Speech Enhancement Techniques for Noisy Speech in Real World Environments

Communication between computer andhuman has become increasingly popular in todayworld. Investigation of human emotion importance isalso growing in several domains. But under realworld condition, speech signal is often, corruptedwith several noise types and the accuracy ofrecognition is degraded from these noisy signal.Therefore this paper focuses on the speechenhancement techniques to develop emotionrecognition system for the noisy signal in the realworld environment. The various popularenhancement techniques are analyzed by adding thebackground noise to the clean signal using variousSNR. To test the accuracy of the system, the widelyused MFCC signal features are against with the SVMclassifier. Results after enhancing were compared tothat noisy signal and that clean signal to measure thesystem performance. The experimental results showthe best performance algorithm and all enhancementalgorithms improve the emotion recognition systemperformance under various SNRs level of real worldbackground noise

Keyword Search over Relational Databases

A relational database is often operated bymeans of a structured query language (SQL).When composing SQL-queries one must have anunderstanding of the SQL syntax to be able toproduce a query the database can execute.Additionally, one must be familiar with theattributes and relations in the database to beable to retrieve the data desired. When onewants to search the available data stored in therelational database, these requirements can bediscouraging. In this case keyword-based searchfunctionality could improve the accessibility ofthe data. In recent years, much research onkeyword search on relational database has beendone, and many prototypes. However, there arestill critical problems on the efficiency andeffectiveness of Keyword search systems overrelational database. In this paper, a system thatenables keyword-based search in relationaldatabase (KQRD) is presented. We proposeBest-first (A*) algorithm and Structured PivotedNormalization Weighting method to be able tosearch and retrieve the document from relationaldatabase effectively

A Framework for Querying Relational Database using Keyword Search

Digital library (DL) research and developmenthas concentrated primarily on collections and on theservices to build and access them. And when thelarge quantity of Myanmar document is gettingarchived by the digital libraries, there is a needMyanmar Keyword Search system to easily searchand retrieve these documents. Keyword search hasbeen the most widely used kind of queryingnowadays, especially for searching documents on theweb because of its user-friendly way. Although, thereare so many Keyword Search Systems overRelational Databases that exist on Internet, none ofthese can’t fully support for searching with Myanmarlanguage. Therefore we propose a system that cansearch and retrieve the document from the digitallibrary by using Myanmar language keyword. Atfirst, we translate keyword query into the availablelanguage in the digital library, and then executethem in RDBMS by using Hybrid Algorithm toretrieve top-k results

Учебная программа для специальности: 1-26 02-02 "Менеджмент" направление специальности 1-26 02 02-04 Менеджмент (недвижимости)

Nowadays, replication technique is widely used in data centerstorage systems to prevent data loss. Data popularity is a key factor in datareplication as popular files are accessed most frequently and then they becomeunstable and unpredictable. Moreover, replicas placement is one of key issuesthat affect the performance of the system such as load balancing, data localityetc. Data locality is a fundamental problem to data-parallel applications thatoften happens and this problem leads to the decrease in performance. To addressthese challenges, this paper proposes a dynamic replication management schemebased on data popularity and data locality; it includes replica allocation andreplica placement algorithms. Data locality, disk bandwidth, CPU processingspeed and storage utilization are considered in the proposed data placementalgorithm in order to achieve better data locality and load balancing effectively.Our proposed scheme will be effective for large-scale cloud storage

Replication Based on Data Locality for Hadoop Distributed File System

Replication plays an important role for storage system to improve data availability, throughputand response time for user and control storage cost. Due to different nature of data access pattern, datapopularity is important in replication because of the unstable and unpredictable nature of popular files. Also,replicas placement is important in consideration of system's performance. In data-parallel applications, datalocality is a key issue and this consequence of this issue occurs the decrement of system’ performance.Therefore, this paper proposes a data locality-based replication for Hadoop Distributed File System (HDFS).In replica allocation, data popularity is considered for maintaining less replicas for unpopular data and also,disk bandwidth, CPU utilization and disk utilization are considered in the proposed replica placementalgorithm in order to get better data locality and more effective storage utilization. Our proposed scheme willbe effective for HDFS

Dynamic Replication Management Scheme for Cloud Storage

Nowadays, replication technique is widely used in datacenter storage systems to prevent data loss. Datapopularity is a key factor in data replication as popularfiles are accessed most frequently and then they becomeunstable and unpredictable. Moreover, replicasplacement is one of key issues that affect the performanceof the system such as load balancing, data locality etc.Data locality is a fundamental problem to data-parallelapplications that often happens (i.e., a data block shouldbe copied to the processing node when a processing nodedoes not possess the data block in its local storage), andthis problem leads to the decrease in performance. Toaddress these challenges, this paper proposes a dynamicreplication management scheme based on data popularityand data locality; it includes replica allocation andreplica placement algorithms. Data locality, diskbandwidth, CPU processing speed and storage utilizationare considered in the proposed data placement algorithmin order to achieve better data locality and loadbalancing effectively. Our proposed scheme will beeffective for large-scale cloud storage

Contact Investigation of Multidrug-Resistant Tuberculosis Patients: A Mixed-Methods Study from Myanmar

There is no published evidence on contact investigation among multidrug-resistant tuberculosis (MDR-TB) patients from Myanmar. We describe the cascade of contact investigation conducted in 27 townships of Myanmar from January 2018 to June 2019 and its implementation challenges. This was a mixed-methods study involving quantitative (cohort analysis of programme data) and qualitative components (thematic analysis of interviews of 8 contacts and 13 health care providers). There were 556 MDR-TB patients and 1908 contacts, of whom 1134 (59%) reached the health centres for screening (chest radiography and symptoms). Of the latter, 344 (30%) had presumptive TB and of them, 186 (54%) were investigated (sputum microscopy or Xpert MTB/RIF®). A total of 27 TB patients were diagnosed (six bacteriologically-confirmed including five with rifampicin resistance). The key reasons for not reaching township TB centres included lack of knowledge and lack of risk perception owing to wrong beliefs among contacts, financial constraints related to loss of wages and transportation charges, and inconvenient clinic hours. The reasons for not being investigated included inability to produce sputum, health care providers being unaware of or not agreeing to the investigation protocol, fixed clinic days and times, and charges for investigation. The National Tuberculosis Programme needs to note these findings and take necessary action

Community-based MDR-TB care project improves treatment initiation in patients diagnosed with MDR-TB in Myanmar

<div><p>Background</p><p>The Union in collaboration with national TB programme (NTP) started the community-based MDR-TB care (CBMDR-TBC) project in 33 townships of upper Myanmar to improve treatment initiation and treatment adherence. Patients with MDR-TB diagnosed/registered under NTP received support through the project staff, in addition to the routine domiciliary care provided by NTP staff. Each township had a project nurse exclusively for MDR-TB and 30 USD per month (max. for 4 months) were provided to the patient as a pre-treatment support.</p><p>Objectives</p><p>To assess whether CBMDR-TBC project’s support improved treatment initiation.</p><p>Methods</p><p>In this cohort study (involving record review) of all diagnosed MDR-TB between January 2015 and June 2016 in project townships, CBMDR-TBC status was categorized as “receiving support” if date of project initiation in patient’s township was before the date of diagnosis and “not receiving support”, if otherwise. Cox proportional hazards regression (censored on 31 Dec 2016) was done to identify predictors of treatment initiation.</p><p>Results</p><p>Of 456 patients, 57% initiated treatment: 64% and 56% among patients “receiving support (n = 208)” and “not receiving support (n = 228)” respectively (CBMDR-TBC status was not known in 20 (4%) patients due to missing diagnosis dates). Among those initiated on treatment (n = 261), median (IQR) time to initiate treatment was 38 (20, 76) days: 31 (18, 50) among patients “receiving support” and 50 (26,101) among patients “not receiving support”. After adjusting other potential confounders (age, sex, region, HIV, past history of TB treatment), patients “receiving support” had 80% higher chance of initiating treatment [aHR (0.95 CI): 1.8 (1.3, 2.3)] when compared to patients “not receiving support”. In addition, age 15–54 years, previous history of TB and being HIV negative were independent predictors of treatment initiation.</p><p>Conclusion</p><p>Receiving support under CBMDR-TBC project improved treatment initiation: it not only improved the proportion initiated but also reduced time to treatment initiation. We also recommend improved tracking of all diagnosed patients as early as possible.</p></div