Optimal Serial Distributed Decision Fusion

The problem of distributed detection involving N sensors is considered. The configuration of sensors is serial in the sense that the (j - 1)th sensor passes its decision to the jth sensor and that the jth sensor decides using the decision it receives and its own observation. When each sensor employs the Neyman-Pearson test, the probability of detection is maximized for a given probability of false alarm, at the Nth stage. With two sensors, the serial scheme has a performance better than or equal to the parallel fusion scheme analyzed in the literature. Numerical examples illustrate the global optimization by the selection of operating thresholds at the sensors

Optimal Decision Fusion in Multiple Sensor Systems

The problem of optimal data fusion in the sense of the Neyman- Pearson (N-P) test in a centralized fusion center is considered. The fusion center receives data from various distributed sensors. Each sensor implements a N-P test individually and independently of the other sensors. Due to limitations in channel capacity, the sensors transmit their decision instead of raw data. In addition to their decisions, the sensors may transmit one or more bits of quality information. The optimal, in the N-P sense, decision scheme at the fusion center is derived and it is seen that an improvement in the performance of the system beyond that of the most reliable sensor is feasible, even without quality information, for a system of three or more sensors. If quality information bits are also available at the fusion center, the performance of the distributed decision scheme is comparable to that of the centralized N-P test. Several examples are provided and an algorithm for adjusting the threshold level at the fusion center is provided

Optimal Distributed Decision Fusion

The problem of decision fusion in distributed sensor system is considered. Distributed sensors pass their decisions about the same hypotheses to a fusion center that combines them into a final decision. Assuming that the semor decisions are independent from each other conditioned on each hypothesis, we provide a general proof that the optimal decision scheme that maximizes the probability of detection at the fusion for fixed false alarm probability comists of a Neyman-Pearson test (or a randomized N-P test) at the fusion and likelihood-ratio tests at the sensors

Genetic Algorithms in Antennas and Smart Antennas Design Overview: Two Novel Antenna Systems for Triband GNSS Applications and a Circular Switched Parasitic Array for WiMax Applications Developments with the Use of Genetic Algorithms

Genetic algorithms belong to a stochastic class of evolutionary techniques, whose robustness and global search of the solutions space have made them extremely popular among researchers. They have been successfully applied to electromagnetic optimization, including antenna design as well as smart antennas design. In this paper, extensive reference to literature related antenna design efforts employing genetic algorithms is taking place and subsequently, three novel antenna systems are designed in order to provide realistic implementations of a genetic algorithm. Two novel antenna systems are presented to cover the new GPS/Galileo band, namely, L5 (1176 MHz), together with the L1 GPS/Galileo and L2 GPS bands (1575 and 1227 MHz). The first system is a modified PIFA and the second one is a helical antenna above a ground plane. Both systems exhibit enhanced performance characteristics, such as sufficient front gain, input impedance matching, and increased front-to-back ratio. The last antenna system is a five-element switched parasitic array with a directional beam with sufficient beamwidth to a predetermined direction and an adequate impedance bandwidth which can be used as receiver for WiMax signals

Optimal and Suboptimal Distributed Decision Fusion

The problem of decision fusion in distributed sensor systems is considered. Distributed sensors pass their decisions about the same hypotheses to a fusion center that combines then into a final decision. Assuming that the sensor decisions are independent from each other conditioned on each hypothesis, we provide a general proof that the optimal decision scheme that maximizes the probability of detection for fixed probability of false alarm at the fusion, is the Neymann-Pearson test at the fusion and Likelihood-Ratio tests at the sensors. The optimal set of thresholds is given via a set of nonlinear, coupled equations that depend on the decision policy but not on the priors. The nonlinear threshold equations cannot be solved in general. We provide a suboptimal algorithm for solving for the sensor thresholds through a one dimensional minimization. The algorithm applies to arbitrary type of similar or disimilar sensors. Numerical results have shown that the algorithm yields solutions that are extremely close to the optimal solutions in all the tested cases, and it does not fail in singular cases

Tenotomy-induced muscle atrophy is sex-specific and independent of NFκB

The nuclear factor-κB (NFκB) pathway is a major thoroughfare for skeletal muscle atrophy and is driven by diverse stimuli. Targeted inhibition of NFκB through its canonical mediator IKKβ effectively mitigates loss of muscle mass across many conditions, from denervation to unloading to cancer. In this study, we used gain- and loss-of-function mouse models to examine the role of NFκB in muscle atrophy following rotator cuff tenotomy - a model of chronic rotator cuff tear. IKKβ was knocked down or constitutively activated in muscle-specific inducible transgenic mice to elicit a twofold gain or loss of NFκB signaling. Surprisingly, neither knockdown of IKKβ nor overexpression of caIKKβ significantly altered the loss of muscle mass following tenotomy. This finding was consistent across measures of morphological adaptation (fiber cross-sectional area, fiber length, fiber number), tissue pathology (fibrosis and fatty infiltration), and intracellular signaling (ubiquitin-proteasome, autophagy). Intriguingly, late-stage tenotomy-induced atrophy was exacerbated in male mice compared with female mice. This sex specificity was driven by ongoing decreases in fiber cross-sectional area, which paralleled the accumulation of large autophagic vesicles in male, but not female muscle. These findings suggest that tenotomy-induced atrophy is not dependent on NFκB and instead may be regulated by autophagy in a sex-specific manner

The Concentration of Stress at the Rotator Cuff Tendon-to-Bone Attachment Site Is Conserved across Species

The tendon-to-bone attachment site integrates two distinct tissues via a gradual transition in composition, mechanical properties, and structure. Outcomes of surgical repair are poor, in part because surgical repair does not recreate the natural attachment, and in part because the mechanical features that are most critical to mechanical and physiological function have not been identified. We employed allometric analysis to resolve a paradox about how the architecture of the rotator cuff contributes to load transfer: whereas published data suggest that the mean muscle stresses expected at the tendon-to-bone attachment are conserved across species, data also show that the relative dimensions of key anatomical features vary dramatically, suggesting that the amplification of stresses at the interface between tendon and bone should also vary widely. However, a mechanical model that enabled a sensitivity analysis revealed that the degree of stress concentration was in fact highly conserved across species: the factors that most affected stress amplification were most highly conserved across species, while those that had a lower effect showed broad variation across a range of relative insensitivity. Results highlight how micromechanical factors can influence structure-function relationships and cross-species scaling over several orders of magnitude in animal size, and provide guidance on physiological features to emphasize in surgical and tissue engineered repair of the rotator cuff

Automated Real-time Anomaly Detection in Human Trajectories using Sequence to Sequence Networks

Detection of anomalous trajectories is an important problem with potential applications to various domains, such as video surveillance, risk assessment, vessel monitoring and high-energy physics. Modeling the distribution of trajectories with statistical approaches has been a challenging task due to the fact that such time series are usually non stationary and highly dimensional. However, modern machine learning techniques provide robust approaches for data-driven modeling and critical information extraction. In this paper, we propose a Sequence to Sequence architecture for real-time detection of anomalies in human trajectories, in the context of risk-based security. Our detection scheme is tested on a synthetic dataset of diverse and realistic trajectories generated by the ISL iCrowd simulator. The experimental results indicate that our scheme accurately detects motion patterns that deviate from normal behaviors and is promising for future real-world applications.Comment: AVSS 201

The role of human operators in safety perception of av deployment—insights from a large european survey

Autonomous vehicles are anticipated to play an important role on future mobility offering encouraging solutions to today’s transport problems. However, concerns of the public, which can affect the AVs’ uptake, are yet to be addressed. This study presents relevant findings of an online survey in eight European countries. First, 1639 responses were collected in Spring 2020 on people’s commute, preferred transport mode, willingness to use AVs and demographic details. Data was analyzed for the entire dataset and for vulnerable road users in particular. Results re-confirm the long-lasting discourse on the importance of safety on the acceptance of AVs. Spearman correlations show that age, gender, education level and number of household members have an impact on how people may be using or allowing their children to use the technology, e.g., with or without the presence of a human supervisor in the vehicle. Results on vulnerable road users show the same trend. The elderly would travel in AVs with the presence of a human supervisor. People with disabilities have the same proclivity, however their reactions were more conservative. Next to safety, reliability, affordability, cost, driving pleasure and household size may also impact the uptake of AVs and shall be considered when designing relevant policies