Neural spike sorting is prerequisite to deciphering useful information from electrophysiological data recorded from the brain, in vitro and/or in vivo. Significant advancements in nanotechnology and nanofabrication has enabled neuroscientists and engineers to capture the electrophysiological activities of the brain at very high resolution, data rate and fidelity. However, the evolution in spike sorting algorithms to deal with the aforementioned technological advancement and capability to quantify higher density data sets is somewhat limited. Both supervised and unsupervised clustering algorithms do perform well when the data to quantify is small, however, their efficiency degrades with the increase in the data size in terms of processing time and quality of spike clusters being formed. This makes neural spike sorting an inefficient process to deal with large and dense electrophysiological data recorded from brain. The presented work aims to address this challenge by providing a novel data pre-processing framework, which can enhance the efficiency of the conventional spike sorting algorithms significantly. The proposed framework is validated by applying on ten widely used algorithms and six large feature sets. Feature sets are calculated by employing PCA and Haar wavelet features on three widely adopted large electrophysiological datasets for consistency during the clustering process. A MATLAB software of the proposed mechanism is also developed and provided to assist the researchers, active in this domain.
Solar panels typically consist of photovoltaic (PV) cells covered by a protective glass coating, which generate electricity when subjected to radiations. However, the capability of electricity generation is constrained due to layer of dust on PV modules. In contrast with conventional method of cleaning the modules using water, this paper presents design and development of a robotic cleaner for cleaning PV modules of Quaid-e-Azam Solar Park (QASP). The hardware as well as software architectures of the proposed robotic cleaner are detailed. The novelty of the design lies in its low cost indigenous development and simplicity in design. The mechanism primarily consists of ducted fan, roller brush and blower fan to offer slippage-free motion and cleaning on a glassy surface. Series of experiments and field trials demonstrate efficiency of the mechanism in cleaning the modules effectively.
This paper describes how, because of Canada’s vast geographic region and the importance of trade to the economy, reliable and efficient transportation is vital to the well-being of Canada’s socioeconomics. An essential element in keeping Canada’s transportation sector vibrant is the sustained availability of trained professional and technical personnel. All sorts of organizations, such as universities, technical institutes, provincial/territorial associations of professional engineers, and associations such as the Transportation Association of Canada (TAC), have a stake in ensuring high quantity and quality of these human resources. Therefore in 2001, the TAC established the Education Council to identify the needs and gaps in education and training within the transportation sector and to explore the opportunities and future directions. This paper describes the steps taken by the TAC from 2001 to the future.
There has been a tremendous research going on in the field of nanofluids containing metal and metal oxide nanoparticles. Appreciable enhancements in thermal conductivity have been achieved in recent years. This paper presents a new study of nanofluids comprising nanofibers as previous studies are mostly based on nanofluids containing nanoparticles. Transient hot-wire method is used for thermal conductivity measurements of ethylene glycol based nanofluids. Nanofluids prepared via two-step method by dispersing TiO2 and CuO nanoparticles and CdTiO3 nanofibers in ethylene glycol. Maximum thermal conductivity enhancements are achieved for CuO nanoparticles. Maximum thermal conductivity enhancement of 13.3% and 8.6% achieved for TiO2 and CuO nanoparticles while 10.2% for CdTiO3 nanofibers. The Experimental results are then compared with the theoretical models to estimate the effect of particle shape and concentration on thermal conductivity enhancement.
Deciphering useful information from electrophysiological data recorded from the brain, in-vivo or in-vitro, is dependent on the capability to analyse spike patterns efficiently and accurately. The spike analysis mechanisms are heavily reliant on the clustering algorithms that enable separation of spike trends based on their spatio-temporal behaviors. Literature review report several clustering algorithms over decades focused on different applications. Although spike analysis algorithms employ only a small subset of clustering algorithms, however, not much work has been reported on the compliance and suitability of such clustering algorithms for spike analysis. In our study, we have attempted to comment on the suitability of available clustering algorithms and performance capacity when exposed to spike analysis. In this regard, the study reports a compatibility evaluation on algorithms previously employed in spike sorting as well as the algorithms yet to be investigated for application in sorting neural spikes. The performance of the algorithms is compared in terms of their accuracy, confusion matrix and accepted validation indices. Three data sets comprising of easy, difficult, and real spike similarity with known ground-truth are chosen for assessment, ensuring a uniform testbed. The procedure also employs two feature-sets, principal component analysis and wavelets. The report also presents a statistical score scheme to evaluate the performance individually and overall. The open nature of the data sets, the clustering algorithms and the evaluation criteria make the proposed evaluation framework widely accessible to the research community. We believe that the study presents a reference guide for emerging neuroscientists to select the most suitable algorithms for their spike analysis requirements.
Battery energy storage systems are becoming an integral part of the modern power grid to maximize the utilization of renewable energy sources and negate the intermittences associated with the weather condition and to support grid during extreme operating conditions. Precise and real time knowledge of battery capacity is of paramount importance for optimal and efficient energy management of the power grid with integrated renewable energy sources. This ensures highest utilization of a battery life. State of Charge (SoC) is the most used measure of the battery available capacity that quantifies the percentage of battery capacity that is available at a given instance. An efficient SoC estimation approach for batteries in power grid is expected to possess attributes such as high accuracy, low complexity, near real time estimation capability, chemistry agonistic nature, etc. In the literature, an overwhelming amount of battery SoC approaches with different level of implementation complexity and accuracy have been reported. To present critical analysis of the existing battery SoC estimation approaches, this paper presents a comprehensive review on the commonly used battery SoC estimation approaches. The presented review includes a detailed description of each of the approaches and highlights their pros and cons in power grid applications.
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