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This paper investigates the problem of detection and classification of unmanned aerial vehicles (UAVs) in the presence of wireless interference signals using a passive radio frequency (RF) surveillance system. The system uses a multistage detector to distinguish signals transmitted by a UAV controller from the background noise and interference signals. First, RF signals from any source are detected using a Markov models-based naïve Bayes decision mechanism. When the receiver operates at a signal-to-noise ratio (SNR) of 10 dB, and the threshold, which defines the states of the models, is set at a level 3.5 times the standard deviation of the preprocessed noise data, a detection accuracy of 99.8% with a false alarm rate of 2.8% is achieved. Second, signals from Wi-Fi and Bluetooth emitters, if present, are detected based on the bandwidth and modulation features of the detected RF signal. Once the input signal is identified as a UAV controller signal, it is classified using machine learning (ML) techniques. Fifteen statistical features extracted from the energy transients of the UAV controller signals are fed to neighborhood component analysis (NCA), and the three most significant features are selected. The performance of the NCA and five different ML classifiers are studied for 15 different types of UAV controllers. A classification accuracy of 98.13% is achieved by k-nearest neighbor classifier at 25 dB SNR. Classification performance is also investigated at different SNR levels and for a set of 17 UAV controllers which includes two pairs from the same UAV controller models.

A micromagnetic simulation-based comparative study between perpendicular magnetic recording (PMR) and heat-assisted magnetic recording (HAMR) has been performed. Signal-to-noise ratio (SNR) is adopted as the metric of comparison, and various system parameters are considered, including fly-height (FH), write-track distance, and rotary motion of the head-stack-assembly (skew). It is found that even a HAMR system with a wide 60 nm full-width at half-maximum thermal profile demonstrates better SNR performance when compared to an optimized PMR system at a magnetic FH of 8 nm. The impact of skew on a single reader system is found to be minor for both PMR and HAMR, but the impact on a dual reader 2-D recording system differs substantially. In particular, the resolution for small bit detection depends greatly on reader shield orientation to the HAMR-written curved transition beneath the read free layer.

Ground penetrating radar (GPR) is a common technique used for target through-the-obstacle imaging. However, it is quite difficult to detect the presence of a buried object in the obtained images in GPR, and the reason for this is the presence of clutter. Many methods have been proposed for clutter removal. In the classical approach, it is aimed to suppress the clutter and increase the target signal strength and then target detection algorithms used. Nowadays, region based convolutional neural network (RCNN) algorithm is adapted for the buried target detection which does not need any clutter removal algorithms. The accuracy performance of these algorithms is related to the size of the training data set. In this study, a realistic simulation data set was created with the gprMax and this data set is augmented by using the generative adversarial network (GAN) algorithm. The Faster R-CNN structure has been trained with an enhanced simulated data set and tested both in simulated and real data sets. In the results obtained, it has been observed that increasing the data with GAN achieves a performance increase up to %5-54 and it is shown that target detection can be performed on real data with a realistic simulated data set for GPR.

Millimeter-wave (mmWave) and terahertz (THz) spectrum can support significantly higher data rates compared to lower frequency bands and hence are being actively considered for 5G wireless networks and beyond. These bands have high free-space path loss (FSPL) in line-of-sight (LOS) propagation due to their shorter wavelength. Moreover, in non-line-of-sight (NLOS) scenario, these two bands suffer higher penetration loss than lower frequency bands which could seriously affect the network coverage. It is therefore critical to study the NLOS penetration loss introduced by different building materials at mmWave and THz bands, to help establish link budgets for an accurate performance analysis in indoor environments. In this work, we measured the penetration loss and the attenuation of several common constructional materials at mmWave (28 and 39 GHz) and sub-THz (120 and 144 GHz) bands. Measurements were conducted using a channel sounder based on NI PXI platforms. Results show that the penetration loss changes extensively based on the frequency and the material properties, ranging from 0.401 dB for ceiling tile at 28 GHz, to 16.608 dB for plywood at 144 GHz. Ceiling tile has the lowest measured attenuation at 28 GHz, while clear glass has the highest attenuation of 27.633 dB/cm at 144 GHz. As expected, the penetration loss and attenuation increased with frequency for all the tested materials.

This paper investigates the problem of classification of unmanned aerial vehicles (UAVs) from radio frequency (RF) fingerprints at the low signal-to-noise ratio (SNR) regime. We use convolutional neural networks (CNNs) trained with both RF time-series images and the spectrograms of 15 different off-the-shelf drone controller RF signals. When using time-series signal images, the CNN extracts features from the signal transient and envelope. As the SNR decreases, this approach fails dramatically because the information in the transient is lost in the noise, and the envelope is distorted heavily. In contrast to time-series representation of the RF signals, with spectrograms, it is possible to focus only on the desired frequency interval, i.e., 2.4 GHz ISM band, and filter out any other signal component outside of this band. These advantages provide a notable performance improvement over the time-series signals-based methods. To further increase the classification accuracy of the spectrogram-based CNN, we denoise the spectrogram images by truncating them to a limited spectral density interval. Creating a single model using spectrogram images of noisy signals and tuning the CNN model parameters, we achieve a classification accuracy varying from 92% to 100% for an SNR range from -10 dB to 30 dB, which significantly outperforms the existing approaches to our best knowledge., Comment: 18 pages, 9 figures, 4 tables, journal

This study focuses on the potential role of plug-in electric vehicles (PEVs) as a distributed energy storage unit to provide peak demand minimization in power distribution systems. Vehicle-to-grid (V2G) power and currently available information transfer technology enables utility companies to use this stored energy. The V2G process is first formulated as an optimal control problem. Then, a two-stage V2G discharging control scheme is proposed. In the first stage, a desired level for peak shaving and duration for V2G service are determined off-line based on forecasted loading profile and PEV mobility model. In the second stage, the discharging rates of PEVs are dynamically adjusted in real time by considering the actual grid load and the characteristics of PEVs connected to the grid. The optimal and proposed V2G algorithms are tested using a real residential distribution transformer and PEV mobility data collected from field with different battery and charger ratings for heuristic user case scenarios. The peak shaving performance is assessed in terms of peak shaving index and peak load reduction. Proposed solution is shown to be competitive with the optimal solution while avoiding high computational loads. The impact of the V2G management strategy on the system loading at night is also analyzed by implementing an off-line charging scheduling algorithm. Acknowledgements This work was supported in part by the Scientific and Technological Research Council of Turkey through the International PostDoctoral Fellowship Program under Grant 2219. The authors also would like to acknowledge the support of Baskent Electricity Distribution Company that provided the distribution transformer data within the scope of the project DAGSIS (Impact Analysis and Optimization of Distribution-Embedded Systems) funded by Turkish Energy Market Regulatory Authority (EPDK).

This paper presents a new distributed smart charging strategy for grid integration of plug-in electric vehicles (PEVs). The main goal is to smooth the daily grid load profile while ensuring that each PEV has a desired state of charge level at the time of departure. Communication and computational overhead, and PEV user privacy are also considered during the development of the proposed strategy. It consists of two stages: 1) an offline process to estimate a reference operating power level based on the forecasted mobility energy demand and base loading profile, and 2) a real-time process to determine the charging power for each PEV so that the aggregated load tracks the reference loading level. Tests are carried out both on primary and secondary distribution networks for different heuristic charging scenarios and PEV penetration levels. Results are compared to that of the optimal solution and other state-of-the-art techniques in terms of variance and peak values, and shown to be competitive. Finally, a real vehicle test implementation is done using a commercial-of-the-shelf charging station and an electric vehicle.

Here, a small, low-power, wireless gas sensor platform for selective detection of volatile organic compounds (VOCs) released from plants under different abiotic or biotic stress conditions is described. This sensor platform is implemented based on a capacitive micromachined ultrasonic transducer (CMUT) array, in which elements were functionalized with a variety of materials including polymers, phthalocyanines, and metals to improve selectivity. Input impedance measurements of the functionalized CMUT array were compared to pre-coating measurements to analyze the mechanical loading. The CMUT arrays were then exposed to VOCs known to be emitted by plants with different concentrations under dry air flow at room temperature. The results demonstrated that 1-Octanol created the strongest response across different channels and a resolution of 3-ppb was calculated for the CMUT element functionalized using silver ink when exposed to 1-Octanol. The relative responses of different channels to tested volatiles were observed to be different. The k-nearest neighbor (k-NN) algorithm was used for the gas classification by dividing the data to training and test groups. The k-NN results showed that the gases at low concentrations were successfully classified with better than 97 % accuracy. Finally, to emulate the ambient atmosphere for plants, the gas tests were repeated by adding different levels of humidity to the gas flow. With a minimum 98 % accuracy, the k-NN classifier demonstrated that the functionalized CMUT array can be used for selective detection of the group of plant VOCs used in this study, even at different relative humidity levels in the ambient atmosphere.

Visible light communication (VLC) utilizes light-emitting diodes (LEDs) to transmit wireless data. A VLC network can also be used to localize mobile users in indoor environments, where the global positioning system (GPS) signals are weak. However, the line-of-sight (LOS) links of mobile VLC devices can be blocked easily, which decreases the accuracy of localization. In this paper, we study tracking a VLC user when the availability of VLC access point (AP) link changes over the user's route. We propose a localization method for a single available AP and use known estimation methods when a larger number of APs are available. Tracking mobile users with Kalman filter can increase the accuracy of the positioning, but the generic Kalman filter does not consider instant changes in the measurement method. In order to include this information in the position estimation, we implement an adaptive Kalman filter by modifying the filter parameters based on the availability of APs to the user. Simulation results show that the implemented method decreases the root-mean-square error (RMSE) of the localization down to 30%-50% of the original estimation., Comment: Accepted to be presented in IEEE Military Commun. Conf. (MILCOM), 2019, Norfolk, VA

In this paper, we focus on the potential use of unmanned aerial vehicles (UAVs) for search and rescue (SAR) missions in GPS-denied indoor environments. We consider the problem of navigating a UAV to a wireless signal source, e.g., a smartphone or watch owned by a victim. We assume that the source periodically transmits RF signals to nearby wireless access points. Received signal strength (RSS) at the UAV, which is a function of the UAV and source positions, is fed to a Q-learning algorithm, and the UAV is navigated to the vicinity of the source. Unlike the traditional location-based Q-learning approach that uses the GPS coordinates of the agent, our method uses the RSS to define the states and rewards of the algorithm. It does not require any a priori information about the environment. These, in turn, make it possible to use the UAVs in indoor SAR operations. Two indoor scenarios with different dimensions are created using a ray tracing software. Then, the corresponding heat maps that show the RSS at each possible UAV location are extracted for more realistic analysis. Performance of the RSS-based Q-learning algorithm is compared with the baseline (location-based) Q-learning algorithm in terms of convergence speed, average number of steps per episode, and the total length of the final trajectory. Our results show that the RSS-based Q-learning provides competitive performance with the location-based Q-learning.

This paper focuses on the detection and classification of micro-unmanned aerial vehicles (UAVs) using radio frequency (RF) fingerprints of the signals transmitted from the controller to the micro-UAV. In the detection phase, raw signals are split into frames and transformed into the wavelet domain. A Markov models-based naive Bayes approach is used to check for the presence of a UAV in each frame. In the classification phase, unlike the traditional approaches that rely solely on time-domain signals and corresponding features, the proposed technique uses the energy transient signal. This approach is more robust to noise and can cope with different modulation techniques. First, the normalized energy trajectory is generated from the energy-time-frequency distribution of the raw control signal. Next, the start and end points of the energy transient are detected by searching for the most abrupt changes in the mean of the energy trajectory. Then, a set of statistical features is extracted from the energy transient. Significant features are selected by performing neighborhood component analysis (NCA) to keep the computational cost of the algorithm low. Finally, selected features are fed to several machine learning algorithms for classification. The algorithms are evaluated experimentally using a database containing 100 RF signals from each of 14 different UAV controllers. The signals are recorded wirelessly using a high-frequency oscilloscope. The data set is randomly partitioned into training and test sets for validation with the ratio 4:1. Ten Monte Carlo simulations are run and results are averaged to assess the performance of the methods. All the micro-UAVs are detected correctly and an average accuracy of 96.3% is achieved using the k-nearest neighbor (kNN) classification. Proposed methods are also tested for different signal-to-noise ratio (SNR) levels and results are reported., Comment: 12 pages. To be presented in 2019 IEEE Aerospace Conference

The stochastic nature of plug-in electric vehicle (PEV) driving behavior and distribution grid load profile make it challenging to control vehicle-grid integration in a mutually beneficial way. This study proposes a new adaptive control strategy that manages PEV charging/discharging for peak shaving and load leveling in a distribution grid. For accurate and high fidelity transportation mobility modeling, real vehicle driving test data are collected from the field. Considering the estimated total required PEV battery charging energy, the vehicle-to-grid capabilities of PEVs, and the forecasted non-PEV base load, a reference operating point for the grid is estimated. This reference operating point is updated once at the end of peak hours to guarantee a full final state-of-charge to each PEV. Proposed method provides cost-efficient operation for the utility grid, utmost user convenience free from range anxiety, and ease of implementation at the charging station nodes. It is tested on a real residential transformer, which serves approximately one thousand customers, under various PEV penetration levels and charging scenarios. Performance is assessed in terms of meansquare-error and peak shaving index. Results are compared with those of various reference operating point choices and shown to be superior.

Date of Conference: 13-15 June 2018 The deployment of public charging infrastructure networks has been a major factor in enabling electric vehicle (EV) technology transition, and must continue to support the adoption of this technology. DC fast charging (DCFC) increases customer convenience by lowering charging time, enables long-distance EV travel, and could allow the electrification of high-mileage fleets. Yet, high capital costs and uneven power demand have been major challenges to the widespread deployment of DCFC stations. There is a need to better understand DCFC stations' loading, utilization, and customer service quality (i.e. queuing time, charging duration, and queue length). This study aims to analyze these aspects using one million vehicle-days of travel data within the Columbus, OH, region. Monte Carlo analysis is carried out in three types of areas - urban, suburban, and rural- to quantify the effect of uncertain parameters on DCFC station loading and service quality. This work was supported by the U.S. Department of Energy under Contract No. DE-AC36-08GO28308 with the National Renewable Energy Laboratory. This report and the work described were sponsored by the U.S. Department of Energy (DOE) Vehicle Technologies Office (VTO) under the Systems and Modeling for Accelerated Research in Transportation (SMART) Mobility Laboratory Consortium, an initiative of the Energy Efficient Mobility Systems (EEMS) Program. The authors acknowledge John Smart of Idaho National Laboratory for leading the Alternative Fueling Infrastructure Pillar of the SMART Mobility Laboratory Consortium. The following DOE Office of Energy Efficiency and Renewable Energy managers played important roles in establishing the project concept, advancing implementation, and providing ongoing guidance: David Anderson, Sarah Olexsak, and Rachael Nealer.

In this paper, a multi-modal solution to the people counting problem in a given area is described. The multi-modal system consists of a differential pyro-electric infrared (PIR) sensor and a camera. Faces in the surveillance area are detected by the camera with the aim of counting people using cascaded AdaBoost classifiers. Due to the imprecise results produced by the camera-only system, an additional differential PIR sensor is integrated to the camera. Two types of human motion: (i) entry to and exit from the surveillance area and (ii) ordinary activities in that area are distinguished by the PIR sensor using a Markovian decision algorithm. The wavelet transform of the continuous-time real-valued signal received from the PIR sensor circuit is used for feature extraction from the sensor signal. Wavelet parameters are then fed to a set of Markov models representing the two motion classes. The affiliation of a test signal is decided as the class of the model yielding higher probability. People counting results produced by the camera are then corrected by utilizing the additional information obtained from the PIR sensor signal analysis. With the proof of concept built, it is shown that the multi-modal system can reduce false alarms of the camera-only system and determines the number of people watching a TV set in a more robust manner.

This chapter focuses on infrared (IR) sensors that complement and have similar architectures to radars (both have transmitters/receivers). It presents the feature extraction methods and machine learning algorithms tailored for each specific application. Radars are compared to IR sensors for similar mode of operation highlighting the pros and cons of each. IR sensors have the following two types: active IR sensor; and passive IR (PIR) sensor. The chapter considers PIR sensor. A PIR sensor only has a collector to sense the motion of the subjects in its field of view (FOV). Rather than sensing the motion, the PIR sensor detects the change in the amount of IR radiation impinging upon it. PIR sensors will be used to refer to dual-element type PIR sensors unless otherwise stated. PIR sensor-based indoor monitoring applications can be categorized into the following fields: home security and safety, home automation, and health status monitoring.

In this paper, a multimodal hand gesture detection and recognition system using differential Pyroelectric Infrared (PIR) sensors and a regular camera is described. Any movement within the viewing range of the differential PIR sensors are first detected by the sensors and then checked if it is due to a hand gesture or not by video analysis. If the movement is due to a hand, one-dimensional continuous-time signals extracted from the PIR sensors are used to classify/recognize the hand movements in real-time. Classification of different hand gestures by using the differential PIR sensors is carried out by a new winner-takeall (WTA) hash based recognition method. Jaccard distance is used to compare the WTA hash codes extracted from 1-D differential infrared sensor signals. It is experimentally shown that the multimodal system achieves higher recognition rates than the system based on only the on/off decisions of the analog circuitry of the PIR sensors.

The majority of cultural heritage and archaeological sites, especially in the Mediterranean region, are covered with vegetation, which increases the risk of fires. These fires may also break out and spread towards nearby forests and other wooded land, or conversely start in nearby forests and spread to archaeological sites. Beyond taking precautionary measures to avoid a forest fire, early warning and immediate response to a fire breakout are the only ways to avoid great losses and environmental and cultural heritage damages. The use of terrestrial systems, typically based on video cameras, is currently the most promising solution for advanced automatic wildfire surveillance and monitoring due to its low cost and short response time. Early and accurate detection and localization of flame is an essential requirement of these systems, however, it remains a challenging issue due to the fact that many natural objects have similar characteristics with fire. This paper presents and compares three video-based flame detection techniques, which were developed within the FIRESENSE EU research project, taking into account the chaotic and complex nature of the fire phenomenon and the large variations of flame appearance in video. Experimental results show that the proposed methods provide high fire detection rates with reasonable false alarm ratios.

Our society will face a notable demographic shift in the near future. According to a United Nations report, the ratio of the elderly population (aged 60 years or older) to the overall population increased from 9.2% in 1990 to 11.7% in 2013 and is expected to reach 21.1% by 2050 [1]. According to the same report, 40% of older people live independently in their own homes. This ratio is about 75% in the developed countries. These facts will result in many societal challenges as well as changes in the health-care system, such as an increase in diseases and health-care costs, a shortage of caregivers, and a rise in the number of individuals unable to live independently [2]. Thus, it is imperative to develop ambient intelligence-based assisted living (AL) tools that help elderly people live independently in their homes. The recent developments in sensor technology and decreasing sensor costs have made the deployment of various sensors in various combinations viable, including static setups as well as wearable sensors. This article presents a survey that concentrates on the signal processing methods employed with different types of sensors. The types of sensors covered are pyro-electric infrared (PIR) and vibration sensors, accelerometers, cameras, depth sensors, and microphones.

Heat-assisted magnetic recording is a promising approach for enabling large increases in the storage density of hard disk drives. A laser is used to momentarily heat the recording area of the medium to reduce its coercivity below that of the applied magnetic field from the recording head. In such a system, the recording materials have a very high magnetic anisotropy, which is essential for the thermal stability of the magnetization of the extremely small grains in the medium. This technology involves new recording physics, new approaches to near field optics, a recording head that integrates optics and magnetics, new recording materials, lubricants that can withstand extremely high temperatures, and new approaches to the recording channel design. This paper surveys the challenges for this technology and the progress that has been made in addressing them.

The growing environmental concerns and the increase in oil prices will lead to the proliferation of electric vehicles (EVs) in the near future. The increase in the number of EVs, while providing green and inexpensive solutions to transportation needs, may cause constraints on the operation of the utility grid that should be investigated. In this paper, the real user driving information is collected from individual data tracking devices of passenger vehicle owners instead of assuming randomly distributed trip characteristics. The collected trip data are first analyzed to generate a statistical model of the trip characteristics in terms of home arrival times and state of charge (SOC) levels. The resulting model is then used to simulate and analyze the impact of EV integration in a real grid with different EV penetration levels. For this, real distribution transformer data provided by Baskent Electric Distribution Co. is used. The proposed method produces more realistic results in comparison to the studies assuming random scenarios.

Date of Conference: 26-28 November 2015 Conference Name: 9th International Conference on Electrical and Electronics Engineering, ELECO 2015 Respiratory rate is an essential parameter in many practical applications such as patient and elderly people monitoring. In this paper, a novel contact-free system is introduced to detect the human breathing activity. The system, which consists of two pyro-electric infrared (PIR) sensors, is capable of estimating the respiratory rate and detecting the sleep apnea. Sensors' signals corresponding to the thoracic movements of a human being are sampled using a microprocessor and analyzed on a general-purpose computer. Sampled signals are processed using empirical mode decomposition (EMD) and a new average magnitude difference function (AMDF) is used to detect the periodicity and the period of the processed signals. The resulting period, by using the fact that breathing is almost a periodic activity, is monitored as the respiratory rate. The new AMDF provides a way to fuse the data from the multiple sensors and generate a more reliable estimation of the respiratory rate.

During the defect scanning process on the production line, when asperities are detected on the surface of a magnetic storage medium, that location is marked, and no data are stored to marked locations. However, some asperities go undetected, and cause problems during data recovery. In this paper, we introduce a new method to detect and cancel the thermal asperity effects for perpendicular magnetic recording systems. We compare the new method to the existing methods in terms of the bit error rate after Viterbi detection.

The paper discusses the methodological questions arising in the study of open electrodynamic structures of resonance quasi optics via time-domain technique. As demonstrated, all of the interesting physical characteristics inherent in these objects (including the objects with various frequency-selective elements) can be obtained through the numerical solution of the relevant model initial bowldary-value probhxns. For the first time, a finite difference method equipped with the exact local 'absorbing' conditions on artificial boundaries has been applied for the solution of this kind of open problems. The results of the computational experiments performed have verified the possibility of the efficient selection of oscillations in dispersive open resonators with diffraction gratings, among them the resonators with gratings operating in the quasitotal nonspecular reflection mode.

In this paper, a hand gesture detection and classification system using two differential Pyro-electric Infrared (PIR) sensors and a camera is introduced. Motion presence is investigated in the area of interest using two differential PIR sensors. In the case of any motion detection, a decision whether it belongs to a hand or not is made by using camera and if it is a hand, which gesture belongs to which predefined class is determined by evaluating each system data together. In the stage of hand gesture detection and classification using camera, skin detection and convex hull-defect algorithms are used. Classification of different hand gestures by using differential PIR sensors is carried out by Winner-Take-All (WTA) hash method. The main contribution of this paper is to show that WTA hash codes can be utilized in classification of 1-D signals and gesture recognition accuracy can be improved by multi-sensor fusion.

The response of a heat-assisted magnetic recording (HAMR) system is very sensitive to the laser spot position. The response is determined by transition characteristics like the center, curvature, and length. In this paper, by using the thermal Williams-Comstock model and the microtrack model, the effects of laser spot position on transition characteristics are investigated for both longitudinal and perpendicular recording from a read channel systems perspective. The general trend in their variation is determined and used to explain the resultant change in response. By simulation, we determine the post-Viterbi dominant error events for longitudinal HAMR system. Finally, we apply the well-known MTR codes to mitigate such errors and present their bit-error performance

Our aim in this paper is to obtain the best synthesis of a desired mutual intensity distribution, by filtering in fractional Fourier domains. More specifically, we find the optimal fractional-domain filter that transforms a given (source) mutual intensity distribution into the desired one as closely as possible (in the minimum mean-square error sense). It is observed that, in some cases, closer approximations to the desired profile can be obtained by filtering in fractional Fourier domains, in comparison to filtering in the ordinary space or frequency domains.

In this paper, a flame detection system using a differential Pyro-electric Infrared (PIR) sensor is proposed. A differential PIR sensor is only sensitive to sudden temperature variations within its viewing range and it produces a time-varying signal. The wavelet transform of the differential PIR sensor signal is used for feature extraction and feature vectors are fed to Markov models trained with uncontrolled fire flames and walking person. The model yielding the highest probability is chosen. Results suggest that the system can be used in spacious rooms for uncontrolled fire flame detection.

A novel system design for sensing very high density magnetic recording data, such as that envisioned for two-dimensional magnetic recording, is proposed. The key idea is a rotated sense head, so that the shields are aligned down-track, combined with oversampled signal processing to regain the lost down-track resolution. Based on a random Voronoi grain model, simulation indicates that for bits with dimension of 8 nm × 6 nm, 5.5 nm grains, and a reader with 4 nm × 18 nm × 18 nm free layer and 11 nm shield-shield spacing, the bit error rate can drop from 20.9% for a normally positioned head array to 4.2% for a single-rotated single head with sampling period of 2 nm, a minimum mean squared error equalizer, and pattern-dependent noise prediction detector. The user density computed using the Shannon capacity limit is greatly increased to 10.1 Tb/in2.

A Pyro-electric Infrared (FIR) sensor based flame detection system is proposed using a Markovian decision algorithm. A differential PIR sensor is only sensitive to sudden temperature variations within its viewing range and it produces a time-varying signal. The wavelet transform of the FIR sensor signal is used for feature extraction from sensor signal and wavelet parameters are fed to a set of Markov models corresponding to the flame flicker process of an uncontrolled fire, ordinary activity of human beings and other objects. The final decision is reached based on the model yielding the highest probability among others. Comparative results show that the system can be used for fire detection in large rooms. (c) 2012 Elsevier Ltd. All rights reserved.

The paper gives a brief description of the data storage technologies that are expected to appear in the near future, and the challenges these new systems bring to designing robust read channels. We focus on perpendicular, heat assisted magnetic recording and probe storage systems.

We present a comprehensive analysis of the areal density potential of a bit-patterned media recording. The recording performance is dominated by written-in errors rather than traditional signal-to-noise considerations. Written-in errors are caused by statistical fluctuations of the magnetic properties and the locations of the individual dots. The highest areal densities are obtained with a combination of a pole head, a soft magnetic underlayer, and a storage medium of the composite type. Areal density scenarios of up to 5 Tb/in2 are analyzed

For any given communication channel, it is desirable to recover all the initial timing information during acquisition and/or any phase and frequency changes during tracking as fast as possible. In this paper, we propose the oversampled per-survivor processing (PSP) timing recovery (TR) architecture to achieve fast convergence rate in the applications of magnetic recording channels. Its performance is compared with the symbol-rate PSP-based TR, the oversampled conventional TR (OCTR), and also with the symbol-rate conventional TR (CTR) architecture used in today's magnetic recording read-channel chip architectures. Results indicate that the oversampled PSP (OPSP) TR yields the best bit-error rate (BER) performance among other TR architectures when fast convergence is required.

We propose and investigate the performance of an FSE (oversampled) system in longitudinal and perpendicular recording channels. Results show that the proposed system outperforms the conventional (symbol rate) one whenever there is excess bandwidth, like in longitudinal recording.

One can obtain either exact realizations or useful approximations of linear systems or matrix–vector products that arise in many different applications by implementing them in the form of multistage or multichannel fractional Fourier-domain filters, resulting in space–bandwidth-efficient systems with acceptable decreases in accuracy. Varying the number and the configuration of filters enables one to trade off between accuracy and efficiency in a flexible manner. The proposed scheme constitutes a systematic way of exploiting the regularity or structure of a given linear system or matrix, even when that structure is not readily apparent.

The optical and digital implementations of general linear systems are costly. Through several examples we show that either exact realizations or useful approximations of these systems may be implemented in the form of repeated-filtering operations in consecutive fractional Fourier domains. These implementations are much cheaper than direct implementations of general linear systems. Thus we may significantly decrease the implementation costs of general linear systems with little or no decrease in performance by synthesizing them with the proposed repeated-filtering method. (C) 1998 Optical Society of America.

Although wave optics is the standard method of analyzing systems composed of a sequence of lenses separated by arbitrary distances, it is often easier and more intuitive to ascertain the function and properties of such systems by tracing a few rays through them. Determining the location, magnification or scale factor, and field curvature associated with images and Fourier transforms by tracing only two rays is a common skill. In this paper we show how the transform order, scale factor, and field curvature can be determined in a similar manner for the fractional Fourier transform. Our purpose is to develop the understanding and skill necessary to recognize fractional Fourier transforms and their parameters by visually examining ray traces. We also determine the differential equations governing the propagation of the order, scale, and curvature, and show how these parameters are related to the parameters of a Gaussian beam. © 1997 Published by Elsevier Science B.V.

We form optical systems by using only free space portions and cylindrical lenses, and consider these systems as anamorphic fractional Fourier transformers. We dynamically adjust the transform order, scale factor and field curvature of both orthogonal dimensions of anamorphic fractional Fourier transformation by just changing the focal lengths of cylindrical lenses used in the proposed setups. Here, we also consider two approaches for implementing cylindrical lenses with dynamically adjustable focal lengths. There may also be some other methods to obtain cylindrical lenses having adjustable focal lengths which can successfully be used in these proposed setups.

The propagation of mutual intensity through quadratic graded-index media or free space can be expressed in terms of two-dimensional fractional Fourier transforms for one-dimensional systems and in terms of four-dimensional fractional Fourier transforms for two-dimensional systems. As light propagates, its mutual intensity distribution is continually fractional Fourier transformed. These results can also be generalized to arbitrary first-order optical systems. Furthermore, the Wigner distribution associated with a partially coherent field rotates in the same manner as the Wigner distribution associated with a deterministic field. © 1996 Optical Society of America.

Cataloged from PDF version of article. We define the accumulated Gouy phase shift as the on-axis phase accumulated by a Gaussian beam in passing through an optical system, in excess of the phase accumulated by a plane wave. We give an expression for the accumulated Gouy phase shift in terms of the parameters of the system through which the beam propagates. This quantity complements the beam diameter and the wave-front radius of curvature to constitute three parameters that uniquely characterize the beam with respect to a reference point in the system. Measurement of these parameters allows one to uniquely recover the parameters characterizing the first-order system through which the beam propagates. © 1997 Optical Society of America.