In this paper, we experimentally compare orthogonal frequency-division multiplexing (OFDM) and on-off keying (OOK) modulation in the context of the IEEE 802.15.13-2023 standard at bandwidths up to 50 MHz across a Li-Fi link with distances up to 5 m and a lateral offset up to 51°. Error vector magnitude (EVM) and bit error rate (BER) evaluations confirm that the high peak-to-average power ratio (PAPR) of OFDM limits the achievable transmission distance, but it offers higher data rates due to its higher spectral efficiency. Due to the lower PAPR, OOK-based Pulsed Modulation PHY (PM-PHY) shows a significantly higher link range. As the structure of the PM-PHY is based on OFDM symbols, the two solutions may also be combined to open a wider range of use cases for optical wireless communications.
LINK
Background: Lexical access problems of inflected verbs are common in aphasia. Previous research addressed these problems either in purely linguistic terms (e.g., verb movement) or in terms of lexical characteristics (e.g., frequency). We propose a new measure of verb complexity, which combines linguistic and lexical characteristics and is formulated in terms of Shannon’s information theory. Aims: We aim to explore the complexity of individual verbs and verb paradigms and its effect on lexical access, both in unimpaired people and people with aphasia (PWA). We apply information theory to investigate the impact of verb complexity on reaction time (RT) for lexical decision. Methods & Procedures: 20 non-fluent aphasic subjects and 11 age-matched and education-matched peers performed an auditory lexical decision task containing 286 real and 286 phonotactically legal non-word past tense forms (regulars and irregulars). RTs and error rates were measured. Two information-theoretic measures were calculated: inflectional entropy (reflecting probabilistic variability of forms within a given verbal family) and information load (I) (reflecting complexity of an individual verb form). The effect for these and other more traditional measures on RT were measured. Outcomes & Results: Linear mixed model analyses to the data for each group with participant and verb as crossed random effects were performed. Results show that for all groups inflectional entropy had a facilitatory effect on RT. There was a group effect for inflectional entropy indicating that for the patients with aphasia the effect of inflectional entropy was less pronounced. At the same time, I did correlate with latencies for healthy adults but not for individuals with aphasia. Conclusions: Our results demonstrate that the decrease in lexical processing capacity characteristic for PWA has a measurable effect that can be calculated using information theoretical means. According to our model, these individuals have particular difficulties with processing lexical items of higher complexity, as measured by individual I, and benefit less from the support normally provided (in comprehension) by other members of the corresponding lexical network. Finally, the proposed information-theoretic complexity measures, which encompass both frequency effects and linguistic parameters, provide a superior measure of lexical access, and have a better explanatory power for the analyses of access problems found in non-fluent aphasia, compared to analyses based on frequency only.
LINK
Reducing the use of pesticides by early visual detection of diseases in precision agriculture is important. Because of the color similarity between potato-plant diseases, narrow band hyper-spectral imaging is required. Payload constraints on unmanned aerial vehicles require reduc- tion of spectral bands. Therefore, we present a methodology for per-patch classification combined with hyper-spectral band selection. In controlled experiments performed on a set of individual leaves, we measure the performance of five classifiers and three dimensionality-reduction methods with three patch sizes. With the best-performing classifier an error rate of 1.5% is achieved for distinguishing two important potato-plant diseases.
MULTIFILE
Every year in the Netherlands around 10.000 people are diagnosed with non-small cell lung cancer, commonly at advanced stages. In 1 to 2% of patients, a chromosomal translocation of the ROS1 gene drives oncogenesis. Since a few years, ROS1+ cancer can be treated effectively by targeted therapy with the tyrosine kinase inhibitor (TKI) crizotinib, which binds to the ROS1 protein, impairs the kinase activity and thereby inhibits tumor growth. Despite the successful treatment with crizotinib, most patients eventually show disease progression due to development of resistance. The available TKI-drugs for ROS1+ lung cancer make it possible to sequentially change medication as the disease progresses, but this is largely a ‘trial and error’ approach. Patients and their doctors ask for better prediction which TKI will work best after resistance occurs. The ROS1 patient foundation ‘Stichting Merels Wereld’ raises awareness and brings researchers together to close the knowledge gap on ROS1-driven oncogenesis and increase the options for treatment. As ROS1+ lung cancer is rare, research into resistance mechanisms and the availability of cell line models are limited. Medical Life Sciences & Diagnostics can help to improve treatment by developing new models which mimic the situation in resistant tumor cells. In the current proposal we will develop novel TKI-resistant cell lines that allow screening for improved personalized treatment with TKIs. Knowledge of specific mutations occurring after resistance will help to predict more accurately what the next step in patient treatment could be. This project is part of a long-term collaboration between the ROS1 patient foundation ‘Stichting Merels Wereld’, the departments of Pulmonary Oncology and Pathology of the UMCG and the Institute for Life Science & Technology of the Hanzehogeschool. The company Vivomicx will join our consortium, adding expertise on drug screening in complex cell systems.
![Does Science-Fiction predict [or change] the future?](https://publinova-harvester-content-prod.s3.amazonaws.com/thumbnails/files/previews/pdf/20250506145555255024.adbf7edd-9619-4d0e-9a17-e0e7096adc7f-thumbnail-400x300.png)
