Electrical Impedance Myography (EIM) is a noninvasive neurophysiologic technique to diagnose muscle health. Besides muscle properties, the EIM measurements vary significantly with the change of some other anatomic and nonanatomic factors such as skin fat thickness, shape and thickness of muscle, and electrode size and spacing due to its noninvasive nature of measurement. In this study, genetic algorithm was applied along with finite element model of EIM as an optimization tool in order to figure out an optimized EIM electrode setup, which is less affected by these factors, specifically muscle thickness variation, but does not compromise EIM’s ability to detect muscle diseases. The results obtained suggest that a particular arrangement of electrodes and minimization of electrode surface area to its practical limit can overcome the effect of undesired factors on EIM parameters to a larger extent.

Electrical Impedance Myography (EIM) is a painless, noninvasive approach for assessing muscle conditions through the application of a high-frequency, low-intensity current to the muscle region of interest. However, besides muscle properties, EIM measurements vary significantly with changes in some other anatomical properties such as subcutaneous skin-fat (SF) thickness and muscle girth, as well as non-anatomical factors, such as ambient temperature, electrode shape, inter-electrode distance, etc. This study has been conducted to compare the effects of different electrode shapes in EIM experiments, and to propose an acceptable configuration that is less dependent on factors other than the cellular properties of the muscle. Initially, a finite element model with two different kinds of electrode shapes, namely, rectangular (the conventional shape) and circular (the proposed shape) was designed for a subcutaneous fat thickness ranging from 5 mm to 25 mm. The study concludes, based on the FEM study, that replacing the conventional electrodes with our proposed electrodes can decrease the variation in EIM parameters due to changes in skin-fat thickness by 31.92%. EIM experiments on human subjects with these two kinds of electrode shapes validate our finite element simulation results, and show that circular electrodes can improve EIM effectiveness significantly, irrespective of muscle shape variation.

Wearable alcohol monitoring devices demand noninvasive, real-time measurement of blood alcohol content (BAC) reliably and continuously. A few commercial devices are available to determine BAC noninvasively by detecting transcutaneous diffused alcohol. However, they suffer from a lack of accuracy and reliability in the determination of BAC in real time due to the complex scenario of the human skin for transcutaneous alcohol diffusion and numerous factors (e.g., skin thickness, kinetics of alcohol, body weight, age, sex, metabolism rate, etc.). In this work, a transcutaneous alcohol diffusion model has been developed from real-time captured data from human wrists to better understand the kinetics of diffused alcohol from blood to different skin epidermis layers. Such a model will be a footprint to determine a base computational model in larger studies. Eight anonymous volunteers participated in this pilot study. A laboratory-built wearable blood alcohol content (BAC) monitoring device collected all the data to develop this diffusion model. The proton exchange membrane fuel cell (PEMFC) sensor was fabricated and integrated with an nRF51822 microcontroller, LMP91000 miniaturized potentiostat, 2.4 GHz transceiver supporting Bluetooth low energy (BLE), and all the necessary electronic components to build this wearable BAC monitoring device. The %BAC data in real time were collected using this device from these volunteers’ wrists and stored in the end device (e.g., smartphone). From the captured data, we demonstrate how the volatile alcohol concentration on the skin varies over time by comparing the alcohol concentration in the initial stage (= 10 min) and later time (= 100 min). We also compare the experimental results with the outputs of three different input profiles: piecewise linear, exponential linear, and Hoerl, to optimize the developed diffusion model. Our results demonstrate that the exponential linear function best fits the experimental data compared to the piecewise linear and Hoerl functions. Moreover, we have studied the impact of skin epidermis thickness within ±20% and demonstrate that a 20% decrease in this thickness results in faster dynamics compared to thicker skin. The model clearly shows how the diffusion front changes within a skin epidermis layer with time. We further verified that 60 min was roughly the time to reach the maximum concentration, C_max, in the stratum corneum from the transient analysis. Lastly, we found that a more significant time difference between BAC_max and C_max was due to greater alcohol consumption for a fixed absorption time.

Cognitive wireless networks consist of cognitive radio devices which enable dynamic spectrum access for unlicensed secondary users. Multi-hop cognitive radio network (MHCRN) offers connectivity when transmitter and receiver are not within the communication range of each other using single hop. However, joint effect of multi-hop and dynamic spectrum access introduces many challenges in MAC and routing layer. Note that the devices through MAC layer make spectrum access decision without disturbing the licensed users. Effective routing protocols in MHCRN are required to provide shortest path between transmitter and receiver. In this paper, several MAC layer and routing layer problem and solutions are presented.

This paper investigates the calculation of ampacity values of medium voltage cable in an unfavorable burial method. The ampacity values are obtained using the International Electro-technical Commission (IEC) 60287 for all ac voltages with a 100% load factor in ducts, pipes, trenches. The cables are obtained from Leading Solution (LS) cable data sheet which provides the values for parameters needed to calculate the ampacity. The ampacity values are analyzed with constant and varying ambient temperature and burial depth. An Artificial Neural Network (ANN) is created to allow users to input a cables voltage and ampacity values in different situations to obtain an output of parameters for that specific cable. Ampacity value calculations showed an 8-15% difference from listed to those calculated. The regression factor of the ANN was around 0.996 and the overall error for all fourteen outputs was at maximum between 1-2%.