Daniel C. Cunha

An approach using support vector regression for mobile location in cellular networks

Wireless positioning systems have become very popular in recent years. One of the reasons is the fact that the use of a new paradigm named Internet of Things has been increasing in the scenario of wireless communications. Since a high demand for accurate positioning in wireless networks has become more intensive, especially for location-based services, the investigation of mobile positioning using radiolocalization techniques is an open research problem. Based on this context, we propose a fingerprinting approach using support vector regression to estimate the position of a mobile terminal in cellular networks. Simulation results indicate the proposed technique has a lower error distance prediction and is less sensitive to a Rayleigh distributed noise than the fingerprinting techniques based on COST-231 and ECC-33 propagation models.

On the use of nonlinear methods for low-power CPU frequency prediction based on Android context variables

The objective of this paper is to analyze the use of nonlinear models to predict the CPU frequency that reaches the lowest power consumption of a smartphone based on Android OS context variables. Artificial neural networks (ANNs) and k-nearest neighbors (k-NN) techniques are investigated, and their results are compared to those obtained by the linear method (LM). Experimental results indicate the k-NN technique is the best option in terms of model accuracy and performance when compared to the other prediction models.

On the complexity-performance trade-off in soft-decision decoding for unequal error protection block codes

Unequal error protection (UEP) codes provide a selective level of protection for different blocks of the information message. The effectiveness of two sub-optimum soft-decision decoding algorithms, namely generalized Chase-2 and weighted erasure decoding, is evaluated in this study for each protection class of UEP block codes. The performances of both algorithms are compared to that of the maximum likelihood algorithm in order to evaluate the performance loss of each protection class provided by less complex algorithms as well as their complexities are evaluated according to the number of arithmetic operations performed at each decoding step. Finally, numerical results and examples are provided which indicate that a trade-off between performance and complexity for each protection class is obtained. The results of this study can be used to select appropriate UEP coding and decoding schemes in applications that demand low energy consumption.

Autonomous power management in mobile devices using dynamic frequency scaling and reinforcement learning for energy minimization

Abstract Embedded systems execute applications that execute hardware differently depending on the computation task, generating time-varying workloads. Energy minimization can be reached by using the low-power central processing unit (CPU) frequency for each workload. We propose an autonomous and online approach, capable of reducing energy consumption from adaptation to workload variations even in an unknown environment. In this approach, we improved the AEWMA algorithm into a new algorithm called AEWMA-MSE, adding new functionality to detect workload changes and demonstrating why it is better to use statistical analysis for real user cases in a mobile environment. Also, a new power model for mobile devices based on k-NN algorithm for regression was proposed and validated proving to have a better trade-off between execution time and precision than neural networks and linear regression-based models. AEWMA-MSE and the proposed power model are integrated into a novel algorithm for energy management based on reinforcement learning that suitably selects the appropriate CPU frequency based on workload predictions to minimize energy consumption. The proposed approach is validated through simulation by using real smartphone data from an ARM Cortex A7 processor used in a commercial smartphone. Our proposal proved to have an improvement in the Q-learning cost function and can effectively minimize the average energy consumption by 21% and up to 29% when compared to the already existing approaches.

Autonomous Power Management for Embedded Systems Using a Non-linear Power Predictor

Embedded systems execute applications that exercise the hardware differently depending on the computation task, generating varying workloads with time. Energy minimization can be reached exploring the optimal CPU frequency for each workload. We propose an autonomous and online approach, capable of minimizing energy through adaptation to these workload variations even in an unknown environment. In the proposed approach we use a reinforcement learning algorithm that suitably selects the appropriate CPU frequency based on workload predictions to minimize energy consumption. The proposed approach is validated through simulation using real smartphone data, an ARM Cortex A7 processor used in a commercial smartphone with Android 4.4.4 version was employed. Our proposed approach demonstrated to have an improvement in the Q-learning cost function and can effectively minimize energy consumption by up to 29% compared to the existing approaches.

A hardware and software Web-based environment for Energy Consumption analysis in mobile devices

In recent times, the number of mobile Android devices has been growing exponentially not only in the expanding popularity of low-cost mobile devices but also for the great number of functionalities and applications. This new range of features has highlighted the need for greater capacity in mobile batteries to provide an extended time of use. Several studies have been done to minimize the impact on the uncontrolled growth of applications, as well as analyze the suitable hardware configuration for a range of applications. In this sense, the objective of this work is to provide a Web-based environment which helps the designer to characterize mobile devices through automated testing and multiple devices simultaneously. The Web environment allows the designer to make assessments on the mobile device, remotely, without the need for measurement environment available. The use of multiple devices allows the designer to perform in different parallel measurements simultaneously. As the case study, an analysis involving video streaming, CPU processor load, and CPU fixed-frequency algorithms versus dynamic frequency scaling techniques were performed for two types of Android smartphones.

Optimal power distribution in non-binary LDPC code-based cooperative wireless networks

This work analyzes the optimal power allocation in coded cooperative communication systems with a single relay and using the amplify-and-forward protocol. Non-binary low-density parity-check (LDPC) codes are used at the source and a fast Fourier transform (FFT)-based decoding algorithm is employed at the destination. We study the power distribution between the source and the relay based on the minimization of the LDPC bit error rate (BER) performance at the destination as well as on the information theoretic measures such as the channel capacity and outage probabilities. The optimal power allocation estimated by the LDPC performance simulation corresponds to the capacity/outage probability results. In addition, BER comparisons of the coded systems (cooperative and noncooperative) are carried out for some typical cooperative scenarios.

Aceleração do Algoritmo Fuzzy K-means: Avaliação em Quantização Vetorial de Imagens

Abstract – A method for accelerating the fuzzy k-means algorithm is presented. A performance evaluation of the method is carried out in the scenario of vector quantization codebook design for image compression. Simulation results show that the proposed method leads to a reduction in the number of iterations performed by the fuzzy k-means algorithm. Additionally, it is shown that the convergence speed of the algorithm is increased without sacrificing the quality of the designed codebooks.