Generalized Perceptual Linear Prediction (gPLP) Features for Animal Vocalization Analysis

A new feature extraction model, generalized perceptual linear prediction (gPLP), is developed to calculate a set of perceptually relevant features for digital signal analysis of animalvocalizations. The gPLP model is a generalized adaptation of the perceptual linear prediction model, popular in human speech processing, which incorporates perceptual information such as frequency warping and equal loudness normalization into the feature extraction process. Since such perceptual information is available for a number of animal species, this new approach integrates that information into a generalized model to extract perceptually relevant features for a particular species. To illustrate, qualitative and quantitative comparisons are made between the species-specific model, generalized perceptual linear prediction (gPLP), and the original PLP model using a set of vocalizations collected from captive African elephants (Loxodonta africana) and wild beluga whales (Delphinapterus leucas). The models that incorporate perceptional information outperform the original human-based models in both visualization and classification tasks

Fog Computing in Medical Internet-of-Things: Architecture, Implementation, and Applications

In the era when the market segment of Internet of Things (IoT) tops the chart in various business reports, it is apparently envisioned that the field of medicine expects to gain a large benefit from the explosion of wearables and internet-connected sensors that surround us to acquire and communicate unprecedented data on symptoms, medication, food intake, and daily-life activities impacting one's health and wellness. However, IoT-driven healthcare would have to overcome many barriers, such as: 1) There is an increasing demand for data storage on cloud servers where the analysis of the medical big data becomes increasingly complex, 2) The data, when communicated, are vulnerable to security and privacy issues, 3) The communication of the continuously collected data is not only costly but also energy hungry, 4) Operating and maintaining the sensors directly from the cloud servers are non-trial tasks. This book chapter defined Fog Computing in the context of medical IoT. Conceptually, Fog Computing is a service-oriented intermediate layer in IoT, providing the interfaces between the sensors and cloud servers for facilitating connectivity, data transfer, and queryable local database. The centerpiece of Fog computing is a low-power, intelligent, wireless, embedded computing node that carries out signal conditioning and data analytics on raw data collected from wearables or other medical sensors and offers efficient means to serve telehealth interventions. We implemented and tested an fog computing system using the Intel Edison and Raspberry Pi that allows acquisition, computing, storage and communication of the various medical data such as pathological speech data of individuals with speech disorders, Phonocardiogram (PCG) signal for heart rate estimation, and Electrocardiogram (ECG)-based Q, R, S detection.Comment: 29 pages, 30 figures, 5 tables. Keywords: Big Data, Body Area Network, Body Sensor Network, Edge Computing, Fog Computing, Medical Cyberphysical Systems, Medical Internet-of-Things, Telecare, Tele-treatment, Wearable Devices, Chapter in Handbook of Large-Scale Distributed Computing in Smart Healthcare (2017), Springe

The effects of sensory deprivation on sensory, perceptual, motor, cognitive, and physiological functions

Sensory deprivation effects on human sensory, perceptual, and physiological mechanism

The influence of auditory feedback on speed choice, violations and comfort in a driving simulation game

Two experiments are reported which explore the relationships between auditory feedback (engine noise), speed choice, driving violations and driver comfort. Participants played a driving simulation game with different levels of auditory feedback in the form of engine noise. In Experiment 1, a between-subjects design revealed that no noise and low levels of engine noise (65 dB(A)) resulted in participants driving at faster speeds than in the medium (75 dB(A)) and high (85 dB(A)) levels of engine noise conditions. The low noise feedback conditions were also associated with decreases in driver comfort. Experiment 2 also demonstrated that low levels of engine noise feedback (no feedback and 70 dB(A)) were associated with increases in driving speed, and driving violations relative to higher levels of feedback (75 dB(A) and 80 dB(A)). Implications exist for current car manufacturing trends which emphasise a growing increase in noise insulation for the driver. © 2011 Elsevier Ltd. All rights reserved

Absolute identification by relative judgment

In unidimensional absolute identification tasks, participants identify stimuli that vary along a single dimension. Performance is surprisingly poor compared with discrimination of the same stimuli. Existing models assume that identification is achieved using long-term representations of absolute magnitudes. The authors propose an alternative relative judgment model (RJM) in which the elemental perceptual units are representations of the differences between current and previous stimuli. These differences are used, together with the previous feedback, to respond. Without using long-term representations of absolute magnitudes, the RJM accounts for (a) information transmission limits, (b) bowed serial position effects, and (c) sequential effects, where responses are biased toward immediately preceding stimuli but away from more distant stimuli (assimilation and contrast)

Psychophysics and the judgment of price: judging complex objects on a non-physical dimension elicits sequential effects like those in perceptual tasks

When participants in psychophysical experiments are asked to estimate or identify stimuli which differ on a single physical dimension, their judgments are influenced by the local experimental context — the item presented and judgment made on the previous trial. It has been suggested that similar sequential effects occur in more naturalistic, real-world judgments. In three experiments we asked participants to judge the prices of a sequence of items. In Experiment 1, judgments were biased towards the previous response (assimilation) but away from the true value of the previous item (contrast), a pattern which matches that found in psychophysical research. In Experiments 2A and 2B, we manipulated the provision of feedback and the expertise of the participants, and found that feedback reduced the effect of the previous judgment and shifted the effect of the previous item’s true price from contrast to assimilation. Finally, in all three experiments we found that judgments were biased towards the centre of the range, a phenomenon known as the “regression effect” in psychophysics. These results suggest that the most recently-presented item is a point of reference for the current judgment. The findings inform our understanding of the judgment process, constrain the explanations for local context effects put forward by psychophysicists, and carry practical importance for real-world situations in which contextual bias may degrade the accuracy of judgments

Interpretation of absolute judgments using information theory: channel capacity or memory capacity?

Shannon’s information theory has been a popular component of first-order cybernetics. It quantifies information transmitted in terms of the number of times a sent symbol is received as itself, or as another possible symbol. Sent symbols were events and received symbols were outcomes. Garner and Hake reinterpreted Shannon, describing events and outcomes as categories of a stimulus attribute, so as to quantify the information transmitted in the psychologist’s category (or absolute judgment) experiment. There, categories are represented by specific stimuli, and the human subject must assign those stimuli, singly and in random order, to the categories that they represent. Hundreds of computations ensued of information transmitted and its alleged asymptote, the sensory channel capacity. The present paper critically re-examines those estimates. It also reviews estimates of memory capacity from memory experiments. It concludes that absolute judgment is memory-limited and that channel capacities are actually memory capacities. In particular, there are factors that affect absolute judgment that are not explainable within Shannon’s theory, factors such as feedback, practice, motivation, and stimulus range, as well as the anchor effect, sequential dependences, the rise in information transmitted with the increase in number of stimulus dimensions, and the phenomena of masking and stimulus duration dependence. It is recommended that absolute judgments be abandoned, because there are already many direct estimates of memory capacity

Prospect relativity: how choice options influence decision under risk.

In many theories of decision under risk (e.g., expected utility theory, rank-dependent utility theory, and prospect theory), the utility of a prospect is independent of other options in the choice set. The experiments presented here show a large effect of the available options, suggesting instead that prospects are valued relative to one another. The judged certainty equivalent for a prospect is strongly influenced by the options available. Similarly, the selection of a preferred prospect is strongly influenced by the prospects available. Alternative theories of decision under risk (e.g., the stochastic difference model, multialternative decision field theory, and range frequency theory), where prospects are valued relative to one another, can provide an account of these context effects