Adaptive performance optimization for large-scale traffic control systems

In this paper, we study the problem of optimizing (fine-tuning) the design parameters of large-scale traffic control systems that are composed of distinct and mutually interacting modules. This problem usually requires a considerable amount of human effort and time to devote to the successful deployment and operation of traffic control systems due to the lack of an automated well-established systematic approach. We investigate the adaptive fine-tuning algorithm for determining the set of design parameters of two distinct mutually interacting modules of the traffic-responsive urban control (TUC) strategy, i.e., split and cycle, for the large-scale urban road network of the city of Chania, Greece. Simulation results are presented, demonstrating that the network performance in terms of the daily mean speed, which is attained by the proposed adaptive optimization methodology, is significantly better than the original TUC system in the case in which the aforementioned design parameters are manually fine-tuned to virtual perfection by the system operators

Characterization of the FE-I4B pixel readout chip production run for the ATLAS Insertable B-layer upgrade

The Insertable B-layer (IBL) is a fourth pixel layer that will be added inside the existing ATLAS pixel detector during the long LHC shutdown of 2013 and 2014. The new four layer pixel system will ensure excellent tracking, vertexing and b-tagging performance in the high luminosity pile-up conditions projected for the next LHC run. The peak luminosity is expected to reach 3 x 10^34 cm^-2 s^-1 with an integrated luminosity over the IBL lifetime of 300 fb^-1 corresponding to a design lifetime fluence of 5 x 10^15 n_eq cm^-2 and ionizing dose of 250 Mrad including safety factors. The production front-end electronics FE-I4B for the IBL has been fabricated at the end of 2011 and has been extensively characterized on diced ICs as well as at the wafer level. The production tests at the wafer level were performed during 2012. Selected results of the diced IC characterization are presented, including measurements of the on-chip voltage regulators. The IBL powering scheme, which was chosen based on these results, is described. Preliminary wafer to wafer distributions as well as yield calculations are given

Automatic LQR Tuning Based on Gaussian Process Global Optimization

This paper proposes an automatic controller tuning framework based on linear optimal control combined with Bayesian optimization. With this framework, an initial set of controller gains is automatically improved according to a pre-defined performance objective evaluated from experimental data. The underlying Bayesian optimization algorithm is Entropy Search, which represents the latent objective as a Gaussian process and constructs an explicit belief over the location of the objective minimum. This is used to maximize the information gain from each experimental evaluation. Thus, this framework shall yield improved controllers with fewer evaluations compared to alternative approaches. A seven-degree-of-freedom robot arm balancing an inverted pole is used as the experimental demonstrator. Results of a two- and four-dimensional tuning problems highlight the method's potential for automatic controller tuning on robotic platforms.Comment: 8 pages, 5 figures, to appear in IEEE 2016 International Conference on Robotics and Automation. Video demonstration of the experiments available at https://am.is.tuebingen.mpg.de/publications/marco_icra_201

Coverage prediction and optimization algorithms for indoor environments

A heuristic algorithm is developed for the prediction of indoor coverage. Measurements on one floor of an office building are performed to investigate propagation characteristics and validations with very limited additional tuning are performed on another floor of the same building and in three other buildings. The prediction method relies on the free-space loss model for every environment, this way intending to reduce the dependency of the model on the environment upon which the model is based, as is the case with many other models. The applicability of the algorithm to a wireless testbed network with fixed WiFi 802.11b/g nodes is discussed based on a site survey. The prediction algorithm can easily be implemented in network planning algorithms, as will be illustrated with a network reduction and a network optimization algorithm. We aim to provide an physically intuitive, yet accurate prediction of the path loss for different building types

Design principles for riboswitch function

Scientific and technological advances that enable the tuning of integrated regulatory components to match network and system requirements are critical to reliably control the function of biological systems. RNA provides a promising building block for the construction of tunable regulatory components based on its rich regulatory capacity and our current understanding of the sequence–function relationship. One prominent example of RNA-based regulatory components is riboswitches, genetic elements that mediate ligand control of gene expression through diverse regulatory mechanisms. While characterization of natural and synthetic riboswitches has revealed that riboswitch function can be modulated through sequence alteration, no quantitative frameworks exist to investigate or guide riboswitch tuning. Here, we combined mathematical modeling and experimental approaches to investigate the relationship between riboswitch function and performance. Model results demonstrated that the competition between reversible and irreversible rate constants dictates performance for different regulatory mechanisms. We also found that practical system restrictions, such as an upper limit on ligand concentration, can significantly alter the requirements for riboswitch performance, necessitating alternative tuning strategies. Previous experimental data for natural and synthetic riboswitches as well as experiments conducted in this work support model predictions. From our results, we developed a set of general design principles for synthetic riboswitches. Our results also provide a foundation from which to investigate how natural riboswitches are tuned to meet systems-level regulatory demands

A microchip optomechanical accelerometer

The monitoring of accelerations is essential for a variety of applications ranging from inertial navigation to consumer electronics. The basic operation principle of an accelerometer is to measure the displacement of a flexibly mounted test mass; sensitive displacement measurement can be realized using capacitive, piezo-electric, tunnel-current, or optical methods. While optical readout provides superior displacement resolution and resilience to electromagnetic interference, current optical accelerometers either do not allow for chip-scale integration or require bulky test masses. Here we demonstrate an optomechanical accelerometer that employs ultra-sensitive all-optical displacement read-out using a planar photonic crystal cavity monolithically integrated with a nano-tethered test mass of high mechanical Q-factor. This device architecture allows for full on-chip integration and achieves a broadband acceleration resolution of 10 \mu g/rt-Hz, a bandwidth greater than 20 kHz, and a dynamic range of 50 dB with sub-milliwatt optical power requirements. Moreover, the nano-gram test masses used here allow for optomechanical back-action in the form of cooling or the optical spring effect, setting the stage for a new class of motional sensors.Comment: 16 pages, 9 figure

Emergence of a stable cortical map for neuroprosthetic control.

Cortical control of neuroprosthetic devices is known to require neuronal adaptations. It remains unclear whether a stable cortical representation for prosthetic function can be stored and recalled in a manner that mimics our natural recall of motor skills. Especially in light of the mixed evidence for a stationary neuron-behavior relationship in cortical motor areas, understanding this relationship during long-term neuroprosthetic control can elucidate principles of neural plasticity as well as improve prosthetic function. Here, we paired stable recordings from ensembles of primary motor cortex neurons in macaque monkeys with a constant decoder that transforms neural activity to prosthetic movements. Proficient control was closely linked to the emergence of a surprisingly stable pattern of ensemble activity, indicating that the motor cortex can consolidate a neural representation for prosthetic control in the presence of a constant decoder. The importance of such a cortical map was evident in that small perturbations to either the size of the neural ensemble or to the decoder could reversibly disrupt function. Moreover, once a cortical map became consolidated, a second map could be learned and stored. Thus, long-term use of a neuroprosthetic device is associated with the formation of a cortical map for prosthetic function that is stable across time, readily recalled, resistant to interference, and resembles a putative memory engram