt...

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Proceedings of the 2024 International Conference on Sports Technology and Performance Analysis

作者： Yuhao Cai School of General Education Guangzhou Institute of Science and Technology Guangzhou Guangdong China

ISBN: (纸本)9798400712234

Identifying the anomalies in athletes' training data plays an important role in preventing injuries and optimizing training plans. In this paper, an improved adaptive algorithm is proposed to improve the accuracy and efficiency of anomaly detection in sports training data. Firstly, a depth self-encoder is constructed to learn the low-dimensional representation of sports training data, which captures the key features in the data. Then, the adaptive landmark filtering mechanism is introduced, and landmarks are selected and filtered and optimized based on the peak density to enhance the sensitivity of the algorithm to abnormal data. In the data preprocessing stage, the original data is cleaned and features are selected to reduce noise and extract the most relevant features. A composite loss function including reconstruction loss, affinity loss and sparsity loss is also proposed to further optimize the performance of the algorithm. Finally, in the training and optimization stage of the algorithm, the designed compound loss function is used to train the algorithm, and the parameters are adjusted and the algorithm is verified. The final results show that the improved adaptive algorithm shows the detection accuracy of more than 99% on multiple data, the missed detection rate remains between 0.257% and 0.447%, and the detection delay is lower than that of GAN and K-means algorithms, which shows the efficiency and accuracy of the algorithm in anomaly detection tasks. Therefore, the algorithm not only improves the accuracy of anomaly detection, but also enhances the applicability and effectiveness of the model in the actual sports training scene, showing a broad application prospect.

关键词： adaptive algorithm

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adaptive algorithm for estimating power frequency phasors using dynamic window length

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IET GENERATION TRANSMISSION & DISTRIBUTION 2016年第14期10卷 3423-3430页

作者： Yang, Liming Jiao, Zaibin Kang, Xiaoning Song, Guobing Suonan, Jiale Xi An Jiao Tong Univ Dept Elect Engn Xian 710049 Peoples R China

Estimating phasors as quickly as possible while guaranteeing accuracy is important to ensure reliable fault detection and isolation in power systems. Window length is an important factor for phasor estimation. The conventional fixed window length cannot handle well the many and diverse fault voltage and current signals. By analysing the relationship between accuracy and window length of a phasor estimation using the matrix pencil method, a criterion is established to determine whether or not the estimated phasor is credible. Then, an adaptive algorithm to estimate the phasors using dynamic window length is proposed. Analytical and experimental investigations show that the proposed algorithm can accurately and timely estimate phasors and can enable reliable and quick relay operation.

关键词： phasor measurement fault diagnosis power system faults matrix algebra power system reliability adaptive algorithm power frequency phasor estimation dynamic window length fault detection fault isolation power system current signal diverse fault voltage matrix pencil method

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adaptive algorithm for spreading factor selection in LoRaWAN networks with multiple gateways

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COMPUTER NETWORKS 2020年 182卷 107491-107491页

作者： Loubany, Ali Lahoud, Samer El Chall, Rida St Joseph Univ Beirut Ecole Super Ingn Beyrouth ESIB Fac Engn Beirut Lebanon Lebanese Univ Fac Engn Ctr Rech Sci Ingn CRSI Beirut Lebanon

Recently, LoRaWAN has been considered a promising technology for large-scale IoT applications owing to its ability to achieve low power and long range communications. However, LoRaWAN is limited using Aloha random access scheme. When in dense scenarios, such scheme leads to a high number of collisions, thus severely impacts the reliability and scalability of LoRaWAN. In this paper, we investigate the impact of scalability and densification of nodes and gateways on the system reliability taking into account the capture effect. We propose an optimization problem to derive the node distribution at different spreading factors (SF) in LoRaWAN networks with multiple gateways. We then introduce an adaptive algorithm that enables to easily implement SF optimization by adjusting the signal-to-noise ratio thresholds. Moreover, the performance of the proposed algorithm is compared with the performance of legacy LoRaWAN and relevant algorithms from the state-of-the-art. Simulation results show that the proposed algorithm significantly outperforms the state-of-the-art algorithms, and improves the throughput and packet delivery ratio of the network.

关键词： Internet of thing LoRaWAN Aloha Scalability Spreading factors adaptive algorithm Capture effect

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adaptive algorithm for sparse signal recovery

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DIGITAL SIGNAL PROCESSING 2019年 87卷 10-18页

作者： Bayisa, Fekadu L. Zhou, Zhiyong Cronie, Ottmar Yu, Jun Umea Univ Dept Math & Math Stat Umea Sweden Umea Univ Dept Math & Math Stat Math Stat Umea Sweden

The development of compressive sensing in recent years has given much attention to sparse signal recovery. In sparse signal recovery, spike and slab priors are playing a key role in inducing sparsity. The use of such priors, however, results in non-convex and mixed integer programming problems. Most of the existing algorithms to solve non-convex and mixed integer programming problems involve either simplifying assumptions, relaxations or high computational expenses. In this paper, we propose a new adaptive alternating direction method of multipliers (AADMM) algorithm to directly solve the suggested non-convex and mixed integer programming problem. The algorithm is based on the one-to-one mapping property of the support and non-zero element of the signal. At each step of the algorithm, we update the support by either adding an index to it or removing an index from it and use the alternating direction method of multipliers to recover the signal corresponding to the updated support. Moreover, as opposed to the competing "adaptive sparsity matching pursuit" and "alternating direction method of multipliers" methods our algorithm can solve non-convex problems directly. Experiments on synthetic data and real world images demonstrated that the proposed AADMM algorithm provides superior performance and is computationally cheaper than the recently developed iterative convex refinement (ICR) and adaptive matching pursuit (AMP) algorithms. (C) 2019 Elsevier Inc. All rights reserved.

关键词： Sparsity adaptive algorithm Sparse signal recovery Spike and slab priors

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AN adaptive algorithm FOR MOTION COMPENSATED COLOR IMAGE-CODING

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IEEE TRANSACTIONS ON COMMUNICATIONS 1987年第7期35卷 747-754页

作者： KWATRA, SC LIN, CM WHYTE, WA NASA LEWIS RES CTRCLEVELANDOH 44135

This paper presents an adaptive algorithm for motion compensated color image coding. The algorithm can be used for video teleconferencing or broadcast signals. Activity segmentation is used to reduce the bit rate and a variable stage search is conducted to save computations. The adaptive algorithm is compared with the nonadaptive algorithm and it is shown that with approximately 60 percent savings in computing the motion vector and 33 percent additional compression, the performance of the adaptive algorithm is similar to the nonadaptive algorithm. The adaptive algorithm results also show improvement of up to 1 bit/pel over interframe DPCM coding with nonuniform quantization. The test pictures used for this study were recorded directly from broadcast video in color.

关键词： adaptive algorithm Color Image coding Multimedia communication Broadcasting Teleconferencing Image segmentation Bit rate Quantization Testing

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adaptive algorithm and tool flow for accelerating SystemC on many-core architectures

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MICROPROCESSORS AND MICROSYSTEMS 2015年第8期39卷 1063-1075页

作者： Reder, Simon Roth, Christoph Bucher, Harald Sander, Oliver Becker, Juergen Karlsruhe Inst Technol Inst Informat Proc Technol ITIV D-76131 Karlsruhe Germany

Within this article an adaptive approach for parallel simulation of SystemC RTL Models on future many-core architectures like the Single-chip Cloud Computer (SCC) from Intel is presented. It is based on a configurable parallel SystemC kernel that preserves the partial order defined by the SystemC delta cycles while avoiding global synchronization as far as possible. The underlying algorithm relies on a classification of existing communication relations between parallel processes. The type and topology of communication relations determines the type and number of causality conditions that need to be fulfilled during runtime. The parallel kernel is complemented by an automated tool flow that allows detecting relevant model-specific properties, performing a fine-grained model partitioning, classifying communication relations and configuring the kernel. Experiments by means of a MPSoC model show that pure local synchronization can provide significant performance gains compared to global synchronization. Furthermore, the combination of local synchronization with fine-grained partitioning provides additional degrees of freedom for optimization. (c) 2015 Elsevier B.V. All rights reserved.

关键词： adaptive algorithm Many-core Parallel simulation SystemC

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adaptive algorithm utilizing acceptance rate for eliminating noisy epochs in block-design functional near-infrared spectroscopy data: application to study in attention deficit/hyperactivity disorder children (vol 5, 045001, 2018)

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NEUROPHOTONICS 2018年第4期5卷 045001-045001页

作者： Sutoko, Stephanie Monden, Yukifumi Funane, Tsukasa Tokuda, Tatsuya Katura, Takusige Sato, Hiroki Nagashima, Masako Kiguchi, Masashi Maki, Atsushi Yamagata, Takanori Dand, Ippeita Hitachi Ltd Res & Dev Grp Ctr Exploratory Res Saitama Japan Jichi Med Univ Dept Pediat Shimotsuke Japan Int Univ Hlth & Welf Dept Pediat Shiobara Japan Chuo Univ Res & Dev Initiat Appl Cognit Neurosci Lab Tokyo Japan Jichi Med Univ Ctr Dev Adv Med Technol Shimotsuke Japan

Functional near-infrared spectroscopy (fNIRS) signals are prone to problems caused by motion artifacts and physiological noises. These noises unfortunately reduce the fNIRS sensitivity in detecting the evoked brain activation while increasing the risk of statistical error. In fNIRS measurements, the repetitive resting-stimulus cycle (so-called block-design analysis) is commonly adapted to increase the sample number. However, these blocks are often affected by noises. Therefore, we developed an adaptive algorithm to identify, reject, and select the noise-free and/or least noisy blocks in accordance with the preset acceptance rate. The main features of this algorithm are personalized evaluation for individual data and controlled rejection to maintain the sample number. Three typical noise criteria (sudden amplitude change, shifted baseline, and minimum intertrial correlation) were adopted. Depending on the quality of the dataset used, the algorithm may require some or all noise criteria with distinct parameters. Aiming for real applications in a pediatric study, we applied this algorithm to fNIRS datasets obtained from attention deficit/hyperactivity disorder (ADHD) children as had been studied previously. These datasets were divided for training and validation purposes. A validation process was done to examine the feasibility of the algorithm regardless of the types of datasets, including those obtained under sample population (ADHD or typical developing children), intervention (nonmedication and drug/placebo administration), and measurement (task paradigm) conditions. The algorithm was optimized so as to enhance reproducibility of previous inferences. The optimum algorithm design involved all criteria ordered sequentially (0.047 mM mm of amplitude change, 0.029 mM mm / s of baseline slope, and 0.6 interquartile range of outlier threshold for each criterion, respectively) and presented complete reproducibility in both training and validation datasets. Compared

关键词： functional near-infrared spectroscopy motion and physiological noises adaptive algorithm acceptance rate personalized evaluation controlled rejection attention deficit/hyperactivity disorder

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adaptive algorithm to Improve Trajectory Prediction Accuracy of Climbing Aircraft

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JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2013年第1期36卷 15-24页

作者： Thipphavong, David P. Schultz, Charles A. Lee, Alan G. Chan, Steven H. NASA Ames Res Ctr Moffett Field CA 94035 USA Univ Affiliated Res Ctr Moffett Field CA 94035 USA

Aircraft climb trajectories are difficult to predict, and large errors in these predictions reduce the potential operational benefits of some advanced concepts in the Next Generation Air Transportation System. An algorithm that dynamically adjusts modeled aircraft weights based on observed track data to improve the accuracy of trajectory predictions for climbing flights has been developed. In real-time evaluation with actual Fort Worth Center traffic, the algorithm decreased the altitude root-mean-square error by about 20%. It also reduced the root-mean-square error of predicted time at top of climb by the same amount.

关键词： Boeing 777 Aircraft Performance Next Generation Air Transportation System adaptive algorithm Center TRACON Automation System Automatic Dependent Surveillance Broadcast RADAR Calibrated Airspeed En Route Automation Modernization Federal Aviation Administration

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adaptive algorithm based on accumulated signal processing for fast fading channels with application to OFDM mobile radio

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IEICE TRANSACTIONS ON COMMUNICATIONS 2005年第2期E88B卷 568-574页

作者： Wijesena, PS Taniguchi, T Karasawa, Y Univ Electrocommun Dept Elect Engn Chofu Tokyo 1828585 Japan

In this paper, we propose an adaptive algorithm based on accumulated signal processing, which could be applicable to Post-FFT-type OFDM adaptive array antennas. Proposed scheme calculates the weight of each element at a particular instant t, by considering both post- and pre-received symbols. Because of the use of additional forthcoming information on channel behavior in the weight calculation scheme, one can expect an improved performance in fast fading conditions by using the proposed adaptive algorithm. This paper also discusses the application of the proposed adaptive algorithm to OFDM adaptive array. In OFDM application, a few subchannels are being used to transmit pilot symbols, and at the receiver, the proposed adaptive algorithm is applied to those pilot subchannels, and interpolates the weights for the data subchannels which are allocated between the pilot subchannels. Finally, the system performance improvement with the application of the proposed adaptive algorithm is verified by computer simulation.

关键词： MMSE adaptive array adaptive algorithm accumulated signal processing OFDM

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