Koopman mode decomposition is a flow analysis technique developed by Igor Mezić in 2004, based upon the Koopman operator first proposed by Bernard Koopman in 1931. Via Koopman decomposition any non-chaotic well-sampled dynamic system – linear, non-linear, laminar or turbulent – is broken down into single-frequency repetitive components (modes). This paper presents a survey consolidating published information regarding data-driven Koopman analysis techniques. It is intended to aid researchers exploring the suitability of data-driven Koopman analysis in anticipation of developing their own modeling. A basic mathematical explanation of Koopman analysis is given with emphasis toward the data-driven Dynamic Mode Decomposition (DMD) solution, which converges to the Koopman operator given a highly-sampled dataset. The four primary uses of Koopman analysis: flow analysis, power grid analysis, building thermal analysis, and biomedical analysis are discussed, along with other public. Finally, weaknesses and problems inherent within Koopman analysis/DMD will be enumerated, alongside potential solutions. Koopman analysis is a computationally complex, yet often suitable method for determining periodic motion in any highly-sampled dataset. When compared to a similar analysis method, Proper Orthogonal Decomposition, Koopman analysis often provides additional detail regarding the structure of less significant modes present, albeit at the cost of increased computational complexity.

Atmospheric weather conditions adversely affect the performance of free-space optical communications systems. Fog present in the atmosphere has the largest impact on free-space optical systems. We use simulation techniques to investigate the performance of a slant-path, wavelength-diversified free-space optical link between a ground station and an unmanned aerial vehicle in the presence of radiation fog. The free-space optical link is configured to operate using the simultaneous transmission of three wavelengths, 0.85, 1.55, and $10μm$, connecting a ground station to an unmanned aerial vehicle operating at either a 4- or $8-km$ altitude. The link is further analyzed by combining the multiple carrier wavelengths into either an equal-gain diversity scheme or a selective diversity scheme. The simulation results are obtained using ALTM and PcModWin simulation software from Ontar Corporation. The results obtained demonstrate that the use of either an equal-gain diversity scheme or a selective diversity scheme enables the deployment of a ground-to-air free-space optical communications link. Furthermore, a selective diversity scheme demonstrates a received power approximately three times greater than an equal-gain diversity scheme.

This paper presents a conceptual design of a multizone HVAC controller based on a wireless sensor network that aims to optimize energy consumption while maintaining the comfort level of users. The proposed system is capable of learning the occupancy pattern of the residents in the building. Moreover, it is transparent (requires no manual configuration). Wireless sensor network technology is used to collect the required measurements for the control system, simulation results are used to demonstrate the system performance in terms of power consumption and comfort level Additionally, a comparison between the proposed system and other HVAC control systems is presented. The results show that the proposed system is capable of providing a major energy savings.

Multi-user free-space optical communication (FSOC) is beginning to draw a significant attention for its ability to support increased system network capacity while using single receiving photodiode and satisfying size, weight, and power (SWaP) constraints imposed by space- and aerial-based mobile communication. Despite these advantages, support of multi-user capabilities cause increased system complexity due to accommodating heterogenous users communications with varying transmission and data rate requirements. Machine learning (ML) has recently been considered as a promising approach for introducing cognition into the network to mitigate some of the complexity. A cognitive method based on unsupervised ML was derived for estimating the number of users communicating and sharing time and bandwidth resources with a single-node receiver. A weighted clustering approach was introduced and experimentally validated when users received with similar amplitude information that results in underestimation. Obtained results confirmed that the proposed methodology was able to accurately differentiate the number of simultaneously transmitting users with accuracy greater than 92%— even in the presence of moderate atmospheric turbulence. An experimental analysis was conducted to determine data size and receiver sampling rate requirements for accurate estimation. Furthermore, an empirical model was derived to evaluate the effect of preamble signal length given a particular sampling rate on the accuracy of the estimation. The model was validated for up-to four users.

The cortical motor system can be reorganized following a stroke, with increased recruitment of the contralesional hemisphere. However, it is unknown whether a similar hemispheric shift occurs in the somatosensory system to adapt to this motor change, and whether this is related to movement impairments. This proof-of-concept study assessed somatosensory evoked potentials (SEPs), P50 and N100, in hemiparetic stroke participants and age-matched controls using high-density electroencephalograph (EEG) recordings during tactile finger stimulation. The laterality index was calculated to determine the hemispheric dominance of the SEP and re-confirmed with source localization. The study found that latencies of P50 and N100 were significantly delayed in stroke brains when stimulating the paretic hand. The amplitude of P50 in the contralateral (to stimulated hand) hemisphere was negatively correlated with the Fügl–Meyer upper extremity motor score in stroke. Bilateral cortical responses were detected in stroke, while only contralateral cortical responses were shown in controls, resulting in a significant difference in the laterality index. These results suggested that somatosensory reorganization after stroke involves increased recruitment of ipsilateral cortical regions, especially for the N100 SEP component. This reorganization delays the latency of somatosensory processing after a stroke. This research provided new insights related to the somatosensory reorganization after stroke, which could enrich future hypothesis-driven therapeutic rehabilitation strategies from a sensory or sensory-motor perspective.

Bluetooth Low Energy (BLE) is a ubiquitous technology allowing applications in healthcare, wellness and house-hold sensors to have wireless functionality. Even though a frequency hopping mechanism is employed by BLE to combat fading and interference in the 2.4 GHz Industrial, Scientific and Medical (ISM) band, a noticeable probability of collision is expected to arise when the number of communicating BLE devices in the same vicinity increases. In this paper, an analytical model to find the selection probability of each of the 37 BLE data channels is presented. The results, which are confirmed by brute-force simulation, are then used to find the probability of collision between collocated BLE pairs and their maximum achievable aggregate throughput.