The next generation of mobile radio communication systems — so called 5G — will provide some major changes to those generations to date. The ability to cope with huge increases in data traffic at reduced latencies and improved quality of user experience together with major reduction in energy usage are big challenges. In addition future systems will need to embody connections to billions of objects — the so called Internet of Things (IoT) which raise new challenges. Visions of 5G are now available from regions across the World and research is ongoing towards new standards. The consensus is a flatter architecture that adds a dense network of small cells operating in the millimetre wave bands and which are adaptable and software controlled. But what place for satellites in such a vision? The paper examines several potential roles for satellite including coverage extension, content distribution, providing resilience, improved spectrum utilisation and integrated signalling systems.

The role of satellites in 5G

Deep learning, recently, has been successfully applied to image classification, object recognition and speech recognition. However, the benefits of deep learning and accompanying architectures have been largely unknown for BCI applications. In motor imagery-based BCI, an energy-based feature, typically after spatial filtering, is commonly used for classification. Although this feature corresponds to the estimate of event-related synchronization/desynchronization in the brain, it neglects energy dynamics which may contain valuable discriminative information. Because traditional classiication methods, such as SVM, cannot handle this dynamical property, we proposed an architecture that inputs a dynamic energy representation of EEG data and utilizes convolutional neural networks for classification. By combining this network with a static energy network, we saw a significant increase in performance. We evaluated the proposed method and compared with SVM on a multi-class motor imagery dataset (BCI competition dataset IV-2a). Our method outperforms SVM with static energy features significantly (p < 0.01).

Parallel convolutional-linear neural network for motor imagery classification

In this paper, we develop a new technique for dual-function radar-communications in a transmit multi-sensor array where information embedding is achieved using phase-rotational invariance. A sequence of Q bits is first mapped into a dictionary of 2Q phase rotations. Then, one pair of transmit orthogonal waveforms is used in tandem with 2Q pairs of transmit beamforming weight vectors for embedding a certain entry of the phase-rotation dictionary during each radar pulse. The same pair of waveforms is used during all pulses while the pair of transmit beamforming weight vectors changes from pulse to pulse based on which entry of the phase-rotation dictionary is embedded. During each pulse, the receiver detects the embedded phase rotation and employ it to decipher the transmitted bit sequence. The proposed information embedding technique is angle-dependant and, therefore, the communication process is inherently secure against interception from directions other than the desired communication direction. The performance of the proposed technique is investigated in terms of the bit error rate (BER).

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Dual-function radar-communications using phase-rotational invariance

Accurate and reliable estimation of the heart rate using wear able devices, especially during physical exercise, must deal with noisy signals that contain motion artifacts. We present an approach that is based on photoplethysmographic (PPG) signals which are measured with two wrist-type pulse oximeters. The heart rate is related to intensity changes of the reflected light. Our proposed method suppresses the motion artifacts by adaptively estimating the transfer functions of each of the three-axis acceleration signals that produce the artifacts in the PPG signals. We combined the output of the six adaptive filters into a single enhanced time-frequency domain signal based on which we track the heart rate with a high ac curacy. Our approach is real-time capable, computationally efficient and real data results for a benchmark data set illustrate the superior performance compared to a recently pro posed approach.

A new method for heart rate monitoring during physical exercise using photoplethysmographic signals

In this paper, a novel single image super-resolution (SISR) algorithm is proposed. It is based on the BM3D (Block-Matching and 3D filtering) paradigm, where both sparsity and nonlocal patch self-similarity priors are utilized. The algorithm is derived from a variational formulation of the problem and has a structure typical for iterative back-projection super-resolution methods. They are characterized by updating high-resolution image which is calculated using the previous estimate and upsampled low-resolution error. The developed method is thoroughly compared with the state-of-the-art SISR both for noiseless and noisy data, demonstrating superior performance objectively and subjectively.

Ieee Staff

Papers

2017 25th European Signal Processing Conference (EUSIPCO)