Study on Dynamic Adaptive Bitrate Selection Algorithm for Mobile Streaming Media

Texture compression is an important technique to reduce memory storage and increase rendering speed in graphic processing units (GPUs). To ensure fast decoding and retain random memory access characteristics, the industry standard DXTC compression finds an approximate line segment in color space for each 4×4 block. The idea has been extended to multiline solutions by segmenting the 4×4 block into several partitions and adopted by the new format BPTC. BPTC can provide the highest quality than other texture compression standards. However, the limited partition combinations used in BPTC would smooth the texture detail in some cases. This work introduces an adaptive color grouping and selection algorithm to relax the texture smoothing issue. Simulation results show that the proposed algorithm can improve the average PSNR by 0.78 dB for those blocks.

In Quorum based cross datacenter distributed storage systems, data replication is a widely used technology. Such systems usually choose weak consistency, means they prone to select less number of replicas in read request in order to improve the performance, which lead to the possibility of getting stale data. In this paper, we propose an adaptive replica selection algorithm to make sure the system does not exceed specific stale read ratio while achieving good performance. Firstly, we choose a suitable distribution model from many candidates to estimate the time interval between current read request and nearest write request before it. Then we use Monte-Carlo method to simulate the processing order of read request and write request in different replicas. At last we use those estimated values to determine minimal number of replicas each read request needs to select in order to achieve a specific consistency level. We conduct a comprehensive experiments and the result shows our algorithm is effective.

Editor-in-Chief of Serbian Journal of Electrical Engineering1 requests to correct the first page of the paper A Multi-Layered Image Format for the Web with an Adaptive Layer Selection Algorithm by Milan Tair, Aleksandar Mihajlovic, Nikola Savanovic, Marko Sarac published in the Serbian journal of Electrical Engineering, Vol. 14, No. 2, June 2017, pp. 177 - 197, DOI: https://doi.org/10.2298/SJEE161010001T, since the text is missing in the footnote. The missing text is: An earlier version of this paper was presented at the International Scientific Conference on ICT AND E-Business Related Research, SINTEZA 2016, April 22, 2016 at Singidunum University in Belgrade, Serbia. <br><br><font color="red"><b> Link to the corrected article <u><a href="http://dx.doi.org/10.2298/SJEE161010001T">10.2298/SJEE161010001T</a></b></u>

In this paper, we present a scheme to improve immune cloning selection algorithm. The improved algorithm, which is referred to as adaptive cloning selection algorithm (ACSA), is proposed and then applied to function optimization. At first, we present adaptive gene mutation which decides mutation probability of each code point (locus) based on the quality of antibodies and the number of evolution iterations. Secondly, we present an iteratively increasing method from one locus to the all ones, which can be used in function optimization. Then, the cloning selection process of evolution is divided into two-stages. The first step is to increase locus of antibodies. In the other one, the Baldwin effect learning operator is employed. And finally, an experiment is carried out to verify the theoretical analyses on several testing functions.

Modern sensors have a range of modalities including SAR, EO, and IR. Registration of multimodal imagery from such sensors is becoming an increasingly common pre-processing step for various image exploitation activities such as image fusion for ATR. Over the past decades, several approaches to multisensor image registration have been developed. However, performance of these image registration algorithms is highly dependent on scene content and sensor operating conditions, with no single algorithm working well across the entire operating conditions space. To address this problem, in this paper we present an approach for dynamic selection of an appropriate registration algorithm, tuned to the scene content and feature manifestation of the imagery under consideration. We consider feature-based registration using Harris corners, Canny edge detection, and CFAR features, as well as pixel-based registration using cross-correlation and mutual information. We develop an approach for selecting the optimal combination of algorithms to use in the dynamic selection algorithm. We define a performance measure which balances contributions from convergence redundancy and convergence coverage components calculated over sample imagery, and optimize the measure to define an optimal algorithm set. We present numerical results demonstrating the improvement in registration performance through use of the dynamic algorithm selection approach over results generated through use of a fixed registration algorithm approach. The results provide registration convergence probabilities for geo-registering test SAR imagery against associated EO reference imagery. We present convergence results for various match score normalizations used in the dynamic selection algorithm.

With the continuous progress of UAV technology and the rapid development of mobile edge computing (MEC), the UAV-assisted MEC system has shown great application potential in special fields such as disaster rescue and emergency response. However, traditional deep reinforcement learning (DRL) decision-making methods suffer from limitations such as difficulty in balancing multiple objectives and training convergence when making mixed action space decisions for UAV path planning and task offloading. This article innovatively proposes a hybrid decision framework based on the improved Dynamic Adaptive Genetic Optimization Algorithm (DAGOA) and soft actor–critic with hierarchical action decomposition, an uncertainty-quantified critic ensemble, and adaptive entropy temperature, where DAGOA performs an effective search and optimization in discrete action space, while SAC can perform fine control and adjustment in continuous action space. By combining the above algorithms, the joint optimization of drone path planning and task offloading can be achieved, improving the overall performance of the system. The experimental results show that the framework offers significant advantages in improving system performance, reducing energy consumption, and enhancing task completion efficiency. When the system adopts a hybrid decision framework, the reward score increases by a maximum of 153.53% compared to pure deep reinforcement learning algorithms for decision-making. Moreover, it can achieve an average improvement of 61.09% on the basis of various reinforcement learning algorithms such as proposed SAC, proximal policy optimization (PPO), deep deterministic policy gradient (DDPG), and twin delayed deep deterministic policy gradient (TD3).

The main target of this article is to propose a simple Received Signal Strength Indicator‐ (RSSI‐) based adaptive Physical Layer (PHY) selection approach for Bluetooth Low Energy 5.0 (BLE 5) and compare the performance of adaptive PHY selection with the performance of individual PHY. This article also validates the simulation results acquired from the in‐house developed Ray Tracing (RT) tool with the BLE 5 measurements conducted in a corridor of the TUAS building, Aalto University. The performance metrics considered for the analysis are RSSI, throughput, and outage ratio. The impact of different transmission power on BLE 5 is also analyzed. The obtained simulation results indicate a Root Mean Square Error (RMSE) of about 4.6 dB between the measured and the simulated RSSI at 2.4 GHz frequency. By investigating these results, an adaptive PHY selection algorithm is proposed in this article. The adaptive PHY selection approach enhances the mean throughput of the system and improves the outage ratio as well. In the light of acquired results, the adaptive PHY selection scheme shows significant performance gain and is found more beneficial in the case of an environment with mixed RSSI samples, i.e., a mix of low and high RSSI values.

This paper presents a low-speed sensorless control implementation on a surface-mounted permanent magnet (SPM) motor for an e-bike application, using a low-cost fixed-point DSP. A novel dynamic PLL gain algorithm is proposed to improve the inherent reduced performance of the sensorless control under transient response. The angular offset caused by the magnetic field distortion is verified experimentally and using Finite Element Analysis (FEA). The acoustic noise and system stability are analyzed with different amplitudes and frequencies of the high-frequency (HF) voltage signal injection, and an adaptive frequency selection algorithm is proposed to reduce the acoustic noise production during a wide load condition operation.

A session-based communication network is a store-and-forward message (or packet) switching network in which each session (conversation from logon until logoff) is assigned to a single communication route (or path) until its termination. Examples of such networks are TYMNET and SNA. A route selection algorithm can be either static or dynamic. A static selection is based on given route selection tables which are usually provided to the system by the system generation process. A dynamic selection uses dynamically changing information of the network for determining an optimal route for the new session to be assigned to. This paper develops both static and dynamic route selection algorithms which may be used in a session-based communication network. Route selection algorithms will be classified into several classes depending on the network work-load information available for making decisions. It will be shown that very simple algorithms give good robustness with good response time.

The performance of underwater acoustic target classification decreases and is unstable when the training set contains noisy, redundant or irrelevant samples. In this paper, a new adaptive immune clonal sample selection algorithm (AICISA) is proposed to address this problem. AICISA is aimed at directing generation evolution. An experiment about the application of AICISA using the multi-field features extracted from 4 kinds of underwater acoustic targets was conducted. Experimental results show that AICISA can select effective subsets of samples. Reducing the sample size by 90%, the classification accuracy of SVM is improved by 10%. AICISA also shows good convergence and stability. The optimal subset of samples obtained by AICISA has good generalization ability and can remarkably reduce the classification time.

This paper presents a qualitative study in which we evaluate the core parts of an adaptive algorithm for next-exercise selection in an e-learning system. The algorithm was previously constructed from a series of studies where participants played the role of a teacher and chose the difficulty of a subsequent exercise for a learner based on their performance, mental effort and self esteem. In this paper, we present these findings to real teachers to gain insights into whether the algorithm is effective and appropriate for future inclusion in an intelligent tutoring system. Overall, we found that teachers believed that the recommendations from the algorithm were appropriate.

Cognitive radar is a new framework of radar system proposed by Simon Haykin recently. Adaptive waveform selection is an important problem of intelligent transmitter in cognit i ve radar. In this paper, the problem of adaptive waveform selection is modeled as stochastic dynamic programming model. Then backward dynamic programming, temporal difference learning and Q-learning are used to solve this problem. Optimal waveform selection algorithm and approximate solutions are proposed respectively. The simulation results demonstrate that the two approximate methods approach the optimal waveform selection scheme and have lower uncertainty of state estimation compared to fixed waveform. The performance of temporal difference learning is better than Q-learning, but Q-learning is more suitable to use in radar scene. Finally, the whole paper is summarized.

Time accurate high-speed flow calculations using a three-dimensional adaptive mesh algorithm with a time accurate implicit Navier-Stokes solver are presented in this work. A dynamic grid adaptation algorithm is developed which improves grid orthogonality near boundaries allowing the accurate resolution of both high-Reynolds number turbulent boundary layers and complex inviscid flow features. This is achieved by using a skewness factor raised to an orthogonality factor (magnitude of face unit normal dot product) as coefficients in the grid point movement algorithm. Steadystate calculations of a three-dimensional inlet/isolator flowfield are presented to highlight the ability of the adaptive mesh algorithm to resolve complex flow features. A three-dimensional inlet unstart due to a combustor perturbation is simulated by closing a diffuser ramp which raises exit pressure. The computational results demonstrate the ability to calculate 3-D highspeed inlet unstarts with a solution adaptive mesh algo-

Extracting frequent itemsets from datasets is an important problem in data mining, for which several mining methods including FP‐Growth have been proposed. FP‐Growth is a classical frequent itemset mining method, which generates pattern databases without candidates. Many improvements have been made in the literature due to the high time complexity and memory usage of FP‐Growth. However, most of them still suffer from performance issues on large datasets. In this paper, we design an auxiliary structure, Array Prefix‐Tree (AP‐Tree), and propose a new algorithm, Array Prefix‐Tree Growth (APT‐Growth), which is further parallelized as a Spark workflow, referred to as PAPT‐Growth. Based on a density threshold, we incorporate an adaptive algorithm selection process into PAPT‐Growth to ensure its running time performance. We conduct extensive experiments on different thresholds and multiple datasets, and experimental results show the performance superiority of PAPT‐Growth in comparison with several state‐of‐the‐art methods such as PFP, YAFIM, and DFPS. The analysis on density reveals a changing point, which justifies the necessity and validity of adaptive algorithm selection.

A dynamic adaptive particle swarm optimization and genetic algorithm is presented to solve constrained engineering optimization problems. A dynamic adaptive inertia factor is introduced in the basic particle swarm optimization algorithm to balance the convergence rate and global optima search ability by adaptively adjusting searching velocity during search process. Genetic algorithm–related operators including a selection operator with time-varying selection probability, crossover operator, and n-point random mutation operator are incorporated in the particle swarm optimization algorithm to further exploit optimal solutions generated by the particle swarm optimization algorithm. These operators are used to diversify the swarm and prevent premature convergence. Tests on nine constrained mechanical engineering design optimization problems with different kinds of objective functions, constraints, and design variables in nature demonstrate the superiority of the dynamic adaptive particle swarm optimization and genetic algorithm against several other meta-heuristic algorithms in terms of solution quality, robustness, and convergence rate in most cases.