A LTE MIMO OTA Test System Using Vector Signal Transceivers

A 2 × 2 multiple-input-multiple-output over-the-air (MIMO OTA) test system based on four field-programmable Vector-Signal-Transceiver (VST) modules is presented. The system enables 2 x 2 MIMO OTA testing by assembling of a twochannel Evolved Node B (eNodeB) LTE base station emulator, a 2x2 channel emulator, and a two-channel user equipment (UE) simulator. A two-stage MIMO OTA test method has been demonstrated with downlink Long-Term Evolution Time-Division Duplex (LTE-TDD) mode using different modulation and coding schemes (MCSs). Test results and analysis are shown. This system will allow a systematic study of MIMO OTA metrology needs

A 2.4-GHz CMOS short-range wireless-sensor-network interface for automotive applications

This paper describes a CMOS interface for shortrange wireless sensor networks (CMOS-SRWSN interface). The sensor interface is composed of a sensor readout, electronics for processing and control, a memory, a radio-frequency CMOS transceiver for operation in the 2.4-GHz industrial, scientific, and medical bands, and a planar antenna. The receiver has a sensitivity of −60 dBm and consumes 6.3 mW from a 1.8-V supply. The transmitter delivers an output power of 0 dBm with a power consumption of 11.2 mW. The application of the CMOS-SRWSN interface is in the automotive industry for the reduction of cables and to support the information, communication, and entertainment systems in cars.

Toward Brain Area Sensor Wireless Network

RÉSUMÉ De nouvelles approches d'interfaçage neuronal de haute performance sont requises pour les interfaces cerveau-machine (BMI) actuelles. Cela nécessite des capacités d'enregistrement/stimulation performantes en termes de vitesse, qualité et quantité, c’est à dire une bande passante à fréquence plus élevée, une résolution spatiale, un signal sur bruit et une zone plus large pour l'interface avec le cortex cérébral. Dans ce mémoire, nous parlons de l'idée générale proposant une méthode d'interfaçage neuronal qui, en comparaison avec l'électroencéphalographie (EEG), l'électrocorticographie (ECoG) et les méthodes d'interfaçage intracortical conventionnelles à une seule unité, offre de meilleures caractéristiques pour implémenter des IMC plus performants. Les avantages de la nouvelle approche sont 1) une résolution spatiale plus élevée - en dessous dumillimètre, et une qualité de signal plus élevée - en termes de rapport signal sur bruit et de contenu fréquentiel - comparé aux méthodes EEG et ECoG; 2) un caractère moins invasif que l'ECoG où l'enlèvement du crâne sous une opération d'enregistrement / stimulation est nécessaire; 3) une plus grande faisabilité de la libre circulation du patient à l'étude - par rapport aux deux méthodes EEG et ECoG où de nombreux fils sont connectés au patient en cours d'opération; 4) une utilisation à long terme puisque l'interface implantable est sans fil - par rapport aux deux méthodes EEG et ECoG qui offrent des temps limités de fonctionnement. Nous présentons l'architecture d'un réseau sans fil de microsystèmes implantables, que nous appelons Brain Area Sensor NETwork (Brain-ASNET). Il y a deux défis principaux dans la réalisation du projet Brain-ASNET. 1) la conception et la mise en oeuvre d'un émetteur-récepteur RF de faible consommation compatible avec la puce de capteurs de réseau implantable, et, 2) la conception d'un protocole de réseau de capteurs sans fil (WSN) ad-hoc économe en énergie. Dans ce mémoire, nous présentons un protocole de réseau ad-hoc économe en énergie pour le réseau désiré, ainsi qu'un procédé pour surmonter le problème de la longueur de paquet variable causé par le processus de remplissage de bit dans le protocole HDLC standard. Le protocole adhoc proposé conçu pour Brain-ASNET présente une meilleure efficacité énergétique par rapport aux protocoles standards tels que ZigBee, Bluetooth et Wi-Fi ainsi que des protocoles ad-hoc de pointe. Le protocole a été conçu et testé par MATLAB et Simulink.----------ABSTRACT New high-performance neural interfacing approaches are demanded for today’s Brain-Machine Interfaces (BMI). This requires high-performance recording/stimulation capabilities in terms of speed, quality, and quantity, i.e. higher frequency bandwidth, spatial resolution, signal-to-noise, and wider area to interface with the cerebral cortex. In this thesis, we talk about the general proposed idea of a neural interfacing method which in comparison with Electroencephalography (EEG), Electrocorticography (ECoG), and, conventional Single-Unit Intracortical neural interfacing methods offers better features to implement higher-performance BMIs. The new approach advantages are 1) higher spatial resolution – down to sub-millimeter, and higher signal quality − in terms of signal-to-noise ratio and frequency content − compared to both EEG and ECoG methods. 2) being less invasive than ECoG where skull removal Under recording/stimulation surgery is required. 3) higher feasibility of freely movement of patient under study − compared to both EEG and ECoG methods where lots of wires are connected to the patient under operation. 4) long-term usage as the implantable interface is wireless − compared to both EEG and ECoG methods where it is practical for only a limited time under operation. We present the architecture of a wireless network of implantable microsystems, which we call it Brain Area Sensor NETwork (Brain-ASNET). There are two main challenges in realization of the proposed Brain-ASNET. 1) design and implementation of power-hungry RF transceiver of the implantable network sensors' chip, and, 2) design of an energy-efficient ad-hoc Wireless Sensor Network (WSN) protocol. In this thesis, we introduce an energy-efficient ad-hoc network protocol for the desired network, along with a method to overcome the issue of variable packet length caused by bit stuffing process in standard HDLC protocol. The proposed ad-hoc protocol designed for Brain-ASNET shows better energy-efficiency compared to standard protocols like ZigBee, Bluetooth, and Wi-Fi as well as state-of-the-art ad-hoc protocols. The protocol was designed and tested by MATLAB and Simulink

A Portable and Autonomous Magnetic Detection Platform for Biosensing

This paper presents a prototype of a platform for biomolecular recognition detection. The system is based on a magnetoresistive biochip that performs biorecognition assays by detecting magnetically tagged targets. All the electronic circuitry for addressing, driving and reading out signals from spin-valve or magnetic tunnel junctions sensors is implemented using off-the-shelf components. Taking advantage of digital signal processing techniques, the acquired signals are processed in real time and transmitted to a digital analyzer that enables the user to control and follow the experiment through a graphical user interface. The developed platform is portable and capable of operating autonomously for nearly eight hours. Experimental results show that the noise level of the described platform is one order of magnitude lower than the one presented by the previously used measurement set-up. Experimental results also show that this device is able to detect magnetic nanoparticles with a diameter of 250 nm at a concentration of about 40 fM. Finally, the biomolecular recognition detection capabilities of the platform are demonstrated by performing a hybridization assay using complementary and non-complementary probes and a magnetically tagged 20mer single stranded DNA target

Development of Multi-Robotic Arm System for Sorting System Using Computer Vision

This paper develops a multi-robotic arm system and a stereo vision system to sort objects in the right position according to size and shape attributes. The robotic arm system consists of one master and three slave robots associated with three conveyor belts. Each robotic arm is controlled by a robot controller based on a microcontroller. A master controller is used for the vision system and communicating with slave robotic arms using the Modbus RTU protocol through an RS485 serial interface. The stereo vision system is built to determine the 3D coordinates of the object. Instead of rebuilding the entire disparity map, which is computationally expensive, the centroids of the objects in the two images are calculated to determine the depth value. After that, we can calculate the 3D coordinates of the object by using the formula of the pinhole camera model. Objects are picked up and placed on a conveyor branch according to their shape. The conveyor transports the object to the location of the slave robot. Based on the size attribute that the slave robot receives from the master, the object is picked and placed in the right position. Experiment results reveal the effectiveness of the system. The system can be used in industrial processes to reduce the required time and improve the performance of the production line

Advanced teleoperation and control system for industrial robots based on augmented virtuality and haptic feedback

There are some industrial tasks that are still mainly performed manually by human workers due to their complexity, which is the case of surface treatment operations (such as sanding, deburring, finishing, grinding, polishing, etc.) used to repair defects. This work develops an advanced teleoperation and control system for industrial robots in order to assist the human operator to perform the mentioned tasks. On the one hand, the controlled robotic system provides strength and accuracy, holding the tool, keeping the right tool orientation and guaranteeing a smooth approach to the workpiece. On the other hand, the advanced teleoperation provides security and comfort to the user when performing the task. In particular, the proposed teleoperation uses augmented virtuality (i.e., a virtual world that includes non-modeled real-world data) and haptic feedback to provide the user an immersive virtual experience when remotely teleoperating the tool of the robot system to treat arbitrary regions of the workpiece surface. The method is illustrated with a car body surface treatment operation, although it can be easily extended to other surface treatment applications or even to other industrial tasks where the human operator may benefit from robotic assistance. The effectiveness of the proposed approach is shown with several experiments using a 6R robotic arm. Moreover, a comparison of the performance obtained manually by an expert and that obtained with the proposed method has also been conducted in order to show the suitability of the proposed approach