The widely linear quaternion recursive total least squares

This is the author accepted manuscript. The final version is available from IEEE via http://dx.doi.org/10.1109/ICASSP.2015.7178593A widely linear quaternion recursive total least squares (WLQRTLS) algorithm is introduced for the processing of Q- improper processes contaminated by noise. The total least squares for quaternions (QTLS) is a generalisation of the real-valued total least squares and is introduced rigorously, starting from the existence condition for low-rank approximation of quaternion matrices. Then, a quaternion Rayleigh quotient (QRQ) is defined to establish the link between the QTLS solution and the minimisation of the QRQ. Finally, the rank-one update formula is employed to allow for fast iterative solution based on the QRQ. Through simulations, the WL-QRTLS was shown to exhibit superior performance, under perturbations on both input and output signals, to other adaptive filtering of the same class - the widely linear quaternion least mean squares (WL-QLMS) and the widely linear quaternion recursive least squares (WL-QRLS). The experiments on both synthetic and real-world Q-improper processes supported the analysis

Control of a Realistic Wave Energy Converter Model using Least-Squares Policy Iteration

PublishedThis is the author accepted manuscript. The final version is available from Institute of Electrical and Electronics Engineers via the DOI in this record.An algorithm has been developed for the resistive control of a non-linear model of a wave energy converter using least-squares policy iteration, which incorporates function approximation, with tabular and radial basis functions being used as features. With this method, the controller learns the optimal PTO damping coefficient in each sea state for the maximization of the mean generated power. The performance of the algorithm is assessed against two on-line reinforcement learning schemes: Q-learning and SARSA. In both regular and irregular waves, least-squares policy iteration outperforms the other strategies, especially when starting from unfavourable conditions for learning. Similar performance is observed for both basis functions, with a smaller number of radial basis functions underfitting the Q-function. The shorter learning time is fundamental for a practical application on a real wave energy converter. Furthermore, this work shows that least-squares policy iteration is able to maximize the energy absorption of a wave energy converter despite strongly non-linear effects due to its model-free nature, which removes the influence of modelling errors. Additionally, the floater geometry has been changed during a simulation to show that reinforcement learning control is able to adapt to variations in the system dynamics.This work was supported partly by the Energy Technologies Institute and the Research Councils Energy Programme (grant EP/J500847/), partly by the Engineering and Physical Sciences Research Council (grant EP/J500847/1), and partly by Wave Energy Scotland

Stability-indicating methods for the determination of olanzapine in presence of its degradation products

Simple, sensitive and precise spectrophotometric and chemometric stability indicating techniques were adopted for Olanzapine (OLA) determination in presence of its degradation products over a concentration range of 0.002-0.02 mg/mL. The spectrophotometric technique involves six methods; first method is first derivative (D1) spectrophotometric one, which allows the determination of OLA in presence of its acidic and alkaline degradation products at 261.2 and 260.6 nm with mean percentage recoveries of 99.90±0.48 and 99.95±0.67, respectively. While second derivative spectrophotometry (D2) was used for determination of drug in presence of alkaline degradation products. Second method is first-derivative of the ratio spectra (DR1) for determination of OLA in presence of its acidic and alkaline degradation products at 267.9 and 251.6 nm, respectively with mean percentage recoveries of 99.81±0.64 and 100.53±1.11, respectively. The third method is pH-induced difference method for determination of OLA in presence of its acidic and alkaline degradation products; with mean percentage recoveries 100.09±0.06 and 99.77±0.78, respectively. Fourth method is the Q-analysis (absorption ratio) method, which involves the formation of absorbance equation at 296.3 nm (isosbestic point) and 271 nm (λmax of OLA) for the determination of OLA in presence of its acidic degradation products. The mean percentage recovery is 100.07±1.51. Fifth method based on dual wavelength selection was developed for the determination of OLA in presence of its acidic degradation products with mean percentage recovery of 100.36±0.69. Sixth method based on simple mathematic algorithm by the bivariate calibration was also used for the determination of OLA with the mean percentage recovery of 101.72±1.10. The second technique is chemometrics, which includes determination of OLA in presence of its acidic degradation products using multivariate calibration methods (the classical least squares (CLS), principle component regression (PCR) and partial least squares (PLS)) using the information contained in the absorption spectra

Minimization of Energy and Service Latency Computation Offloading using Neural Network in 5G NOMA System

The future Internet of Things (IoT) era is anticipated to support computation-intensive and time-critical applications using edge computing for mobile (MEC), which is regarded as promising technique. However, the transmitting uplink performance will be highly impacted by the hostile wireless channel, the low bandwidth, and the low transmission power of IoT devices. Using edge computing for mobile (MEC) to offload tasks becomes a crucial technology to reduce service latency for computation-intensive applications and reduce the computational workloads of mobile devices. Under the restrictions of computation latency and cloud computing capacity, our goal is to reduce the overall energy consumption of all users, including transmission energy and local computation energy. In this article, the Deep Q Network Algorithm (DQNA) to deal with the data rates with respect to the user base in different time slots of 5G NOMA network. The DQNA is optimized by considering more number of cell structures like 2, 4, 6 and 8. Therefore, the DQNA provides the optimal distribution of power among all 3 users in the 5G network, which gives the increased data rates. The existing various power distribution algorithms like frequent pattern (FP), weighted least squares mean error weighted least squares mean error (WLSME), and Random Power and Maximal Power allocation are used to justify the proposed DQNA technique. The proposed technique which gives 81.6% more the data rates when increased the cell structure to 8. Thus 25% more in comparison to other algorithms like FP, WLSME Random Power and Maximal Power allocation

LMS-Based RF BIST Architecture for Multistandard Transmitters

Article accepté pour publicationInternational audienceSoftware defined radios (SDR) platforms are increasingly complex systems which combine great flexibility and high performance. These two characteristics, together with highly integrated architectures make production test a challenging task. In this paper, we introduce an Radio Frequency (RF) Built-in Self-Test (BIST) strategy based on Periodically Nonuniform Sampling of the signal at the output stages of multistandard radios. We leverage the I/Q ADC channels and the DSP resources to extract the bandpass waveform at the output of the power amplifier (PA). Analytic expressions and simulations show that our time-interleaved conversion scheme is sensitive to time-skew. We propose a time-skew estimation technique based on a Least Mean Squares (LMS) algorithm to solve this problem. Simulation results show that we can effectively reconstruct the bandpass signal of the output stage using this architecture, opening the way for a complete RF BIST strategy for multistandard radios