Sodium Reduction Through Salt Substitute Mixtures, Visual Cues, and Salt Level Statements, and Its Effects on Consumer Perception: A Case of Barbecue Sauce

Cardiovascular diseases are the leading causes of death in the USA and associated with excessive sodium (Na) intake. Thus, reducing the Na intake is vital to alleviate health concerns. One serving of the average Barbecue sauce (BBQs) may contribute around 15%-20% of the daily Na allowance (2,300 mg). No research has evaluated salt substitutes or visual cues on salty taste perception of BBQs, thus it was the aim of this research. Consumer studies followed a Balanced Incomplete Block (BIB) designs. For Study I, BBQs prototypes followed a 3-components (NaCl : KCl : Glycine) mixture design, with 38% - 87% less Na than the average BBQs in the US. In the first part (t=10, k=3, r=9, b=30), 300 consumers rated their saltiness and bitterness liking (9-point), emotion (5-point) and purchase intent (PI) before and after disclosing a sodium claim corresponding to each sample. In the second part (t=8, k=3, r=9, b=24), 240 consumers rated their expected salty and bitter taste intensity evoked solely by the brown color of BBQs, and then their perceived taste intensity (3-point JAR scale). In Study II, BBQs prototypes followed a factorial combination of three brown color levels and two salt levels (with and without). For the consumer test (t=6, k=2, r=5, b=15), 225 consumers rated their expected salty taste evoked by color only, then the perceived taste intensity before and after disclosing the sodium content of the samples, using a JAR scale. In this study, disclosing the sodium content favored the positive emotions of the consumers towards reduced and low sodium BBQs, which in turn enhanced the odds of positive purchase of these products. Also, the brown color of BBQs modulated the expected and actual salty taste perception (

AN RFQ DIRECT INJECTION SCHEME FOR THE ISODAR HIGH INTENSITY H+₂ CYCLOTRON

IsoDAR is a novel experiment designed to measure neutrino oscillations through e disappearance, thus providing a definitive search for sterile neutrinos. In order to generate the necessary anti-neutrino flux, a high intensity primary proton beam is needed. In IsoDAR, H+2 is accelerated and is stripped into protons just before the target, to overcome space charge issues at injection. As part of the design, we have refined an old proposal to use an RFQ to axially inject bunched H+2 ions into the driver cyclotron. This method has several advantages over a classical low energy beam transport (LEBT) design: (1) The bunching efficiency is higher than for the previously considered two-gap buncher and thus the overall injection efficiency is higher. This relaxes the constraints on the H+2 current required from the ion source. (2) The overall length of the LEBT can be reduced. (3) The RFQ can also accelerate the ions. This enables the ion source platform high voltage to be reduced from 70 kV to 30 kV, making underground installation easier. We are presenting the preliminary RFQ design parameters and first beam dynamics simulations from the ion source to the spiral inflector entrance.National Science Foundation (U.S.). Division of Physics (NSF-PHY-1148134)MIT Energy Initiative Seed Fund Progra

CASanDRA: A framework to provide Context Acquisition Services ANd Reasoning Algorithms for Ambient Intelligence Applications

The development of ambient intelligence (AmI) applications usually implies dealing with complex sensor access and context reasoning tasks, which may significantly slow down the application development cycle when vertically assumed. To face this issue, we present CASanDRA, a middleware which provides easily consumable context information about a given user and his environment, retrieving and fusing data from personal mobile devices and external sensors. The framework is built following a layered service oriented approach. The output data from every CASanDRA's layer are fully accessible through semantic interfaces; this allows AmI applications to retrieve raw context features, aggregated context data and complex `images of context', depending on their information needs. Moreover, different query modes -subscription, event-based, continuous and on-demand- are available. The current `mobile-assisted' version of CASanDRA is composed by a CASanDRA Server, developed on an applications container and hosting the system intelligence, and CASanDRA Lite, a mobile client bundling a set of sensor level acquisition services. How an AmI application may be effortlessly built on CASanDRA is described in the paper through the design of an `Ambient Home Care Monitor'

Real time calibration for RSS indoor positioning systems

Due to the random characteristics of the indoor propagation channel, received signal strength-based localization systems usually need to be manually calibrated once and again to guarantee their best performance. Calibration processes are costly in terms of time and resources, so they should be eliminated or reduced to a minimum. In this direction, this paper presents an optimization algorithm to automatically calibrate a propagation channel model by using a Least Mean Squares technique: RSS samples gathered in a number of reference points (with known positions) are used by a LMS algorithm to calculate those values for the channel model's constants that minimize the error computed by a hyperbolic triangulation positioning algorithm. Preliminary results on simulated and real data show that the localization error in distance is effectively reduced after a number of training samples. The LMS algorithm's simplicity and its low computational and memory costs make it adequate to be used in real systems

Comparison of Localization Methods Using Calibrated and Simulated Fingerprints for Indoor Systems Based on Bluetooth and WLAN Technologies

This paper compares two different localization algorithms to face the problem of indoor positioning using Bluetooth and WLAN technologies, which we have called: the fusion algorithm and the combination algorithm. The first algorithm is based on the construction of a fusion map using WiFi and Bluetooth power values. Considering the three lowest values of a defined distance, we compute the coordinates of the target point that we want to localize. In the second algorithm, the location determination is carried out independently with every single technology; then, results are combined to obtain a final estimated position. The performance of these methods has been tested experimentally using a simulated map and a real calibrated one. Using a real calibrated map, the localization errors obtained with the fusion algorithm are smaller than with the combination one, while when using a simulated map there is almost no difference between both algorithms. The results of the experiments made with the real calibrated map are a little better than using the simulated map, but the improvement obtained using the real map is not enough to confirm that using this one is worth, because of the effort necessary to build it

Dynamic chanel model LMS updating for RSS-based localization

Received signal strength-based localization systems usually rely on a calibration process that aims at characterizing the propagation channel. However, due to the changing environmental dynamics, the behavior of the channel may change after some time, thus, recalibration processes are necessary to maintain the positioning accuracy. This paper proposes a dynamic calibration method to initially calibrate and subsequently update the parameters of the propagation channel model using a Least Mean Squares approach. The method assumes that each anchor node in the localization infrastructure is characterized by its own propagation channel model. In practice, a set of sniffers is used to collect RSS samples, which will be used to automatically calibrate each channel model by iteratively minimizing the positioning error. The proposed method is validated through numerical simulation, showing that the positioning error of the mobile nodes is effectively reduced. Furthermore, the method has a very low computational cost; therefore it can be used in real-time operation for wireless resource-constrained nodes

Enhancing Activity Recognition by Fusing Inertial and Biometric Information

Activity recognition is an active research field nowadays, as it enables the development of highly adaptive applications, e.g. in the field of personal health. In this paper, a light high-level fusion algorithm to detect the activity that an individual is performing is presented. The algorithm relies on data gathered from accelerometers placed on different parts of the body, and on biometric sensors. Inertial sensors allow detecting activity by analyzing signal features such as amplitude or peaks. In addition, there is a relationship between the activity intensity and biometric response, which can be considered together with acceleration data to improve the accuracy of activity detection. The proposed algorithm is designed to work with minimum computational cost, being ready to run in a mobile device as part of a context-aware application. In order to enable different user scenarios, the algorithm offers best-effort activity estimation: its quality of estimation depends on the position and number of the available inertial sensors, and also on the presence of biometric information

Deploying context-aware services: A case study of rapid prototyping

In this contribution, a real experience of rapid design and deployment of context-aware services for an exhibition hall is detailed. The prototype has been built on a combination of a commercial system (which has been customized and improved to satisfy the prototype needs) with an in-home developed context acquisition framework. In order to partially overcome device fragmentation issues, we have focused on the development of web-based context-aware applications. The whole system has been deployed from scratch under real constraints of time and environment. The objective has been to test the integration problems of context-aware systems, in order to infer some conclusions on what it is needed to generalize them

Gesture recognition using mobile phone's inertial sensors

The availability of inertial sensors embedded in mobile devices has enabled a new type of interaction based on the movements or “gestures” made by the users when holding the device. In this paper we propose a gesture recognition system for mobile devices based on accelerometer and gyroscope measurements. The system is capable of recognizing a set of predefined gestures in a user-independent way, without the need of a training phase. Furthermore, it was designed to be executed in real-time in resource-constrained devices, and therefore has a low computational complexity. The performance of the system is evaluated offline using a dataset of gestures, and also online, through some user tests with the system running in a smart phone