Conventional Detection Schemes Research Articles

Fourier transform photoabsorption spectroscopy based on scanning tunneling microscopy

A scheme of photoabsorption spectroscopy (PAS) based on scanning tunneling microscopy (STM) applicable to semiconductors has been devised to circumvent experimental difficulties inherent in STM-PAS measurements so far conducted in the conventional lock-in detection scheme. The present scheme uses multiplexed lights from an interferometer for spectroscopic lights and the modulated current flowing through the STM tip for the signal from which the photoabsorption spectrum is deduced by Fourier transformation. The technique under an optimized condition has been applied to Si samples deposited with epitaxially grown Ge1−xSnx nanodots, which proved the spatial resolution of a few nanometers.

Magneto-optic measurement of Brownian relaxation of magnetic nanoparticles

We demonstrate a magneto-optic technique to measure Brownian relaxation of magnetic nanoparticles suspended in liquids. We used AC susceptibility data as a function of frequency of the applied AC magnetic field to verify that the results agree with those obtained via a conventional inductive detection technique. However, compared with a commercial AC susceptometer using the conventional detection scheme, our magneto-optic setup is able to detect a density of nanoparticles at least three orders smaller. This technique has the potential of being used as a sensor for magnetic nanoparticles such as in local temperature, viscoelasticity, or molecular-binding measurements.

A Simplified Maximum Likelihood Detection for MIMO Systems

SUMMARY In this paper, we propose a simplified Maximum Likelihood (ML) detection scheme for Multiple-Input Multiple-Output (MIMO) system that has much less computational complexity than the conventional ML detection scheme. Simulation results verify that the bit error rate (BER) performance of the proposed scheme is close to that of the ML detection

Vertically integrated optics for ballistic electron emission luminescence: Device and microscopy characterizations

By integrating a p-i-n photodiode photodetector directly into a ballistic electron emission luminescence (BEEL) heterostructure with GaAs quantum-well active region, we have obtained a photon detection efficiency of more than 10%. This is many orders of magnitude higher than conventional far-field detection scheme with the most sensitive single-photon counters, enabling BEEL microscopy in systems with no optical components. Detailed analysis shows found a parasitic bipolar injection in parallel with the desired optical coupling between the BEEL heterostructure and the integrated photodiode beyond a characteristic collector bias, which may be solved by improved device design or limiting the operating window of the collector bias. Preliminary BEEL microscopy images of a homogeneous GaAs quantum-well luminescent layer show lateral variations of photon emission correlated with the collector current injection level modulated by surface features or interface defects.

Synthesis of terahertz electromagnetic wave pulses using amplitude-and-phase masks

In the conventional terahertz (THz) electromagnetic emission and detection scheme using a semiconductor as a THz wave emitter and a photoconductive antenna as a THz wave receiver, we generated THz electromagnetic waves having various temporal waveforms by inserting simple plastic plates between off-axis parabolic mirrors. We simulated the observed waveforms using a simple model, where the THz field is expressed by the summation of fields passing through each sub-section of the whole cross-section of the collimated THz beam. The results show a new simple method to shape THz pulses with complicated temporal waveforms.

An Iterative ICI Cancellation and Decoding Scheme for Coded OFDM Systems in Mobile Channels

Recently, it has been shown that the received signal in the orthogonal frequency-division multiplexing (OFDM) systems can be viewed as the synchronous code-division multiple access (SCDMA) signal in receiver after some mathematical manipulation, and the ICI term in the OFDM system is equivalent to the multiple access interference (MAI) term in SCDMA system. They proposed a successive interference cancellation multicarrier detector and it performs better than the conventional single-carrier detection schemes. To further improve the performance of OFDM systems in the presence of ICI, we propose to use a soft-in soft-out (SISO) multicarrier detector and a SISO convolutional decoder in an iterative (turbo) way. Note that full-complexity turbo multicarrier detector and turbo decoder are not used to strike a balance between performance and complexity of OFDM systems. And the transmitter structure of OFDM systems is unchanged. The simulation results show that the first iteration of the proposed scheme demonstrates 2--3 dB gain compared to the previous multicarrier detection schemes. The second and third iterations improve the performance further.

Defending against spoofed DDoS attacks with path fingerprint

In this paper, we propose a new scheme, called ANTID, for detecting and filtering DDoS attacks which use spoofed packets to circumvent the conventional intrusion detection schemes. The proposed anti-DDoS scheme intends to complement, rather than replace conventional schemes. By embedding in each IP packet a unique path fingerprint that represents the route an IP packet has traversed, ANTID is able to distinguish IP packets that traverse different Internet paths. In ANTID, a server maintains for each of its communicating clients the mapping from the client's IP address to the corresponding path fingerprint. The construction and renewal of these mappings is performed in an on-demand fashion that helps to reduce the cost of maintenance. With presence of the mapping table, the onset of a spoofed DDoS attack can be detected by observing a surge of spoofed packets. Consequently, spoofed attack packets are filtered so as to sustain the quality of protected Internet services. ANTID is lightweight, robust, and incrementally deployable. Our experiment results showed that the proposed scheme can detect 99.95% spoofed IP packets and can discard them with little collateral damage to legitimate clients. It also showed that the higher the aggregated attack rate is, the sooner the attack can be detected.

Optimal optical measurement of small displacements

We derive the quantum noise limit for the optical beam displacement of a TEM00 mode. Using a multimodal analysis, we show that the conventional split detection scheme for measuring beam displacement is non-optimal with 80% efficiency. We propose a new displacement measurement scheme that is optimal for small beam displacement. This scheme utilises a homodyne detection setup that has a TEM10 mode local oscillator. We show that although the quantum noise limit to displacement measurement can be surpassed using squeezed light in appropriate spatial modes for both schemes, the TEM10 homodyning scheme out-performs split detection for all values of squeezing.

X-ray energy spectrum measurements by an annular superconducting tunnel junction with trapped magnetic flux quanta

We report on the Fe55 x-ray pulse height spectrum of an annular superconducting tunnel junction (STJ) collected without an external magnetic field. An energy resolution of 100eV was obtained for the Kα line of the top electrode. The Josephson critical current and Fiske resonances were suppressed by the presence of trapped magnetic flux quanta which created a persistent current and self-generated magnetic field. The obtained energy resolution and the stability of the trapped magnetic flux under irradiation demonstrate the capability of an annular STJ device with trapped flux quanta to detect radiation without using the external magnetic field that is required in the conventional detection scheme.

Discrete magnitude-squared channel modeling, equalization, and detection for volume holographic storage channels

As storage density increases, the performance of volume holographic storage channels is degraded, because intersymbol interference and noise also increase. Equalization and detection methods must be employed to mitigate the effects of intersignal interference and noise. However, the output detector array in a holographic storage system detects the intensity of the incident light's wave front, leading to loss of sign information. This sign loss precludes the applicability of conventional equalization and detection schemes. We first address channel modeling under quadratic nonlinearity and develop an efficient model named the discrete magnitude-squared channel model. We next introduce an advanced equalization method called the iterative magnitude-squared decision feedback equalization (IMSDFE), which takes the channel nonlinearity into account. The performance of IMSDFE is quantified for optical-noise-dominated channels as well as for electronic-noise-dominated channels. Results indicate that IMSDFE is a good candidate for a high-density, high-intersignal-interference volume holographic storage channel.

A Reduced-Complexity Maximum-Likelihood Method for Multiuser Detection

Multiuser or joint detection has recently been receiving significant research interest because of its potential for the significant increase in system capacity and performance. Among the conventional multiuser detection schemes such as the minimum mean-squared error and successive interference cancellation-based ones, maximum-likelihood multiuser detection has the best system performance. However, the complexity of maximum-likelihood detection (MLD) increases exponentially in the number of users and constellation size. In this paper, a low-complexity MLD scheme based on the use of a sensitive-bits algorithm is proposed. It is demonstrated that the proposed method can greatly reduce the computational complexity with a minimal penalty in performance compared with the exhaustive optimal MLD scheme.

Annular superconducting tunnel junction with injected current as a new configuration of radiation detector

We have experimentally demonstrated the capability of an annular superconducting tunnel junction with trapped flux quanta to detect X-ray without external magnetic field which is required in the conventional detection scheme. With this device, we have collected the pulse height spectrum with acceptable resolution.

A survey of multiuser/multisubchannel detection schemes based on recurrent neural networks

AbstractMultiuser/multisubchannel detection schemes based on a recurrent neural network (RNN) structure are a promising alternative to conventional detection schemes, both in terms of performance and complexity. This paper gives an overview of different approaches that make use of the results of RNN research for multiuser/multisubchannel detection. The detectors' results are compared with the results of conventional detection schemes in simulations of the uplink of a DS‐CDMA packet transmission system. Copyright © 2002 John Wiley & Sons, Ltd.

Planar waveguides for ultra-high sensitivity of the analysis of nucleic acids

In the first part of this paper, the need for analytical techniques capable of highly parallel and sensitive nucleic acid analysis, with the capability of achieving very low limits of detection (LODs) and of resolving small differences in concentration, is described. Whereas the requirement for performing simultaneously multi-analyte detection is solved by the approach of nucleic acid microarrays, requirements on sensitivity can often not be satisfied by classical detection technologies. Inherent limitations of conventional fluorescence excitation and detection schemes are identified, and the implementation of planar waveguides as analytical platforms for nucleic acid microarrays, with fluorescence excitation in the evanescent field associated with the guided excitation light, is proposed. The relevant parameters for an optimization of sensitivity are discussed. In the second part of this paper, the specific formats of our planar waveguide platforms, which are compatible with established industrial standard formats allowing for integration into industrial high throughput environments, are presented, as well as the dedicated optical system for fluorescence excitation and detection that we developed. In a direct comparison with a state-of-the-art scanner, it is demonstrated that the implementation of genomic microarrays on planar waveguide platforms allows for unprecedented, direct detection of low-abundant genes in limited amounts of sample. Otherwise, when using conventional fluorescence excitation and detection configurations, the detection of such low amounts of nucleic acids requires massive sample preparation and signal or target amplification steps.

Polarization analyses of readout signals by a solid immersion lens in phase change recording material

Comprehensive analyses based on the rigorous vector dif- fraction theory were used to simulate the polarized readout signals using a solid immersion lens (SIL) in reading phase change (PC) disks. For an x-polarized incident beam, the signal contrast in x and y polarization was a different function of air gap between the bottom surface of the SIL and recording medium. Compared with conventional nonpolarized detection schemes for PC media, the x-polarized contrast is a factor 1.0 to 1.35 higher at different air gaps, at a small cost of total intensity loss. © 2001

A model-based neural network for edge characterization

In this paper, we investigate the feasibility of characterizing significant image features using model-based neural network with modular architecture. Instead of employing traditional mathematical models for characterization, we ask human users to select what they regard as significant features on an image, and then incorporate these selected features directly as training examples for the network. As a first step, we consider the problem of the characterization of edges, which are usually regarded as significant image features by humans. Unlike conventional edge detection schemes where decision thresholds have to be specified, the current NN-based edge characterization scheme implicitly represents these decision parameters in the form of network weights which are updated during the training process, and which thus allow automatic generation of the final binary edge map without further parameter adjustments. Experiments have confirmed that the resulting network is capable of generalizing this previously acquired knowledge to identify important edges in images not included in the training set. In particular, one of the important attributes characterizing the current approach is its robustness against noise contaminations: the network can be directly applied to noisy images without any re-training and alteration of architecture, as opposed to conventional edge detection algorithms where re-adjustment of the threshold parameters are usually required.

Performance of timing recovery methods in turbo coded magnetic recording channels

Turbo codes, with potential for 5 dB coding gain over the conventional Viterbi detection schemes, are good candidates for the next generation detection/decoding schemes for magnetic recording systems. In this paper, timing recovery is included in the evaluation of turbo codes. Three timing recovery methods, namely voltage control oscillator (VCO) method, interpolated timing recovery (ITR) method and adaptive filter timing recovery (AFTR) method are examined for two turbo decoder architectures, full turbo and serial turbo. Bit error rate (BER) results from simulations suggest that full turbo decoder is more robust than the serial turbo decoder. For serial turbo decoder, error floor is seen earlier in VCO and ITR methods than AFTR methods. Using s-random interleaver seems to be able to remove such an error floor.

RIE process time optimization from real time sensory data

This paper addresses the issue of end point detection and etch time control for a reactive ion etch process. Our approach involves the use of neural networks to model functional relationship between an end point detection signal as well as various in situ measurements, and the resulting film thickness remaining. An optimization algorithm is then employed to determine the optimal etch time based on the neural network model in order to achieve the desired level of film thickness remaining. This circumvents the need for monitoring and operating on noisy end point detection signal typically associated with conventional detection schemes. Furthermore, a self-organizing map is incorporated as a process classifier for identifying the current operating condition of the reactive ion etch process, which provides an alternative for tracking process variations and uncertainties, without the need tor the on-line adaptation of model or controller parameters. Tested on data from 77 randomly selected wafers, our controller yields a film thickness distribution with the standard deviation of 6.54, a 50% improvement over the scheme currently implemented in production.

Production data based optimal etch time control design for a reactive ion etching process

This paper addresses the issue of end point detection and etch time control for a reactive ion etch process. Our approach involves the use of neural networks to model the functional relationship between an end point detection signal, as well as various in situ measurements, and the resulting film thickness remaining. An optimization algorithm is then employed to determine the optimal etch time based on the neural network model in order to achieve the desired level of film thickness remaining. This circumvents the need for monitoring and operating on noisy end point detection signals typically associated with conventional detection schemes. Simulation studies based on production data are presented to further demonstrate the associated design procedures and the feasibility of the algorithm. Tested on data from 89 randomly selected wafers, our controller yields a film thickness distribution with the standard deviation of 6.42 /spl Aring/, a 50% improvement over the scheme currently implemented in production.

Joint Bayesian model selection and estimation of noisy sinusoids via reversible jump MCMC

In this paper, the problem of joint Bayesian model selection and parameter estimation for sinusoids in white Gaussian noise is addressed. An original Bayesian model is proposed that allows us to define a posterior distribution on the parameter space. All Bayesian inference is then based on this distribution. Unfortunately, a direct evaluation of this distribution and of its features, including posterior model probabilities, requires evaluation of some complicated high-dimensional integrals. We develop an efficient stochastic algorithm based on reversible jump Markov chain Monte Carlo methods to perform the Bayesian computation. A convergence result for this algorithm is established. In simulation, it appears that the performance of detection based on posterior model probabilities outperforms conventional detection schemes.