On Cyclic Delay Diversity OFDM Based Channels

Orthogonal Frequency Division Multiplexing, so called OFDM, has found a prominent place in various wireless systems and networks as a method of encoding data over multiple carrier frequencies. OFDM-based communication systems, however, lacking inherent diversity, are capable of benefiting from different spatial diversity schemes. One such scheme, Cyclic Delay Diversity (CDD) is a method to provide spatial diversity which can be also interpreted as a Space-Time Block Coding (STBC) step. The main idea is to add more transmit antennas at the transmitter side sending the same streams of data, though with differing time delays. In [1], the capacity of a point-to-point OFDM-based channel with CDD is derived for inputs with Gaussian and discrete constellations. In this dissertation, we use the same approach for an OFDM-based single-input single-output (SISO) two-user interference channel (IC). In our model, at the receiver side, the interference is treated as noise. Moreover, since the channel is time-varying (slow-fading), the Shannon capacity in the strict sense is not well-defined, so the expected value of the instantaneous capacity is calculated instead. Furthermore, the channel coefficients are unknown to the transmitters. Thus, in this setting, the probability of outage emerges as a reasonable performance measure. Adding an extra antenna in the transmitters, the SISO IC turns into an MISO IC, which results in increasing the diversity. Both the continuous and discrete inputs are studied and it turns out that decoding interference is helpful in some cases. The results of the simulations for discrete inputs indicate that there are improvements in terms of outage capacity compared to the ICs with single-antenna transmitters

Bacterial etiology and antibiotic susceptibility pattern of female patients with urinary tract infection referred to Imam Khomeini Hospital, Ahvaz, Iran, 2019

 Urinary tract infection (UTI) is one of the most frequent infections among women, and if untreated could lead to severe complications. The treatment of UTI is difficult due to the appearance of pathogens with increasing resistance to antimicrobial agents. This study thus aimed to determine the bacterial etiological pathogens of UTI and the antibiotic sensitivity pattern of the pathogens isolated. This descriptive cross-sectional study was performed from March to September 2019 on a total of 339 women referred to Imam Khomeini Teaching Hospital in Ahvaz, Iran. Mid-stream urine samples were collected from the patients and were cultured. The presence of significant bacteriuria was determined using the plate count method. The antimicrobial susceptibility test was done by the standard disk diffusion method. The most frequently isolated pathogens were Escherichia coli (54.8%), Klebsiella (18.2%), Pseudomonas aeruginosa (9.9%), Proteus (8%), and Acinetobacter (5.1%). E. coli, as the most common pathogen of UTIs, showed the most resistance to cephalosporins and the least resistance to imipenem. According to the findings, E. coli was the most common cause of UTI in our region. Considering the rate of UTI, and the importance of preventing its severe complications, a survey of regional resistance patterns and timely treatment can control the development of its resistant bacteria

Towards Development of a Robust Asynchronous EEG-BCI using Error-related Potentials

The primary goal of brain-computer interface (BCI) research is to provide a means of communication for individuals with severe motor impairments. For BCIs to be widely adopted by patients, they need to have a reliable accuracy. One method to achieve this goal is to automatically correct erroneous classifications by exploiting error-related potentials (ErrPs). The first objective of this research is to explore how does the introduction of error-based autocorrection impact the accuracy of a multi-class active BCI based on non-motor imagery (MI) cognitive tasks. Another unavoidable step toward making BCIs clinically viable is to develop self-paced BCIs that users can control whenever they intend to. The second objective of this research is to investigate the impact of ErrP-based autocorrection on the accuracy of self-paced BCIs based on non-MI cognitive tasks. The first two studies were designed to answer the first research question. In the first study, participants performed multiple iterations of five different cognitive tasks. To simulate errors, a random subset of 20% of the trials were followed by incorrect feedback. An average area-under-the-curve of 0.83 was reached for the detection of ErrPs which confirmed the presence of ErrPs in such BCIs. The second study explored the effect of ErrP-guided error correction in an online three-class active BCI (idle state and two personally selected cognitive tasks). ErrP-based error correction modestly but significantly improved the average online task classification accuracy (+7%) and the information transfer rate (+0.9 bits/min) of the BCI across participants. The third study was designed to answer the second research question. Using a self-paced EEG-BCI based on cognitive tasks, the possibility of improving the BCI performance using ErrPs was investigated. The BCI continuously analyzed the EEG data and displayed real-time feedback as soon as it detected a cognitive task. Then, the BCI analyzed the EEG data after the feedback onset to detect ErrPs. The average of post-error correction success rate across participants improved significantly compared to the pre-error correction value (+7%). The findings of these studies support the addition of ErrP-informed correction to maximize the accuracy of cue-based and self-paced BCIs based on non-MI cognitive tasks.Ph.D

Structurally random fourier domain compressive sampling and frequency domain beamforming for ultrasound imaging

Advances in ultrasound technology have fueled the emergence of Point-Of-Care Ultrasound (PoCU) imaging, including improved ease-of-use, superior image quality, and lower cost ultrasound. One of the approaches that can make the adoption of PoCU universal is to make the data acquisition module as simple as a "stethoscope" while further processing and image construction can be done using cloud-based processors. Toward this goal, we use Structurally Random Matrices (SRM) for compressive sensing of ultrasound data, Fourier sparsifying matrix for recovery in 1D, and frequency domain approach for 2D ultrasound image reconstruction. This approach is demonstrated through wire phantom and in vivo carotid arteries data from ultrasound system using 25%, 12.5%, and 6.25% of the full data rate and ultrasound images of similar perceived quality quantified by Structural Similarity Index Metric (SSIM).The authors would like to thank the Canadian funding agencies, OCE VIP I (22984) and NSERC Engage Grant (485353 2015)