In computed tomography (CT) imaging, optimizing the balance between radiation dose and image quality is crucial due to the potentially harmful effects of radiation on patients. Although subjective assessments by radiologists are considered the gold standard in medical imaging, these evaluations can be time-consuming and costly. Thus, objective methods, such as the peak signal-to-noise ratio and structural similarity index measure, are often employed as alternatives. However, these metrics, initially developed for natural images, may not fully encapsulate the radiologists' assessment process. Consequently, interest in developing deep learning-based image quality assessment (IQA) methods that more closely align with radiologists' perceptions is growing. A significant barrier to this development has been the absence of open-source datasets and benchmark models specific to CT IQA. Addressing these challenges, we organized the Low-dose Computed Tomography Perceptual Image Quality Assessment Challenge in conjunction with the Medical Image Computing and Computer Assisted Intervention 2023. This event introduced the first open-source CT IQA dataset, consisting of 1,000 CT images of various quality, annotated with radiologists' assessment scores. As a benchmark, this challenge offers a comprehensive analysis of six submitted methods, providing valuable insight into their performance. This paper presents a summary of these methods and insights. This challenge underscores the potential for developing no-reference IQA methods that could exceed the capabilities of full-reference IQA methods, making a significant contribution to the research community with this novel dataset. The dataset is accessible at https://zenodo.org/records/7833096.

Ground-level ozone is formed by chemical reactions between nitrogen oxides (NOx) and volatile organic compounds (VOC) under sunlight, primarily from industrial and vehicular emissions.

In the low-error rate regime, message-passing (MP) decoding for low-density parity-check (LDPC) codes is known to have performance degradation due to trapping sets (TSs), which often limits the use of LDPC codes for applications with low target error rates like storage devices. This work proposes a novel deep-learning based decoding algorithm which is tailored for breaking TSs. In particular, when MP decoding fails due to TSs, there exist pairs of unsatisfied check nodes (CNs) which are connected through paths only with error variable nodes (VNs), i.e., VNs with erroneous hard-decision results. The proposed algorithm efficiently identifies the paths with error VNs between unsatisfied CNs with the aid of deep-learning techniques. Then, the decoding failures are resolved by repeating the MP decoding after re-initializing the channel outputs for the error VNs in the identified paths. In addition, by analyzing the behaviors of the deep-learning based algorithm, we propose a low-complexity algorithm, called adaptive-error-path (AEP) detector. Simulation results show that the proposed algorithms efficiently break the TSs and significantly improve the error-floor performance in the low error-rate regime.

In modern tape storage, user data are recorded and retrieved along multiple tracks of rapidly moving, flexible magnetic medium that give rise to a variety of channel impediments including occasional long erasures, more frequent amplitude fades as well as a large amount of random errors. This work considers reliable recovery of data from such tape channels using a novel concatenation of an inner Reed-Solomon (RS) code and an outer nonbinary low-density parity-check (LDPC) code. This particular concatenation scheme and a highly tailored iterative decoding algorithm are chosen to efficiently handle the assortment of the tape channel impediments while meeting the stringent target error rate constraint as well as key practical requirements of the mass tape storage system. Despite the use of a nonbinary LDPC code, the proposed scheme allows excellent performance-complexity tradeoffs. In stark contrast to any existing coding schemes that involve LDPC codes, the proposed concatenation strategy allows semianalytic error rate performance evaluation at rates below what is possible using modern computers, thus providing an ability to ensure satisfactory low-error-rate performance.

Trying to automatically detect laughter and other nonlin-guistic events in speech raises a fundamental question: Is it appropriate to simply adopt acoustic features that have tradi-tionally been used for analyzing linguistic events? Thus we take a step back and propose syllabic-level features that may show a contrast between laughter and speech in their intensity-, pitch-, and timbral-contours and rhythmic patterns. We mo-tivate and define our features and evaluate their effectiveness in correctly classifying laughter from speech. Inclusion of our features in the baseline feature set for the Social Signals Sub-Challenge of the Computational Paralinguistics Challenge yielded an improvement of 2.4 % in Unweighted Average Area Under the Curve (UAAUC). But beyond objective metrics, an-alyzing laughter at a phonetically meaningful level has allowed us to examine the characteristic contours of laughter and to rec-ognize the importance of the shape of its intensity envelope.

Multilevel flash memories store more than one bit per storage cell and are further characterized by large word (page) sizes and very low target error rates. In this paper, a high-rate error control scheme is presented that uses inner trellis-coded modulation (TCM) for storing two bits per cell with five possible charge levels. The coded subset-label bits and the uncoded signal-label bits of TCM are independently protected by separate outer Reed-Solomon (RS) codes. The resulting scheme permits multistage decoding. Errors made by the TCM decoder in the subset-label bits occur in bursts and are corrected by the associated first RS decoder prior to determining signal-label bits and correcting errors in those bits by the associated second RS decoder. The multi-stage decoding avoids the significant spread of errors from subset-label bits into the generally larger number of signal-label bits which is typical for conventional serial RS-TCM concatenation when the inner TCM system operates at relatively low SNR. The error performance of the proposed scheme is evaluated at low error rates by a mixed simulation-analytic method. It is shown that the proposed scheme exhibits highly favorable performance vs. complexity tradeoffs compared to the other schemes.