As members of an increasingly aging society, one of our major priorities is to develop tools to detect the earliest stage of age-related disorders such as Alzheimer's Disease (AD). The goal of this paper is to evaluate the possibility of using unobtrusively collected activity-aware smart home behavior data to detect the multimodal symptoms that are often found to be impaired in AD. After gathering longitudinal smart home data for 29 older adults over an average duration of 2 years, we automatically labeled the data with corresponding activity classes and extracted time-series statistics containing ten behavioral features. Mobility, cognition, and mood were evaluated every six months. Using these data, we created regression models to predict symptoms as measured by the tests and a feature selection analysis was performed. Classification models were built to detect reliable absolute changes in the scores predicting symptoms and SmoteBOOST and wRACOG algorithms were used to overcome class imbalance where needed. Results show that all mobility, cognition, and depression symptoms can be predicted from activity-aware smart home data. Similarly, these data can be effectively used to predict reliable changes in mobility and memory skills. Results also suggest that not all behavioral features contribute equally to the prediction of every symptom. Future work therefore can improve model sensitivity by including additional longitudinal data and by further improving strategies to extract relevant features and address class imbalance. The results presented herein contribute toward the development of an early change detection system based on smart home technology.

Last-generation and future wireless communication standards, such as DVB-T2 or DVB-NGH, are including multi-antenna transmission and reception in order to increase bandwidth efficiency and receiver robustness. The main goal is to combine diversity and spatial multiplexing in order to fully exploit the multiple-input multiple-output (MIMO) channel capacity. Full-rate full-diversity (FRFD) space-time codes (STC) such as the Golden code are studied for that purpose. However, despite their larger achievable capacity, most of them present high complexity for soft detection, which hinders their combination with soft-input decoders in bit-interleaved coded modulation (BICM) schemes. This article presents a novel low-complexity soft detection algorithm for the reception of FRFD space-frequency block codes in BICM orthogonal frequency-division multiplexing (OFDM) systems. The proposed detector maintains a reduced and fixed complexity, avoiding the variable nature of the list sphere decoder (LSD) due to its dependence on the noise and channel conditions. Complexity and simulation-based performance results are provided which show that the proposed detector performs close to the optimal log-maximum a posteriori (MAP) detection in a variety of DVB-T2 broadcasting scenarios.

This contribution analyzes the architecture design and FPGA implementation of high-throughput multiuser vector precoders. The most complex task of such precoders, i.e. the search of the perturbing vector over an infinite lattice, has been carried out by means of two tree search algorithms: a novel fully-pipelined complex-valued K-Best scheme and a fixed-sphere encoder (FSE). Both designs have been implemented in a Virtex-6 device, leading to precoding throughputs of up to 2.4 Gbps with 16-QAM modulation.

Recent and next-generation wireless broadcasting standards, such as DVB-T2 or DVB-NGH, are considering distributed multi-antenna transmission in order to increase bandwidth efficiency and signal quality. Full-rate full-diversity (FRFD) space-time codes (STC), such as the Golden code, have been reported to be excellent candidates, being their main drawback their detection complexity, which is enhanced when soft output is required when combined with a bit interleaved coded modulation (BICM) scheme based on low-density parity check (LDPC) codes. We present a novel low-complexity soft detection algorithm for the reception of Golden codes in LDPC based orthogonal frequency-division multiplexing (OFDM) systems. Complexity and simulation-based performance results are provided which show that the proposed detector performs close to the optimal detector in a variety of DVB-T2 broadcasting scenarios.

Besides the optimal but impractically complex dirty paper coding (DPC), linear precoding and vector precoding (VP) have been proposed for the vector broadcast channel (BC). Linear precoding is simple, as only a linear transform is applied to the data signals, whereas VP necessitates a closest-point search in a lattice to find the perturbation signal which reduces the necessary transmit power. In this paper, we analyze the high signal-to-noise ratio (SNR) performance of VP assuming optimal decoding. As the perturbation process hinders the analytical assessment of the VP performance, lower and upper bounds on the expected data rate of VP systems are reviewed and proposed. Based on these bounds, VP is compared to linear precoding w.r.t. the weighted sum rate, the power resulting from a quality of service (QoS) formulation, and the performance when balancing the rates.