Real-time Signal Measurement Research Articles

A Real-Time Signal Measurement System Using FPGA-Based Deep Learning Accelerators and Microwave Photonic

A Real-Time Signal Measurement System Using FPGA-Based Deep Learning Accelerators and Microwave Photonic

Active Cancellation Stealth Technology Analysis and Verification in Experiment

As a Radar Countermeasure technique, Active cancellation could further decrease the radar detection probability of a stealthy target in some interest directions accomplishing with some traditional stealth techniques. According to radar equation, the received power of radar from the scattering of the target is positively linearly relative to the Radar Cross Section (RCS) of the target. The traditional stealth techniques aim at reducing the RCS by shaping and materials, but the active methods center the parameters of radar wave. Practically, there are many difficulties such as the real time signal measurement, the accurate anti-signal production, etc. In this paper, a type of structure of active cancellation system is proposed, and verified by measurement results its technique indexes This paper designs, and. At last, a verification experiment system and the confirm our conclusions about active cancellation stealth technology.

Self-Organizing Map for Fingerprinting-Based Cooperative Localization in Dynamic Indoor Environments

Wireless Local Area Network (WLAN) fingerprinting methods based on 802.11 signal strength are becoming increasingly the dominating indoor positioning techniques, due to their independence from radio propagation models and cost-effectiveness in terms of hardware and deployment requirements. However, frequent environmental changes cause inconsistency between the fingerprints stored in the radio map and the current radio behavior, thus jeopardizing their accuracy. Re-calibration of the area for updating the radio map incurs considerable amount of time and manual effort. In this paper, we aim to overcome this limitation by adapting to the new radio characteristics through user cooperation and thus eliminating the need of re-calibration. To that end, we propose a cooperative learning algorithm, whereby users exchange their real-time signal measurements in order to refine their estimated locations. The refinement process relies on the neural network structure of self-organizing map (SOM) which is of special interest for localization due to the key property of its neurons in self-organizing in geographic structures based on their similarity to a high-dimensional input data. In our solution, each user is regarded as a neuron of its local SOM network and runs in distributed fashion a modified version of SOM learning algorithm by considering its signal relationship with its neighboring users. Performance evaluation results demonstrate accuracy improvement over both the baseline deterministic and probabilistic fingerprinting approaches, while keeping the communication and computational overheads low.

Signal-based deployment planning for robot teams in tunnel-like fading environments

Deploying a multi-robot team in confined environments poses multiple challenges that involve task and motion planning, localization and mapping, safe navigation, coordination of robots and also communications among all of them. In recent years, increasing attention has been paid to these challenges by the robotics community, but many problems remain unresolved. In this paper we address a technique for planning the deployment of a robot team in so-called fading environments, such as tunnels or galleries, where signal propagation presents specific characteristics. In order to maintain constant connectivity and high signal quality in the communication network formed by the robots and the base station, the robot deployment is driven by real-time signal measurements. First, an analysis of the signal propagation to obtain the general characteristic parameters of the signals in this kind of environment is carried out. Second, a technique which uses these parameters to drive the deployment is developed. A general strategy for this kind of environment in which the signals exhibit similar behavior is implemented. A complete system involving all of the above-mentioned robotics tasks has been developed. Finally, the system has been evaluated by means of simulation and in a real scenario.

Electrical percolation-based biosensor for real-time direct detection of staphylococcal enterotoxin B (SEB)

Electrical percolation-based biosensing is a new technology. This is the first report of an electrical percolation-based biosensor for real-time detection. The label-free biosensor is based on electrical percolation through a single-walled carbon nanotubes (SWNTs)–antibody complex that forms a network functioning as a “Biological Semiconductor” (BSC). The conductivity of a BSC is directly related to the number of contacts facilitated by the antibody–antigen “connectors” within the SWNT network. BSCs are fabricated by immobilizing a pre-functionalized SWNTs–antibody complex directly on a poly(methyl methacrylate) (PMMA) and polycarbonate (PC) surface. Each BSC is connected via silver electrodes to a computerized ohmmeter, thereby enabling a continuous electronic measurement of molecular interactions (e.g. antibody–antigen binding) via the change in resistance. Using anti-staphylococcal enterotoxin B (SEB) IgG to functionalize the BSC, we demonstrate that the biosensor was able to detect SEB at concentrations as low as 5 ng/mL at a signal to baseline (S/B) ratio of 2. Such measurements were performed on the chip in wet conditions. The actuation of the chip by SEB is immediate, permitting real-time signal measurements. In addition to this “direct” label-free detection mode, a secondary antibody can be used to “label” the target molecule bound to the BSC in a manner analogous to an immunological sandwich “indirect” detection-type assay. Although a secondary antibody is not needed for direct detection, the indirect mode of detection may be useful as an additional measurement to verify or amplify signals from direct detection in clinical, food safety and other critical assays. The BSC was used to measure SEB both in buffer and in milk, a complex matrix, demonstrating the potential of electrical percolation-based biosensors for real-time label-free multi-analyte detection in clinical and complex samples. Assembly of BSCs is simple enough that multiple sensors can be fabricated on the same chip, thereby creating “Biological Central Processing Units (BCPUs)” capable of parallel processing and sorting out information on multiple analytes simultaneously which may be used for complex analysis and for point of care diagnostics.

A carbon fiber-based radiation sensor for dosimetric measurement in radiotherapy

A prototype sensor was designed and fabricated to explore the application of carbon fibers for absorbed dose measurements in the ionizing radiation field used for therapeutic radiology. The carbon fiber-based sensor has a 1.8 × 1.8 cm 2 sensing area. Both 6 and 15 MV X-ray beams produced by a Varian Clinac 21 EX medical linear accelerator were used to test the sensor. Radiation dosimetric measurements were performed by varying the dose rates, total dose, and field sizes to characterize various properties of the sensor. For the given dose range from 0 to 600 monitor units, the sensor displayed an excellent linear response. This sensor can be used for dosimetric measurements and real-time dose rate monitoring. Compared to existing MV X-ray measurement technologies in radiotherapy, this prototype sensor provides many advantages over commercially available dosimeters: (i) ease of operation; (ii) low noise at room temperature; (iii) light in mass; (iv) low voltage requirements; (v) potential for miniaturization; and (vi) real-time signal measurement. A higher spatial resolution can be achieved by miniaturizing the physical size of the current sensor design.