Dataset on the human body as a signal propagation medium for body coupled communication.

Signal loss models are frequently utilized by wireless communication researchers and engineers to predict received signal strength, optimize system parameters, and conduct feasibility studies. However, novel communication methods such as Body-Coupled Communication (BCC) that are suitable for Body Area Networks formed by wearable devices currently lack readily available signal propagation models. In this data article, we present a galvanic-coupled BCC signal loss and bioimpedance dataset, which serves as a foundation for building such models. This extensive dataset consists of experimental data recorded from 30 volunteer test subjects. The experimental setup involves a tunable signal generator transmitting continuous wave signals, along with two oscilloscopes recording the transmitter-side and receiver-side voltages. From these measurements, we compute the signal loss over the body, and the transmitter-side impedance. The transmitted signal frequencies range from 50 kHz to 20 MHz, with discrete steps. The primary application of this dataset is to enable empirical-data-supported modeling in the human body as a BCC signal propagation medium, which will help to explore how the properties of the human body, the measurement locations, and the signal frequency impact the signal loss.

Adaptive Signal-to-Noise Ratio Indicator for Wearable Bioimpedance Monitoring.

Bioimpedance monitoring is an increasingly important non-invasive technique for assessing physiological parameters such as body composition, hydration levels, heart rate, and breathing. However, sensor signals obtained from real-world experimental conditions invariably contain noise, which can significantly degrade the reliability of the derived quantities. Therefore, it is crucial to evaluate the quality of measured signals to ensure accurate physiological parameter values. In this study, we present a novel wrist-worn wearable device for bioimpedance monitoring, and propose a method for estimating signal quality for sensor signals obtained on the device. The method is based on the continuous wavelet transform of the measured signal, identification of wavelet ridges, and assessment of their energy weighted by the ridge duration. We validate the algorithm using a small-scale experimental study with the wearable device, and explore the effects of variables such as window size and different skin/electrode coupling agents on signal quality and repeatability. In comparison with traditional wavelet-based signal denoising, the proposed method is more adaptive and achieves a comparable signal-to-noise ratio.

ETAM: Next Generation Event Timer for Picosecond-Precision Time and Amplitude Measurements.

Riga Event Timers have the ability to measure the interval between events with high resolution, on the order of picoseconds. However, they have several drawbacks, such as sensitivity to environmental temperature changes and an inability to capture the amplitude of the events. In this work, we present the ETAM: a next generation Event Timer. Its innovative features include adaptive correction of measurement errors based on an internal temperature sensor, and integrated peak-detector circuit to determine the amplitude of nanosecond-duration pulses. Evaluation shows that the ETAM has high thermal stability with a root mean square error (RMSE) of <3 ps in a temperature range between 0 and +40 °C, and accurate event amplitude measurement capability, with <2.3 mV RMSE in the 100-1000 mV range. These improvements allow the ETAM to be used in satellite laser ranging, optical time-domain reflectometry, and other field applications that require temperature- and amplitude-based time correction in addition to high robustness, performance, and stability.

TSCH-Sim: Scaling Up Simulations of TSCH and 6TiSCH Networks.

TSCH (Time-Slotted Channel Hopping) and 6TiSCH (IPv6 over the TSCH mode of IEEE 802.15.4e) low-power wireless networks are becoming prominent in the industrial Internet of Things (IoT) and other areas where high reliability is needed in conjunction with energy efficiency. Due to the complexity of IoT deployments, network simulations are typically used for pre-deployment design and validation. However, it is currently difficult and time-consuming to simulate large-scale IoT networks with thousands of nodes. This paper proposes TSCH-Sim: a new discrete event simulator for IEEE 802.15.4-2015 TSCH and 6TiSCH networks. The evaluation shows that simulation results obtained with TSCH-Sim show a good match with results from other simulators that are commonly used to investigate TSCH networks. At the same time, TSCH-Sim is faster than these alternatives at least by an order of magnitude, making it more practical to carry out simulations of large networks.

From Bits of Data to Bits of Knowledge-An On-Board Classification Framework for Wearable Sensing Systems.

Wearable systems constitute a promising solution to the emerging challenges of healthcare provision, feeding machine learning frameworks with necessary data. In practice, however, raw data collection is expensive in terms of energy, and therefore imposes a significant maintenance burden to the user, which in turn results in poor user experience, as well as significant data loss due to improper battery maintenance. In this paper, we propose a framework for on-board activity classification targeting severely energy-constrained wearable systems. The proposed framework leverages embedded classifiers to activate power-hungry sensing elements only when they are useful, and to distil the raw data into knowledge that is eventually transmitted over the air. We implement the proposed framework on a prototype wearable system and demonstrate that it can decrease the energy requirements by one order of magnitude, yielding high classification accuracy that is reduced by approximately 5%, as compared to a cloud-based reference system.

Total enzyme activity constraint and homeostatic constraint impact on the optimization potential of a kinetic model.

The application of biologically and biochemically relevant constraints during the optimization of kinetic models reduces the impact of suggested changes in processes not included in the scope of the model. This increases the probability that the design suggested by model optimization can be carried out by an organism after implementation of design in vivo. A case study was carried out to determine the impact of total enzyme activity and homeostatic constraints on the objective function values and the following ranking of adjustable parameter combinations. The application of constraints on the model of sugar cane metabolism revealed that a homeostatic constraint caused heavier limitations of the objective function than a total enzyme activity constraint. Both constraints changed the ranking of adjustable parameter combinations: no "universal" constraint-independent top-ranked combinations were found. Therefore, when searching for the best subset of adjustable parameters, a full scan of their combinations is suggested for a small number of adjustable parameters, and evolutionary search strategies are suggested for a large number. Simultaneous application of both constraints is suggested.

SpaceScanner: COPASI wrapper for automated management of global stochastic optimization experiments.

MOTIVATION: Due to their universal applicability, global stochastic optimization methods are popular for designing improvements of biochemical networks. The drawbacks of global stochastic optimization methods are: (i) no guarantee of finding global optima, (ii) no clear optimization run termination criteria and (iii) no criteria to detect stagnation of an optimization run. The impact of these drawbacks can be partly compensated by manual work that becomes inefficient when the solution space is large due to combinatorial explosion of adjustable parameters or for other reasons. RESULTS: SpaceScanner uses parallel optimization runs for automatic termination of optimization tasks in case of consensus and consecutively applies a pre-defined set of global stochastic optimization methods in case of stagnation in the currently used method. Automatic scan of adjustable parameter combination subsets for best objective function values is possible with a summary file of ranked solutions. AVAILABILITY AND IMPLEMENTATION: https://github.com/atiselsts/spacescanner . CONTACT: egils.stalidzans@lu.lv. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.