D. Mitchell Wilkes

Acoustical properties of speech as indicators of depression and suicidal risk

Acoustic properties of speech have previously been identified as possible cues to depression, and there is evidence that certain vocal parameters may be used further to objectively discriminate between depressed and suicidal speech. Studies were performed to analyze and compare the speech acoustics of separate male and female samples comprised of normal individuals and individuals carrying diagnoses of depression and high-risk, near-term suicidality. The female sample consisted of ten control subjects, 17 dysthymic patients, and 21 major depressed patients. The male sample contained 24 control subjects, 21 major depressed patients, and 22 high-risk suicidal patients. Acoustic analyses of voice fundamental frequency (F/sub 0/), amplitude modulation (AM), formants, and power distribution were performed on speech samples extracted from audio recordings collected from the sample members. Multivariate feature and discriminant analyses were performed on feature vectors representing the members of the control and disordered classes. Features derived from the formant and power spectral density measurements were found to be the best discriminators of class membership in both the male and female studies. AM features emerged as strong class discriminators of the male classes. Features describing F/sub 0/ were generally ineffective discriminators in both studies. The results support theories that identify psychomotor disturbances as central elements in depression and suicidality.

Development of reusable, configurable, extensible holonic manufacturing system

Modern manufacturing system have to deal with dynamic situations such as customer specification changes, machine break-down, emergency orders and other kinds of disturbances. This requires the manufacturing system to be adaptive to handle such situations. The holonic concept has been proposed as an efficient paradigm for developing such an adaptive manufacturing system. There are still some critical issues to be investigated such as how to define the holons for a given application context and make them reusable, what should be an appropriate system architecture and how to design the system cooperation mechanisms for better system performance. The Intelligent Machine Architecture (IMA) is a software architecture developed at Vanderbilt University. It provides a framework for developing reusable components and constructing reconfigurable, extensible systems. Although it is mainly used for robots, it is also a suitable development tool for designing adaptive manufacturing systems. The authors explain how to build reusable, reconfigurable and extensible holonic manufacturing systems based on the IMA framework.

Experimental Characterization of Radiation-Induced Charge Sharing

Charge collection by multiple junctions is investigated using broadbeam heavy-ion and backside laser current transient measurements. The probability that significant charge is collected by more than one junction is greater for laser-generated events compared to heavy-ion measurements, which is attributed to the larger carrier generation track radius for the laser. With both sources, the probability of collecting charge on multiple junctions saturates at high charge generation levels. Effects caused by the interaction between junctions on the transient current waveforms is determined using position-correlated two-photon absorption laser analysis. The total charge collected for ion strikes between four adjacent junctions is shown to be approximately the same as for a direct strikes on a single junction, even though the incident ion does not pass through the depletion region of a biased junction.

The human agent: a work in progress toward human-humanoid interaction

At the Intelligent Robotics Laboratory of the Center for Intelligent Systems at Vanderbilt University, we have been developing a humanoid system called ISAC, (Intelligent Soft-Arm Control), over the past several years. As people work with a humanoid system, the interaction should be natural and robust. Our framework for human-humanoid interaction (HHI) considers various aspects of HHI and the paper presents information about the development of one key element of this system: the human agent.

Enhanced rational signal modeling

Abstract In various signal processing applications involving theoretical or empirical considerations, it is desired to appropriately modify a given data set so that the modified data set possesses prescribed properties. These properties are usually chosen so as to identify information signals believed to be contained within the data. The modification of the data then serves as a cleansing process whereby corrupting noise, measurement distortion or theoretical mismatch is removed. In this paper, a recently developed signal enhancement algorithm is described which achieves this objective. Particular attention is directed towards properties that are describable using a singular value decomposition (SVD) of a data generated matrix. Examples are given demonstrating a significant improvement in the performance of subspace-based frequency estimation techniques.

Enhancing minimum spanning tree-based clustering by removing density-based outliers

Traditional minimum spanning tree-based clustering algorithms only make use of information about edges contained in the tree to partition a data set. As a result, with limited information about the structure underlying a data set, these algorithms are vulnerable to outliers. To address this issue, this paper presents a simple while efficient MST-inspired clustering algorithm. It works by finding a local density factor for each data point during the construction of an MST and discarding outliers, i.e., those whose local density factor is larger than a threshold, to increase the separation between clusters. This algorithm is easy to implement, requiring an implementation of iDistance as the only k-nearest neighbor search structure. Experiments performed on both small low-dimensional data sets and large high-dimensional data sets demonstrate the efficacy of our method.

A fast MST-inspired kNN-based outlier detection method

Todays real-world databases typically contain millions of items with many thousands of fields. As a result, traditional distribution-based outlier detection techniques have more and more restricted capabilities and novel k-nearest neighbors based approaches have become more and more popular. However, the problems with these k-nearest neighbors based methods are that they are very sensitive to the value of k, may have different rankings for top n outliers, are very computationally expensive for large datasets, and doubts exist in general whether they would work well for high dimensional datasets. To partially circumvent these problems, we propose in this paper a new global outlier factor and a new local outlier factor and an efficient outlier detection algorithm developed upon them that is easy to implement and can provide competing performances with existing solutions. Experiments performed on both synthetic and real data sets demonstrate the efficacy of our method. A new k-nearest neighbors (kNN) based outlier detection scheme is proposed.It is built upon two new MST-inspired outlier scores, a global one and a local one.A set of state-of-the-art outlier detectors are applied to some high dimensional data.A fast approximate kNN search algorithm is used to accelerate the mining process.The proposed method can provide competing performances with existing solutions.

System status evaluation: monitoring the state of agents in a humanoid system

In order for a humanoid robot to be accepted in society and perform as an intelligent human assistant or companion, it must exhibit robust human-humanoid interaction (HHI). Additionally, the humanoid must be able to recognize if it is working properly, and if not, why. We describe our development of a framework for HHI. This framework includes a high-level agent called the Self Agent; part of its role is to maintain information about the status of the humanoid. To accomplish this, we have developed a technique called system status evaluation that the humanoid uses to detect and localize failures. We present the results of the use of SSE on a humanoid system called ISAC.

Human tracking based on attention distraction

The face tracker system of a humanoid, ISAC (Intelligent Soft Arm Control), is integrated with two modalities for localizing humans in order to direct ISACs attention and to prevent ISAC from being quickly distracted. The sound source localization and passive infrared (PIR) motion detection systems are used to provide the face tracker system with candidate regions for finding a face. However, the sensing modules should not directly gain control of the tracking if the system has recently acquired a new face. Our goal is to allow a human to redirect the attention of the system, but give the system a method to ignore the distraction if recently engaged.

Acoustical correlates of near‐term suicidal risk

In the course of many years of clinical work in emergency rooms and office consultation with suicidal patients, clinicians have often successfully predicted suicidality based on the vocal patterns of the patients, independent of the content. Vocal sound and clinical substance reciprocally augmented each other in determining the near‐term risk [M. K. Silverman and S. E. Silverman, ‘‘From sound to silence: A preliminary investigation of the use of vocal parameters in the prediction of near‐term suicidal risk,’’ submitted to J. Med. Psychotheraphy]. Vocal patterns heard as representing a ‘‘hollow,’’ ‘‘toneless’’ sound were designated unanimously as the most compelling feature in suicidal voices. Motivated by qualitative descriptions of experienced clinicians, a quantitative study was carried out that investigated the acoustic correlates of near‐term risk. The audio tapes selected for this research were suicide notes left on tapes donated by survivors, recordings of several patients who had been hospitalized,...