Mohammad Mortazavi

The design of a low-power high-speed current comparator in 0.35-μm CMOS technology

A novel low power with high performance low current comparator is proposed in this paper which comprises of low input impedance using a simple biasing method. It aimed for low power consumption and high speed designs compared with other high speed designs. The simulation results from HSPICE demonstrate the propagation delay is about 0.7 ns and the average power consumption is 130 μW for 100 nA input current at supply voltage of 1.8 V using 0.35 micron CMOS technology.

Simple lossless and near-lossless medical image compression based on enhanced DPCM transformation

Medical images include information about human body which are used for different purposes such as surgical and diagnostic plans. Compression of medical images is used in some applications such as profiling patients data and transmission systems. Regard to importance of medical images information, lossless or near-lossless compression is preferred. Lossless JPEG, JPEG-LS and lossless version of JPEG2000 are few well-known methods for lossless compression. JPEG2000 is one of the latest and provides good compression ratio, however, it is complex [1]. In real time application which needs hardware implementation, low complex algorithm accelerates compression process. In this paper, a lossless and near-lossless compression method is introduced which is efficient due to its high compression ratio and simplicity. The method is consists of a new transformation method called Enhanced DPCM Transformation (EDT) which has a good energy compaction and a suitable Huffman encoding. After introducing the compression method, it is applied on different test cases and the results are evaluated.

Using augmented reality and automatic speech recognition techniques to help deaf and hard of hearing people

Recently, many researches show Augmented Reality (AR) and Automatic Speech Recognition (ASR) can help people with disabilities. In this paper we implement an innovative system for helping deaf people by combining AR, ASR, and AVSR technologies. This system can instantly take narrators speech and converts it into readable text and shows it directly on AR display. We show that our systems accuracy becomes over 85 percent on average, by using different ASR engines near using an AVSR engine in different noisy environments. We also show in a survey that more than 90 percent of deaf people on average need such system as assistant in portable devices, near using only text or only sign-language for communications.

Practical design of low-cost instrumentation for industrial electrical impedance tomography (EIT)

Electrical Impedance Tomography (EIT), is one of the medical imaging technologies. It can also be used in industrial process monitoring. In this method, the image of the electrical conductivity distribution of the inner part of a conductive subject can be reconstructed. The image reconstruction process is done by injecting an accurate current into the boundary of a conductive subject (e.g. body), measuring the voltages around the boundary and transmitting them to a computer, and processing on acquired data with a software (e.g., MATLAB). The images are obtained from the peripheral data by using an algorithm. Precise EIT instrumentation plays an important role in the final images quality. In this paper, we have proposed a practical design of low-cost precise EIT hardware, containing a high output impedance current source with a pulse generation part and also an accurate voltage demodulator and measurement part. All the subsystems have been tested accurately. By using this structure, electrical impedance tomography have been implemented successfully. The quality of reconstructed images, confirms the accuracy of the proposed EIT hardware.

Combining Augmented Reality and Speech Technologies to Help Deaf and Hard of Hearing People

Augmented Reality (AR), Automatic Speech Recognition (ASR) and Text-to-Speech Synthesis (TTS) can be used to help people with disabilities. In this paper, we combine these technologies to make a new system for helping deaf people. This system can take the narrators speech and convert it into a readable text and show it directly on AR display. To improve the accuracy of the system, we use Audio-Visual Speech Recognition (AVSR) as a backup for the ASR engine in noisy environments. In addition, we use the TTS system to make our system more usable for deaf people. The results of testing the system show that its accuracy is over 85 percent on average in different places. Also, the result of a survey shows that more than 90 percent of deaf people on average are very interested in using our system as an assistant in portable devices for communication.

An efficient lossless medical image transformation method by improving prediction model

Medical images include human body picture and it is used in diagnosis purpose [1]. Lossless compression of medical image is an application of medical imaging. During lossless compression task, transformation algorithm can be used to increase compression ratio. In Real time applications such as telemedicine and online diagnosis, hardware implementation accelerates the process. Hence, for such purposes medical compression is better to be simple. Lossless JPEG and JPEG2000 are some compression method. JPEG2000 gives better compression ratio. However, it is complex. In this paper an efficient method of lossless image transformation has been introduced by improving prediction model. Simulation results show that the new method has reduced the entropy compared to Differential Pulse Code Modulation (DPCM) and Discrete Wavelet Transform (DWT) which are used in JPEG and JPEG2000 respectively. Moreover, this method is cost effective due to computational complexity and entropy reduction.

Packet loss replacement in voip using a recursive low-order autoregressive model-based speech

In real-time packet-based communication systems one major problem is misrouted or delayed packets which results in degraded perceived voice quality. When some speech packets are not available on time, tghe packet is known as lost packet in real-time communication systems. The easiest task of a network terminal receiver is to replace silence for the duration of lost speech segments. In a high quality communication system in order to avoid quality reduction due to packet loss a suitable method and/or algorithm is needed to replace the missing segments of speech. In this paper, we introduce a recursive low order autoregressive (AR) model for replacement of lost speech segment. The evaluaton results show that this method has a lower mean square error (MSE) and low complexity compared to the other efficient methods like high-order AR model without any substantial degradation in perceived voice quality.

Analyzing Area Penalty of 32-Bit Fault Tolerant ALU Using BCH Code

In this paper we have presented a hardware implementation of 32-bit Fault-tolerant ALU (Arithmetic and Logic Unit) which is compared with the current techniques, Residue code, Triple Modular Redundancy (TMR) with single voting and TMR with triplicated voter that are widely used in space application to mitigate the upsets, in terms of area penalty. We consider BCH (Bose, Chaudhuri, and Hocquenghem) codec (encoder, decoder) using the prototyping FPGA (Field Programmable Gate Array). The new implementation of ALU employing BCH code on Spartan-3 FPGA has been provided. The results show that our fault tolerant method has the lowest hardware overhead and it can correct any 5-bit error in any position of 32-bit input registers of ALU.

Simple and efficient remote sensing image transformation for lossless compression

Remote Sensing (RS) images or satellite images include information about earth. Compression of RS images is important in the field of satellite transmission systems and mass storage purposes. Because of importance of information and existent of large amount of details, lossless compression preferred. Real time compression technique is applied on satellite and aerial transmission systems [1]. A simple algorithm accelerates the whole process in real time purposes. Lossless JPEG, JPEG-LS and JPEG2000 are some famous lossless compression methods. Transformation is the first step of these methods. In this paper, a simple and efficient method of lossless image transformation has been introduced by improving prediction ability which leads to more energy compaction. After mathematical proof for efficiency of new method, it compared with previous transformations of JPEG and JPEG2000 respectively by comparing their entropy value. Finally, we conclude that, our new method is cost effective for real time applications.

Efficient medical image transformation method for lossless compression by considering real time applications

Medical images contain human body pictures and used widely in diagnosis and surgical purposes [1]. Compression is needed for medical images for some applications such as profiling patients data or transmission systems Due to the importance of the information of medical images, lossless or visually lossless compression preferred. Lossless compression mainly consists of transformation and encoding steps. On the other hand, hardware implementation of lossless compression algorithm accelerates real time tasks such as online diagnosis and telemedicine. Lossless JPEG, JPEG-LS and lossless version of JPEG2000 are few well known methods for lossless compression. This paper is focused on the transformation step of compression and introduced a new transformation which is efficient in both entropy reduction and computational complexity. A new method is then achieved by improving the perdition model which is used in lossless JPEG. Our new transformation increases the energy compaction of prediction model and as a result reduces entropy value of transformed image. However, our new method is low complex. After a mathematical proof for efficiency of the new method, it is applied to more than hundreds of test-cases and the results are compared with previous methods and it shows about 8 percent improvement in average. As a result, the new algorithm shows a better efficiency for transforming lossless medical images, especially for online applications.