Compact low-power cortical recording architecture for compressive multichannel data acquisition

This paper introduces an area- and power-efficient approach for compressive recording of cortical signals used in an implantable system prior to transmission. Recent research on compressive sensing has shown promising results for sub-Nyquist sampling of sparse biological signals. Still, any large-scale implementation of this technique faces critical issues caused by the increased hardware intensity. The cost of implementing compressive sensing in a multichannel system in terms of area usage can be significantly higher than a conventional data acquisition system without compression. To tackle this issue, a new multichannel compressive sensing scheme which exploits the spatial sparsity of the signals recorded from the electrodes of the sensor array is proposed. The analysis shows that using this method, the power efficiency is preserved to a great extent while the area overhead is significantly reduced resulting in an improved power-area product. The proposed circuit architecture is implemented in a UMC 0.18 [Formula: see text]m CMOS technology. Extensive performance analysis and design optimization has been done resulting in a low-noise, compact and power-efficient implementation. The results of simulations and subsequent reconstructions show the possibility of recovering fourfold compressed intracranial EEG signals with an SNR as high as 21.8 dB, while consuming 10.5 [Formula: see text]W of power within an effective area of 250 [Formula: see text]m × 250 [Formula: see text]m per channel.

A computer-aided diagnostic system to characterize CT focal liver lesions: design and optimization of a neural network classifier

In this paper, a computer-aided diagnostic (CAD) system for the classification of hepatic lesions from computed tomography (CT) images is presented. Regions of interest (ROIs) taken from nonenhanced CT images of normal liver, hepatic cysts, hemangiomas, and hepatocellular carcinomas have been used as input to the system. The proposed system consists of two modules: the feature extraction and the classification modules. The feature extraction module calculates the average gray level and 48 texture characteristics, which are derived from the spatial gray-level co-occurrence matrices, obtained from the ROIs. The classifier module consists of three sequentially placed feed-forward neural networks (NNs). The first NN classifies into normal or pathological liver regions. The pathological liver regions are characterized by the second NN as cyst or "other disease." The third NN classifies "other disease" into hemangioma or hepatocellular carcinoma. Three feature selection techniques have been applied to each individual NN: the sequential forward selection, the sequential floating forward selection, and a genetic algorithm for feature selection. The comparative study of the above dimensionality reduction methods shows that genetic algorithms result in lower dimension feature vectors and improved classification performance.

Evidence Based Development of a Novel Lateral Fibula Plate (VariAx Fibula) Using a Real CT Bone Data Based Optimization Process During Device Development

Development of novel implants in orthopaedic trauma surgery is based on limited datasets of cadaver trials or artificial bone models. A method has been developed whereby implants can be constructed in an evidence based method founded on a large anatomic database consisting of more than 2.000 datasets of bones extracted from CT scans. The aim of this study was the development and clinical application of an anatomically pre-contoured plate for the treatment of distal fibular fractures based on the anatomical database. 48 Caucasian and Asian bone models (left and right) from the database were used for the preliminary optimization process and validation of the fibula plate. The implant was constructed to fit bilaterally in a lateral position of the fibula. Then a biomechanical comparison of the designed implant to the current gold standard in the treatment of distal fibular fractures (locking 1/3 tubular plate) was conducted. Finally, a clinical surveillance study to evaluate the grade of implant fit achieved was performed. The results showed that with a virtual anatomic database it was possible to design a fibula plate with an optimized fit for a large proportion of the population. Biomechanical testing showed the novel fibula plate to be superior to 1/3 tubular plates in 4-point bending tests. The clinical application showed a very high degree of primary implant fit. Only in a small minority of cases further intra-operative implant bending was necessary. Therefore, the goal to develop an implant for the treatment of distal fibular fractures based on the evidence of a large anatomical database could be attained. Biomechanical testing showed good results regarding the stability and the clinical application confirmed the high grade of anatomical fit.

Population-based design of mandibular fixation plates with bone quality and morphology considerations

In this paper we present a new population-based implant design methodology, which advances the state-of-the-art approaches by combining shape and bone quality information into the design strategy. The method may enhance the mechanical stability of the fixation and reduces the intra-operative in-plane bending which might impede the functionality of the locking mechanism. The computational method is presented for the case of mandibular locking fixation plates, where the mandibular angle and the bone quality at screw locations are taken into account. The method automatically derives the mandibular angle and the bone thickness and intensity values at the path of every screw from a set of computed tomography images. An optimization strategy is then used to optimize the two parameters of plate angle and screw position. The method was applied to two populations of different genders. Results for the new design are presented along with a comparison with a commercially available mandibular locking fixation plate (MODUS(®) TriLock(®) 2.0/2.3/2.5, Medartis AG, Basel, Switzerland). The proposed designs resulted in a statistically significant improvement in the available bone thickness when compared to the standard plate. There is a higher probability that the proposed implants cover areas of thicker cortical bone without compromising the bone mineral density around the screws. The obtained results allowed us to conclude that an angle and screw separation of 129° and 9 mm for females and 121° and 10 mm for males are more suitable designs than the commercially available 120° and 9 mm.

Anatomical study of the human middle ear for the design of implantable hearing aids

OBJECTIVE: To generate anatomical data on the human middle ear and adjacent structures to serve as a base for the development and optimization of new implantable hearing aid transducers. Implantable middle ear hearing aid transducers, i.e. the equivalent to the loudspeaker in conventional hearing aids, should ideally fit into the majority of adult middle ears and should utilize the limited space optimally to achieve sufficiently high maximal output levels. For several designs, more anatomical data are needed. METHODS: Twenty temporal bones of 10 formalin-fixed adult human heads were scanned by a computed tomography system (CT) using a slide thickness of 0.63 mm. Twelve landmarks were defined and 24 different distances were calculated for each temporal bone. RESULTS: A statistical description of 24 distances in the adult human middle ear which may limit or influence the design of middle ear transducers is presented. Significant inter-individual differences but no significant differences for gender, side, age or degree of pneumatization of the mastoid were found. Distances, which were not analyzed for the first time in this study, were found to be in good agreement with the results of earlier studies. CONCLUSION: A data set describing the adult human middle ear anatomy quantitatively from the point of view of designers of new implantable hearing aid transducers has been generated. In principle, the method employed in this study using standard CT scans could also be used preoperatively to rule out exclusion criteria.

Quantile-Based Optimization of Noisy Computer Experiments with Tunable Precision

This article addresses the issue of kriging-based optimization of stochastic simulators. Many of these simulators depend on factors that tune the level of precision of the response, the gain in accuracy being at a price of computational time. The contribution of this work is two-fold: first, we propose a quantile-based criterion for the sequential design of experiments, in the fashion of the classical expected improvement criterion, which allows an elegant treatment of heterogeneous response precisions. Second, we present a procedure for the allocation of the computational time given to each measurement, allowing a better distribution of the computational effort and increased efficiency. Finally, the optimization method is applied to an original application in nuclear criticality safety. This article has supplementary material available online. The proposed criterion is available in the R package DiceOptim.

On the use of Cramér-Rao minimum variance bounds for the design of magnetic resonance spectroscopy experiments

Localized Magnetic Resonance Spectroscopy (MRS) is in widespread use for clinical brain research. Standard acquisition sequences to obtain one-dimensional spectra suffer from substantial overlap of spectral contributions from many metabolites. Therefore, specially tuned editing sequences or two-dimensional acquisition schemes are applied to extend the information content. Tuning specific acquisition parameters allows to make the sequences more efficient or more specific for certain target metabolites. Cramér-Rao bounds have been used in other fields for optimization of experiments and are now shown to be very useful as design criteria for localized MRS sequence optimization. The principle is illustrated for one- and two-dimensional MRS, in particular the 2D separation experiment, where the usual restriction to equidistant echo time spacings and equal acquisition times per echo time can be abolished. Particular emphasis is placed on optimizing experiments for quantification of GABA and glutamate. The basic principles are verified by Monte Carlo simulations and in vivo for repeated acquisitions of generalized two-dimensional separation brain spectra obtained from healthy subjects and expanded by bootstrapping for better definition of the quantification uncertainties.

Adapting agricultural land management to climate change: a regional multi-objective optimization approach

In several regions of the world, climate change is expected to have severe impacts on agricultural systems. Changes in land management are one way to adapt to future climatic conditions, including land-use changes and local adjustments of agricultural practices. In previous studies, options for adaptation have mostly been explored by testing alternative scenarios. Systematic explorations of land management possibilities using optimization approaches were so far mainly restricted to studies of land and resource management under constant climatic conditions. In this study, we bridge this gap and exploit the benefits of multi-objective regional optimization for identifying optimum land management adaptations to climate change. We design a multi-objective optimization routine that integrates a generic crop model and considers two climate scenarios for 2050 in a meso-scale catchment on the Swiss Central Plateau with already limited water resources. The results indicate that adaptation will be necessary in the study area to cope with a decrease in productivity by 0–10 %, an increase in soil loss by 25–35 %, and an increase in N-leaching by 30–45 %. Adaptation options identified here exhibit conflicts between productivity and environmental goals, but compromises are possible. Necessary management changes include (i) adjustments of crop shares, i.e. increasing the proportion of early harvested winter cereals at the expense of irrigated spring crops, (ii) widespread use of reduced tillage, (iii) allocation of irrigated areas to soils with low water-retention capacity at lower elevations, and (iv) conversion of some pre-alpine grasslands to croplands.

An application of combinatorial optimization in the printing industry

Offset printing is a common method to produce large amounts of printed matter. We consider a real-world offset printing process that is used to imprint customer-specific designs on napkin pouches. The print- ing technology used yields a number of specific constraints. The planning problem consists of allocating designs to printing-plate slots such that the given customer demand for each design is fulfilled, all technologi- cal and organizational constraints are met and the total overproduction and setup costs are minimized. We formulate this planning problem as a mixed-binary linear program, and we develop a multi-pass matching-based savings heuristic. We report computational results for a set of problem instances devised from real-world data.

Conceptual design and simulation of a water Cherenkov muon veto for the XENON1T experiment

XENON is a dark matter direct detection project, consisting of a time projection chamber (TPC) filled with liquid xenon as detection medium. The construction of the next generation detector, XENON1T, is presently taking place at the Laboratori Nazionali del Gran Sasso (LNGS) in Italy. It aims at a sensitivity to spin-independent cross sections of 2 10-47 c 2 for WIMP masses around 50 GeV2, which requires a background reduction by two orders of magnitude compared to XENON100, the current generation detector. An active system that is able to tag muons and muon-induced backgrounds is critical for this goal. A water Cherenkov detector of ~ 10 m height and diameter has been therefore developed, equipped with 8 inch photomultipliers and cladded by a reflective foil. We present the design and optimization study for this detector, which has been carried out with a series of Monte Carlo simulations. The muon veto will reach very high detection efficiencies for muons (>99.5%) and showers of secondary particles from muon interactions in the rock (>70%). Similar efficiencies will be obtained for XENONnT, the upgrade of XENON1T, which will later improve the WIMP sensitivity by another order of magnitude. With the Cherenkov water shield studied here, the background from muon-induced neutrons in XENON1T is negligible.

Differentiating the Multipoint Expected Improvement for Optimal Batch Design

This work deals with parallel optimization of expensive objective functions which are modelled as sample realizations of Gaussian processes. The study is formalized as a Bayesian optimization problem, or continuous multi-armed bandit problem, where a batch of q > 0 arms is pulled in parallel at each iteration. Several algorithms have been developed for choosing batches by trading off exploitation and exploration. As of today, the maximum Expected Improvement (EI) and Upper Confidence Bound (UCB) selection rules appear as the most prominent approaches for batch selection. Here, we build upon recent work on the multipoint Expected Improvement criterion, for which an analytic expansion relying on Tallis’ formula was recently established. The computational burden of this selection rule being still an issue in application, we derive a closed-form expression for the gradient of the multipoint Expected Improvement, which aims at facilitating its maximization using gradient-based ascent algorithms. Substantial computational savings are shown in application. In addition, our algorithms are tested numerically and compared to state-of-the-art UCB-based batchsequential algorithms. Combining starting designs relying on UCB with gradient-based EI local optimization finally appears as a sound option for batch design in distributed Gaussian Process optimization.