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More and more applications are promoting cus-tomized or personalized services. In order for these applications to provide meaningful output, it collects users’ personal information over time. Some personal information (e.g. education level or income level) can only be captured by users actively updating their profile to reflect these changes. We refer to these as long-term time series data, as they do not change frequently. If applications can keep up to date on a diverse and large set of personal features, they can provide higher quality service. However, this quality of service comes at the cost of the user sacrificing their privacy. There has been numerous research on protecting privacy of time series data for context aware services, but the privacy leakage of personal information updates during the whole life-cycle of the series has received only scant attention.Motivated users concerned about their privacy, we discuss in detail the privacy leakage risk, focusing on long-term time-series data from the perspective of game theory. Then, we propose a reward-privacy model, targeting the privacy-aware data-updates for the entire life-cycle in context-aware services by leveraging a three-party Stackelberg game. We theoretically prove that a Nash Equilibrium exists in the proposed model, and then use simulations to validate that a Nash Equilibrium exists for different parameters of the productivity function. By using our proposed framework, users have guidance to decide not only the timing of submitting personal updates, but also the granularity or obscurity level for their data.

An updated approach to configure a microwave-photonics-based long-term radio-frequency memory using recirculating delay line structure is proposed and discussed. As clear validated by the results of modeling and experimental research, the proposed circuit in comparison with the known approaches is provided the trade-off between output power drop and noise rise with increasing number of re-circulations. Specifically, more than 5-fold shorter delay step, better transmission gain flatness of super-wide bandwidth radio-frequency signal, smaller dimensions mainly due to multicore fiber application, and lower power consumption using less number of needed optical amplifiers are demonstrated.

In this paper, we will develop new mathematical tools for mapping tissue microstructural properties via the use of space-time fractional calculus methods. We propose the following two new computational multi-term time-space fractional diffusion models in three dimensions, which can be used to simulate multi-term time-space fractional Bloch-Torrey models in three dimensions: Model I: Finite element method for the multi-term time-space fractional diffusion model with Riesz fractional operator; Model II: Finite difference method for the multi-term time-space fractional diffusion equation with fractional Laplacian operator. Firstly, the three-dimensional multi-term time-space fractional Bloch-Torrey models are decoupled; the problem is then equivalent to solving the three-dimensional time-space fractional diffusion equations (Model I and Model II). Secondly, we propose the finite element method for Model I and finite difference method for Model II, respectively. These methods can be directly used to simulate three-dimensional multi-term time-space fractional Bloch-Torrey models. Finally, some numerical examples are given to demonstrate the versatility and application of the models.

In this paper, we propose a framework for time synchronized sensing of temperature with applications in supply-chain monitoring. At the core of the proposed framework is a chip-scale self-powered timing device that has been shown to be robust to manufacturing artifacts and has been shown to precisely track absolute time for years. In this paper we exploit two key characteristics of the timer device: its sensitivity to temperature, and its ability to synchronize time across multiple devices that are subjected to identical environmental conditions. Here, we also propose a mathematical model that captures the time-temperature behavior of the timers more accurately. Sensor/timer prototypes were fabricated on a standard 0.5 µm CMOS process and experiments were conducted in a thermally-controlled chamber with continuous monitoring using commercial-off-the-shelf (COTS) sensors. Results show that variations in the recovered temperature readings from our proposed framework were comparable to that of measured temperature using the COTS sensor. Due to its small form factor and robust behavior, we believe this sensor presents a solution for integrating and monitoring every individual samples in a supply-chain.

In this study, a deep denoising recurrent temporal restricted Boltzmann machine network is proposed for long-term prediction of time series. The network is a deep dynamic network model which is stacked by multiple denoising recurrent temporal restricted Boltzmann machines with strong modeling ability for complex high noise time series data. To better deal with high noise data, a random noise is added to original data to form a new input sample and the model is forced to fit the original data without the noise to improve the denoising ability of the model. To train and predict real data directly, the Gaussian distribution is introduced into the energy function of the model to establish a Gaussian network model. After training the network layer by layer, this paper proposes to use the back-propagation algorithm to fine-tune the whole network and improve the modeling performance. Through the actual data simulation in the production process of the iron and steel enterprise, the comparative analysis shows that the proposed method can effectively improve the prediction accuracy and achieve satisfactory results.

Time series forecasting is an efficient approach for future prediction based on past observations. It is useful for real world applications such as, predicting gain, loss and market trends in future, production planning, weather forecasting, flood forecasting, etc. Having larger network size and symmetrical behavior, the recently proposed Bi-Swapped Networks (BSN) is an excellent class of networks for the efficient parallel implementation of such applications in comparison to OTIS networks. It is well accepted that moving average is the best-suited approach for short-term time series forecasting. In this article, we present the parallel mapping of the weighted moving averages of time series forecasting over √n × √n BSN mesh. It requires 5√n-1 intra-group (electronic) and 1 inter-part (optical) moves. We also present network scalability and compare the mapping of proposed parallel algorithm over BSN mesh with its counterpart OTIS mesh network [6]. The proposed approach claims to be cost effective and demands fewer communication moves.

The most important feature of GPS-receivers in time keeping is long-term phase stability and reliability. MIKES and Nokia have studied in cooperation the performance of medium price commercial GPS receivers both in the short term and long-term sense. By comparing time differences of the receivers, both short term and long-term stability can be evaluated assuming that all receivers do not drift into the same direction.

This paper will investigate the effect of layered snow in alpine regions on microwave emission at Ku and Ka bands, using both experimental data and model simulations. A multilayer dense-medium radiative transfer model (DMRT), was implemented under the quasi-crystalline approximation (ML-QCA), to account for the effects of snow layers on the emission from dry snow covers. The model then evaluated the sensitivity of two microwave indices, based on frequency and polarization combinations, to snow parameters. Model simulations were compared with radiometric dual frequency/polarization measurements of snow covers, collected during long-term experiments carried out over three winter seasons between 2007 and 2011 in the Eastern Italian Alps. This comparison has the twofold purpose of validating the model with experimental data and verifying the influence of snow layering on microwave emission and related frequency and polarization indices. The wide variations of snow characteristics over several winter seasons allowed for an extended validation of the model, which was demonstrated to account for the complex stratigraphy (up to 15 layers) of snow. The measured brightness temperatures at Ku and Ka bands were compared with those simulated through the multi (ML-QCA) and single-layer (SL-QCA) models, by using the observed snow parameters as inputs. In the case of SL, we used the average value of all layers weighted for the layer thickness. The results showed that the ML-QCA model was better correlated to the radiometric the radiometric measurements than the SL-QCA. A direct comparison of measured and simulated data showed that the slope of correlation for the single layer ranged between 0.4 and 0.5, with determination coefficient lower than 0.3. The slope in the multi-layer approach ranged between 0.7 and 1.0, with determination coefficients between 0.51 and 0.75, and Root Mean Square Error (RmSe) between 11K and 15K.

Deep learning is being widely used in various applications, and diverse neural networks have been proposed. A form of neural network, such as the novel feed-forward sequential memory network (FSMN), aims to forecast prospective data by extracting the time-series feature. FSMN is a standard feed-forward neural network equipped with time-domain filters, and it can forecast without recurrent feedback. In this paper, we propose a field-programmable gate-array (FPGA) architecture for this model, and exhibit that the resource does not increase exponentially as the network scale increases.