Unique State Transitions Research Articles

Exploiting Unique State Transitions to Capture Micro-Doppler Signatures of Human Actions Using CW Radar

Exploiting Unique State Transitions to Capture Micro-Doppler Signatures of Human Actions Using CW Radar

S-MDP: Streaming With Markov Decision Processes

In recent years, finding adaptive players competing for network resources at a single bottleneck link has become common. This competition generally occurs at routers in household local area networks. Such competition severely reduces viewers’ quality of experience, such as fairness and stability. Researchers harness Markov decision process (MDP) models to optimize the adaptive video streaming process. Typically, players follow a policy based on numerous parameters, such as buffer occupancy or average bandwidth. In this study, we defied this traditional decentralized client-side MDP approach by allowing players to share a network state among themselves, which we called a streaming MDP (S-MDP). This state includes a discrete data rate measurement (DRM) value. The DRM value is a normalized value of a player's incoming bitrate and is an example of an interval measurement scale. Players use video bitrates to produce unique state transition matrices. The S-MDP reward matrix penalizes excessive switching along the DRM interval scale and thus encourages stability. At intervals during streaming, players create unique policies. The near-real-time update of policies enables players DRM values to converge. S-MDP shows relatively good performance in emulation experiments compared with four streaming methods, namely, k-chunk MDP, stochastic dynamic programming for adaptive streaming over hypertext transfer protocol (sdpDASH), MDP-based DASH, and RTRA_S. In Internet experiments, we compare the performance of streaming methods with a Roku, Amazon Fire TV, and Apple TV. We also compared it against users who play online games, download files, and/or chat online, and S-MDP outperforms the other methods in terms of both objective and subjective visual quality, except in the presence of transmission control protocol long-lived flows, such as Skype.

Cold-Adapted Protein Kinases and Thylakoid Remodeling Impact Energy Distribution in an Antarctic Psychrophile.

The Antarctic psychrophile Chlamydomonas sp. UWO241 evolved in a permanently ice-covered lake whose aquatic environment is characterized not only by constant low temperature and high salt but also by low light during the austral summer coupled with 6 months of complete darkness during the austral winter. Since the UWO241 genome indicated the presence of Stt7 and Stl1 protein kinases, we examined protein phosphorylation and the state transition phenomenon in this psychrophile. Light-dependent [γ-33P]ATP labeling of thylakoid membranes from Chlamydomonas sp. UWO241 exhibited a distinct low temperature-dependent phosphorylation pattern compared to Chlamydomonas reinhardtii despite comparable levels of the Stt7 protein kinase. The sequence and structure of the UWO241 Stt7 kinase domain exhibits substantial alterations, which we suggest predisposes it to be more active at low temperature. Comparative purification of PSII and PSI combined with digitonin fractionation of thylakoid membranes indicated that UWO241 altered its thylakoid membrane architecture and reorganized the distribution of PSI and PSII units between granal and stromal lamellae. Although UWO241 grown at low salt and low temperature exhibited comparable thylakoid membrane appression to that of C. reinhardtii at its optimal growth condition, UWO241 grown under its natural condition of high salt resulted in swelling of the thylakoid lumen. This was associated with an upregulation of PSI cyclic electron flow by 50% compared to growth at low salt. Due to the unique 77K fluorescence emission spectra of intact UWO241 cells, deconvolution was necessary to detect enhancement in energy distribution between PSII and PSI, which was sensitive to the redox state of the plastoquinone pool and to the NaCl concentrations of the growth medium. We conclude that a reorganization of PSII and PSI in UWO241 results in a unique state transition phenomenon that is associated with altered protein phosphorylation and enhanced PSI cyclic electron flow. These data are discussed with respect to a possible PSII-PSI energy spillover mechanism that regulates photosystem energy partitioning and quenching.

Automatic Truck Processing Time Extraction at Marine Container Terminal Gates Using Low-Frame-Rate Images

Truck processing time, including the processing time per lane and per truck at a specific timestamp, at marine container terminal gates is crucial for measuring the performance of terminals. Truck processing time has traditionally been collected for short periods of time (e.g., a few hours) through field observation. From the surveillance cameras widely available at terminal gates, researchers have attempted to collect truck processing time data by manually reviewing camera images frame by frame. However, this manual review process is labor-intensive and time-consuming. This study is motivated by the need for effectively collecting truck processing times over a long period of time. An image-processing algorithm to automatically extract truck processing time data using the low-frame-rate images (less than 1 frame per second, fps) is first proposed. The proposed algorithm includes three steps: (1) design of two region of interest (ROIs) per lane to capture truck trajectories, (2) a frame-differencing change-detection algorithm addressing the low frame rate and cast shadow issue, and (3) a unique state transition model with a set of decision rules, considering perspective occlusion and other potential sources of false positive detections, to reliably detect truck departures. An experimental test using one day's actual images in varying conditions was conducted to evaluate the performance of the proposed algorithm. Experimental tests have demonstrated the robustness of the proposed algorithm's ability to meet the unique technical challenges at a terminal gate, including the following: day and night conditions, cast shadows, occlusion by work vehicles, people, and nearby trucks. An experimental test using 7,225 images (6,567 day and 658 night operation images) was conducted to evaluate the performance of the proposed algorithm. Experimental tests have also demonstrated the robustness of the proposed algorithm for successfully detecting truck departures under several challenging situations, including perspective occlusion, cast shadows, nighttime and various lighting conditions, and multiple-lane departures. The correct detection rate is 98.1% for daytime images and 90.8% for nighttime images, giving our data a correct detection rate of 97.6%.

Acoustic Chord Transcription and Key Extraction From Audio Using Key-Dependent HMMs Trained on Synthesized Audio

We describe an acoustic chord transcription system that uses symbolic data to train hidden Markov models and gives best-of-class frame-level recognition results. We avoid the extremely laborious task of human annotation of chord names and boundaries-which must be done to provide machine learning models with ground truth-by performing automatic harmony analysis on symbolic music files. In parallel, we synthesize audio from the same symbolic files and extract acoustic feature vectors which are in perfect alignment with the labels. We, therefore, generate a large set of labeled training data with a minimal amount of human labor. This allows for richer models. Thus, we build 24 key-dependent HMMs, one for each key, using the key information derived from symbolic data. Each key model defines a unique state-transition characteristic and helps avoid confusions seen in the observation vector. Given acoustic input, we identify a musical key by choosing a key model with the maximum likelihood, and we obtain the chord sequence from the optimal state path of the corresponding key model, both of which are returned by a Viterbi decoder. This not only increases the chord recognition accuracy, but also gives key information. Experimental results show the models trained on synthesized data perform very well on real recordings, even though the labels automatically generated from symbolic data are not 100% accurate. We also demonstrate the robustness of the tonal centroid feature, which outperforms the conventional chroma feature.

Markov Model Assessment of Subjects' Clinical Skill Using the E-Pelvis Physical Simulator

Inherent difficulties evaluating clinical competence of physicians has led to the widespread use of subjective skill assessment techniques. Inspired by an analogy between spoken language and surgical procedure, a generalized methodology using Markov models (MMs), independent of the modality under study, was developed. The methodology applied to an endoscopic experiment in "Generalized approach for modeling minimally invasive surgery as a stochastic process using a discrete Markov model" by J. Rosen et al. (IEEE Trans. Biomed. Eng., Vol. 53, No. 3, pp. 399-413, Mar. 2006) is modified and applied to data collected with the E-Pelvis physical simulator. The simulator incorporates five contact pressure sensors located in key anatomical landmarks. Two 32-state fully connected MMs are used, one for each skill level. Each state corresponds to a unique five-dimensional signature of contact pressures. Statistical distances measured between models representing subjects with different skill levels are sensitive enough to provide an objective measure of medical skill level. The method was tested with 41 expert subjects and 41 novice subjects in addition to the 30 subjects used for training the MM. Of the 82 subjects, 76 (92%) were classified correctly. Unique state transitions as well as pressure magnitudes for corresponding states were found to be skill dependent. The "white box" nature of the model provides insight into the examination process performed.

A hidden Markov model-based approach for identifying timing differences in gene expression under different experimental factors

Time series experiments of cDNA microarrays have been commonly used in various biological studies and conducted under a lot of experimental factors. A popular approach of time series microarray analysis is to compare one gene with another in their expression profiles, and clustering expression sequences is a typical example. On the other hand, a practically important issue in gene expression is to identify the general timing difference that is caused by experimental factors. This type of difference can be extracted by comparing a set of time series expression profiles under a factor with those under another factor, and so it would be difficult to tackle this issue by using only a current approach for time series microarray analysis. We have developed a systematic method to capture the timing difference in gene expression under different experimental factors, based on hidden Markov models. Our model outputs a real-valued vector at each state and has a unique state transition diagram. The parameters of our model are trained from a given set of pairwise (generally multiplewise) expression sequences. We evaluated our model using synthetic as well as real microarray datasets. The results of our experiment indicate that our method worked favourably to identify the timing ordering under different experimental factors, such as that gene expression under heat shock tended to start earlier than that under oxidative stress. Supplementary data are available at Bioinformatics online.

Robust decoding of variable-length encoded Markov sources using a three-dimensional trellis

In this letter, we present an improved index-based a-posteriori probability (APP) decoding approach for the error-resilient transmission of packetized variable-length encoded Markov sources. The proposed algorithm is based on a novel two-dimensional (2D) state representation which leads to a three-dimensional trellis with unique state transitions. APP decoding on this trellis is realized by employing a 2D version of the BCJR algorithm where all available source statistics can be fully exploited in the source decoder. For an additional use of channel codes the proposed approach leads to an increased error-correction performance compared to a one-dimensional state representation.

Theory and application of nongroup cellular automata for synthesis of easily testable finite state machines

The paper reports some of the interesting properties and relationships of a nongroup cellular automata (CA) and its dual. A special class of nongroup cellular automata denoted as D1*CA is analytically investigated. Based on such analysis, D1*CA has been proposed as an ideal test machine which can be efficiently embedded in a finite state machine to enhance the testability of the synthesized design. A state encoding algorithm has been formulated to embed the D1*CA based test machine in the synthesized FSM while minimizing the hardware overhead. The unique state transition properties of D1*CA are then used in designing an easy testing scheme for the FSM. Experiments on FSM benchmarks have shown that the scheme achieves 100% coverage of all single stuck at faults at the cost of hardware overhead and circuit delay that are comparable, if not better, to that incurred for scan path based designs. However, the major advantage of the scheme is the significant reduction of test time overhead due to integration of an embedded test machine in the design at the synthesis phase.