Fizza Ghulam Nabi

This report aims to present a study on redesigning the production floor layout in an electronic appliance manufacturing company using a product layout approach. The study was initiated to improve the overall efficiency of the production process and reduce production costs. The report provides an in-depth analysis of the existing layout of the production floor, highlighting the key strengths and weaknesses of the current design. The study identifies the areas of the production floor that are causing bottlenecks and delays and recommends potential changes to the layout that could reduce these issues. The report describes the proposed layout design and its potential benefits, including improved flow of materials, reduced handling and transportation time, and increased production process efficiency. The study concludes with a discussion of the potential challenges that may arise while implementing the new layout and recommends strategies for overcoming them. Overall, this study is expected to result in significant improvements in production efficiency, reduced cycle times, and increased throughput, thereby enhancing the company's competitiveness in the market. This study found that batch size changes from 20% to 10%, and 33.3% of space is utilized for external cabinets.

Objective: Breath sound has information about underlying pathology and condition of subjects. The purpose of this study is to examine asthmatic acuteness levels (Mild, Moderate, Severe) using frequency features extracted from wheeze sounds. Further, analysis has been extended to observe behavior of wheeze sounds in different datasets. Method: Segmented and validated wheeze sounds was collected from 55 asthmatic patients from the trachea and lower lung base (LLB) during tidal breathing maneuvers. Segmented wheeze sounds have been grouped in to nine datasets based on auscultation location, breath phases and a combination of phase and location. Frequency based features F25, F50, F75, F90, F99 and mean frequency (MF) has been calculated from normalized power spectrum. Subsequently, multivariate analysis has been performed for analysis. Result: Generally frequency features observe statistical significance (p < 0.05) for the majority of datasets to differentiate severity level ? = 0.432-0.939, F(12, 196-1534) = 2.731-11.196, p < 0.05, ????2 = 0.061-0.568. It was observed that selected features performed better (higher effect size) for trachea related samples ? = 0.432-0.620, F(12, 196-498) = 6.575-11.196, p < 0.05, ????2 = 0.386-0.568. Conclusion: The results demonstrate that severity levels of asthmatic patients with tidal breathing can be identified through computerized wheeze sound analysis. In general, auscultation location and breath phases produce wheeze sounds with different characteristics. Keywords: Asthma, Breath Sounds, Wheeze Detection, Airway Obstruction, Severity Level Continuous...

Objective: This study aimed to statistically analyze the behavior of time-frequency features in digital recordings of wheeze sounds obtained from patients with various levels of asthma severity (mild, moderate, and severe), and this analysis was based on the auscultation location and/or breath phase. Method: Segmented and validated wheeze sounds were collected from the trachea and lower lung base (LLB) of 55 asthmatic patients during tidal breathing maneuvers and grouped into nine different datasets. The quartile frequencies F₂₅, F₅₀, F₇₅, F₉₀ and F₉₉, mean frequency (MF) and average power (AP) were computed as features, and a univariate statistical analysis was then performed to analyze the behavior of the time-frequency features. Results: All features generally showed statistical significance in most of the datasets for all severity levels [χ² = 6.021–71.65, p < 0.05, η² = 0.01–0.52]. Of the seven investigated features, only AP showed statistical significance in all the datasets. F₂₅, F₇₅, F₉₀ and F₉₉ exhibited statistical significance in at least six datasets [χ² = 4.852–65.63, p < 0.05, η² = 0.01–0.52], and F₂₅, F₅₀ and MF showed statistical significance with a large η² in all trachea-related datasets [χ² = 13.54–55.32, p < 0.05, η² = 0.13–0.33]. Conclusion: The results obtained for the time-frequency features revealed that (1) the asthma severity levels of patients can be identified through a set of selected features with tidal breathing, (2) tracheal wheeze sounds are more sensitive and specific predictors of severity levels and (3) inspiratory and expiratory wheeze sounds are almost equally informative.

Wheezes are high pitched continuous respiratory acoustic sounds which are produced as a result of airway obstruction. Computer-based analyses of wheeze signals have been extensively used for parametric analysis, spectral analysis, identification of airway obstruction, feature extraction and diseases or pathology classification. While this area is currently an active field of research, the available literature has not yet been reviewed. This systematic review identified articles describing wheeze analyses using computer-based techniques on the SCOPUS, IEEE Xplore, ACM, PubMed and Springer and Elsevier electronic databases. After a set of selection criteria was applied, 41 articles were selected for detailed analysis. The findings reveal that 1) computerized wheeze analysis can be used for the identification of disease severity level or pathology, 2) further research is required to achieve acceptable rates of identification on the degree of airway obstruction with normal breathing, 3) analysis using combinations of features and on subgroups of the respiratory cycle has provided a pathway to classify various diseases or pathology that stem from airway obstruction.