26 research outputs found
On the Performance of Short Block Codes over Finite-State Channels in the Rare-Transition Regime
As the mobile application landscape expands, wireless networks are tasked
with supporting different connection profiles, including real-time traffic and
delay-sensitive communications. Among many ensuing engineering challenges is
the need to better understand the fundamental limits of forward error
correction in non-asymptotic regimes. This article characterizes the
performance of random block codes over finite-state channels and evaluates
their queueing performance under maximum-likelihood decoding. In particular,
classical results from information theory are revisited in the context of
channels with rare transitions, and bounds on the probabilities of decoding
failure are derived for random codes. This creates an analysis framework where
channel dependencies within and across codewords are preserved. Such results
are subsequently integrated into a queueing problem formulation. For instance,
it is shown that, for random coding on the Gilbert-Elliott channel, the
performance analysis based on upper bounds on error probability provides very
good estimates of system performance and optimum code parameters. Overall, this
study offers new insights about the impact of channel correlation on the
performance of delay-aware, point-to-point communication links. It also
provides novel guidelines on how to select code rates and block lengths for
real-time traffic over wireless communication infrastructures
Performance Analysis of Block Codes over Finite-state Channels in Delay-sensitive Communications
As the mobile application landscape expands, wireless networks are tasked with supporting different connection profiles, including real-time traffic and delay-sensitive communications. Among many ensuing engineering challenges is the need to better understand the fundamental limits of forward error correction in non-asymptotic regimes. This dissertation seeks to characterize the performance of block codes over finite-state channels with memory and also evaluate their queueing performance under different encoding/decoding schemes.
In particular, a fading formulation is considered where a discrete channel with correlation over time introduces errors. For carefully selected channel models and arrival processes, a tractable Markov structure composed of queue length and channel state is identified. This facilitates the analysis of the stationary behavior of the system, leading to evaluation criteria such as bounds on the probability of the queue exceeding a threshold. Specifically, this dissertation focuses on system models with scalable arrival profiles based on Poisson processes, and finite-state memory channels. These assumptions permit the rigorous comparison of system performance for codes with arbitrary block lengths and code rates. Based on this characterization, it is possible to optimize code parameters for delay-sensitive applications over various channels. Random codes and BCH codes are then employed as means to study the relationship between code-rate selection and the queueing performance of point-to-point data links. The introduced methodology offers a new perspective on the joint queueing-coding analysis for finite-state channels, and is supported by numerical simulations.
Furthermore, classical results from information theory are revisited in the context of channels with rare transitions, and bounds on the probabilities of decoding failure are derived for random codes. An analysis framework is presented where channel dependencies within and across code words are preserved. The results are subsequently integrated into a queueing formulation. It is shown that for current formulation, the performance analysis based on upper bounds provides a good estimate of both the system performance and the optimum code parameters. Overall, this study offers new insights about the impact of channel correlation on the performance of delay-aware communications and provides novel guidelines to select optimum code rates and block lengths
Virtual histological staining of unlabeled autopsy tissue
Histological examination is a crucial step in an autopsy; however, the
traditional histochemical staining of post-mortem samples faces multiple
challenges, including the inferior staining quality due to autolysis caused by
delayed fixation of cadaver tissue, as well as the resource-intensive nature of
chemical staining procedures covering large tissue areas, which demand
substantial labor, cost, and time. These challenges can become more pronounced
during global health crises when the availability of histopathology services is
limited, resulting in further delays in tissue fixation and more severe
staining artifacts. Here, we report the first demonstration of virtual staining
of autopsy tissue and show that a trained neural network can rapidly transform
autofluorescence images of label-free autopsy tissue sections into brightfield
equivalent images that match hematoxylin and eosin (H&E) stained versions of
the same samples, eliminating autolysis-induced severe staining artifacts
inherent in traditional histochemical staining of autopsied tissue. Our virtual
H&E model was trained using >0.7 TB of image data and a data-efficient
collaboration scheme that integrates the virtual staining network with an image
registration network. The trained model effectively accentuated nuclear,
cytoplasmic and extracellular features in new autopsy tissue samples that
experienced severe autolysis, such as COVID-19 samples never seen before, where
the traditional histochemical staining failed to provide consistent staining
quality. This virtual autopsy staining technique can also be extended to
necrotic tissue, and can rapidly and cost-effectively generate artifact-free
H&E stains despite severe autolysis and cell death, also reducing labor, cost
and infrastructure requirements associated with the standard histochemical
staining.Comment: 24 Pages, 7 Figure
The unique features of hyperparathyroidism in developing countries and the problem of vitamin D deficiency
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Anetoderma in a patient with a history of primary syphilis.
Anetoderma is a rare cutaneous disorder presenting with atrophic skin lesions. It can be associated with several autoimmune and infectious diseases. With the current resurgence of syphilis, clinicians must be aware of its association with anetoderma
On the Performance of Block Codes Over Finite-State Channels in the Rare-Transition Regime
Genetic Linkages for Thyroxine Released in Response to Thyrotropin Stimulation in Three Sets of Recombinant Inbred Mice Provide Evidence for Shared and Novel Genes Controlling Thyroid Function
Albuminuria and its correlates in an Iranian type 2 diabetic population
Abstract Objective To study the prevalence and correlates of increased urinary albumin excretion (UAE) in an Iranian type 2 diabetic population. Methods Over a one year period since October 2002, 400 consecutive type 2 diabetic patients referred to an outpatient diabetes clinic, were enrolled in a cross sectional study. Subjects had no history of renal impairment or overt proteinuria. Data concerning demographic characteristics and cardiovascular risk factors were recorded and height, weight and blood pressure were measured. Glucose, cholesterol, HDL-C, LDL-C, triglyceride, apoprotein B, lipoprotein a, creatinine, and HbA1c were measured in fasting blood samples. Overnight twelve-hour UAE were assessed by immunoturbidometry method. Regression analyses were employed to determine the correlates of UAE. Results Out of 400 patients, 156 (40%) subjects had increased UAE (UAE ≥ 30 mg/24 hour). The UAE was higher in males compared to females (145.5 vs. 72.1 mg/day; p Conclusion In this study, increased UAE was considerably frequent among type 2 diabetic patients without any significant history of renal dysfunction. Albuminuria was found to be associated with dyslipidemia (low HDL-C), long duration of diabetes, and uncontrolled glycemia revealed by higher HbA1c.</p