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.

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.

Retinal thickness may serve as a biomarker in Parkinson’s disease (PD). In this prospective longitudinal study, we aimed to determine if PD patients present accelerated thinning rate in the parafoveal ganglion cell-inner plexiform layer (pfGCIPL) and peripapillary retinal nerve fiber layer (pRNFL) compared to controls. Additionally, we evaluated the relationship between retinal neurodegeneration and clinical progression in PD. A cohort of 156 PD patients and 72 controls underwent retinal optical coherence tomography, visual, and cognitive assessments between February 2015 and December 2021 in two Spanish tertiary hospitals. The pfGCIPL thinning rate was twice as high in PD (β [SE] = −0.58 [0.06]) than in controls (β [SE] = −0.29 [0.06], p < 0.001). In PD, the progression pattern of pfGCIPL atrophy depended on baseline thickness, with slower thinning rates observed in PD patients with pfGCIPL below 89.8 µm. This result was validated with an external dataset from Moorfields Eye Hospital NHS Foundation Trust (AlzEye study). Slow pfGCIPL progressors, characterized by older at baseline, longer disease duration, and worse cognitive and disease stage scores, showed a threefold increase in the rate of cognitive decline (β [SE] = −0.45 [0.19] points/year, p = 0.021) compared to faster progressors. Furthermore, temporal sector pRNFL thinning was accelerated in PD (β_{time x group} [SE] = −0.67 [0.26] μm/year, p = 0.009), demonstrating a close association with cognitive score changes (β [SE] = 0.11 [0.05], p = 0.052). This study suggests that a slower pattern of pfGCIPL tissue loss in PD is linked to more rapid cognitive decline, whereas changes in temporal pRNFL could track cognitive deterioration.

Characterizing the effect of age and sex on macular retinal layer thicknesses and foveal pit morphology is crucial to differentiating between natural and disease-related changes. We applied advanced image analysis techniques to optical coherence tomography (OCT) to: 1) enhance the spatial description of age and sex effects, and 2) create a detailed open database of normative retinal layer thickness maps and foveal pit shapes. The maculae of 444 healthy subjects (age range 21–88) were imaged with OCT. Using computational spatial data analysis, thickness maps were obtained for retinal layers and averaged into 400 (20 x 20) sectors. Additionally, the geometry of the foveal pit was radially analyzed by computing the central foveal thickness, rim height, rim radius, and mean slope. The effect of age and sex on these parameters was analyzed with multiple regression mixed-effects models. We observed that the overall age-related decrease of the total retinal thickness (TRT) (-1.1% per 10 years) was mainly driven by the ganglion cell-inner plexiform layer (GCIPL) (-2.4% per 10 years). Both TRT and GCIPL thinning patterns were homogeneous across the macula when using percentual measurements. Although the male retina was 4.1 μm thicker on average, the greatest differences were mainly present for the inner retinal layers in the inner macular ring (up to 4% higher TRT than in the central macula). There was an age-related decrease in the rim height (1.0% per 10 years) and males had a higher rim height, shorter rim radius, and steeper mean slope. Importantly, the radial analysis revealed that these changes are present and relatively uniform across angular directions. These findings demonstrate the capacity of advanced analysis of OCT images to enhance the description of the macula. This, together with the created dataset, could aid the development of more accurate diagnosis models for macular pathologies.

The development of a fingerprint verification system on a low-cost embedded platform is an open issue in nowadays biometrics. Our paper describes a low-cost fingerprint minutiae extraction and matching system based on a Spartan3 family FPGA with an embedded Leon2 open core processor. The proposed system architecture incorporates a floating point unit and a discrete Fourier transform coprocessor to accelerate the minutiae extraction process. The whole verification algorithm is based on the NFIS version 2 open source software developed by the national institute of standards and technology (NIST). The results on execution time reduction and FPGA occupation for different system configurations show that the proposed architecture improves substantially the performance of the baseline system architecture.

In the multiuser MIMO broadcast channel, the use of precoding techniques is required in order to detect the signal at the users' terminals without any cooperation between them. This contribution presents the design and hardware implementation of a high-capacity precoder based on vector perturbation. The most challenging part of the vector precoding (VP) scheme, that is, the search for the perturbing signal in an infinite lattice, has been completed by the reduced-complexity albeit high-performance Fixed Sphere Encoder (FSE) algorithm. The most remarkable feature of the FSE is its fixed complexity, which makes it highly suitable for hardware implementation on field-programmable gate arrays (FPGA), where the parallelization and pipelining of resources can be applied to enhance the system throughput. An optimized reduced-complexity implementation is proposed, which achieves high performance with a reduced hardware resource usage.