Design Robustness Analysis of Neuromorphic Circuits

Conventional Von Neumann architecture faces significant challenges as the device dimensions are scaled down. Power consumption and device reliability have become major concerns. Therefore, new computational paradigms are being proposed to overcome these challenges. Brain-inspired computing has emerged as a promising direction in that regard. Its massive-parallelism, potential for scalability, and power efficiency make it attractive. In addition, neuromorphic computing has shown better performance in complex tasks such as pattern recognition. Few attempts have been made to investigate the effect of silicon failures beyond the circuit level. In this thesis, a method is proposed to evaluate the impact of process and environmental variations on the overall performance of biologically inspired spiking neural networks. In this method, transistor-level and behavioral level analysis are carried out. Then, the results of the transistor-level simulation is mapped to the application layer to determine effect of variability on the performance of the system. Monte Carlo analysis of a brain-inspired digital neuromorphic circuit in the presence of voltage, and temperature (PVT) variations is performed. A commercial 90nm technology process is utilized to synthesize and simulate the design. The functionality of the circuit is demonstrated through a behavioral model of a neural network that implements a character recognition system. Errors are injected in the network to obtain its fault resilience characteristics. The result from PVT variations analysis are projected into a behavioral model to estimate the effect of the circuit failures on the operation of the neural network. Furthermore, the influence of key parameters on the system's performance is examined. These are the supply voltage, at the circuit level, and the structure, at the application level. The experimental results have demonstrated the robustness of the networks with respect to the targeted variation effects

Consistent metagenomic biomarker detection via robust PCA

BACKGROUND: Recent developments of high throughput sequencing technologies allow the characterization of the microbial communities inhabiting our world. Various metagenomic studies have suggested using microbial taxa as potential biomarkers for certain diseases. In practice, the number of available samples varies from experiment to experiment. Therefore, a robust biomarker detection algorithm is needed to provide a set of potential markers irrespective of the number of available samples. Consistent performance is essential to derive solid biological conclusions and to transfer these findings into clinical applications. Surprisingly, the consistency of a metagenomic biomarker detection algorithm with respect to the variation in the experiment size has not been addressed by the current state-of-art algorithms. RESULTS: We propose a consistency-classification framework that enables the assessment of consistency and classification performance of a biomarker discovery algorithm. This evaluation protocol is based on random resampling to mimic the variation in the experiment size. Moreover, we model the metagenomic data matrix as a superposition of two matrices. The first matrix is a low-rank matrix that models the abundance levels of the irrelevant bacteria. The second matrix is a sparse matrix that captures the abundance levels of the bacteria that are differentially abundant between different phenotypes. Then, we propose a novel Robust Principal Component Analysis (RPCA) based biomarker discovery algorithm to recover the sparse matrix. RPCA belongs to the class of multivariate feature selection methods which treat the features collectively rather than individually. This provides the proposed algorithm with an inherent ability to handle the complex microbial interactions. Comprehensive comparisons of RPCA with the state-of-the-art algorithms on two realistic datasets are conducted. Results show that RPCA consistently outperforms the other algorithms in terms of classification accuracy and reproducibility performance. CONCLUSIONS: The RPCA-based biomarker detection algorithm provides a high reproducibility performance irrespective of the complexity of the dataset or the number of selected biomarkers. Also, RPCA selects biomarkers with quite high discriminative accuracy. Thus, RPCA is a consistent and accurate tool for selecting taxanomical biomarkers for different microbial populations. REVIEWERS: This article was reviewed by Masanori Arita and Zoltan Gaspari. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13062-017-0175-4) contains supplementary material, which is available to authorized users

Conversion Function Theory of Power Converter Systems

Modern power systems are increasingly relying on switching power converters to operate and control the power flow. This is mainly due to the need for power conditioning with the addition of new and diverse elements such as renewable energy sources and HVDC. These systems extensively utilize multiple types of power converters including DC-DC converters, AC-DC rectifiers, and DC/AC inverters. Moreover, power converters are used in a wide array of applications such as transportation (hybrid and electric vehicles, more electric aircraft, and more electric ship), motor drives, energy storage and management, and microgrids. This has led to complex structures which pose a challenge in terms of modelling and simulation. In this dissertation, the power conversion theory is presented: a general framework for modelling and simulation of systems with extensive presence of switching power converters. In this theory, power conversion functions are developed to represent power converters. These functions are defined based on the wanted quantity which corresponds to the application’s needs. Furthermore, a systematic approach to implement these functions with switching circuits is introduced. The theory presented here provides a method to refer one side of the converter to the other as an equivalent apparent element. This facilitates the reduction of a large system to a simplified circuit model. This model provides an intuitive insight into the operation and interaction between the components of the system. In addition, it offers a fast and accurate simulation approach for the study of the dynamics and performance of multiconverter systems. Furthermore, this theory can be extended to analyze multi-physics systems by employing the generalized gyrator theory, which allows the representation of elements in one physics domain with an equivalent element in another. Therefore, a multi-domain system can be analyzed in a single domain after all elements of the system are represented by their equivalent in the domain of interest

Conversion Function Theory of Power Converter Systems

Modern power systems are increasingly relying on switching power converters to operate and control the power flow. This is mainly due to the need for power conditioning with the addition of new and diverse elements such as renewable energy sources and HVDC. These systems extensively utilize multiple types of power converters including DC-DC converters, AC-DC rectifiers, and DC/AC inverters. Moreover, power converters are used in a wide array of applications such as transportation (hybrid and electric vehicles, more electric aircraft, and more electric ship), motor drives, energy storage and management, and microgrids. This has led to complex structures which pose a challenge in terms of modelling and simulation. In this dissertation, the power conversion theory is presented: a general framework for modelling and simulation of systems with extensive presence of switching power converters. In this theory, power conversion functions are developed to represent power converters. These functions are defined based on the wanted quantity which corresponds to the application’s needs. Furthermore, a systematic approach to implement these functions with switching circuits is introduced. The theory presented here provides a method to refer one side of the converter to the other as an equivalent apparent element. This facilitates the reduction of a large system to a simplified circuit model. This model provides an intuitive insight into the operation and interaction between the components of the system. In addition, it offers a fast and accurate simulation approach for the study of the dynamics and performance of multiconverter systems. Furthermore, this theory can be extended to analyze multi-physics systems by employing the generalized gyrator theory, which allows the representation of elements in one physics domain with an equivalent element in another. Therefore, a multi-domain system can be analyzed in a single domain after all elements of the system are represented by their equivalent in the domain of interest

A Novel PV Maximum Power Point Tracking Based on Solar Irradiance and Circuit Parameters Estimation

This research paper presents a novel maximum power point taking (MPPT) algorithm. The algorithm uses an adaptive calculation block to estimate the solar irradiance and the PV I–V curve circuit parameters based on the PV panel’s measured output current and voltage. In the proposed algorithm, the output power does not oscillate around the maximum power point (MPP) compared to conventional MPPT methods. Moreover, the proposed algorithm does not require expensive solar irradiance sensors compared with trackers that depend on measured solar irradiance. In addition, the proposed MPPT can handle the fast variation in solar irradiance. The PV panel nonlinear I–V curve was modeled using a single-diode PV. The algorithm with the adaptive block was tested separately to verify the ability of the system to estimate the solar irradiance and the circuit parameters. The solar system was then simulated using MATLAB/Simulink to evaluate the robustness of the proposed method under steady-state and during sudden changes in solar irradiance and load. The proposed solar system reaches the steady-state in 8 ms after a step-change in the solar irradiance. In the worst-case scenario, the proposed system achieves a relative error of around 2.64% in estimating the solar irradiance at 600 W/m2 with an efficiency of 99.3%

Evidence-based nursing practice and improving pediatric patient care outcomes in the prevention of infection transmission: Emergency department findings.

BackgroundReducing the risk of infection transmission by getting emergency care for pediatric patients is a challenging task.AimThe study aim was to assess emergency nurses' readiness to provide care for pediatric patients with infectious diseases.MethodTwo hundred Jordanian emergency department nurses were surveyed using a descriptive design.ResultsThe study revealed that insufficient safety and infection control procedures were put into place, starting with family support to allow nurses to work 145 (78%), family care plans intended to assist caregivers 139 (74.7%), the availability of respiratory protection and a backup plan for standard precautions, training requirements, and equipment 131 (70.4%), create a unit pandemic safety strategy 124 (66.7%), have a plan for emergencies for at-risk staff 116 (62.4%), have a hospital pandemic safety plan 113 (60.8%), manage inventory 102 (54.8%), use reuse guidelines if there will be severe shortages 99 (53.2%), create a strategy for nurses' access to healthcare for themselves and their families 96 (51.6%), and end with any required system updates for new policies 88 (47.3%). Staff nurses made up a large proportion of participants (145; 78%; 115; 62.8%) who said they lacked experience with care for pediatric patients with infectious illnesses who were critically sick. A 62.8% of nurses reported they did not have training in infectious disease emergency prevention and control for pediatric patients. What nurses prioritize it was determined that the concept of crisis standards of care (34.9%) was the most important educational topic for training emergency room nurses to care for pediatric patients who are critically ill with infectious infections, while the clarity of communication pathways was ranked lowest.ConclusionMore training and support are needed for emergency room nurses to properly care for children's patients with infectious illnesses

Electric Vehicles in Jordan: Challenges and Limitations

An increasing number of electric vehicles (EVs) are replacing gasoline vehicles in the automobile market due to the economic and environmental benefits. The high penetration of EVs is one of the main challenges in the future smart grid. As a result of EV charging, an excessive overloading is expected in different elements of the power system, especially at the distribution level. In this paper, we evaluate the impact of EVs on the distribution system under three loading conditions (light, intermediate, and full). For each case, we estimate the maximum number of EVs that can be charged simultaneously before reaching different system limitations, including the undervoltage, overcurrent, and transformer capacity limit. Finally, we use the 19-node distribution system to study these limitations under different loading conditions. The 19-node system is one of the typical distribution systems in Jordan. Our work estimates the upper limit of the possible EV penetration before reaching the system stability margins

Participants’ demographic characteristics (N = 92).

Participants’ demographic characteristics (N = 92).</p

Effectiveness of a Culturally-Tailored Smoking Cessation Intervention for Arab-American Men

To date, no smoking cessation programs are available for Arab American (ARA) men, who are a vulnerable population with high rates of smoking. Thus, the primary aim of this one group pre-test/post-test study was to assess the effectiveness of Sehatack—a culturally and linguistically tailored smoking cessation program for ARA men. The study sample was 79 ARA men with a mean age of 43 years who smoked between 5 and 40 cigarettes (mean = 19.75, SD = 9.1) per day (98.7%). All of the participants reported more interest in smoking cessation post-intervention and many of the participants in the baseline (38.5%) and post-intervention phases (47.7%) wanted to quit smoking ”very much”. For daily smokers who completed the smoking cessation program, the median number of cigarettes smoked daily was significantly lower than those in the post-intervention phase (Z = −6.915, p &lt; 0.001). Results of this preliminary study indicate that: (a) Sehatack may be a promising way for ARA men to quit smoking, and (b) culturally relevant smoking cessation counselors can be trained to recruit and retain ARA smokers in an intensive group smoking cessation program. Strengths of this study were community engagement and rapport between three faith organizations and the University of Florida College of Nursing. However, a larger trial is needed to address study limitations and to confirm benefits in this population

Perceptions of participants toward telenursing (N = 92).

Perceptions of participants toward telenursing (N = 92).</p