Abstract

The design and fabrication of a rolled dielectric elastomer actuator is described and the parametric dependence of the displacement and blocked force on the actuator geometry, elastomer layer thickness, voltage, and number of turns is analyzed. Combinations of different elastomers and carbon nanotube electrodes are investigated and optimized to meet performance characteristics appropriate to tactile display applications, namely operation up to 200 Hz with a combination of a 1 N blocked force and free displacement of 1 mm, all within a volume of less than 1 cm3. Lives in excess of 50 000 cycles have been obtained. Key to meeting these objectives is control of the multilayering fabrication process, the carbon nanotube electrode concentration, the selection of a soft elastomer with low viscous losses, and a proof‐testing procedure for enhancing life cycle reliability.

Abstract

A master equation has been constructed for a global system–bath interaction in the both absence and presence of non-Markovian noise. For the memoryless case, it has been exactly solved for a paradigmatic class of two qubit states in high- and zero-temperature thermal environment. For the non-Markovian model, it has been solved for zero-temperature bath. The evolution of quantum coherence and entanglement has been observed in the presence of the above-mentioned interactions. We show that the global part of the system–bath interaction compensates for the decoherence, resulting in slowdown of coherence and entanglement decay. For an appropriately defined limiting case, both coherence and entanglement show freezing behavior for the high-temperature bath. In case of zero-temperature bath, the mentioned interaction not only stabilizes the non-classical correlations, but also enhances them …

Abstract

The focus of this paper is on evaluating different spatial interpolation methods for the construction of radio environment map using field measurements obtained by cyclostationary-based mobile sensors. Mobile sensing devices employing cyclostationary detectors provide lot of advantages compared to the widely used energy detectors, such as robustness to noise uncertainty and ability to distinguish among different primary user signals. However, mobile sensing results are not available at locations between the sensors making it difficult for a secondary user (possibly without a spectrum sensor) to decide whether to use primary user resources at that location. To overcome this, spatial interpolation of test statistics measured at limited number of locations can be carried out to create a channel occupancy map at unmeasured locations between the sensors. For this purpose, different spatial interpolation techniques for the cyclostationary test statistic have been employed in this paper such as inverse distance weighting, ordinary kriging, and universal kriging. The effectiveness of these methods is demonstrated by applying them on extensive real-world field measurement data obtained by mobile-phone-compliant spectrum sensors. The field measurements were carried out using four mobile spectrum sensors measuring eight digital video broadcasting-terrestrial (DVB-T) channels at more than hundred locations encompassing roughly one-third of the area of the city of Espoo in Finland. The accuracy of the spatial interpolation results based on the field measurements is determined using the cross-validation approach with the widely used root mean square error as the metric. Field measurement results indicate that reliable results with spatial coverage can be achieved using kriging for cyclostationary based test statistics. Comparison of spatial interpolation results of cyclostationary test statistics is also carried out with those of energy values obtained during the measurement campaign in the form of received signal strength indicator. Comparison results clearly show the performance improvement and robustness obtained using cyclostationary based detectors instead of energy detectors.

Abstract

The focus of this paper is on a feature detector for filter bank multicarrier (FBMC) signal in cognitive radio. In this paper, we first prove that the FBMC signal samples are uncorrelated with each other. However, if the FBMC signal is processed by our proposed method, then the autocorrelation function (ACF) of FBMC signal becomes non-zero at the lag equal to number of subcarriers. On the other hand, additive white Gaussian noise (AWGN) samples after the same proposed processing remain uncorrelated. Using this feature, an autocorrelation based feature detector is proposed to detect FBMC signal in noise. The main advantage of the proposed detector is that, unlike blind detectors, this detector can distinguish between FBMC signal and noise (or interference). Next, the distribution of the test statistic of the proposed detector is derived under noise-only scenario so that the threshold of the Neyman-Pearson detector can be designed to maintain constant false alarm rate while maximizing the probability of detection. Simulation results demonstrate the efficacy of the proposed detector

Abstract

In this paper, we investigate a distributed and heterogeneous cognitive radio network (CRN), comprising of secondary users (SUs) employing either energy detector (ED) or autocorrelation detector (AD) to detect the presence or absence of an orthogonal frequency-division multiplexing (OFDM) based primary user (PU). For the considered heterogeneous cooperative spectrum sensing (CSS), the optimal soft combining rule is derived. The performance of this optimal fusion rule and different hard combining schemes such as OR, AND, and MAJOR- ITY is presented for the case when the noise variance is exactly known. Later, the effect of noise uncertainty is also presented. The proposed heterogeneous CSS is shown to combine the excellent performance of the EDs (when the noise variance is exactly known) and robustness of the ADs to the noise uncertainty.

Abstract

In this paper, we propose two two-stage spectrum sensing schemes for a single secondary user (SU) or cognitive radio (CR) with multiple antennas to detect a primary user (PU) transmission. For both the proposed schemes, the first stage involves low-complexity coarse-sensing using simple energy detection (ED). The second stage for both methods involve high-performance fine-sensing using beamformed energy detection (BFED) in the estimated direction of arrival (DoA) of the PU signal. In the two-stage method, the second stage is conditional and sensing process goes to the second stage only if certain performance criteria is not met in the first stage. The two proposed methods differ in the performance criteria, which decides if the second stage of BFED is needed or not. The first two-stage method is designed to reduce complexity when there is no PU transmission while the second method is designed to reduce complexity when the PU signal is present. It is shown through simulations that the proposed two-stage schemes have significantly lower complexity as compared to only employing single-stage BFED with little or no performance loss.

Abstract

With the success of deep learning based approaches in tackling challenging problems in computer vision, a wide range of deep architectures have recently been proposed for the task of visual odometry (VO) estimation. Most of these proposed solutions rely on supervision, which requires the acquisition of precise ground-truth camera pose information, collected using expensive motion capture systems or high-precision IMU/GPS sensor rigs. In this work, we propose an unsupervised paradigm for deep visual odometry learning. We show that using a noisy teacher, which could be a standard VO pipeline, and by designing a loss term that enforces geometric consistency of the trajectory, we can train accurate deep models for VO that do not require ground-truth labels. We leverage geometry as a selfsupervisory signal and propose "Composite Transformation Constraints (CTCs)", that automatically generate supervisory signals for training and enforce geometric consistency in the VO estimate. We also present a method of characterizing the uncertainty in VO estimates thus obtained. To evaluate our VO pipeline, we present exhaustive ablation studies that demonstrate the efficacy of end-to-end, self-supervised methodologies to train deep models for monocular VO. We show that leveraging concepts from geometry and incorporating them into the training of a recurrent neural network results in performance competitive to supervised deep VO methods.

Abstract

The core of Reinforcement learning lies in learning from experiences. The performance of the agent is hugely impacted by the training conditions, reward functions and exploration policies. Deep Deterministic Policy Gradient(DDPG) is a well known approach to solve continuous control problems in RL. We use DDPG with intelligent choice of reward function and exploration policy to learn various driving behaviors(Lanekeeping, Overtaking, Blocking, Defensive, Opportunistic) for a simulated car in unstructured environments. In cluttered scenes, where the opponent agents are not following any driving pattern, it is difficult to anticipate their behavior and henceforth decide our agent’s actions. DDPG enables us to propose a solution which requires only the sensor information at current time step to predict the action to be taken. Our main contribution is generating a behavior based motion model for simulated cars, which plans for every instant

Abstract

The analyzed system model in this paper is a distributed parallel detection network in which each secondary user (SU) evaluates the energy-based test statistic from the received observations and sends it to a fusion center (FC), which makes the final decision. Uncertainty in the noise variance at each SU is modeled as an unknown constant in a certain interval around the nominal noise variance. It is assumed that the SUs are heterogeneous in that the nominal noise variances and the uncertainty intervals can be different for different SUs. Moreover, the received signal power at each SU may be different. For the considered system model, the paper presents important results for two inter-related themes on cooperative energy detection (CED) in the presence of noise uncertainty (NU). First, the expressions for generalized signal-to-noise ratio (SNR) walls are derived for the classical CED fusion rule, i.e., sum of energies from all SUs. Second, a Dempster-Shafer theory based CED is proposed in the presence of NU with heterogeneous sensors. In the proposed scheme, the test statistic from each SU is the energy-based basic mass assignment values, which are first discounted depending on the uncertainty level associated with the SU and then fused at the FC using the Dempster rule of combination to arrive at the global decision. It is shown that the proposed scheme outperforms the traditional sum fusion rule in terms of detection performance as well as the location of SNR wall.

Abstract

In buildings, a large chunk of energy is spent on heating, ventilation and air conditioning systems. One way to optimize their usage is to make them demand-driven depending on human occupancy. This paper focuses on accurately estimating the number of occupants in a room by leveraging multiple heterogeneous sensor nodes and machine learning models. For this purpose, low-cost and non-intrusive sensors such as CO 2 , temperature, illumination, sound and motion were used. The sensor nodes were deployed in a room in a star configuration and measurements were recorded for a period of four days. A regression based method is proposed for calculating the slope of CO 2 , a new feature derived from real-time CO 2 values. Supervised learning algorithms such as linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), support vector machine (SVM) and random forest (RF) were used on several different combinations of feature sets. Moreover, multiple performance metrics such as accuracy, F1 score and confusion matrix were used to evaluate the performance of our models. Experimental results demonstrate a maximum accuracy of 98.4% and a high F1 score of 0.953 for estimating the number of occupants in the room. Principal component analysis (PCA) was also applied to evaluate the performance of a dataset with reduced dimensionality.