Improving the performance of Invertible Bloom Lookup Tables

Invertible Bloom Lookup Tables (IBLTs) have been recently introduced as an extension of traditional Bloom filters. IBLTs store key-value pairs. Unlike traditional Bloom filters, IBLTs support both a lookup operation (given a key, return a value) and an operation that lists out all the key-value pairs stored. One issue with IBLTs is that there is a probability that a lookup operation will return “not found” for a key. In this paper, a technique to reduce this probability without affecting the storage requirement and only moderately increasing the search time is presented and evaluated. The results show that it can significantly reduce the probability of not returning a value that is actually stored in the IBLT. The overhead of the modified search procedure, compared to the standard IBLT search procedure, is small and has little impact on the average search time.     

Spatio-temporal hotspots and application on a disease analysis case via GIS

Hotspot analysis is a spatial analysis that uses cluster techniques for determining areas with elevated concentrations of localized events. We use the consolidated Extended Fuzzy C-Means algorithm to determine the hotspot areas on the map as circles, moreover the advantages of this technique are the linear computational complexity, the robustness to noise and outliers, the automatic determination of the optimal number C of clusters (in the classical FCM algorithm C is chosen a priori). Furthermore it prevents the problem of shifting the clusters with low density area of data points in areas with higher density of such points. We apply this method to study the spatio-temporal variations of the hotspot areas by testing this process on a specific disease problem, precisely we have clusterized 5,000 point-events correspondent to cases of brain cancer detected in the state of New Mexico from 1973 to 1991. We also show that the same results are obtained by using the Extended Gustafson–Kessel algorithm which gives elliptical clusters. We have implemented both algorithms in a Geographic Information System environment. Thus we establish the areas which seem not interested from the incidence of the disease and those areas in which the phenomenon appears to be temporarily attenuated either increased or constant or quite disappeared.     

Unsupervised tissue classification of brain MR images for voxel‐based morphometry analysis

In this article, a fully unsupervised method for brain tissue segmentation of T1‐weighted MRI 3D volumes is proposed. The method uses the Fuzzy C‐Means (FCM) clustering algorithm and a Fully Connected Cascade Neural Network (FCCNN) classifier. Traditional manual segmentation methods require neuro‐radiological expertise and significant time while semiautomatic methods depend on parameter's setup and trial‐and‐error methodologies that may lead to high intraoperator/interoperator variability. The proposed method selects the most useful MRI data according to FCM fuzziness values and trains the FCCNN to learn to classify brain’ tissues into White Matter, Gray Matter, and Cerebro‐Spinal Fluid in an unsupervised way. The method has been tested on the IBSR dataset, on the BrainWeb Phantom, on the BrainWeb SBD dataset, and on the real dataset “University of Palermo Policlinico Hospital” (UPPH), Italy. Sensitivity, Specificity, Dice and F‐Factor scores have been calculated on the IBSR and BrainWeb datasets segmented using the proposed method, the FCM algorithm, and two state‐of‐the‐art brain segmentation software packages (FSL and SPM) to prove the effectiveness of the proposed approach. A qualitative evaluation involving a group of five expert radiologists has been performed segmenting the real dataset using the proposed approach and the comparison algorithms. Finally, a usability analysis on the proposed method and reference methods has been carried out from the same group of expert radiologists. The achieved results show that the segmentations of the proposed method are comparable or better than the reference methods with a better usability and degree of acceptance.     

AMOLED displays with transfer‐printed integrated circuits

Abstract— Small integrated circuits of crystalline silicon (chiplets) transfer‐printed onto a flat‐panel‐display substrate provide greatly improved electrical performance and uniformity in active‐matrix organic light‐emitting‐diode (OLED) displays. The integrated circuits are formed in high‐performance crystalline silicon using conventional photolithographic processes and then transfer‐printed onto a substrate using a stamp that transfers hundreds or thousands of chiplets at once. The chiplets are connected to an external controller and to pixel elements using conventional photolithographic substrate processing methods. Active‐matrix OLED (AMOLED) displays using transfer‐printed chiplets have good yields, excellent uniformity, and electrical performance and are thermally robust.     

A semantic approach to remove incoherent items from a user profile and improve the accuracy of a recommender system

Recommender systems usually suggest items by exploiting all the previous interactions of the users with a system (e.g., in order to decide the movies to recommend to a user, all the movies she previously purchased are considered). This canonical approach sometimes could lead to wrong results due to several factors, such as a change in user preferences over time, or the use of her account by third parties. This kind of incoherence in the user profiles defines a lower bound on the error the recommender systems may achieve when they generate suggestions for a user, an aspect known in literature as magic barrier. This paper proposes a novel dynamic coherence-based approach to define the user profile used in the recommendation process. The main aim is to identify and remove, from the previously evaluated items, those not semantically adherent to the others, in order to make a user profile as close as possible to the user’s real preferences, solving the aforementioned problems. Moreover, reshaping the user profile in such a way leads to great advantages in terms of computational complexity, since the number of items considered during the recommendation process is highly reduced. The performed experiments show the effectiveness of our approach to remove the incoherent items from a user profile, increasing the recommendation accuracy.     

Micromixing with spark-generated cavitation bubbles

The intrinsic laminarity of microfluidic devices impedes the mixing of multiple fluids over short temporal or spatial scales. Despite the existence of several mixers capable of stirring and stretching the flows to promote mixing, most approaches sacrifice temporal or spatial control, portability, or flexibility in terms of operating flow rates. Here, we report a novel method for rapid micromixing based on the generation of cavitation bubbles. By using a portable battery-powered electric circuit, we induce a localized electric spark between two tip electrodes perpendicular to the flow channel that results in several cavitation events. As a result, a vigorous stirring mechanism is induced. We investigate the spatiotemporal dynamics of the spark-generated cavitation bubbles and quantify the created flow disturbance. We demonstrate rapid (in the millisecond timescale) and efficient micromixing (up to 98%) within a length scale of only 200 µm and over a flow rate ranging from 5 to 40 µL/min.     

Design and control of an IPMC wormlike robot.

This paper presents an innovative wormlike robot controlled by cellular neural networks (CNNs) and made of an ionic polymer-metal composite (IPMC) self-actuated skeleton. The IPMC actuators, from which it is made of, are new materials that behave similarly to biological muscles. The idea that inspired the work is the possibility of using IPMCs to design autonomous moving structures. CNNs have already demonstrated their powerfulness as new structures for bio-inspired locomotion generation and control. The control scheme for the proposed IPMC moving structure is based on CNNs. The wormlike robot is totally made of IPMCs, and each actuator has to carry its own weight. All the actuators are connected together without using any other additional part, thereby constituting the robot structure itself. Worm locomotion is performed by bending the actuators sequentially from "tail" to "head," imitating the traveling wave observed in real-world undulatory locomotion. The activation signals are generated by a CNN. In the authors' opinion, the proposed strategy represents a promising solution in the field of autonomous and light structures that are capable of reconfiguring and moving in line with spatial-temporal dynamics generated by CNNs.