Rapidly diffusing 'industry 4.0' technologies stand to impact a broad range of stakeholders. Prior to implementation, forward looking formative analyses can identify systems and policy designs to promote equitable benefit. We investigate this potential through an analysis of stakeholders to a potential drone implementation on a small commercial farm in Rwanda. Translating stakeholders' imaginaries within a post-colonial frame, we identify hopes and concerns related to agency and influenced by global and local systems of power. The findings highlight constraints that recommend system designs promoting local agency and control and policies designed to balance local data management against potentially 'extractive' multinational data transfer processes.

Chronic electrocorticography (ECoG) studies will be necessary to understand how end-users of assistive brain-computer interface (BCI) technologies will control their devices on a long-term scale. A BCI platform consisting of a deep brain stimulator (DBS) and a cortical electrode strip, theoretically suitable for use over multi-year periods, is validated in a single human subject with essential tremor (ET). At the time of this writing, stable ECoG recordings have been demonstrated for four months after implantation. Additionally, the patient has performed a BCI cursor-control task with better-than-chance accuracy using both (1) an overt movement control scheme and (2) a motor imagery control scheme. These results validate the sensing-enabled DBS with cortical strip system as an exciting platform for chronic ECoG BCI experiments and as a scientific tool for long-term cortical recordings in different patient populations.

Implantable cardioverter defibrillators (ICDs) include small, battery-powered generators, the longevity of which depends on a patient's rate of consumption. Generator replacement, however, involves risks, including death. Hence, a trade-off exists between prematurely exposing the patient to these risks and allowing for the possibility that the device is unable to deliver therapy when needed. Currently, replacements are performed using a one-size-fits-all approach. Here, we develop a Markov decision process model to determine patient-specific optimal replacement policies as a function of patient age and the remaining battery capacity. We analytically establish that the optimal policy is of threshold-type in the remaining capacity, but not necessarily in patient age. Based on clinical data, we conduct a large computational study that suggests that under the optimal policy, patients undergoing initial implantation at age 30–40, 41–60, and 61–80 see an approximate decrease in the total expected number of replacements of 8%–14%, 8%–15% and 8%–19%, respectively, while achieving the same or greater expected lifetime.

This paper explores the possible effects on the performance of IEEE 802.11 when high-rate real-time applications such as video streaming are transmitted simultaneously with data over the network. While most of the studies conducted have been limited to 802.11b and 802.11g; this study focuses on the newer 802.11n. It considers the effect on performance parameters like throughput, jitter and packets lost when video transmission is introduced in a network carrying purely data traffic. It also gives insight on the approximate number of users streaming high rate videos that can be supported over different environments (commercial, residential, and isolated) on 802.11n.

Atlas-driven morphometric analysis has received great attention for studying anatomical shape variation across clinical populations in neuroimaging research as it provides a local coordinate representation for understanding the family of anatomic observations. We present a procedure for generating atlas of subcortical and ventricular structures, including amygdala, hippocampus, caudate, putamen, globus pallidus, thalamus, and lateral ventricles, using the large deformation diffeomorphic metric atlas generation algorithm. The atlas was built based on manually labeled volumes of 41 subjects randomly selected from the database of Open Access Series of Imaging Studies (OASIS, 10 young adults, 10 middle-age adults, 10 healthy elders, and 11 patients with dementia). We show that the estimated atlas is representative of the population in terms of its metric distance to each individual subject in the population. In the application of detecting shape variations, using the estimated atlas may potentially increase statistical power in identifying group shape difference when comparing with using a single subject atlas. In shape-based classification, the metric distances between subjects and each of within-class estimated atlases construct a shape feature space, which allows for performing a variety of classification algorithms to distinguish anatomies.