More than Meets the Eye: Step by Step, Exploring Connecticut\u27s Great Mountain Forest

The 6,300-acre Great Mountain Forest in northwestern Connecticut, once an industrial landscape of charcoal making for iron ore smelting, today is one of the most wild and remote tracts in southern New England

Finding an Unforeseen Labrador: From the St. Lawrence to Ungava

A memoir of a 1988 trip to Labrador begins with a long train ride to Schefferville and proceeds to long days paddling two rivers north toward Ungava Bay

Implementation and Testing of Surface Acoustic Wave Strain Sensors for Harsh Environment Applications

Static and dynamic strain sensing is needed in high-temperature, harsh environment applications for structural health monitoring, condition-based maintenance, process efficiency monitoring, and operator safety in power plants, oil wells, metallurgy, aerospace, and automotive industries. Some challenges for sensors in these environments include device integrity, stability, mounting, packaging, and data acquisition techniques. In addition, it is desirable for sensors in high-temperature harsh-environments to be compact, operate without a battery, and have wireless interrogation capabilities so that they can be installed in small, hard-to-reach locations that otherwise could not be monitored. Surface acoustic wave resonator (SAWR) sensors can respond to the demands of high-temperature, harsh-environment applications due to: (i) the existence of piezoelectric substrates and thin film electrode technology capable of operating at high temperatures (above 1000°C); (ii) sensor response to static and dynamic strain components; (iii) small sensor size; (iv) wireless interrogation capability; (v) and battery-free operation. SAWR strain sensing for harsh-environment applications needs to address some of the issues inherent to these environments, such as: (i) sensor mounting techniques to metal parts, (ii) stability of the sensor and sensor mounting technique, (iii) packaging of the sensor, and (iv) cross-sensitivity between strain and temperature. In this work, langasite (LGS) SAWR sensors were used, due to the proven performance of these devices at high temperature at UMaine, for static and dynamic strain measurements. Simulation of the strain due to thermal expansion and mechanical loads was performed to determine where there were concentrations of high strain at the adhesive/LGS and adhesive/metal interfaces as well as adhesive shaping designs aimed at minimizing this strain. Wireless interrogation of SAWR static and dynamic strain sensors using inductive coupling techniques was achieved up to 400°C. After temperature cycling, it was determined that cracking was taking place within the ceramic adhesive layer and along the borders of the SAWR sensor chip that causes degradation and inconsistency in the SAWR strain response. Based on these results, further investigation of static and dynamic strain sensors using alternative adhesives was done limited to 200°C. Two polymer epoxy adhesives showed stability after temperature cycling between 50°C and 250°C. Using the polymer epoxy that showed greater stability for the static strain, dynamic strain was measured. The test setup implementation was investigated towards improving the stability of dynamic strain sensor measurements after temperature cycling. Finally, a method for extracting temperature and the dynamic strain magnitude and spectral components was devised and implemented using a single SAWR sensor

Efficiency of a Brownian information machine

A Brownian information machine extracts work from a heat bath through a feedback process that exploits the information acquired in a measurement. For the paradigmatic case of a particle trapped in a harmonic potential, we determine how power and efficiency for two variants of such a machine operating cyclically depend on the cycle time and the precision of the positional measurements. Controlling only the center of the trap leads to a machine that has zero efficiency at maximum power whereas additional optimal control of the stiffness of the trap leads to an efficiency bounded between 1/2, which holds for maximum power, and 1 reached even for finite cycle time in the limit of perfect measurements.Comment: 9 pages, 2 figure

The left superior temporal gyrus is a shared substrate for auditory short-term memory and speech comprehension: evidence from 210 patients with stroke

Competing theories of short-term memory function make specific predictions about the functional anatomy of auditory short-term memory and its role in language comprehension. We analysed high-resolution structural magnetic resonance images from 210 stroke patients and employed a novel voxel based analysis to test the relationship between auditory short-term memory and speech comprehension. Using digit span as an index of auditory short-term memory capacity we found that the structural integrity of a posterior region of the superior temporal gyrus and sulcus predicted auditory short-term memory capacity, even when performance on a range of other measures was factored out. We show that the integrity of this region also predicts the ability to comprehend spoken sentences. Our results therefore support cognitive models that posit a shared substrate between auditory short-term memory capacity and speech comprehension ability. The method applied here will be particularly useful for modelling structure–function relationships within other complex cognitive domains

Breakdown of universality in multi-cut matrix models

We solve the puzzle of the disagreement between orthogonal polynomials methods and mean field calculations for random NxN matrices with a disconnected eigenvalue support. We show that the difference does not stem from a Z2 symmetry breaking, but from the discreteness of the number of eigenvalues. This leads to additional terms (quasiperiodic in N) which must be added to the naive mean field expressions. Our result invalidates the existence of a smooth topological large N expansion and some postulated universality properties of correlators. We derive the large N expansion of the free energy for the general 2-cut case. From it we rederive by a direct and easy mean-field-like method the 2-point correlators and the asymptotic orthogonal polynomials. We extend our results to any number of cuts and to non-real potentials.Comment: 35 pages, Latex (1 file) + 3 figures (3 .eps files), revised to take into account a few reference

An Utterance Verification System for Word Naming Therapy in Aphasia

Anomia (word finding difficulties) is the hallmark of aphasia an acquired language disorder, most commonly caused by stroke. Assessment of speech performance using picture naming tasks is therefore a key method for identification of the disorder and monitoring patient’s response to treatment interventions. Currently, this assessment is conducted manually by speech and language therapists (SLT). Surprisingly, despite advancements in ASR and artificial intelligence with technologies like deep learning, research on developing automated systems for this task has been scarce. Here we present an utterance verification system incorporating a deep learning element that classifies ‘correct’/‘incorrect’ naming attempts from aphasic stroke patients. When tested on 8 native British-English speaking aphasics the system’s performance accuracy ranged between 83.6% to 93.6%, with a 10 fold cross validation mean of 89.5%. This performance was not only significantly better than one of the leading commercially available ASRs (Google speech-to-text service) but also comparable in some instances with two independent SLT ratings for the same dataset