PC sound card based instrumentation and control

The availability of inexpcnsivc PC sound cards that can simultaneously play and rcrord stereo digital audio files pennits a single PC OT laptop or netbook to function as both a signal generator and as a dual-channel recording digital oscilloscope. This paper presents how sound card with Matlab Data Acquisition Toolbox can be utili7.ed for inSlrumcntation and control. Th~ buff~r hardware circuit or signal conditiooillg circuit is developed and implemellted to enablc sound card to be functioned like oscilloscope. Preliminary results showed that sound card with the developed software has a good potential fOT instnllTlentation and contro

Protein coding identification using modified gabor wavelet transform on multicore system

The gene identification problem, which identifies the protein-coding regions (exons) in DNA sequences through computational means, is of great importance nowadays. A DNA sequence can be divided into genes and intergenic spaces. In eukaryotic genes, these regions can be divided into two sub-regions called coding regions (exons) and non-coding regions (introns). The intergenic and intronic regions make up most of the genome. For example, in the human genome, the exonic fraction is as low as 2%. It is well known that protein-coding regions of DNA sequences tend to exhibit a period-3 pattern because of the codon structure involved in the translation of base sequences into amino acids [1-4]. Many researchers have regarded the period-3 property to be a good indicator ofgene location

On the use of edge features and exponential decaying number of nodes in the hidden layers for handwritten signature recognition

Handwritten signatures are playing an important role in finance, banking and education and more because it is considered the “seal of approval” and remains the most preferred means of authentication. In this paper, an offline handwritten signature authentication algorithm is proposed using the edge features and deep feedforward neural network (DFNN). The number of hidden layers in DFNN is configured to be at least one layer and more. In this paper, an exponential decaying number of nodes in the hidden layers was proposed to achieve better recognition rate with reasonable training time. Of the six edge algorithms evaluated, Roberts operator and Canny edge detectors were found to produce better recognition rate. Results showed that the proposed exponential decaying number of nodes in the hidden layers outperform other structure. However, more training data was required so that the proposed DFNN structure could have more efficient learning

Recent advances in IoT, AI, and national technology resilience

Internet of Things (IoT) and Artificial Intelligence (AI) are the critical enablers of the Industrial Revolution 4.0. IoT can be used in many applications that require precision, such as agriculture, industrial automation, education, automotive, and smart cities, to name a few. In other words, IoT is a powerful technology that can solve various business problems. Nevertheless, its integration with AI can help to take automation to the next level. This talk aims to discuss the recent advances in IoT, edge computing, and its applications. First, the IoT and edge commercial adoption survey 2021 will be highlighted. Then, the IoT framework will be introduced to solve a complex problem, including Things, Connect, Collect, Learn, and Do. Especially, the Learn part is very much related to AI. Then, some applications using IoT and edge computing will be presented. Finally, national technology resilience is now a necessity rather than necessary due to the current world situation. Therefore, future directions to enhance national technology resilience will be elaborated

Multilayers Fast Mode Decision Algorithms for Scalable Video Coding

Abstract: Scalable video coding (SVC) is the extension of H.264/AVC standard. The features in SVC are also developed from the H.264/AVC standard, so that SVC has more features compared to H.264/AVC standard. This provides higher coding complexity in SVC encoder which causes higher encoding time for SVC. SVC is gaining great interest because of its ability and scalability to adapt in various network conditions. SVC allows partial transmission and decoding of a bitstream. This research deals with multilayers fast mode decision algorithm for decreasing encoding time or fastening the mode decision process of the SVC encoder. The proposed fast mode decision scheme has been implemented and is successfully decrease encoding time with negligible loss of quality and bitrate requirement. The simulation result shows the proposed fast mode decision algorithm provides time saving up to 45 % while maintaining video quality with negligible PSNR loss

Video streaming evaluation Of H.264 SVC on IEEE 802.11g wireless network

Scalable video coding (SVC) is gaining great interest because of its ability and scalability to adapt in various conditions of network. The term of scalability is referring to the removal of parts of the video bitstream in order to adapt it to the various needs or preferences of end users as well as to varying terminal capabilities or network conditions. SVC allows partial transmission and decoding of a bitstream [1]. It contains the base layer and the enhancement layers. The base layer should be transmitted with very high reliability. On the other hand, the enhancement layers might be dropped or only transmitted partially according to the available network bitrates [2, 3]. This allows very fast and accurate network adaptation to variable bit rate channel

Forward masking threshold estimation using neural networks and its application to parallel speech enhancement

Forward masking models have been used successfully in speech enhancement and audio coding. Presently, forward masking thresholds are estimated using simplified masking models which have been used for audio coding and speech enhancement applications. In this paper, an accurate approximation of forward masking threshold estimation using neural networks is proposed. A performance comparison to the other existing masking models in speech enhancement application is presented. Objective measures using PESQ demonstrates that our proposed forward masking model, provides significant improvements (5-15 %) over four existing models, when tested with speech signals corrupted by various noises at very low signal to noise ratios. Moreover, a parallel implementation of the speech enhancement algorithm was developed using Matlab parallel computing toolbox

Speech coding techniques and algorithms

Due to the growing need for bandwidth conservation and enhanced quality in wireless cellular and satellite communication, the research of low bit rate speech coder with acceptable quality is becoming increasingly important. Applications like Digital cellular and satellite telephony, video conferencing and internet voice communications, all have an increasing demand efficient use of bandwidth without compromising the qualit

Forward masking threshold estimation using neural networks and its application to parallel speech enhancement

Forward masking models have been used successfully in speech enhancement and audio coding. Presently, forward masking thresholds are estimated using simplified masking models which have been used for audio coding and speech enhancement applications. In this paper, an accurate approximation of forward masking threshold estimation using neural networks is proposed. A performance comparison to the other existing masking models in speech enhancement application is presented. Objective measures using PESQ demonstrates that our proposed forward masking model, provides significant improvements (5-15 %) over four existing models, when tested with speech signals corrupted by various noises at very low signal to noise ratios. Moreover, a parallel implementation of the speech enhancement algorithm was developed using Matlab parallel computing toolbox