Far-field thermal imaging below diffraction limit.

Non-uniform self-heating and temperature hotspots are major concerns compromising the performance and reliability of submicron electronic and optoelectronic devices. At deep submicron scales where effects such as contact-related artifacts and diffraction limits accurate measurements of temperature hotspots, non-contact thermal characterization can be extremely valuable. In this work, we use a Bayesian optimization framework with generalized Gaussian Markov random field (GGMRF) prior model to obtain accurate full-field temperature distribution of self-heated metal interconnects from their thermoreflectance thermal images (TRI) with spatial resolution 2.5 times below Rayleigh limit for 530nm illumination. Finite element simulations along with TRI experimental data were used to characterize the point spread function of the optical imaging system. In addition, unlike iterative reconstruction algorithms that use ad hoc regularization parameters in their prior models to obtain the best quality image, we used numerical experiments and finite element modeling to estimate the regularization parameter for solving a real experimental inverse problem.

Effect of substrate temperature on pattern formation of nanoparticles from volatile drops.

This study investigates pattern formation during evaporation of water-based nanofluid sessile droplets placed on a smooth silicon surface at various temperatures. An infrared thermography technique was employed to observe the temperature distribution along the air-liquid interface of evaporating droplets. In addition, an optical interferometry technique is used to quantify and characterize the deposited patterns. Depending on the substrate temperature, three distinctive deposition patterns are observed: a nearly uniform coverage pattern, a "dual-ring" pattern, and multiple rings corresponding to "stick-slip" pattern. At all substrate temperatures, the internal flow within the drop builds a ringlike cluster of the solute on the top region of drying droplets, which is found essential for the formation of the secondary ring deposition onto the substrate for the deposits with the "dual-ring" pattern. The size of the secondary ring is found to be dependent on the substrate temperature. For the deposits with the rather uniform coverage pattern, the ringlike cluster of the solute does not deposit as a distinct secondary ring; instead, it is deformed by the contact line depinning. In the case of the "stick-slip" pattern, the internal flow behavior is complex and found to be vigorous with rapid circulating flow which appears near the edge of the drop.

Cervical and ocular vestibular evoked myogenic potentials in multiple sclerosis participants.

BACKGROUND: Multiple sclerosis (MS) is a chronic neurological disease that affects brain and spinal cord. The infratentorial region contains the cerebellum and brainstem. Vestibular evoked myogenic potentials (VEMPs) are short-latency myogenic responses. Cervical vestibular evoked myogenic potential (cVEMP) is a manifestation of vestibulocolic reflex and ocular vestibular evoked myogenic potential (oVEMP) contributes to the linear vestibular-ocular reflex. The aim of this study was to evaluate cVEMP and oVEMP in MS patients with and without infratentorial plaques and compare the findings with normal controls. METHODS: In this cross-sectional study, latency and amplitude of cVEMP and oVEMP were recorded in 15 healthy females with mean age of 31.13±9.27 years, 17 female MS patients with infratentorial plaque(s) and mean age of 29.88±8.93 years, and 17 female MS patients without infratentorial plaque(s) and mean age of 30.58±8.02 years. All patients underwent a complete clinical neurological evaluation and brain MRI scanning. Simple random sampling method was used in this study and data were analyzed using one way ANOVA through SPSS v22. RESULTS: The latency of N1-P1 and P13 in MS participants with and without infratentorial plaques were significantly prolonged compared to normal controls (p<0.001). Additionally latency of P13- N23-N1 and P1 in MS patients with infratentorial plaques were significantly prolonged compared to patients without infratentorial plaques subjects (p<0.001). CONCLUSION: Abnormality of both cVEMP and oVEMP in MS patient with infratentorial plaque are more than that of MS patient without infratentorial plaque. Recording both ocular and cervical VEMPs are appropriate electrophysiologic methods assessing the function of both ascending and descending central vestibular pathways.

IRIS: Integrated Retinal Functionality in Image Sensors.

Neuromorphic image sensors draw inspiration from the biological retina to implement visual computations in electronic hardware. Gain control in phototransduction and temporal differentiation at the first retinal synapse inspired the first generation of neuromorphic sensors, but processing in downstream retinal circuits, much of which has been discovered in the past decade, has not been implemented in image sensor technology. We present a technology-circuit co-design solution that implements two motion computations-object motion sensitivity and looming detection-at the retina's output that could have wide applications for vision-based decision-making in dynamic environments. Our simulations on Globalfoundries 22 nm technology node show that the proposed retina-inspired circuits can be fabricated on image sensing platforms in existing semiconductor foundries by taking advantage of the recent advances in semiconductor chip stacking technology. Integrated Retinal Functionality in Image Sensors (IRIS) technology could drive advances in machine vision applications that demand energy-efficient and low-bandwidth real-time decision-making.

Opportunities for neuromorphic computing algorithms and applications.

Neuromorphic computing technologies will be important for the future of computing, but much of the work in neuromorphic computing has focused on hardware development. Here, we review recent results in neuromorphic computing algorithms and applications. We highlight characteristics of neuromorphic computing technologies that make them attractive for the future of computing and we discuss opportunities for future development of algorithms and applications on these systems.

Molecular and microscopic prevalence of intestinal microsporidia among HIV+/AIDS patients in the Alborz province, Iran.

BACKGROUND: Microsporidia are a large family of obligate intracellular protozoa; these medically important species are recognized as opportunistic agents in intestinal complications in HIV+/AIDS patients. METHODS: The current cross-sectional study was designed and conducted from October 2018 to June 2019 to determine intestinal microsporidia in HIV+/AIDS patients by trichrome/Zeihl-Neelsen staining and SYBR Green-based real-time PCR. RESULTS: Out of 80 HIV+/AIDS patients, 23.75% (n=19) and 12.5% (n=10) were identified by molecular and microscopic methods, respectively. The predominant species in patients was Encephalitozoon (94%), which was found by quantitative real-time PCR and its high resolution melting tool. CONCLUSION: As far as we know, this is the first report from the Alborz region. The prevalence of intestinal microsporidiosis in this area in HIV+/AIDS patients was higher than both the global and national average. In addition to the need for further studies to prove protozoan pathogenicity in the aforementioned group, preventive measures should be considered.

Bayesian Multi-objective Hyperparameter Optimization for Accurate, Fast, and Efficient Neural Network Accelerator Design.

In resource-constrained environments, such as low-power edge devices and smart sensors, deploying a fast, compact, and accurate intelligent system with minimum energy is indispensable. Embedding intelligence can be achieved using neural networks on neuromorphic hardware. Designing such networks would require determining several inherent hyperparameters. A key challenge is to find the optimum set of hyperparameters that might belong to the input/output encoding modules, the neural network itself, the application, or the underlying hardware. In this work, we present a hierarchical pseudo agent-based multi-objective Bayesian hyperparameter optimization framework (both software and hardware) that not only maximizes the performance of the network, but also minimizes the energy and area requirements of the corresponding neuromorphic hardware. We validate performance of our approach (in terms of accuracy and computation speed) on several control and classification applications on digital and mixed-signal (memristor-based) neural accelerators. We show that the optimum set of hyperparameters might drastically improve the performance of one application (i.e., 52-71% for Pole-Balance), while having minimum effect on another (i.e., 50-53% for RoboNav). In addition, we demonstrate resiliency of different input/output encoding, training neural network, or the underlying accelerator modules in a neuromorphic system to the changes of the hyperparameters.

Mechanisms of pattern formation from dried sessile drops.

The formation of patterns after the evaporation of colloidal droplets deposited on a solid surface is an everyday natural phenomenon. During the past two decades, this topic has gained broader audience due to its numerous applications in biomedicine, nanotechnology, printing, coating, etc. This paper presents a detailed review of the experimental studies related to the formation of various deposition patterns from dried droplets of complex fluids (i.e., nanofluids, polymers). First, this review presents the fundamentals of sessile droplet evaporation including evaporation modes and internal flow fields. Then, the most observed dried patterns are presented and the mechanisms behind them are discussed. The review ends with the categorisation and exhaustive investigation of a wide range of factors affecting pattern formation.

Full-field thermal imaging of quasiballistic crosstalk reduction in nanoscale devices.

Understanding nanoscale thermal transport is of substantial importance for designing contemporary semiconductor technologies. Heat removal from small sources is well established to be severely impeded compared to diffusive predictions due to the ballistic nature of the dominant heat carriers. Experimental observations are commonly interpreted through a reduction of effective thermal conductivity, even though most measurements only probe a single aggregate thermal metric. Here, we employ thermoreflectance thermal imaging to directly visualise the 2D temperature field produced by localised heat sources on InGaAs with characteristic widths down to 100 nm. Besides displaying effective thermal performance reductions up to 50% at the active junctions in agreement with prior studies, our steady-state thermal images reveal that, remarkably, 1-3 µm adjacent to submicron devices the crosstalk is actually reduced by up to fourfold. Submicrosecond transient imaging additionally shows responses to be faster than conventionally predicted. A possible explanation based on hydrodynamic heat transport, and some open questions, are discussed.

Staged Inference using Conditional Deep Learning for energy efficient real-time smart diagnosis.

Recent progress in biosensor technology and wearable devices has created a formidable opportunity for remote healthcare monitoring systems as well as real-time diagnosis and disease prevention. The use of data mining techniques is indispensable for analysis of the large pool of data generated by the wearable devices. Deep learning is among the promising methods for analyzing such data for healthcare applications and disease diagnosis. However, the conventional deep neural networks are computationally intensive and it is impractical to use them in real-time diagnosis with low-powered on-body devices. We propose Staged Inference using Conditional Deep Learning (SICDL), as an energy efficient approach for creating healthcare monitoring systems. For smart diagnostics, we observe that all diagnoses are not equally challenging. The proposed approach thus decomposes the diagnoses into preliminary analysis (such as healthy vs unhealthy) and detailed analysis (such as identifying the specific type of cardio disease). The preliminary diagnosis is conducted real-time with a low complexity neural network realized on the resource-constrained on-body device. The detailed diagnosis requires a larger network that is implemented remotely in cloud and is conditionally activated only for detailed diagnosis (unhealthy individuals). We evaluated the proposed approach using available physiological sensor data from Physionet databases, and achieved 38% energy reduction in comparison to the conventional deep learning approach.

Effect of Substrate Temperature on Pattern Formation of Bidispersed Particles from Volatile Drops.

In this study, pattern formation during evaporation of bidispersed drops (containing 1 and 3.2 µm particles) placed on a smooth substrate at different temperatures is investigated. Five distinctive deposition patterns are observed depending on the substrate temperature: a relatively uniform pattern enclosed by a disk-shaped ring, a nearly nonuniform pattern inside a thick outer ring, a "dual-ring" pattern, a "rose-like" pattern, and a set of concentric rings corresponding to the "stick-slip" pattern. At drops edge, the particle size effect leads to the formation of three rings: an outermost ring formed by the nonvolatile additives smaller than 1 µm, a middle ring built by particles with size of 1 µm, and an innermost ring formed by the mixture of 1 and 3.2 µm. For temperatures between 64 and 99 °C, the depinning of the contact line causes the same particle sorting at the other deposition lines in the interior of the drop. However, the width of the zone between the outermost ring and the middle ring at the initial edge of the drop is found to be smaller than that at the other deposition lines. The size of the width is found to be dependent on the contact angle. Particle velocity is measured by tracking particles during the evaporation. It is shown that particle velocity slightly increases with time, but it rapidly increases at the last stage of the drying process, known as "rush-hour" behavior. The sudden change in the increase of the velocity occurs between the normalized time of 0.7 and 0.8 for temperatures from 22 to 81 °C. The increasing trend of velocity with time matches well with the theoretical model. The tracer particles are also used to measure the distance between the contact line and the nearest turning point of those particles return back toward the top of the drop due to the inward Marangoni flow. It is found that this distance decreases with increasing the substrate temperature.