State-of-the-art neural microprobes contain hundreds of electrodes within a single shaft. Due to hardware and wiring restrictions, it is usually only possible to measure a small subset of the available electrodes simultaneously. The selection of the best channels is typically performed offline either manually or automatically. However, having a fixed selection for long-term observation does not allow the system to react to changes in the neural activity, and may therefore lead to the loss of important information. In this paper, we formulate the process of autonomously selecting the best subset of electrodes as a combinatorial multi-armed bandit problem with non-stationary rewards, thus allowing the probe to adapt its selection policies online. In order to minimize exploratory actions of the probe, we furthermore take advantage of the existing dependencies between neighboring channels. Our approach is an adaptation of the discounted upper confidence bounds (D-UCB) algorithm, and identifies the electrodes providing the largest amount of non-redundant information. To the best of our knowledge, this is the first online approach for the problem of electrode selection. In extensive experiments, we demonstrate that our solution is not only able to converge towards an average optimal selection policy, but it is also able to react to changes in the neural activity or to damages of the recording electrodes.

This paper presents a self-powered interface circuit for micromachined piezoelectric energy harvesters (PEH). The system implements the Synchronous Electric Charge Extraction (SECE) technique. Micro- and millimeter-scale PEHs are usually limited to tens or hundreds of microwatts with output voltages that typically do not exceed 1-3 V. Through system- and circuit-level optimizations the proposed interface can operate from as little as 1.5 μW, and can cold-start from a completely discharged state with PEH open circuit AC voltages as small as 900 mVpk. The system can operate up to 7 Vpkwith extraction efficiency as high as 86%. The circuit is designed for the XFAB 0.35 μm CMOS process.

High-resolution microprobes are used to record single neuron activity in the brain. This technology is envisaged to be a central component for brain-controlled computers and robots. Current neural probes, however, allow for recording only a small number of the densely spaced electrodes simultaneously. Therefore, we address the problem of autonomously choosing, for a given number, the subset of electrodes with the corresponding size so as to extract as much information as possible. We first present an approach for predicting neural spikes across different channels of the probe. Our method employs nonparametric sparse Gaussian process regression to predict the signal of a channel given the signals recorded at neighboring sites. Second, we utilize the signal predictions for efficiently seeking for the subset of electrodes that minimizes the overall prediction error. In experiments carried out using real neural data, we demonstrate that our selection procedure provides highly accurate results. Furthermore, the solutions found in our experiments are close to the optimal solution.

Recently, autonomous miniature airships have become a growing research field. Whereas airships are attractive as they can move freely in the three-dimensional space, their high-dimensional state space and the restriction to small and lightweight sensors are demanding constraints with respect to self-localization. Furthermore, their complex second-order kinematics makes the estimation of their pose and velocity through dead reckoning odometry difficult and imprecise. In this paper, we consider the problem of estimating the velocity of a miniature blimp with lightweight air flow sensors. We present a probabilistic sensor model that accurately models the uncertainty of the flow sensors and thus allows for robust state estimation using a particle filter. In experiments carried out with a real airship we demonstrate that our method precisely estimates the velocity of the blimp and outperforms the standard velocity estimates of the motion model as applied in many existent autonomous blimp navigation systems.

Recently developed CMOS-based microprobes contain hundreds of electrodes on a single shaft with inter-electrode distances as small as 30 μm. So far, neuroscientists needed to select electrodes manually from hundreds of electrodes. Here we present an electronic depth control algorithm that allows to select electrodes automatically, hereby allowing to reduce the amount of data and locating those electrodes that are close to neurons. The electrodes are selected according to a new penalized signal-to-noise ratio (PSNR) criterion that demotes electrodes from becoming selected if their signals are redundant with previously selected electrodes. It is shown that, using the PSNR, interneurons generating smaller spikes are also selected. We developed a model that aims to evaluate algorithms for electronic depth control, but also generates benchmark data for testing spike sorting and spike detection algorithms. The model comprises a realistic tufted pyramidal cell, non-tufted pyramidal cells and inhibitory interneurons. All neurons are synaptically activated by hundreds of fibers. This arrangement allows the algorithms to be tested in more realistic conditions, including backgrounds of synaptic potentials, varying spike rates with bursting and spike amplitude attenuation.

Due to their unique properties, electromagnetic bandgap (EBG) materials are of high interest for applications in communication technology for many frequency bands from microwave up to optical frequencies. We have investigated in both simulation and experiment a two dimensionally periodic EBG structure made by reactive ion etching of silicon with a bandgap for transverse magnetic waves in the millimeter wave range around 100 GHz. The structure comprises both a large bandgap and a high mechanical stability due to interconnecting dielectric bridges.

An integrated circuit (IC) approach to thermal microsensors is presented. The focus is on thermal sensors with on-chip bias and signal conditioning circuits made by industrial complementary metal-oxide-semiconductor (CMOS) IC technology in combination with post-CMOS micromachining or deposition techniques. CMOS materials and physical effects pertinent to thermal sensors are summarized together with basic structures used for microheaters, thermistors, thermocouples, thermal isolation, and heat sinks. As examples of sensors using temperature measurement, we present micromachined CMOS radiation sensors and thermal converters. Examples for sensors based on thermal actuation include thermal flow and pressure sensors, as well as thermally excited microresonators for position and chemical sensing. We also address sensors for the characterization of process-dependent thermal properties of CMOS materials, such as thermal conductivity, Seebeck coefficient, and heat capacity, whose knowledge is indispensable for thermal sensor design. Last, two complete packaged microsystems-a thermoelectric air-flow sensor and a thermoelectric infrared intrusion detector-are reported as demonstrators.