Fishery stock assessment based on asymmetric logistic model

Open House, ISM in Tachikawa, 2014.6.13統計数理研究所オープンハウス（立川）、H26.6.13ポスター発

Machine Learning for Microcontroller-Class Hardware -- A Review

The advancements in machine learning opened a new opportunity to bring intelligence to the low-end Internet-of-Things nodes such as microcontrollers. Conventional machine learning deployment has high memory and compute footprint hindering their direct deployment on ultra resource-constrained microcontrollers. This paper highlights the unique requirements of enabling onboard machine learning for microcontroller class devices. Researchers use a specialized model development workflow for resource-limited applications to ensure the compute and latency budget is within the device limits while still maintaining the desired performance. We characterize a closed-loop widely applicable workflow of machine learning model development for microcontroller class devices and show that several classes of applications adopt a specific instance of it. We present both qualitative and numerical insights into different stages of model development by showcasing several use cases. Finally, we identify the open research challenges and unsolved questions demanding careful considerations moving forward.Comment: Accepted for publication at IEEE Sensors Journa

Detection of microsleeps from the eeg via optimized classification techniques.

Microsleeps are complete breaks in responsiveness for 0.5–15 s. They can lead to multiple fatalities in certain occupational fields (e.g., transportation and military) due to the need in such occupations for extended and continuous vigilance. Therefore, an automated microsleep detection system may assist in the reduction of poor job performance and occupational fatalities. An EEG-based microsleep detector offers advantages over a videobased microsleep detector, including speed and temporal resolution. A series of software modules were implemented to examine different feature sets to determine the optimal circumstances for automated EEG-based microsleep detection. The microsleep detection system was organized in a similar manner to an EEG-based brain-computer interface (BCI). EEG data underwent baseline removal and filtering to remove overhead noise. Following this, feature extraction generated spectral features based upon an estimate of the power spectrum or its logarithmic transform. Following this, feature selection/reduction (FS/R) was used to select the most relevant information across all the spectral features. A trained classifier was then tested on data from a subject it had not seen before. In certain cases, an ensemble of classifiers was used instead of a single classifier. The performance measures from all cases were then averaged together in leave-one-out crossvalidation (LOOCV). Sets of artificial data were generated to test a prototype EEG-based microsleep detection system, consisting of a combination of EEG and 2-s bursts of 15 Hz sinusoids of varied signal-to-noise ratios (SNRs) ranging from 16 down to 0.03. The balance between events and non-events was varied between evenly balanced and highly imbalanced (e.g., events occurring only 2% of the time). Features were spectral estimates of various EEG bands (e.g., alpha band power) or ratios between them. A total of 34 features for each of the 16 channels yielded a total of 544 features. Five minutes of EEG from eight subjects were used in the generation of the dummy data, and each subject yielded a matrix of 300 observations of 544 features. Datasets from two prior microsleep studies were employed after validating the system on the artificial data. The first, Study A (N = 8), had 16 channels sampled at 256 Hz from two 1-hour sessions per subject and the second, Study C (N = 10), had one 50-min session with 30-62 channels per subject sampled at 250 Hz. A vector of 34 spectral features from each channel was concatenated into a feature vector for each 2-s interval, with each interval having a 1-s overlap with the prior one. In both cases, microsleeps had been identified via a combination of video recording and performance on a continuous tracking task. Study A provided four datasets to compare effects of various preprocessing techniques on performance: (1) Study A bipolar EEG with Independent Component Analysis (ICA) preprocessing and artefact pruning (total automated rejection of artefact-containing epochs) and logarithmic transforms of the spectral features (SABIL); (2) Study A bipolar EEG with ICA-based eye blink removal and artefact removal with pruning of epochs with major artefacts, and linear spectral features (SABIS); (3) Study A referential EEG unprocessed by ICA with spectral features (SARUS); and (4) Study A bipolar EEG unprocessed by ICA with spectral features (SABUS). The second study had one primary feature set, the Study C referential EEG ICA preprocessed spectral feature (SCRIS) variant. LOOCV was evaluated based on the phi correlation coefficient. After replicating prior work, several FS/R and classifier structures were investigated with both the artificially balanced and unbalanced data. Feature selection/reduction methods included principal component analysis (PCA), common spatial patterns (CSP), projection to latent structures (PLS), a new method based on average distance between events and nonevents (ADEN), ADEN normalized with a z-score transform (ADENZ), genetic algorithms in concert with ADEN (GADEN), and genetic algorithms in concert with ADENZ (GADENZ). Several pattern recognition algorithms were investigated: linear discriminant analysis (LDA), radial basis functions (RBFs), and Support Vector Machines with Gaussian (SVMG) and polynomial (SVMP) kernels. Classifier structures examined included single classifiers, bagging, boosting, stacking, and adaptive boosting (AdaBoost). The highest LOOCV results on artificial data (SNR = 0.3) corresponded to GADEN with 10 features and a single LDA classifier with a mean phi value of 0.96. Of the four Study A datasets, PCA with 150 features and a stacking ensemble achieved the highest mean phi of 0.40 with the SABIL feature set, and ADEN with 20 features with a single LDA classifier achieved the highest mean phi of 0.10 with Study C. Other machine-learning methodologies, such as training on artificially balanced data, decreasing the training size, within-subject training and testing, and randomly mixed data from across subjects, were also examined. Training on artificially balanced data did not improve performance. An issue found by performing within-subject training and testing was that, for certain subjects, a classifier trained on one-half of the subject’s data and then tested on the other half was that classifier performance dropped to random guessing. The low phi values on within-subject tests occurred independently of the feature selection/reduction method explored. As such, performance of a standard LOOCV was often dependent on whether a particular testing subject had a low (< 0.15) within-subjects mean phi correlation coefficient. Training on only the higher mean phi values did not boost performance. Additional tests found correlations (r = 0.57, p = 0.003 for Study A and r = 0.67, p 0.15) and longer mean microsleep durations. Other individual subject characteristics, such as number of microsleeps and subject age, did not have significant differences. The primary findings highlighted the strengths and limitations of supervised feature selection and linear classifiers trained upon highly variable between-subject features across two studies. Findings suggested that a classifier performs best when individuals have high mean microsleep durations. On the configurations investigated, preprocessing factors, such as ICA preprocessing, feature extraction method, and artefact pruning, affected the performance more than changing specific module configurations. No significant differences between the SABIL features and the lower performing Study A feature sets were found due to overlapping ranges of performance (p = 0.15). The findings suggest that the investigated techniques plateaued in performance on the Study A data, reaching a point of diminishing returns without fundamentally changing the nature of the classification problem. The different number of channels of varying quality across all subjects in Study C rendered microsleep classification extremely difficult, but even a linear classifier can properly generalize if exposed to a large enough variety of data from across the entire set. Many of the techniques explored are also relevant to other fields, such as braincomputer interface (BCI) and machine learning

Pattern Recognition

Pattern recognition is a very wide research field. It involves factors as diverse as sensors, feature extraction, pattern classification, decision fusion, applications and others. The signals processed are commonly one, two or three dimensional, the processing is done in real- time or takes hours and days, some systems look for one narrow object class, others search huge databases for entries with at least a small amount of similarity. No single person can claim expertise across the whole field, which develops rapidly, updates its paradigms and comprehends several philosophical approaches. This book reflects this diversity by presenting a selection of recent developments within the area of pattern recognition and related fields. It covers theoretical advances in classification and feature extraction as well as application-oriented works. Authors of these 25 works present and advocate recent achievements of their research related to the field of pattern recognition

Advances in Computer Recognition, Image Processing and Communications, Selected Papers from CORES 2021 and IP&C 2021

As almost all human activities have been moved online due to the pandemic, novel robust and efficient approaches and further research have been in higher demand in the field of computer science and telecommunication. Therefore, this (reprint) book contains 13 high-quality papers presenting advancements in theoretical and practical aspects of computer recognition, pattern recognition, image processing and machine learning (shallow and deep), including, in particular, novel implementations of these techniques in the areas of modern telecommunications and cybersecurity

Forecasting: theory and practice

Forecasting has always been in the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The lack of a free-lunch theorem implies the need for a diverse set of forecasting methods to tackle an array of applications. This unique article provides a non-systematic review of the theory and the practice of forecasting. We offer a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts, including operations, economics, finance, energy, environment, and social good. We do not claim that this review is an exhaustive list of methods and applications. The list was compiled based on the expertise and interests of the authors. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of the forecasting theory and practice

Forecasting: theory and practice

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases