This PDF file contains the front matter associated with SPIE Proceedings Volume 6592, including the Title Page, Copyright information, Table of Contents, Introduction, and the Conference Committee listing.

In previous publications,^1-6 several approaches targeting the problem of seizure prediction⁷ in epilepsy⁸ have been proposed. In this contribution recent results based on an EEG-signal prediction algorithm will be presented and discussed in detail. Therefore segmented data aquired by multi-electrode Stereoelectroencephalography (SEEG) and Electrocorticography (ECoG) are presented to a delay-type DTCNN with linear weight functions and a 3×1 network topology. This leads to series of signal predictors and according to that to series of prediction errors. These prediction error series are arranged in a 2 dimensional representation called error profile.⁹ This profile enables the choice of optimal positions for implanting long time electrodes, by means of which perhaps a mostly effective seizure prediction may become possible. So far data of different patients have been studied in detail and some distinct electrode points were found showing distinct changes before a seizure onset.

Electroencephalograph (EEG) recording systems offer a versatile, noninvasive window on the brain's spatio-temporal activity for many neuroscience and clinical applications. Our research aims at improving the spatial resolution and mobility of EEG recording by reducing the form factor, power drain and signal fanout of the EEG acquisition node in a scalable sensor array architecture. We present such a node integrated onto a dimesized circuit board that contains a sensor's complete signal processing front-end, including amplifier, filters, and analog-to-digital conversion. A daisy-chain configuration between boards with bit-serial output reduces the wiring needed. The circuit's low power consumption of 423 &mgr;W supports EEG systems with hundreds of electrodes to operate from small batteries for many hours. Coupling between the bit-serial output and the highly sensitive analog input due to dense integration of analog and digital functions on the circuit board results in a deterministic noise component in the output, larger than the intrinsic sensor and circuit noise. With software correction of this noise contribution, the system achieves an input-referred noise of 0.277 &mgr;Vrms in the signal band of 1 to 100 Hz, comparable to the best medical-grade systems in use. A chain of seven nodes using EEG dry electrodes created in micro-electrical-mechanical system (MEMS) technology is demonstrated in a real-world setting.

We describe wireless networking systems for close proximity biological sensors, as would be encountered in artificial skin. The sensors communicate to a "base station" that interprets the data and decodes its origin. Using a large bundle of ultra thin metal wires from the sensors to the "base station" introduces significant technological hurdles for both the construction and maintenance of the system. Fortunately, the Address Event Representation (AER) protocol provides an elegant and biomorphic method for transmitting many impulses (i.e. neural spikes) down a single wire/channel. However, AER does not communicate any sensory information within each spike, other that the address of the origination of the spike. Therefore, each sensor must provide a number of spikes to communicate its data, typically in the form of the inter-spike intervals or spike rate. Furthermore, complex circuitry is required to arbitrate access to the channel when multiple sensors communicate simultaneously, which results in spike delay. This error is exacerbated as the number of sensors per channel increases, mandating more channels and more wires. We contend that despite the effectiveness of the wire-based AER protocol, its natural evolution will be the wireless AER protocol. A wireless AER system: (1) does not require arbitration to handle multiple simultaneous access of the channel, (2) uses cross-correlation delay to encode sensor data in every spike (eliminating the error due to arbitration delay), and (3) can be reorganized and expanded with little consequence to the network. The system uses spread spectrum communications principles, implemented with a low-power integrate-and-fire neurons. This paper discusses the design, operation and capabilities of such a system. We show that integrate-and-fire neurons can be used to both decode the origination of each spike and extract the data contained within in. We also show that there are many technical obstacles to overcome before this version of wireless AER can be practical.

Address Event Representation (AER) is an emergent neuromorphic interchip communication protocol that allows a real-time virtual massive connectivity between huge number neurons, located on different chips. By exploiting high speed digital communication circuits (with nano-seconds timings), synaptic neural connections can be time multiplexed, while neural activity signals (with mili-seconds timings) are sampled at low frequencies. Also, neurons generate "events" according to their activity levels. More active neurons generate more events per unit time, and access the interchip communication channel more frequently, while neurons with low activity consume less communication bandwidth. When building multi-chip muti-layered AER systems, it is absolutely necessary to have a computer interface that allows (a) reading AER interchip traffic into the computer and visualizing it on the screen, and (b) converting conventional frame-based video stream in the computer into AER and injecting it at some point of the AER structure. This is necessary for test and debugging of complex AER systems. In the other hand, the use of a commercial personal computer implies to depend on software tools and operating systems that can make the system slower and un-robust. This paper addresses the problem of communicating several AER based chips to compose a powerful processing system. The problem was discussed in the Neuromorphic Engineering Workshop of 2006. The platform is based basically on an embedded computer, a powerful FPGA and serial links, to make the system faster and be stand alone (independent from a PC). A new platform is presented that allow to connect up to eight AER based chips to a Spartan 3 4000 FPGA. The FPGA is responsible of the network communication based in Address-Event and, at the same time, to map and transform the address space of the traffic to implement a pre-processing. A MMU microprocessor (Intel XScale 400MHz Gumstix Connex computer) is also connected to the FPGA to allow the platform to implement eventbased algorithms to interact to the AER system, like control algorithms, network connectivity, USB support, etc. The LVDS transceiver allows a bandwidth of up to 1.32 Gbps, around ~66 Mega events per second (Mevps).

Address Event Representation (AER) is an emergent neuromorphic interchip communication protocol that allows real-time virtual massive connectivity among huge number of neurons located on different chips.[1] By exploiting high speed digital communication circuits (with nano-seconds timing), synaptic neural connections can be time multiplexed, while neural activity signals (with mili-seconds timings) are sampled at low frequencies. Neurons generate "events" according to their activity levels. That is, more active neurons generate more events per unit time and access the interchip communication channel more frequently than neurons with low activity. In Neuromorphic system development, AER brings some advantages to develop real-time image processing system: (1) AER represents the information like time continuous stream not like a frame; (2) AER sends the most important information first (although this depends on the sender); (3) AER allows to process information as soon as it is received. When AER is used in artificial vision field, each pixel is considered like a neuron, so pixel's intensity is represented like a sequence of events; modifying the number and the frequency of these events, it is possible to make some image filtering. In this paper we present four image filters using AER: (a) Noise addition and suppression, (b) brightness modification, (c) single moving object tracking and (d) geometrical transformations (rotation, translation, reduction and magnification). For testing and debugging, we use USB-AER board developed by Robotic and Technology of Computers Applied to Rehabilitation (RTCAR) research group. This board is based on an FPGA, devoted to manage the AER functionality. This board also includes a micro-controlled for USB communication, 2 Mbytes RAM and 2 AER ports (one for input and one for output).

Even so called "simple" organisms as insects are able to fastly adapt to changing conditions of their environment. Their behaviour is affected by many external influences and only its variability and adaptivity permits their survival. An intensively studied example concerns hexapod walking.^1,2 Complex walking behaviours in stick insects have been analysed and the results were used to construct a reactive model that controls walking in a robot. This model is now extended by higher levels of control: as a bottom-up approach the low-level reactive behaviours are modulated and activated through a medium level. In addition, the system grows up to an upper level for cognitive control of the robot: Cognition - as the ability to plan ahead - and cognitive skills involve internal representations of the subject itself and its environment. These representations are used for mental simulations: In difficult situations, for which neither motor primitives, nor whole sequences of these exist, available behaviours are varied and applied in the internal model while the body itself is decoupled from the controlling modules. The result of the internal simulation is evaluated. Successful actions are learned and applied to the robot. This constitutes a level for planning. Its elements (movements, behaviours) are embodied in the lower levels, whereby their meaning arises directly from these levels. The motor primitives are situation models represented as neural networks. The focus of this work concerns the general architecture of the framework as well as the reactive basic layer of the bottom-up architecture and its connection to higher level functions and its application on an internal model.

In this paper a new methodology for action-oriented perception will be introduced. It is based on a previous method that used Turing Patterns in CNNs for the arousal of "perceptual states" as representation of the environmental condition. The emerging patterns were associated to codes which gave rise to learnable actions on a moving robot. Recently the new paradigm of Winnerless Competition (WLC) was taken into consideration to represent a suitable, bioinspired and efficient method to generate sequences of neural activations, strictly related to the spatial-temporal activity of input sensors. This fascinating property was recently peculiarly measured in the olfactory system, in particular in groups of neurons belonging to the insects' Antennal Lobe and to the mammalians' Olfactory Bulb. Taking inspiration from these experimental results and from the analytical model of the WLC, a cellular nonlinear model generating sequences of cell activation, representing the input pattern at the sensory level, will be used in an action-oriented perception framework. In fact simulation results showed the potentiality of the WLC approach to design dynamic networks for discrimination and classification, with a potentially huge memory capacity. In the present manuscript the WLC principle, implemented in a network of FitzHugh Nagumo neurons will be used within the whole framework for action-oriented perception, and the results will be applied to a roving robot.

In order to solve the navigation problem of a mobile robot in an unstructured environment a versatile sensory system and efficient locomotion control algorithms are necessary. In this paper an innovative sensory system for action-oriented perception applied to a legged robot is presented. An important problem we address is how to utilize a large variety and number of sensors, while having systems that can operate in real time. Our solution is to use sensory systems that incorporate analog and parallel processing, inspired by biological systems, to reduce the required data exchange with the motor control layer. In particular, as concerns the visual system, we use the Eye-RIS v1.1 board made by Anafocus, which is based on a fully parallel mixed-signal array sensor-processor chip. The hearing sensor is inspired by the cricket hearing system and allows efficient localization of a specific sound source with a very simple analog circuit. Our robot utilizes additional sensors for touch, posture, load, distance, and heading, and thus requires customized and parallel processing for concurrent acquisition. Therefore a Field Programmable Gate Array (FPGA) based hardware was used to manage the multi-sensory acquisition and processing. This choice was made because FPGAs permit the implementation of customized digital logic blocks that can operate in parallel allowing the sensors to be driven simultaneously. With this approach the multi-sensory architecture proposed can achieve real time capabilities.

In this paper we consider the problem of tracking the real-time positions of a diagnostic capsule in the gastrointestinal (GI) tract. Our solution is a fully developed estimation algorithm that utilizes radio frequency signals converted to voltage on the received signal strength indicator (RSSI) output of an array of transceivers. We employed a modified form of the traditional radio-map based deterministic model that requires an estimate of initial position vector. Data capture was implemented with commercial off-the-shelf transceivers featuring RSSI outputs. At the intermediate processing stages, trilateration was employed as a mathematical tool to determine the approximate 2-D coordinates of unknown capsule locations by linearizing the resulting equations. This approach facilitates a PC-based implementation of fully automated real-time position measurements by eliminating the need to measure angles. In the final algorithm based on non linear least squares approximations, the Newton's iteration of the resulting Jacobian matrices were used to generate a more accurate position coordinates. Test Results from laboratory experiments demonstrate the accuracy of the solution in the centimeter range. This results in a position tracking measurements with an average value error of less than 25%. This kind of results guarantees that our solution can be adapted into telemetry capsules for use in diagnosing intestinal malfunctions.

In this work we present a system for the detection of labeled DNA by means of a two-color amorphous silicon photosensor. The device is a p-i-n-i-p structure, whose spectral response is controlled by tuning the voltage applied to its electrodes. The thicknesses of the different layers has been optimized to match the emission spectra of the two utilized fluorochromes. Minima detectable concentrations range in the order of few nmol/l. Very good linearity in the photosensor responses, comparable with those of commercial equipment, has been achieved.

Artificial sensitive skins are intended to emulate the human skin to improve the skills of robots and machinery in complex unstructured environments. They are basically smart arrays of pressure sensors. As in the case of artificial retinas, one problem to solve is the management of the huge amount of information that such arrays provide, especially if this information should be used by a central processing unit to implement some control algorithms. An approach to manage such information is to increment the signal processing performed close to the sensor in order to extract the useful information and reduce the errors caused by long wires. This paper proposes the use of voltage to frequency converters to implement a quite straightforward analog to digital conversion as front end interface to digital circuitry in a smart tactile sensor. The circuitry commonly implemented to read out the information from a piezoresistive tactile sensor can be modified to turn it into an array of voltage to frequency converters. This is carried out in this paper, where the feasibility of the idea is shown through simulations and its performance is discussed.

Porous silicon (PSi) nanostructures have remarkable optical properties that can be used for biosensing applications. In this paper we report first on the fabrication of heavily doped p-type PSi with pore diameters in the range of 400-4000 nm. The nonspecific and specific binding of the Glucose Oxidase protein (GOX) was then studied onto the PSi mirrorlike substrate. Adsorption of GOX was tuned by the pH of the protein solution (pI = 4.2) depending of the surface charge. PSi matrixes were first stabilized by thermal oxidation and GOX adsorption was performed once directly on the oxidized PSi surface, and also on previously functionalized PSi surfaces. In the latter case the GOX was coupled to the PSi via the S-H group of the 3-(mercaptopropyl)trimethoxysilane (MPTS). The silane-GOX and GOX interactions on the PSi surface were monitored by the Fourier Transformed Infrared spectra that display characteristic bands of the linked molecules. The interference spectrum shows a large blue shift in the Fabry-Perot interference pattern caused by the change in the refractive index of the medium implying a decrease in the effective optical thickness. Quantitative analysis shows that chemically modified PSi samples admit approximately 24% of GOX. Activity assay proved that the protein preserves its catalyst properties under these adsorption conditions.

We report current developments in biomimetic flow-sensors based on flow sensitive mechano-sensors of crickets. Crickets have one form of acoustic sensing evolved in the form of mechanoreceptive sensory hairs. These filiform hairs are highly perceptive to low-frequency sound with energy sensitivities close to thermal threshold. In this work we describe hair-sensors fabricated by a combination of sacrificial poly-silicon technology, to form silicon-nitride suspended membranes, and SU8 polymer processing for fabrication of hairs with diameters of about 50 &mgr;m and up to 1 mm length. The membranes have thin chromium electrodes on top forming variable capacitors with the substrate that allow for capacitive read-out. Previously these sensors have been shown to exhibit acoustic sensitivity. Like for the crickets, the MEMS hair-sensors are positioned on elongated structures, resembling the cercus of crickets. In this work we present optical measurements on acoustically and electrostatically excited hair-sensors. We present adaptive control of flow-sensitivity and resonance frequency by electrostatic spring stiffness softening. Experimental data and simple analytical models derived from transduction theory are shown to exhibit good correspondence, both confirming theory and the applicability of the presented approach towards adaptation.

In the paper we present a massively parallel VLSI architecture for future smart CMOS camera chips with up to one billion transistors. To exploit efficiently the potential offered by future micro- or nanoelectronic devices traditional on central structures oriented parallel architectures based on MIMD or SIMD approaches will fail. They require too long and too many global interconnects for the distribution of code or the access to common memory. On the other hand nature developed self-organising and emergent principles to manage successfully complex structures based on lots of interacting simple elements. Therefore we developed a new as Marching Pixels denoted emergent computing paradigm based on a mixture of bio-inspired computing models like cellular automaton and artificial ants. In the paper we present different Marching Pixels algorithms and the corresponding VLSI array architecture. A detailed synthesis result for a 0.18 &mgr;m CMOS process shows that a 256×256 pixel image is processed in less than 10 ms assuming a moderate 100 MHz clock rate for the processor array. Future higher integration densities and a 3D chip stacking technology will allow the integration and processing of Mega pixels within the same time since our architecture is fully scalable.

As it is known, there are five types of neurons in the mammalian retinal layer allowing the detection of several important characteristics of the visual image impinging onto the visual system, namely, photoreceptors, horizontal cells, amacrine, bipolar and ganglion cells. And it is a well known fact too, that the amacrine neuron architecture allows a first detection for objects motion, being the most important retinal cell to this function. We have already studied and simulated the Dowling retina model and we have verified that many complex processes in visual detection is performed with the basis of the amacrine cell synaptic connections. This work will show how this structure may be employed for motion detection.

Common design of a robot searching for a target emitting sensory stimulus (e.g. odor or sound) makes use of the gradient of the sensory intensity. However, the intensity may decay rapidly with distance to the source, then weak signal-to-noise ratio strongly limits the maximal distance at which the robot performance is still acceptable. We propose a simple deterministic platform for investigation of the searching problem in an uncertain environment with low signal to noise ratio. The robot sensory layer is given by a differential sensor capable of comparing the stimulus intensity between two consecutive steps. The sensory output feeds the motor layer through two parallel sensory-motor pathways. The first "reflex" pathway implements the gradient strategy, while the second "integrating" pathway processes sensory information by discovering statistical dependences and eventually correcting the results of the first fast pathway. We show that such parallel sensory information processing allows greatly improve the robot performance outside of the robot safe area with high signal to noise ratio.

In this work a biologically inspired network of spiking neurons is used for robot navigation control. The two tasks taken into account are obstacle avoidance and landmark-based navigation. The system learns the correlation among unconditioned stimuli (pre-wired sensors) and conditioned stimuli (high level sensors) through Spike Timing Dependent Plasticity (STDP). In order to improve the robot behaviours not only the synaptic weight but also the synaptic delay is subject to learning. Modulating the synaptic delay the robot is able to store the landmark position, like in a short time memory, and to use this information to smooth the turning actions prolonging the landmark effects also when it is no more visible. Simulations are carried out in a dynamic simulation environment and the robotic system considered is a cockroach-inspired hexapod robot. The locomotion signals are generated by a Central Pattern Generator and the spiking network is devoted to control the heading of the robot acting on the amplitude of the leg steps. Several scenarios have been proposed, for instance a T-shaped labyrinth, used in laboratory experiments with mice to demonstrate classical and operant conditioning, has been considered. Finally the proposed adaptive navigation control structure can be extended in a modular way to include other features detected by new sensors included in the correlation-based learning process.

Locomotion control of legged robots is nowadays a field in continuous evolution. In this work a bio-inspired control architecture based on the stick insect is applied to control the hexapod robot Gregor. The control scheme is an extension of Walknet, a decentralized network inspired by the stick insect, that on the basis of local reflexes generates the control signals needed to coordinate locomotion in hexapod robots. Walknet has been adapted to the specific mechanical structure of Gregor that is characterized by specialized legs and a sprawled posture. In particular an innovative hind leg geometry, inspired by the cockroach, has been considered to improve climbing capabilities. The performances of the new control architecture have been evaluated in dynamic simulation environments. The robot has been endowed with distance and contact sensors for obstacle detection. A heading control is used to avoid large obstacles, and an avoidance reflex, as can be found in stick insects, has been introduced to further improve climbing capabilities of the structure. The reported results, obtained in different environmental configurations, stress the adaptive capabilities of the Walknet approach: Even in unpredictable and cluttered environments the walking behaviour of the simulated robot and the robot prototype, controlled through a FPGA based board, remained stable.

In this paper a new methodology for landmark navigation will be introduced. Either for animals or for artificial agents, the whole problem of landmark navigation can be divided into two parts: first, the agent has to recognize, from the dynamic environment, space invariant objects which can be considered as suitable landmarks for driving the motion towards a goal position; second, it has to use the information on the landmarks to effectively navigate within the environment. Here, the problem of determining landmarks has been addressed by processing the external information through a spiking network with dynamic synapses plastically tuned by an STDP algorithm. The learning processes establish correlations between the incoming stimuli, allowing the system to extract from the scenario important features which can play the role of landmarks. Once established the landmarks, the agent acquires geometric relationships between them and the goal position. This process defines the parameters of a recurrent neural network (RNN). This in turn drives the agent navigation, filtering the information about landmarks given within an absolute reference system (e.g the North). When the absolute reference is not available, a safety mechanism acts to control the motion maintaining a correct heading. Simulation results showed the potentiality of the proposed architecture: this is able to drive an agent towards the desired position in presence of stimuli subject to noise and also in the case of partially obscured landmarks.

Some recent robot controllers for hexapod walking have been developed based on investigations of stick insects. These animals live in an unpredictable environment that consists of twigs and leaves. Supports like twigs, leaves and branches induce a considerable amount of movement to the legs and their elastic joints. Earlier studies proposed negative feedback PD-controllers to regulate the angles of the knee joints to handle this situation. Recent studies suggest that the behaviour of the joint controller depends on the compliance of the substrate the insect is standing on. On highly elastic substrates (e.g. leaves) the joint controller exhibits an I-characteristic. Deviations from the original position are compensated completely. On moderately elastic substrates (e.g. twigs) the joint controller comprises a P-characteristic. The leg attains a resting position that differs from the original position through application of a specific compensation force. On stiff substrates the knee joint seems to be controlled by a D-controller. If the leg endpoint is forced away from the original position by an external disturbance (e.g. a moving branch), the controller compensates this deviation by activation of the according muscle which results in a counter force. After some time the controller seems to "give up." The force decreases to zero. To model these results, we propose a self-adjusting joint controller that changes its own setpoint in dependance of the substrate stiffness. The substrate stiffness is determined by means of a correlator circuit that compares (superimposed) movement commands with the actual responses of the leg joint. The new controller can be used for the control of legged robots.

In this paper we study the problem of obstacle avoidance for a redundant manipulator. The manipulator is controlled through an already developed recurrent neural network, called MMC-model (Mean of Multiple Computation), able to solve the kinematics of manipulators in any configuration. This approach solves both problems of direct and inverse kinematics by simple numerical iterations. The MMC-model here proposed is constituted by a linear part that performs the topological analysis without any constraint and by a second layer, with nonlinear blocks used to add the constraints related to both the mechanical structure of the manipulator and the obstacles located in the operative space. The control architecture was evaluated in simulation for a planar manipulator with three links. The robot starting from a given initial configuration is able to reach a target position chosen in the operative space avoiding collisions with an obstacle placed in the plane. The obstacle is identified by simulated sensors placed on each link, they can measure the distance between link and obstacle. The reaction to the obstacle proximity can be modulated through a damping factor that improves the smoothing of the robot trajectory. The good results obtained open the way to a hardware implementation for the real-time control of a redundant manipulator.

This work presents the design and simulations of an interdigitated micro-electrode array aimed to discriminate cancer cells, with potential applications in predictive oncology, diagnostics and anti-tumor drug research. The device has been designed and a technological fabrication process has been defined. The microsystem consists of a quartz substrate with gold electrodes and a microfabricated three-dimensional structure (glass/SU8) for cells confinement. The designed device consists of three separated part: two circular areas for confinement, detection and moving and a narrow channel between those areas for the transportation of the cells. The detection can be done by measuring the charge variations associated to the different membrane capacitances and conductivities of tumor and normal cells. Alternating electric field over the electrodes is used for moving the cells between the circular areas (Travelling Wave Dielectrophoresis TwDEP) as well as for levitating and trapping cells on the electrodes (positive-negative Dielectrophoresis p-nDEP). Analytical and Finite Elements simulations have been performed in order to verify the system reliability and to estimate the parameters in use, like operative frequency, voltage, expected velocity.

Optical manipulation techniques have become an important research tool for single cell experiments in microbiology. Using optical tweezers, single cells can be trapped and held during long experiments without risk of cross contamination or compromising viability. However, it is often desirable to not only control the position of a cell, but also to control its environment. We have developed a method that combines optical tweezers with a microfluidic device. The microfluidic system is fabricated by soft lithography in which a constant flow is established by a syringe pump. In the microfluidic system multiple laminar flows of different media are combined into a single channel, where the fluid streams couple viscously. Adjacent media will mix only by diffusion, and consequently two different environments will be separated by a mixing region a few tens of micrometers wide. Thus, by moving optically trapped cells from one medium to another we are able to change the local environment of the cells in a fraction of a second. The time needed to establish a change in environment depends on several factors such as the strength of the optical traps and the steepness of the concentration gradient in the mixing region. By introducing dynamic holographic optical tweezers several cells can be trapped and analyzed simultaneously, thus shortening data acquisition time. The power of this system is demonstrated on yeast (Saccharomyces cerevisiae) subjected to osmotic stress, where the volume of the yeast cell and the spatial localization of green fluorescent proteins (GFP) are monitored using fluorescence microscopy.

A new non-invasive real-time system for the monitoring and control of microfluidodynamic phenomena is proposed. The general purpose design of such system is suitable for in vitro and in vivo experimental setup and therefore for microfluidic application in the biomedical field such as lab-on-chip and for research studies in the field of microcirculation. The system consists of an ad hoc optical setup for image magnification providing images suitable for image acquisition and processing. The optic system was designed and developed using discrete opto-mechanic components mounted on a breadboard in order to provide an optic path accessible at any point where the information needs to be acquired. The optic sensing, acquisition, and processing were performed using an integrated vision system based on the Cellular Nonlinear Networks (CNNs) analogic technology called Focal Plane Processor (FPP, Eye-RIS, Anafocus) and inserted in the optic path. Ad hoc algorithms were implemented for the real-time analysis and extraction of fluido-dynamic parameters in micro-channels. They were tested on images recorded during in vivo microcirculation experiments on hamsters and then they were applied on images optically acquired and processed in real-time during in vitro experiments on a continuous microfluidic device (serpentine mixer, ThinXXS) with a two-phase fluid.

Lasers are adequate tools for the production of patterns with high spatial resolution owing to the high focusing power of their radiation. Laser induced forward transfer (LIFT) is a direct-writing technique allowing the deposition of tiny amounts of material from a donor thin film through the action of a pulsed laser beam. A laser pulse is focused onto the donor thin film through a transparent support, what results in the transference of a small area of the film onto a receptor substrate that is placed parallel to the film-support system. Although LIFT was originally developed to operate with solid films, it has been demonstrated that deposition is also viable from liquid films. In this case, a small amount of liquid is directly ejected from the film onto the receptor substrate, where it rests deposited in the form of a microdroplet. This makes LIFT adequate for biosensors preparation, since biological solutions can be transferred onto solid substrates to produce micrometric patterns of biomolecules. In this case, the liquid solvent acts as transport vector of the biomolecules. The viability of the technique has been demonstrated through the preparation of functional miniaturized biosensors showing similar performances and higher scales of integration than those prepared through more conventional techniques.

We studied the enzyme glucose oxidase (GOx) immobilization on silicon oxide surfaces. In particular, we optimized the immobilization protocol and verified that it fulfills both requirements of enzyme preservation (measured by enzymatic activity) and VLSI compatibility. The immobilization consists of four steps: oxide activation, silanization, linker molecule deposition and GOx immobilization. It is crucial to form an uniform linker layer on the sample surface in order to maximize the sites available for enzyme bonding and achieving the best enzyme deposition. In this study, utilizing glutaraldehyde as bifunctional reagent, we monitored its uniformity on the surface through X-ray Photoelectron Spectroscopy (XPS). Once optimized, the same protocol was used to anchor the enzyme in a porous silicon dioxide matrix. Gold labeled GOx molecules were monitored by electron diffraction X-ray (EDX) measurements coupled with scanning electron microscopy (SEM). The enzymatic activity was also monitored to confirm the goodness of the proposed immobilization method. Finally, the electrical characterization of MOS capacitors, showing a shift of about 1 V in the flat band voltage, demonstrated the possibility to use this approach for electrical detection.

Exploiting the concepts learnt from nature to build new nanomaterials from the bottom-up is critical for the efficient design of complex nanodevices. We demonstrate for the first time that the capacity of the &agr;-synuclein protein to assemble into nanofibers can be used for the synthesis of metallic nanowires. Silver and platinum nanowires with controlled diameters, ranging from 15 to 125nm, have been synthesized on an &agr;-synuclein protein fiber scaffold.

Nanoscale functionalization of semiconductor quantum dots (SQDs) with biomedical structures is promising for many applications and novel studies of intrinsic properties of both constituent systems. Results of our study of structural properties of the nanoscale functionalized SQDs such as CdS, and ZnS-capped CdSe SQDs, conjugated with biomolecules such as short peptides and cells are presented. We study CdS SQDs functionalized with peptides specially composed of the following amino acid chains: CGGGRGDS, CGGGRVDS, CGGIKVAV, and CGGGLDV, where R is arginine, D - aspartic acid, S - serine, V - valine, K - lysine and L is Levine. As will be seen the cysteine (C) amino acid links to CdS SQDs via the thiol link, the GGG sequences of glycine (G) amino acid, provide a spacer in the amino acid chain. At the same time the RGDS, RVDS, IKAV, and LDV sequences have selective bonding affinities to specialized transmembrane cellular structures known as integrins of neurons and MDA-MB-435 cancer cells, respectively. We found that the quantum confinement and functionalizing in biomedical environments plays in altering and determining the electronic, optical, and vibrational properties of these nanostructures as well as demonstrated the effectiveness to use semiconductor quantum dots as integrin sensitive biotags.

The ability to anchor cells in predefined patterns on a surface has become very important for the development of cell-based sensors, tissue-engineering applications, and the understanding of basic cell functions. Currently, the most widely used technique to generate micrometer or sub-micrometer-sized patterns for various biological applications is microcontact printing (&mgr;CP). However, the fidelity of the final pattern may be compromised by deformation of the PDMS stamps used during printing. A novel technique for accurately patterning and positioning biological cells is presented, which can overcome this obstacle. We have fabricated a chip on a silicon wafer using standard photolithographic and deposition processes consisting of gold patterns on top of PECVD silicon dioxide. A hydrophobic self-assembled monolayer (SAM) derived from 1-hexadecanethiol (HDT) was coated on the gold surface to prevent cell growth, and a hydrophilic SAM derived from (3-trimethoxysilyl propyl)-diethylenetriamine (DETA) was coated on the exposed PECVD silicon dioxide surface to promote cell growth. Immortalized mouse hypothalamic neurons (GT1-7) were cultured in vitro on the chip, and patterned cells were fluorescently stained and visualized by fluorescence microscopy. By our method, hydrophobic and hydrophilic regions can be reliably generated and easily visualized under a microscope prior to cell culturing. Cell growth was precisely controlled and limited to specific areas. The achieved resolution was 2 microns, and it could be improved with high resolution photolithographic methods.

Functionalization of semiconductors (SC) has been widely used for various electronic, photonic and biomedical applications. In this paper, we report on selective functionalization achieved by peptides that reveal specific recognition of the SC surfaces. A M13 bacteriophage library was used to screen 10¹⁰ different 12-mer peptide on various SC substrates to successfully isolate after 3 cycles one specific peptide for the majority of semiconductors. Our results conclude that GaAs(100) and GaN(0001) retain the same sequence of 12-mer peptide, suggesting that the specificity does not depend on the crystallographic structure but it depends on the chemical composition and the electronegativity of the surface, thus on the orientation of the material. We also note the presence of at least one proline (Pro) amino acid in each peptide, and the presence of the histidine (His) in the specific peptides for the II-VI class SC. Pro imprints a constraint to the peptide to facilitate adhesion to the surface, whereas the basic side chain His is known for its affinity towards some of the elements of class II SC. Finally, fluorescence microscopy has been employed to demonstrate the preferential attachment of the peptide to their specific SC surface in close proximity to a surface of different chemical and structural composition. The use of selected peptides expressed by phage display can be extended to encompass a variety of nanostructured semiconductor based devices.

A complete innovative and portable, for on-field operation, Surface Plasmon Resonance (SPR) biosensing system for wine quality monitoring was designed, realized and tested; the system takes advantage of the innovative module Spreeta™, an integrated transducer designed by Texas Instruments able to recognize the Surface Resonance Phenomena and to represent it through electrical signals. The system, based on an 8 bit microprocessor board, acquires through a 12 bit A/D converter, elaborates and sends to a PC data from the Spreeta™ sensor. A proprietary high-level software calculates the refractive index related to biological solution flowing on the surface sensor and lets to detect suitable substances, custom defined. At the moment this system was finalized to Ochratoxin-A (OTA) detection in wine.

Immobilization of ion channels requires of a methodology able to retain the physical properties of the lipid bilayer where their activity is performed. However, most of lipid membrane immobilization methods have been observed to alter the structural properties of the bilayers. Use of sol-gel routes seems to be an interesting alternative, although unstable liposomes were obtained when conventional sol-gel methodology was employed for immobilizing. Recently, we have suggested that use of alcohol-free sol-gel routes combined with negatively charged lipids could minimize effects exerted by host matrix on liposome structure, increasing its stability. Here we confirm this assumption by analysing the physical properties of a series of zwitterionic and anionic liposomes entrapped in a sol-gel matrix and we develop a methodology able to retain the physical properties of the lipid bilayer. This methodology has been successfully used to immobilize the transmembrane ion channel peptide gramicidin. Gramicidin was reconstituted in anionic liposomes and its immobilization was confirmed from changes observed in the photophysical properties of the tryptophan residues. Ion channel activity was determined using the fluorescent dye pyrene-1,3,6,8-tetrasulphonic acid (PTSA) and long term stability of the immobilized system was checked from steady-state fluorescence anisotropy measurements.

Most of the conjugated polymer employed as fluorescent biosensors present low solubility and emission in aqueous environment. In order to solve this feature, we have reconstituted, in buffer phosphate, a neutral conjugated poly[9,9- bis(6'-bromohexil)-2,7-fluorene-co-alt-1,4-phenylene], as PFPBr₂ (insoluble in water), in the presence of an artificial zwitterionic phospholipids bilayers, as 1,2-dimyristoyl-sn-glycero-3-phospho-choline (DMPC). Quantum yield of PFPBr₂-DMPC was around 20% in phosphate buffer, it was identical value calculated from ammonium polyelectrolytes (PFPNMe³⁺). In addition, the maximum of bluish emission for buffer solution of PFPBr₂-DMPC was at 420nm, a red-shift emission with regard to chloroform solution (at 410 nm). The structural study at different concentrations of PFPBr₂ and DMPC was carried out using different approaches: steady state fluorescence spectroscopy, confocal fluorescence microscopy and calorimetry. A positive interaction takes place involving neutral conjugated polymer and zwitterionic phospholipids bilayer. Novels complexes or associations of poly(fluorene-phenylene) (PFPBr₂) and zwitterionic phospholipids (DMPC) have been suggested and visualized by epifluorescence. Phase transitions of the liposomes have been also detected by differential scanning calorimetry.

Entropy as a measure of information and uncertainty can be calculated in a pixel basis through the use of a spatial/spatial-frequency distribution. A normalized windowed pseudo-Wigner distribution (PWD) and the generalized Renyi entropy have been selected to define locally pixel-wise entropy. The PWD has been calculated in a 1-D window basis, adding directionality to the analysis and allowing in such way an anisotropic image evaluation. By means of this, a value of entropy can be assigned to each pixel of the image and therefore a histogram of these entropy values can be obtained. Statistical parameters of such entropy distribution have been derived to define a new image quality metric that can be interpreted as measure associated to the anisotropy of images. The purpose of this paper is to show how such metric constitutes a useful tool to assess both the fidelity and quality of images. Experimental results have been presented for assessing different noisy and blurred images and for the quality evaluation of resolution enhanced images.

The performance evaluation of image fusion algorithms is a difficult task using standard objective metrics specially because there is no reference image to compare with. In this paper we present a nonreference image fusion quality assessment procedure based on the use of the mutual information between the source images and the generalized Tsallis entropy. This procedure will be useful in other image quality evaluation scenarios as well, where the absence of a ground truth reference image can hamper the assessment of the results.