7 research outputs found
Design of Soft Viterbi Algorithm Decoder Enhanced With Non-Transmittable Codewords for Storage Media
Viterbi Algorithm Decoder Enhanced with Non-transmittable Codewords is one of
the best decoding algorithm which effectively improves forward error correction
performance. HoweverViterbi decoder enhanced with NTCs is not yet designed to
work in storage media devices. Currently Reed Solomon (RS) Algorithm is almost
the dominant algorithm used in correcting error in storage media. Conversely,
recent studies show that there still exist low reliability of data in storage
media while the demand for storage media increases drastically. This study
proposes a design of the Soft Viterbi Algorithm decoder enhanced with
Non-transmittable Codewords (SVAD-NTCs) to be used in storage media for error
correction. Matlab simulation was used in this design in order to investigate
behavior and effectiveness of SVAD-NTCs in correcting errors in data retrieving
from storage media.Sample data of one million bits are randomly generated,
Additive White Gaussian Noise (AWGN) was used as data distortion model and
Binary Phase- Shift Keying (BPSK) was applied for simulation modulation.
Results show that,behaviors of SVAD-NTC performance increase as you increase
the NTCs, but beyond 6NTCs there is no significant change and SVAD-NTCs design
drastically reduce the total residual error from 216,878 of Reed Solomon to
23,900
A Comparative Study on Performance of SVM and CNN in Tanzania Sign Language Translation Using Image Recognition
Sign language is an effective form of communication for speech impaired people. However, there is a challenge for people without impairment to communicate with speech impaired people because most are unaware of the language. There are several Machine Learning techniques that have been used in sign language translation. However, no study has been found in Tanzania Sign Language which is the sign language used by speech impaired people in Tanzania. This study seeks to compare the performance of SVM and CNN on translating sign language through the image recognition. The study employs Tanzanian Sign Language images as datasets. Principal Component Analysis was employed for feature extraction. Furthermore, the study used Combined 5x2cv F test to compare the two techniques. The findings indicate that CNN scored 96% in all of the parameters which are accuracy, recall, and precision while SVM scored similar rate in precision but lag behind on recall and accuracy. Additionally, the results show that there is significant difference in performance between the techniques. Therefore, the study recommends the use of CNN since it has high accuracy
Fuel Adulteration in Tanzania and its Consequencies: An Overview
A research article was published by Research Journal in Engineering and Applied Sciences 2(4)Adulteration of petrol and diesel fuel with kerosene is very common in Tanzania. It increases the tailpipe
emissions of harmful pollutants from vehicles. These leads to this study, which tends to show the rate of
emission of CO (carbon II oxide) and PM (Particulate matters) from engines when they are run on adulterated
fuel. Petroleum products are essential inputs needed in economic activities, hence a necessity for the economy
of any Country. Adulteration of petroleum products especially petrol (motor gasoline) and diesel (high speed
diesel (HSD)), has become a serious problem. Expensive consumer products are often adulterated by integrating
with cheaper low quality materials having similar physical and chemical properties. Kerosene is the most
important domestic fuel for economically weaker sections of society and hence is heavily subsidized. The large
differences in the prices of petrol, diesel and kerosene, the easy availability of kerosene and the fact that it is
miscible in petrol and diesel, make the unhealthy and unethical practice of adulteration of petrol and diesel a
very plausible proposition. Effects of adulteration on the environment and on human health were discussed and
possible solutions to combat adulteration of fuel were highlighted
Forward Error Correction for Storage Media: An Overview
Research Article published byInternational Journal of Computer Science and Information Security (IJCSIS), Vol. 13, No. 12, December 2015As the adoption of Information and Communication
Technology (ICT) tools in production and service rendering
sectors increases, the demand for digital data storage with large
storage capacity also increases. Higher storage media systems
reliability and fault tolerance are among the key factors that the
existing systems sometimes fail to meet and therefore, resulting
into data loss. Forward error correction is one of the techniques
applied to reduce the impact of data loss problem in digital data
storage. This paper presents a survey conducted in different
digital data storage companies in Dar es Salam, Tanzania. Data
were collected and analyzed using Statistical Package for Social
Sciences (SPSS). Secondary data were captured from user and
manufacturer technical reports. It was revealed that data loss is
still a predominant challenge in the digital data storage industry.
Therefore, the study proposes the new storage media FEC model
using locked convolutional encoder with the enhanced NTCViterbi
decoder
Performance of Soft Viterbi Decoder enhanced with Non-Transmittable Codewords for storage media
The introduction of Non-Transmittable Codewords (NTCs) into Viterbi Algorithm Decoder has emerged as one of the best ways of improving performance of the Viterbi Algorithm Decoder. However, the performance has been tested only in hard decision Viterbi Decoder in telecommunication systems, but not in soft decision Viterbi Decoder and storage media. Most storage media use Reed Solomon (RS) Algorithm Decoder. Yet, the field experience still shows failure of the algorithm in correcting burst errors in reading data from the storage media; leading into data loss. This paper introduces the Soft Viterbi Algorithm Decoding enhanced with Non-Transmittable Codewords for storage media. Matlab software was used to simulate the algorithm and the performance was measured by comparing residual errors in a data length of one million bits. Additive White Gaussian Noise model was applied to distort the stored data. The performance comparison was made against the Reed Solomon code, Normal Soft Viterbi and Hard decision Viterbi enhanced with NTCs. The results showed that the Soft Viterbi Algorithm enhanced with NTCs performed remarkably better by 88.98% against RS, 84.31% against Normal Soft Viterbi and 67.26% against Hard Viterbi enhanced with NTCs
First results of phase 3 trial of RTS,S/AS01 malaria vaccine in african children
Background An ongoing phase 3 study of the efficacy, safety, and immunogenicity of candidate malaria vaccine RTS,S/AS01 is being conducted in seven African countries. Methods From March 2009 through January 2011, we enrolled 15,460 children in two age categories - 6 to 12 weeks of age and 5 to 17 months of age - for vaccination with either RTS,S/AS01 or a non-malaria comparator vaccine. The primary end point of the analysis was vaccine efficacy against clinical malaria during the 12 months after vaccination in the first 6000 children 5 to 17 months of age at enrollment who received all three doses of vaccine according to protocol. After 250 children had an episode of severe malaria, we evaluated vaccine efficacy against severe malaria in both age categories. Results In the 14 months after the first dose of vaccine, the incidence of first episodes of clinical malaria in the first 6000 children in the older age category was 0.32 episodes per person-year in the RTS,S/AS01 group and 0.55 episodes per person-year in the control group, for an efficacy of 50.4% (95% confidence interval [CI], 45.8 to 54.6) in the intention-to-treat population and 55.8% (97.5% CI, 50.6 to 60.4) in the per-protocol population. Vaccine efficacy against severe malaria was 45.1% (95% CI, 23.8 to 60.5) in the intention-to-treat population and 47.3% (95% CI, 22.4 to 64.2) in the per-protocol population. Vaccine efficacy against severe malaria in the combined age categories was 34.8% (95% CI, 16.2 to 49.2) in the per-protocol population during an average follow-up of 11 months. Serious adverse events occurred with a similar frequency in the two study groups. Among children in the older age category, the rate of generalized convulsive seizures after RTS,S/AS01 vaccination was 1.04 per 1000 doses (95% CI, 0.62 to 1.64). Conclusions The RTS,S/AS01 vaccine provided protection against both clinical and severe malaria in African children. (Funded by GlaxoSmithKline Biologicals and the PATH Malaria Vaccine Initiative; RTS,S ClinicalTrials.gov number, NCT00866619 .