Showing papers in "Journal of Electrical and Computer Engineering in 2015"
TL;DR: This paper presents an accelerometer sensor-based approach for human activity recognition that recognized ten activities with an average accuracy of 95.6% using only a single triaxial accelerometer.
Abstract: Human activity recognition via triaxial accelerometers can provide valuable information for evaluating functional abilities. In this paper, we present an accelerometer sensor-based approach for human activity recognition. Our proposed recognition method used a hierarchical scheme, where the recognition of ten activity classes was divided into five distinct classification problems. Every classifier used the Least Squares Support Vector Machine (LS-SVM) and Naive Bayes (NB) algorithm to distinguish different activity classes. The activity class was recognized based on the mean, variance, entropy of magnitude, and angle of triaxial accelerometer signal features. Our proposed activity recognition method recognized ten activities with an average accuracy of 95.6% using only a single triaxial accelerometer.
44Â citations
TL;DR: A novel forest fire smoke video detection based on spatiotemporal features and dynamic texture features that can effectively detect smoke image recorded from different scenes is presented.
Abstract: Smoke detection is a very key part of fire recognition in a forest fire surveillance video since the smoke produced by forest fires is visible much before the flames. The performance of smoke video detection algorithm is often influenced by some smokelike objects such as heavy fog. This paper presents a novel forest fire smoke video detection based on spatiotemporal features and dynamic texture features. At first, Kalman filtering is used to segment candidate smoke regions. Then, candidate smoke region is divided into small blocks. Spatiotemporal energy feature of each block is extracted by computing the energy features of its 8- neighboring blocks in the current frame and its two adjacent frames. Flutter direction angle is computed by analyzing the centroid motion of the segmented regions in one candidate smoke video clip. Local Binary Motion Pattern (LBMP) is used to define dynamic texture features of smoke videos. Finally, smoke video is recognized by Adaboost algorithm. The experimental results show that the proposed method can effectively detect smoke image recorded from different scenes.
36Â citations
TL;DR: A video based framework is proposed that efficiently identifies abnormal activities happening at the ATM installations and generates an alarm during any untoward incidence and is able to distinguish the normal and abnormal activities like money snatching, harm to the customer by virtue of fight, or attack on the customer with an average accuracy.
Abstract: Automated teller machines (ATM) are widely being used to carry out banking transactions and are becoming one of the necessities of everyday life. ATMs facilitate withdrawal, deposit, and transfer of money fromone account to another round the clock. However, this convenience is marred by criminal activities like money snatching and attack on customers, which are increasingly affecting the security of bank customers. In this paper, we propose a video based framework that efficiently identifies abnormal activities happening at the ATM installations and generates an alarm during any untoward incidence. The proposed approach makes use of motion history image (MHI) and Humoments to extract relevant features fromvideo. Principle component analysis has been used to reduce the dimensionality of features and classification has been carried out by using support vector machine. Analysis has been carried out on different video sequences by varying the window size of MHI. The proposed framework is able to distinguish the normal and abnormal activities like money snatching, harm to the customer by virtue of fight, or attack on the customer with an average accuracy of 95.73%.
26Â citations
TL;DR: This paper mainly introduces a method to build a simulation model for the power distribution system, which is based on detailed component models, and shows that steady state performance and transient state performance of the model can fulfill the requirements of aircraftPower distribution system in the realistic application.
Abstract: The More Electric Aircraft concept is a fast-developing trend in modern aircraft industry. With this new concept, the performance of the aircraft can be further optimized and meanwhile the operating and maintenance cost will be decreased effectively. In order to optimize the power system integrity and have the ability to investigate the performance of the overall system in any possible situations, one accurate simulation model of the aircraft power system will be very helpful and necessary. This paper mainly introduces a method to build a simulation model for the power distribution system, which is based on detailed component models. The power distribution system model consists of power generation unit, transformer rectifier unit, DC-DC converter unit, and DCAC inverter unit. In order to optimize the performance of the power distribution system and improve the quality of the distributed power, a feedback control network is designed based on the characteristics of the power distribution system. The simulation result indicates that this new simulation model is well designed and it works accurately. Moreover, steady state performance and transient state performance of the model can fulfill the requirements of aircraft power distribution system in the realistic application.
25Â citations
TL;DR: This paper focuses on a four-wire shunt active power filter (APF) control scheme proposed to improve the performance of the APF and confirmed the theoretical developments for balanced and unbalanced nonlinear loads.
Abstract: This paper focuses on a four-wire shunt active power filter (APF) control scheme proposed to improve the performance of the APF. This filter is used to compensate harmonic distortion in three-phase four-wire systems. Several harmonic suppression techniques have been widely proposed and applied to minimize harmonic effects. The proposed control scheme can compensate harmonics and reactive power of the nonlinear loads simultaneously. This approach is compared to the conventional shunt APF reference compensation strategy. The developed algorithm is validated by simulation tests using MATLAB Simulink. The obtained results have demonstrated the effectiveness of the proposed scheme and confirmed the theoretical developments for balanced and unbalanced nonlinear loads.
23Â citations
TL;DR: The empirical results strongly indicate that IHS algorithm can effectively provide better results for solving the distribution network planning problem compared to other optimization algorithms.
Abstract: Distribution network planning because of involving many variables and constraints is a multiobjective, discrete, nonlinear, and large-scale optimization problem. Harmony search (HS) algorithm is a metaheuristic algorithm inspired by the improvisation process ofmusic players. HS algorithmhas several impressive advantages, such as easy implementation, less adjustable parameters, and quick convergence. But HS algorithm still has some defects such as premature convergence and slow convergence speed. According to the defects of the standard algorithm and characteristics of distribution network planning, an improved harmony search (IHS) algorithm is proposed in this paper. We set up a mathematical model of distribution network structure planning, whose optimal objective function is to get the minimum annual cost and constraint conditions are overload and radial network. IHS algorithm is applied to solve the complex optimization mathematical model. The empirical results strongly indicate that IHS algorithm can effectively provide better results for solving the distribution network planning problem compared to other optimization algorithms.
22Â citations
TL;DR: This paper analyzes the generalization abilities of six AdaBoost variants in terms of classification margins and compares them to each other by using experiments to verify the analyses.
Abstract: As a machine learning method, Ada Boost is widely applied to data classification and object detection because of its robustness and efficiency. AdaBoost constructs a global and optimal combination ofweak classifiers based on a sample reweighting. It is known that this kind of combination improves the classification performance tremendously. As the popularity of AdaBoost increases, many variants have been proposed to improve the performance of AdaBoost. Then, a lot of comparison and review studies for AdaBoost variants have also been published. Some researchers compared different AdaBoost variants by experiments in their own fields, and others reviewed various AdaBoost variants by basically introducing these algorithms. However, there is a lack of mathematical analysis of the generalization abilities for different AdaBoost variants. In this paper, we analyze the generalization abilities of six AdaBoost variants in terms of classification margins. The six compared variants are Real AdaBoost, Gentle AdaBoost, Modest AdaBoost, Parameterized AdaBoost, Margin-pruning Boost, and Penalized AdaBoost. Finally, we use experiments to verify our analyses.
21Â citations
TL;DR: A zero-watermarking scheme for vector map data is proposed that is robust to geometrical attacks, vertex attacks, and object attacks and the results of extensive experiments demonstrate the robustness of the proposed scheme.
Abstract: With the rapid development of GIS and computer techniques, vector map data has been widely used in many fields. Since the production of map data is very costly, illegal copying will result in huge loss for data owners. In order to protect the copyright of vector data, digital watermarking has been employed in recent years. In this paper, a zero-watermarking scheme for vector map data is proposed. In the proposed scheme, FVDR (feature vertex distance ratio) is constructed based on the feature vertices of objects. The feature data, FVDR, is combined with watermark to generate the zero-watermark. Due to the specially designed cover data, the proposed scheme is robust to geometrical attacks, vertex attacks, and object attacks. The results of extensive experiments also demonstrate the robustness of the proposed scheme.
21Â citations
TL;DR: A security privacy-preserving data aggregation model, which adopts a mixed data aggregation structure, which is robust to many attacks and has a lower communication overhead.
Abstract: With the rapid development and widespread use of wearable wireless sensors, data aggregation technique becomes one of the most important research areas. However, the sensitive data collected by sensor nodes may be leaked at the intermediate aggregator nodes. So, privacy preservation is becoming an increasingly important issue in security data aggregation. In this paper, we propose a security privacy-preserving data aggregation model, which adopts a mixed data aggregation structure. Data integrity is verified both at cluster head and at base station. Some nodes adopt slicing technology to avoid the leak of data at the cluster head in innercluster. Furthermore, a mechanism is given to locate the compromised nodes.The analysis shows that the model is robust to many attacks and has a lower communication overhead.
20Â citations
TL;DR: The results of simulation show that the positioning accuracy of Gaussian-weighted model is better than statistical average model and Gaussian model and it has a high positioning accuracy after correcting positioning error correction.
Abstract: Aiming at the large positioning errors of traditional coal mine underground locomotive, an improved received signal strength indication (RSSI) positioning algorithm for coal mine underground locomotive was proposed.TheRSSI value fluctuates heavily due to the poor environment of coal mine underground.The nodes with larger RSSI value corrected by Gaussian-weighted model were selected as beacon nodes. In order to reduce the positioning error further, the estimated positions of the locomotives were corrected by the weighted distance correction method. The difference between actual position and estimated position of beacon node was regarded as the positioning error andwas given a correspondingweight.Theresults of simulation showthat the positioning accuracy of Gaussian-weighted model is better than statistical average model and Gaussian model and it has a high positioning accuracy after correcting positioning error correction. In the 10m of communication range, positioning error can be maintained at 0.5m.
20Â citations
TL;DR: A new model based on wavelet transformand the least squares support vector machine (LSSVM) which is optimized by fruit fly algorithm (FOA) for short-term load forecasting is proposed which demonstrates that the hybrid model can be used in the short- term forecasting of the power system.
Abstract: Electric power is a kind of unstorable energy concerning the national welfare and the people's livelihood, the stability of which is attracting more and more attention. Because the short-term power load is always interfered by various external factors with the characteristics like high volatility and instability, a single model is not suitable for short-term load forecasting due to low accuracy. In order to solve this problem, this paper proposes a new model based on wavelet transformand the least squares support vector machine (LSSVM) which is optimized by fruit fly algorithm (FOA) for short-term load forecasting. Wavelet transform is used to remove error points and enhance the stability of the data. Fruit fly algorithm is applied to optimize the parameters of LSSVM, avoiding the randomness and inaccuracy to parameters setting.The result of implementation of short-term load forecasting demonstrates that the hybrid model can be used in the short-term forecasting of the power system.
TL;DR: MSE could effectively assess the noise level on the real ECG signals, and this study provided a valuable reference for applying MSE method to the practical signal quality assessment of mobile ECG.
Abstract: This study explored the performance of multiscale entropy (MSE) for the assessment of mobile ECG signal quality, aiming to provide a reasonable application guideline. Firstly, the MSE for the typical noises, that is, high frequency (HF) noise, low frequency (LF) noise, and power-line (PL) noise, was analyzed.The sensitivity of MSE to the signal to noise ratio (SNR) of the synthetic artificial ECG plus different noises was further investigated. The results showed that the MSE values could reflect content level of various noises contained in the ECG signals. For the synthetic ECG plus LF noise, the MSE was sensitive to SNR within higher range of scale factor. However, for the synthetic ECG plus HF noise, the MSE was sensitive to SNR within lower range of scale factor. Thus, a recommended scale factor range within 5 to 10 was given. Finally, the results were verified on the real ECG signals, which were derived from MIT-BIH Arrhythmia Database and Noise Stress Test Database. In all, MSE could effectively assess the noise level on the real ECG signals, and this study provided a valuable reference for applying MSE method to the practical signal quality assessment of mobile ECG.
TL;DR: An accurate and computationally efficient background subtraction method to reduce the data dimensionality of image frame based on compressive sensing and in the meanwhile apply sparse representation to build the current background by a set of preceding background images.
Abstract: Robust and efficient foreground extraction is a crucial topic in many computer vision applications. In this paper, we propose an accurate and computationally efficient background subtraction method. The key idea is to reduce the data dimensionality of image frame based on compressive sensing and in the meanwhile apply sparse representation to build the current background by a set of preceding background images. According to greedy iterative optimization, the background image and background subtracted image can be recovered by using a few compressive measurements. The proposed method is validated through multiple challenging video sequences. Experimental results demonstrate the fact that the performance of our approach is comparable to those of existing classical background subtraction techniques.
TL;DR: The results suggested that the LS-SVM based on K-CV optimization was one of effective methods for diagnosing elevator malfunctions.
Abstract: Several common elevator malfunctions were diagnosed with a least square support vector machine (LS-SVM). After acquiring vibration signals of various elevator functions, their energy characteristics and time domain indicators were extracted by theoretically analyzing the optimal wavelet packet, in order to construct a feature vector of malfunctions for identifying causes of the malfunctions as input of LS-SVM. Meanwhile, parameters about LS-SVM were optimized by K-fold cross validation (KCV). After diagnosing deviated elevator guide rail, deviated shape of guide shoe, abnormal running of tractor, erroneous rope groove of traction sheave, deviated guide wheel, and tension of wire rope, the results suggested that the LS-SVM based on K-CV optimization was one of effective methods for diagnosing elevator malfunctions.
TL;DR: The experiments show thatlinear regression model is good enough to model performance counters, nonlinear regression is better than linear regression model for modeling system utilization, and support vector regression model are better than polynomial and exponential regression models.
Abstract: As cloud data center consumes more and more energy, both researchers and engineers aim to minimize energy consumption while keeping its services available. A good energy model can reflect the relationships between running tasks and the energy consumed by hardware and can be further used to schedule tasks for saving energy. In this paper, we analyzed linear and nonlinear regression energy model based on performance counters and system utilization and proposed a support vector regression energy model. For performance counters, we gave a general linear regression framework and compared three linear regression models. For system utilization, we compared our support vector regression model with linear regression and three nonlinear regression models. The experiments show that linear regression model is good enough to model performance counters, nonlinear regression is better than linear regression model for modeling system utilization, and support vector regression model is better than polynomial and exponential regression models.
TL;DR: A novel method of candidate smoke region segmentation based on rough set theory is presented and Kalman filtering is used to update video background in order to exclude the interference of static smoke-color objects, such as blue sky.
Abstract: Candidate smoke region segmentation is the key link of smoke video detection; an effective and prompt method of candidate smoke region segmentation plays a significant role in a smoke recognition system. However, the interference of heavy fog and smoke-color moving objects greatly degrades the recognition accuracy. In this paper, a novel method of candidate smoke region segmentation based on rough set theory is presented. First, Kalman filtering is used to update video background in order to exclude the interference of static smoke-color objects, such as blue sky. Second, in RGB color space smoke regions are segmented by defining the upper approximation, lower approximation, and roughness of smoke-color distribution. Finally, in HSV color space small smoke regions are merged by the definition of equivalence relation so as to distinguish smoke images from heavy fog images in terms of V component value variety from center to edge of smoke region. The experimental results on smoke region segmentation demonstrated the effectiveness and usefulness of the proposed scheme.
TL;DR: A formal verification methodology that can be used to check the correctness of object code programs that implement the safety-critical control functions of DDD mode pacemakers and was able to verify a control program with millions of transitions against the simple specification with only 10 transitions.
Abstract: Pacemakers are safety-critical devices whose faulty behaviors can cause harm or even death. Often these faulty behaviors are caused due to bugs in programs used for digital control of pacemakers. We present a formal verification methodology that can be used to check the correctness of object code programs that implement the safety-critical control functions of DDD mode pacemakers. Our methodology is based on the theory of Well-Founded Equivalence Bisimulation (WEB) refinement, where both formal specifications and implementation are treated as transition systems. We develop a simple and general formal specification for DDD mode pacemakers. We also develop correctness proof obligations that can be applied to validate object code programs used for pacemaker control. Using our methodology, we were able to verify a control program with millions of transitions against the simple specification with only 10 transitions. Our method also found several bugs during the verification process.
TL;DR: An improved probabilistic routing algorithm (IPRA) is proposed, where the history information of contacts for the immediate encounter and two-hop neighbors has been jointly used to make an informed decision for message forwarding.
Abstract: We investigate an opportunistic routing protocol in delay/disruption tolerant networks (DTNs) where the end-to-end path between source and destination nodes may not exist for most of the time. Probabilistic routing protocol using history of encounters and transitivity (PRoPHET) is an efficient history-based routing protocol specifically proposed for DTNs, which only utilizes the delivery predictability of one-hop neighbors to make a decision for message forwarding. In order to further improve the message delivery rate and to reduce the average overhead of PRoPHET, in this paperwe propose an improved probabilistic routing algorithm (IPRA), where the history information of contacts for the immediate encounter and two-hop neighbors has been jointly used to make an informed decision for message forwarding. Based on the Opportunistic Networking Environment (ONE) simulator, the performance of IPRA has been evaluated via extensive simulations.The results showthat IPRA can significantly improve the average delivery rate while achieving a better or comparable performance with respect to average overhead, average delay, and total energy consumption compared with the existing algorithms.
TL;DR: This work proposes a new recognition algorithm based on Curvelet and Shape Context that can improve the speed and accuracy of maize leaf disease recognition and has guiding significance for other diseases recognition to an extent.
Abstract: Because the corn vein and noise influence the contour extraction of the maize leaf disease, we put forward a new recognition algorithm based on Curvelet and Shape Context (SC). This method can improve the speed and accuracy of maize leaf disease recognition. Firstly, we use Seeded Regional Growing (SRG) algorithm to segment the maize leaf disease image. Secondly, Curvelet Modulus Correlation (CMC) method is put forward to extract the effective contour of maize leaf disease. Thirdly, we combine CMC with the SC algorithm to obtain the histogram features and then use these features we obtain to calculate the similarities between the template image and the target image. Finally, we adopt n-fold cross-validation algorithm to recognize diseases on maize leaf disease database. Experimental results show that the proposed algorithm can recognize 6 kinds of maize leaf diseases accurately and achieve the accuracy of 94.446%. Meanwhile this algorithm has guiding significance for other diseases recognition to an extent.
TL;DR: The fundamental function by the proposed UAV/CD&;R using Quasi-ADS-B is verified with effective signal broadcasting for surveillance and efficient collision alert and avoidance performance to low altitude flights.
Abstract: A Conflict Detection and Resolution (CD&;R) system for manned/unmanned aerial vehicle (UAV) based on Automatic Dependent Surveillance-Broadcast (ADS-B) concept is designed and verified in this paper. The 900 MHz XBee-Pro is selected as data transponder to broadcast flight information among participating aircraft in omnirange. Standard Compact Position Report (CPR) format packet data are automatically broadcasted by ID sequencing under Quasi-ADS-B mechanism. Time Division Multiple Access (TDMA) monitoring checks the designated time slot and reallocates the conflict ID. This mechanism allows the transponder to effectively share data with multiple aircraft in near airspace. The STM32f103 microprocessor is designed to handle RF, GPS, and flight data with Windows application on manned aircraft and ground control station simultaneously. Different conflict detection and collision avoidance algorithms can be implemented into the system to ensure flight safety. The proposed UAV/CD&;R using Quasi-ADS-B transceiver is tested using ultralight aircraft flying at 100-120 km/hr speed in small airspace for mission simulation. The proposed hardware is also useful to additional applications to mountain hikers for emergency search and rescue. The fundamental function by the proposed UAV/CD&;R using Quasi-ADS-B is verified with effective signal broadcasting for surveillance and efficient collision alert and avoidance performance to low altitude flights.
TL;DR: Novel designs of current-mode Ternary minimum (AND) and maximum (OR) and combined together in order to generate both outputs concurrently in an integrated design are proposed in this paper based on Carbon NanoTube Field Effect Transistors.
Abstract: Novel designs of current-mode Ternary minimum (AND) and maximum (OR) are proposed in this paper based on Carbon NanoTube Field Effect Transistors (CNTFET). First, these Ternary operators are designed separately. Then, they are combined together in order to generate both outputs concurrently in an integrated design. This integration results in the elimination of common parts when both functions are required at the same time. The third proposed current-mode integrated circuit generates both ternary operators with the usage of only 30 transistors. The new designs are composed of three main parts: (1) the part which converts current to voltage; (2) threshold detectors; and (3) the parallel paths through which the output current flows. Unlike the previously presented structure, there is no need for any constant current source within the new designs. This elimination leads to less static power dissipation.The second proposed current-mode segregated Ternary minimum operates 43% faster and consumes 40% less power in comparison with a previously presented structure.
TL;DR: The results show that the donor-type and acceptor-type ITs have the great influence on DC characteristic at midgap, and the flat band shift changes obviously and differently in the AC analysis, which results in contrast of peak shift of Miller capacitor Cgd for n-type TFETs with donor-like andacceptor-like ITs.
Abstract: We demonstrate the impact of semiconductor/oxide interface traps (ITs) on the DC and AC characteristics of tunnel field-effect transistors (TFETs). Using the Sentaurus simulation tools, we show the impacts of trap density distribution and trap type on the n-type double gate- (DG-) TFET. The results show that the donor-type and acceptor-type ITs have the great influence on DC characteristic at midgap. Donor-like and acceptor-like ITs have different mechanism of the turn-on characteristics. The flat band shift changes obviously and differently in the AC analysis, which results in contrast of peak shift of Miller capacitor Cgd for n-type TFETs with donor-like and acceptor-like ITs.
TL;DR: A small sized and highly accurate economic signal generator based on DDS technology has been developed, which is capable of providing wave signals commonly used in experiments and introduced the basic principles of DDS.
Abstract: Signal generators are widely used in experimental courses of universities. However, most of the commercial tests signal generators are expensive and bulky. In addition, a majority of them are in a fixed working mode with many little-used signals. In order to improve this situation, a small sized and highly accurate economic signal generator based on DDS technology has been developed, which is capable of providing wave signals commonly used in experiments. Firstly, it is introduced the basic principles of DDS and is determined the overall scheme of the signal generator. Then, it proposes a design of the hardware, which include power supply module, display module, keyboard module, waveform generating module based on DDS chip, and the minimum system module based on C8051F010. The signal generator was designed to output sine and square waveforms, and the other achieved performances included the frequency range 0.1Hz-12.5MHz, the frequency resolution 0.05Hz-0.1Hz, the output amplitude 1.0- 4.5V, the frequency accuracy Kfmin = 94.12% and Kfmax = 99.99%, and the signal distortion RTHDmin = 0.638% and RTHDmaz = 11.67%.
TL;DR: Compared to the most existing fault diagnosis approaches, a fault diagnosis method for Interturn short circuit (ITSC) fault of five-phase PMSM based on the trust region algorithm is presented and the effectiveness of the proposed parameter estimation method is validated.
Abstract: Taking advantage of the high reliability, multiphase permanent magnet synchronous motors (PMSMs), such as five-phase PMSM and six-phase PMSM, are widely used in fault-tolerant control applications. And one of the important fault-tolerant control problems is fault diagnosis. In most existing literatures, the fault diagnosis problem focuses on the three-phase PMSM. In this paper, compared to the most existing fault diagnosis approaches, a fault diagnosis method for Interturn short circuit (ITSC) fault of five-phase PMSM based on the trust region algorithm is presented. This paper has two contributions. (1) Analyzing the physical parameters of the motor, such as resistances and inductances, a novel mathematic model for ITSC fault of five-phase PMSM is established. (2) Introducing an object function related to the Interturn short circuit ratio, the fault parameters identification problem is reformulated as the extreme seeking problem. A trust region algorithm based parameter estimation method is proposed for tracking the actual Interturn short circuit ratio. The simulation and experimental results have validated the effectiveness of the proposed parameter estimation method.
TL;DR: Bymodelling multiuser cooperative relay as a labour market, a contract model with moral hazard for relay incentive is proposed and numerical simulation results demonstrate the effectiveness of the proposed contract design scheme for cooperative relay.
Abstract: Cooperative relay can effectively improve spectrum efficiency by exploiting the spatial diversity in the wireless networks. However, wireless nodes may acquire different network information with various users' location and mobility, channels' conditions, and other factors, which results in asymmetric information between the source and the relay nodes (RNs). In this paper, the relay incentive mechanism between relay nodes and the source is investigated under the asymmetric information. Bymodelling multiuser cooperative relay as a labour market, a contract model with moral hazard for relay incentive is proposed. To effectively incentivize the potential RNs to participate in cooperative relay, the optimization problems are formulated to maximize the source's utility while meeting the feasible conditions under both symmetric and asymmetric information scenarios. Numerical simulation results demonstrate the effectiveness of the proposed contract design scheme for cooperative relay.
TL;DR: A fast two-step energy detection algorithm for spectrum sensing via improving the sampling process of conventional energy detection, which adaptively selects N-point or 2N-point sampling by comparing its observed energy with prefixed double thresholds, which is superior in sampling time and detection speed.
Abstract: Spectrum sensing is one of the key tasks in cognitive radio. This paper proposes a fast two-step energy detection (FED) algorithm for spectrum sensing via improving the sampling process of conventional energy detection (CED). The algorithm adaptively selects N-point or 2N-point sampling by comparing its observed energy with prefixed double thresholds, and thereby is superior in sampling time and detection speed. Moreover, under the constraint of constant false alarm, this paper optimizes the thresholds from maximizing detection probability point of view. Theoretical analyses and simulation results show that, compared with CED, the proposed FED can achieve significant gain in detection speed at the expense of slight accuracy loss. Specifically, within high signal-to-noise ratio regions, as much as 25% of samples can be reduced.
TL;DR: Experimental results demonstrate the superiority of the proposed robust watermarking algorithm on robustness against content-preserving operations and incidental distortions such as JPEG compression, Gaussian noise, median filter, resizing, cropping, and sharpening.
Abstract: Digital watermarking has received extensive attention as a new method for copyright protection. This paper proposes a robust watermarking algorithm based on maximum wavelet coefficient modification and optimal threshold technique. The medium wavelet coefficients are randomly permutated according to a secret key and divided into subgroups. We modify the maximum wavelet in each subgroup according to the embedded watermark bits, which can effectively resist attacks. During the extraction process, an optimal threshold value calculated by iterative computation is used to extract the watermark from the watermarked image under different attacks, without using the original image or watermark. Numerous experiments are conducted to evaluate the watermarking performance. Experimental results demonstrate the superiority of our scheme on robustness against content-preserving operations and incidental distortions such as JPEG compression, Gaussian noise, median filter, resizing, cropping, and sharpening.
TL;DR: The novel approach of dual source provides an effective technique for enhancing the drive current and it is found that this structure can offer four tunneling junctions by increasing a source region.
Abstract: This paper presents tunneling field-effect transistor (TFET) with dual source regions. It explores the physics of drive current enhancement. The novel approach of dual source provides an effective technique for enhancing the drive current. It is found that this structure can offer four tunneling junctions by increasing a source region. Meanwhile, the dual source structure does not influence the excellent features of threshold slope (SS) of TFET. The number of the electrons and holes would be doubled by going through the tunneling junctions on the original basis. The overlap length of gate-source is also studied. The dependence of gate-drain capacitance Cgd and gate-source capacitance Cgs on gate-to-source voltage Vgs and drain-to-source voltage Vds was further investigated. There are simulation setups and methodology used for the dual source TFET (DS-TFET) assessment, including delay time, total energy per operation, and energy-delay product. It is confirmed that the proposed TFET has strong potentials for VLSI.
TL;DR: A novel cellular and WiFi roaming decision and AP selection scheme based on state of the art, 3GPP TS24.312 that increased the utilization and balanced the traffic load of access points and improved user's experienced throughput.
Abstract: The existing IEEE and 3GPP standards have laid the foundation for integrating cellular and WiFi network to deliver a seamless experience for the end-users when roaming across multiple access networks. However, in recent studies, the issue of making roaming decision and intelligently selecting the most preferable Point of Service to optimize network resource and improve end user's experience has not been considered properly. In this paper, we propose a novel cellular and WiFi roaming decision and AP selection scheme based on state of the art, 3GPP TS24.312 and IEEE 802.11u, k standards. Our proposed scheme assists the mobile nodes to decide the right timing to make roaming decision and select preferable point of service based on the operator's policies and real-time network condition. We also introduce our simulation model of a heterogeneous network with cellular and WiFi interworking as well as 3GPP ANDSF, TS24.312. It is a complete end-to-end system model from application to physical layer with considering user's mobility and realistic traffic model. The proposed scheme outperformed the conventional WiFi selection scheme in terms of dynamically steering mobile node's data traffic from macrocell to available Access Points. The proposed scheme increased the utilization and balanced the traffic load of access points and improved user's experienced throughput.
TL;DR: In this paper, a Matrix Pencil Method (MPM) is used to decompose the signal into a set of exponentially damped sinusoids, which are then plotted in the time-frequency plane.
Abstract: This paper discusses time-frequency analysis of clinical percussion signals produced by tapping over human chest or abdomen with a neurological hammer and recorded with an air microphone. The analysis of short, highly damped percussion signals using conventional time-frequency distributions (TFDs) meets certain difficulties, such as poor time-frequency localization, cross terms, and masking of the lower energy features by the higher energy ones. The above shortcomings lead to inaccurate and ambiguous representation of the signal behavior in the time-frequency plane. This work describes an attempt to construct a TF representation specifically tailored to clinical percussion signals to achieve better resolution of individual components corresponding to physical oscillation modes. Matrix Pencil Method (MPM) is used to decompose the signal into a set of exponentially damped sinusoids, which are then plotted in the time-frequency plane. Such representation provides better visualization of the signal structure than the commonly used frequency-amplitude plots and facilitates tracking subtle changes in the signal for diagnostic purposes. The performance of our approach has been verified on both ideal and real percussion signals.TheMPM-based time-frequency analysis appears to be a better choice for clinical percussion signals than conventional TFDs, while its ability to visualize damping has immediate practical applications.