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Due to the large mover mass and the low power density, the development of traditional stator-magnet moving-iron linear oscillatory motor (SMLOM) is hindered. In this paper, a new type of SMLOM is proposed, with simplified structure and low manufacturing difficulty. By multiplying the module, it is also easy to be extended by using the multi-unit method to satisfy the requirement of variable load thrust. In this paper, the structure and principle of the improved SMLOM are described, and a two dimensional analytical model based on the size parameters of the motor is established. The accuracy of the analytical model of the motor is verified by comparing the analytical results of the motor performance parameters with the finite element algorithm (FEA). Based on the deduced analytical model, the dimensional parameters of the motor are optimized by multi-objective orthogonal method, and the modeling analysis is carried out under the on-load condition. Comprehensive simulation results have fully demonstrated the advantage of low thrust harmonic and high power density.

It is an important part of an intelligent pipeline nondestructive testing robot to detect the welded circular seams from a frame image which is captured by the robot’s camera. By use of prior knowledge of the contour shape of a welded circular seam, a circular convolution along a circular ring is deigned and an auto-encoding deep learning network is proposed based the circular convolutional neural network this paper. In the stage of training the auto-encoding network, a training frame image is automatically partitioned into concentricity rings. Each ring is visually and easily notated as a welded seam ring or a background ring and then used as a sample to train the network. In the stage of testing, the testing frame is automatically partitioned into concentricity rings according to the sample frame and then the each ring is auto-encoded and recognized if it is welded seam ring. The welded seam is thus coarsely segmented by the circular convolution based auto-encoding deep network. Then OTSU is used to segment out the welded seam from the welded seam ring finely and effectively. The experimental results show that the proposed circular convolution based auto-encoding deep network and segmentation algorithm are effective and efficient.

For beginners in sprinting, receiving timely and effective guidance and feedback can not only help them master the movement, but also effectively avoid sports injuries. However, the existing guidance and improvement methods are inconvenient and inaccurate. This paper makes an in-depth study of the human body posture synthesis model based on deep neural network, and uses it to obtain posture and joint data of athletes, and judges whether the posture is correct and standard through the correction system. It is found that the model in this paper can obtain more accurate human body data. The position correction system has a higher posture recognition rate, which can effectively judge whether the position posture is standard or not, it is of great significance for actual position guidance.

Finding the source of a hidden hazardous substance in an unknown environment is of great importance both in military and civil use. This paper presents a coordinated search algorithm that enables robots to perceive the environment and plan the route autonomously. Gaussian Process Regression is applied to estimate the intensity distribution of the hazardous source within the search area. A bi-level nested optimization algorithm, in which the first level optimizes a temporary target and the second level finds the optimal path for each robot to the temporary target, is implemented to search for the hazardous source location via a multi-robot system. A contrast simulation between multi-robot search and single-robot search is carried out for an indoor heat source search scenario, proving the feasibility and effectiveness of the proposed algorithm.

This paper presents a design concept and a prototype of a polyimide-coated Fibre Bragg Grating (FBG) thermal sensor system based on a fast scan tunable laser source (up to 1 kHz sensing frequency). This system was deployed to perform a comprehensive 2-D real-time radial and axial thermal profiling of an electromagnetic coil - the short duty cycle electric machine (EM) windings. The thermal measurements were performed using an equivalent prototype consisting of an FBG interrogator, a multi-FBG string temperature sensor, power resistors (thermal objects), and conventional thermocouples used for measurement comparison. The FBG measurements of copper windings were subsequently carried out using a copper coil with FBG sensors system to map the radial and axial temperature distribution of the coil. The experimental results show that Polyimide-coated FBG sensor can be used for multi-point temperature monitoring of electromagnetic coils with a sensing temperature range of 20-200°C, a spatial resolution of 6cm, and a measurement error of ± 2%. The obtained thermal maps can be used for the identification of hot spot locations in electromagnetic coil systems.

As a recent swarm-based intelligent optimization algorithm, Pigeon Inspired Optimization (PIO), is motivated by the natural bio-mechanism of pigeons for their superior skills in destination finding and navigating. The standard PIO has been successfully implemented to solve complex optimization problems. Similar to other swarm intelligent techniques, PIO is suitable to solve global optimization problems for its robustness in adapting to dynamic environments, where the convergence rate is generally limited, since the algorithm does not employ much local information to establish a most promising direction for optima searching. In this paper, we propose a novel hybridized optimization algorithm, Nelder-Mead Pigeon Inspired Optimization (NMPIO), incorporating the global optimization ability of PIO and the capability of fast local convergent of the Nelder-Mead Simplex method. With the implementation of synthesizing a bio-inspired optimization algorithm and a direct local search method, feasible global optimal solution can be found with a faster convergence rate, compared with the original PIO algorithm. Numerical experiments for several well-known benchmarks are conducted to study the performance of this algorithm. The results reveal that our hybridization strategy is effective and efficient for solving global optimization problems.

This paper reports on the recent commercial product development on the 10 Gbps RF direct modulated Distributed Bragg Reflector (DBR) tunable lasers, featuring 8 nm wavelength tuning range, 20 mW output power, high SMSR ratio (> 40 dB), and high burn-in reliability performance. This development has paved the way towards low cost, reliable, and industrial standard high speed tunable lasers for 5G and metro network upgrading applications.

For the weak data interaction between the platforms of the distribution network, the low degree of information fusion between the systems and the massive operational monitoring data, the traditional distribution network operation state control system can not meet the monitoring requirements. Therefore, this paper proposes a distribution network state control system based on big data and cloud platform, by introducing micro-service technology, massive data distributed storage technology and cross-region collaboration technology, the system can realize access processing and deep mining analysis of massive data, dynamic configuration of resources and flexible expansion of the system. In turn, the operation level of the distribution network is improved, and the lean management level of the distribution network is improved.

Aiming at the problem that the communication function program of the intelligent device in distribution automation system needs to be re-encoded under different embedded hardward environments and different application functions, this paper proposes a method for modular and rapid development of embedded device communication functions based on plug-in technology. The method uses a dedicated modeling tool to modify the channel model of a plug-in communication service framework which composed of a communication processing plug-in, a communication medium plug-in, and a communication protocol processing plug-in. The plug-in communication service framework performs the configuration change of the model according to the configuration change content noticed by communication processing plug-in, completes the change of the communication mode, and realizes the rapid construction and online update of the embedded communication function.

Substation equipments are faced with incomplete information, multiple system data cannot be effectively shared, and maintenance personnel are not aware of the status of the equipment. In view of the above problems, this paper proposes a substation intelligent monitoring decision platform based on panorama data, which can realize the centralized access of substation main equipment and auxiliary equipment datas. The equipment state assessment model and intelligent load transfer decision-making base are established based on the decision platform, which realize the intelligent judgment of quantifying equipment fault and abnormal state by comprehensive evaluation method, and provide transfer strategy for substation based on device status discrimination result. Finally, The transfer strategy and model are validated by an actual engineering data, the results show that based on the method proposed in this paper, all optional load transfer schemes are formed and the best solution is given, which can improve the emergency repair and pre-control ability and have great practical value in the automation system of substation and deserves extensive application.

Single image super-resolution(SISR) is to reconstruct a high resolution(HR) image from a single low resolution(LR) image. In this paper, with generative adversarial networks(GAN) model as the basic component, we build a dual discriminator super-resolution reconstruction network(DDSRRN) to improve the quality of image super-resolution reconstruction. By adding another discriminator based on GAN, we combine the Kullback-Leibler(KL) with reverse KL divergence to make a unified objective function to train the two discriminators, and by using the complementary statistical characteristics of these two divergences, the prediction density is effectively dispersed in multi-mode, which can avoid collapse of the network model during the reconstruction process and improve the stability of model training. We build the content loss function using the Charbonnier loss and use the intermediate features information of the two discriminators to build the perceptual loss function and style loss function. The experimental results show that the proposed method has sharp edges and rich details in subjective vision, and obtains better subjective visual evaluation and objective quantitative evaluation.

Domain adaptation has shown promising advances for alleviating domain shift problem. However, recent visual domain adaptation works usually focus on non-sequential object recognition with a global coarse alignment, which is inadequate to transfer effective knowledge for sequence-like text images with variable-length fine-grained character information. In this paper, we develop a Sequence-to-Sequence Domain Adaptation Network (SSDAN) for robust text image recognition, which could exploit unsupervised sequence data by an attention-based sequence encoder-decoder network. In the SSDAN, a gated attention similarity (GAS) unit is introduced to adaptively focus on aligning the distribution of the source and target sequence data in an attended character-level feature space rather than a global coarse alignment. Extensive text recognition experiments show the SSDAN could efficiently transfer sequence knowledge and validate the promising power of the proposed model towards real world applications in various recognition scenarios, including the natural scene text, handwritten text and even mathematical expression recognition.

We investigate, for the first time, the use of an annular pupil in optical scanning holography (OSH) for edge extraction of a 3-D object. Preliminary results show excellent edge extraction capability of the annular pupil for a multi-section object.

Keyword spotting (KWS) system constitutes a critical component of human-computer interfaces, which detects the specific keyword from a continuous stream of audio. The goal of KWS is providing a high detection accuracy at a low false alarm rate while having small memory and computation requirements. The DNN-based KWS system faces a large class imbalance during training because the amount of data available for the keyword is usually much less than the background speech, which overwhelms training and leads to a degenerate model. In this paper, we explore the focal loss for the training of a small-footprint KWS system. It can automatically down-weight the contribution of easy samples during training and focus the model on hard samples, which naturally solves the class imbalance and allows us to efficiently utilize all data available. Furthermore, many keywords of Chinese conversational assistants are repeated words due to the idiomatic usage, such as ‘XIAO DU XIAO DU’. We propose a double-edge-triggered detecting method for the repeated keyword, which significantly reduces the false alarm rate relative to the single threshold method. Systematic experiments demonstrate significant further improvements compared to the baseline system.

Searching and locating the position of the signal source is of great significance in wireless sensor network, mobile communication, public safety, and so on. A traditional signal source search and localization method requires either the signal source to be in the line-of-sight or a large number of operations to compute, which is difficult to satisfy in a complex environment for an unknown signal source with limited computational resources. In this paper, we propose a hybrid gradient-free optimization method combining the advantages of the Nelder-Mead Simplex algorithm and the Particle Swarm Optimization to study the search and localization of a signal source by using received signal strengths with a multi-agent system. Integrating a direct search method with a bio-inspired evolutionary method enables a feasible optimal solution to be found with a rapid convergence rate. To validate our proposal, numerical experiments are conducted to investigate the localization performance; three cases are studied, including two standard objective test functions and a complex 2.4GHz mobile signal strength distribution. The findings demonstrate the approving achievements of the proposed method in terms of global optimization, accuracy, and rate of convergence.

For massive multiple-input multiple-output (MIMO) detection, be- lief propagation $({\text{BP}})$ based on graphical models has become a pop- ular detection algorithm since it provides a good tradeoff between performance and complexity. To further lower the complexity of BP detection, an efficient BP detection based on channel harden- ing (BP-CH) is proposed. In this paper, the comparison in terms of both performance and complexity between proposed BP-CH and general BP is firstly investigated exhaustively. Simulation results have shown that the proposed BP-CH achieves similar performance behavior as general BP while keeping lower computational complex- ity. Additionally, an folded hardware architecture for proposed BP- CH detector is designed to improve the implementation efficiency. Meanwhile, VLSI implementation results have verified the great ad- vantage of BP-CH regarding hardware overhead, especially for sce- narios with large system loading factor.

Spatial modulation (SM), a modulation scheme that maps transmitted information onto the antenna parameter, appears as an attractive option for the emerging large-scale multiple-input multiple-output (MIMO) systems. Due to the reduced inter-channel interference (ICI) and signal processing complexity, SM has attracted vast attention to motivate further vigorous research, one of which is Huffman-based SM. To further decrease the bit error rate (BER) of SM with acceptable overhead signal processing complexity and power assumption, a new modulation scheme combining BCH coding and Huffman coding is proposed in this paper. Additionally, an adaptive quantization algorithm is derived and applied to the processing procedure of the probability matrix. Numerical results have illustrated that the proposed algorithm achieves considerable performance improvement over the existing schemes.

For massive multiple-input multiple-output (MIMO) detection, minimum mean square error (MMSE) method can achieve close-to-optimal performance. However, its computational complexity is proportional to the third power of antenna elements due to the inevitable matrix inversion operation. Especially when the number of transmitting antennas is very large, exact MMSE detection method becomes disabled owing to the excessive complexity. In this paper, an adaptive damped Jacobi (DJ) method is proposed firstly to iteratively realize MMSE performance without the intractable matrix inversion. By automatically updating the optimal relaxation factor w with the increase of iterations, the adaptive scheme does not require careful selection of initial relaxation factor compared to the conventional fixed-w DJ method. Simulation results have shown that the proposed adaptive DJ is more applicable to the correlated MIMO channels for its great flexibility of relaxation factor ω. Additionally, the hardware architecture design is also presented in this paper.

Accurate transmission line parameters are critical for power system analysis, operation, and control. In this paper, the influence of data selection scheme on estimation accuracy is study. Three factors affecting the data selection are analyzed, including the size of time window, number of data points used and data sorting method. With simulated data following operating mode, the influence of the factors on estimation accuracy and stability is checked with the least square estimation model. According to the analysis, a data selection scheme for transmission line parameters is proposed. Some transmission lines are estimated with field measurement data to verify the feasibility of the proposed data selection method.

For massive multiple-input multiple-output (MIMO) systems, signal detection is always a key concern. Traditional detection methods such as minimum mean square error (MMSE) all suffer from a variety of problems in respect of complexity and performance when applied to large-scale MIMO systems. In this paper, expectation propagation (EP) algorithm is employed to guarantee high-accuracy and low-complexity of symbol detection in high-dimensional MIMO systems. Nevertheless, in the iterative updating process of EP algorithm, the inevitable matrix inversion operation is one of the key challenges to the realistic hardware implementation. Therefore, a new improved EP, which is termed as expectation propagation with Neumann-series approximation (EP-NSA), is firstly proposed to accommodate the complexity as well as the performance by executing an approximate matrix inversion with a small number of Neumann-series terms. Simulation results have shown that the proposed EP-NSA with two items achieves notable performance improvement compared to belief propagation (BP) detection when the system loading factor is relatively large. For antenna configurations with large loading factor, this approach achieves similar performance to MMSE while keeping lower computational complexity.

3D reconstruction using multiple views allows for the restoration of a complete geometric model, but does not produce textural effects. Most of the existing texture mapping methods are aimed at the scanning reconstruction models, and few of them can be used to deal with the models with large amount of data and complex structure. We propose a high-realistic texture mapping algorithm based on image sequences. Firstly, the image sequence is sampled by using the spatio-temporal adaptive method. Then, the parameters of the camera and the size of each image are extracted by means of the Bundler, and the optimal texture image is selected for each triangular patch through the Markov random field. Finally, due to excessive loading of image data, it is necessary to reduce the texture data and only retain the effective parts mapped onto each triangular patch. Our method ensures that the resolution of the model after texture mapping is higher, and there is no obvious texture seam.

With the development of new energy distributed power generation technology, power system encountered a variety of problems, the development of micro-grid control technology requirements are getting higher and higher. On the one hand, scholars from all countries are actively studying more advanced control technology. On the other hand, when the existing control system can not stabilize the local micro-grid, you need a safe and stable control system that can trip on the new energy side in time. With the safety and stability control function of the new energy remote trip engineering system, which is characterized by a large number of devices, each station between the higher communication requirements, so to the engineering system simulation test has brought a lot of difficulties. Therefore, this paper presents a simulation test device for a new energy remote trip control system. At the same time, the RTDS simulation scheme of the system is proposed, which can be used for reference in the future development of new energy system.

In this paper, belief propagation (BP) detection based on max-sum (MS) algorithm for massive multiple-input multiple-output (MIMO) systems is therefore proposed to reduce computational complexity of general belief propagation. Owing to employing the approximation strategy, complexity reduction of MS is at the expense of detection performance loss. Based on MS algorithm, two effective approaches are proposed to compensate the performance loss resulting from MS detection. By introducing a normalized factor or an offset factor, performance and complexity can strike a balance to some extent for large-scale MIMO systems. Simulation results show that, for asymmetric antenna configuration, MS detection suffers negligible performance loss while keeping lower hardware complexity compared to original BP detection. Furthermore, in comparison with MS algorithm, the two modified algorithms can achieve noticeable performance improvement with minor overhead hardware complexity for symmetrical antenna configurations, which is applicable to independent identically distributed (i.i.d.) and correlated channels.

In this paper, an extended type-reduction method for spiked concave type-2 fuzzy sets has been studied. First, the concave type-2 fuzzy set is decomposed with α-level, and the α-level sets are obtained. Second, to the certain α-level set, there exists more than one combination (vertical interval sets defined on U) for the concave type-2 fuzzy set that parallels to the x-u plane; then, enhanced Karnik-Mendel (EKM) algorithm can be utilized to resolve the problem of type-reduction of the α-level set. Third, based on the type-reduction result that is a union of the horizontal interval set, and the type-reduction result is parallel to the x-axis. Spikes are considered for centroid calculation for the general concave type-2 fuzzy set, and the centroid for each discretized point on the x-axis is modified to have impact on the EKM results. Finally, examples on Gaussian concave type-2 fuzzy set and triangular concave type-2 fuzzy set are presented to show that spikes have impact on the concave type-2 fuzzy set's type-reduction, and the spikes should be considered if its happened whether in fuzzy modeling or fuzzy inference process.

A 3D point cloud model reconstructed based on image usually contains many outliers. These outliers may exist in the form of isolation or aggregated clusters, or connected to surface of model. Based on the distribution characteristics of outliers, an integrated outlier detection method is proposed. Firstly, the spatial topological relation of point cloud data is established by dividing cell method. When the surface of model sampling is insufficient, the points of surface will be split into a number of clusters with different sizes instead of a complete cluster, the boundary matching method can be used to preserve the good points and remove outlier clusters. Finally, the improved K-means algorithm is adopted to cluster all points based on colors, and remove the outliers which are connected to surface. Simulation results show that the proposed algorithm can effectively detect outliers with the different distribution state in the reconstructed point clouds.

A new automatic extraction algorithm of the boundary feature of the point cloud model is proposed. We use space partitioning strategy to rasterize the minimum bounding box of the point cloud model to get the spatial raster index and topology structure of each spatial discrete point; According to the number of points in the neighboring grids of each point to extract boundary feature of model preliminarily, and obtain the initial boundary feature points. Put forward the concept of grid proxy point, and create a micro tangent plane with the initial boundary point and their neighborhood proxy points by using least-square fitting method, and project them into this plane. Filter the initial boundary points according to the angle between the corresponding projection points of the initial boundary point and its neighbor proxy points in the plane, and obtain the accurate model boundary characteristics. The examples show that our algorithm can identify the boundary feature of the point cloud model accurately with lower running times.

This article proposes a tensor product model transformation based fractional order decoupled sliding mode control (TPFrDSMC) scheme for cart-pole system with friction model. In the design of TPFrDSMC, the fractional order sliding surface is incorporated into the main sliding surface, and the number of tuning the sliding surface slopes is reduced to 5. Based on fuzzy logic, parameters auto-tuning laws for the sliding surface slopes are implemented. Finally, control law of the proposed TPFrDSMC is designed according to Lyapunov stability theorem. The proposal is validated through a simulated cart-pole system with Dahl's friction model. The robustness has been evaluated through simulation in the presence of frictions and disturbances, and comparisons are performed with respect to classical approach show a better performance of the newly proposed method.

Information and communication technology are the important support technology to effective energy distribution and consumption. With the national power reform and electricity sale opened, the new requirements and goals for user demand side management and response were put forward. The efficient mining and application of consumption data should be the important basis of demand side management and response. But these data have some difficult problems as following: massive amounts, complexity of processing and analysis; In this paper, based on big data and cloud computing technology, the overall function modules of intelligent power consumption management platform were completed, and the multivariate, multi-dimensional intelligence analysis model was proposed and applied. Example as residential electricity data, the parallel outliers algorithm based on density was researched to complete the abnormal behavior and mine abnormal electricity user type; Meanwhile, Some residents in Shanghai, Beijing, Nanchang and Yinchuan were grouped, empirical research of power demand response were carried out, under the effective interaction and good incentives, the peak load can be effectively reduced; With intelligent industrial park enterprises in Gansu province as industry user example, the adaptive scheduling algorithm was proposed and applied to actual production of some enterprise with orderly electricity consumption management, the experimental result show effectively load reduction. Therefore, big data technology will provide effective support for smart power consumption in the future.

This paper presents the experimental results of ultra-high power narrow band tuning Distributed Bragg Reflector (DBR) lasers for telecom applications. Tuning range is about 8 – 10 nm, maximum output power is above 100 mW, Side Mode Suppression Ratio (SMSR) is around 55 – 60 dB, Relative Intensity Noise (RIN) is < −135 dB/Hz, and linewidth is between 2.5 MHz and 5 MHz.

This paper presents a novel design of a “Sensor on a Chip” spectrometer, based on silicon on silica (SOI) material. It employs a series of silicon micro-ring resonators with varied diameters whose resonance wavelengths evenly space out across the CO 2 emission spectrum range. This provides a resonators-based detection platform for the observation of the Earth's atmospheric CO 2 levels, as well as for the monitoring of the air quality related CO 2 level and car exhauster CO 2 pollution level. This chip-based spectrometer can collect light, propagate it along a bus waveguide, coupling the light into the ring resonators at their resonant wavelengths, and then drop off the light onto an output waveguide which is coupled with a broadband photodiode detector. The chip can be readily made by conventional silicon based semiconductor process technology.

The accuracy of transformer parameters is critical for power system analysis and control decision. In practice, the parameters of a transformer may deviate from its factory values due to the influence of environment and equipment aging. An improved model is presented in this paper to identify transformer parameters based on WAMS and SCADA data. Relative errors are considered in the objective function. Cases with different objective functions are examined. The accuracy and convergence of the different method are compared.

Taking into account human visual characteristics, when isolated distortion pixels disperse sufficiently in an image, they have minimal effect on the visual image. This paper presents two color calibration methods based on excluding isolated pixels with brightness distortion under las color space. The experimental results show that the methods can obtain better brightness and color uniformity, as well as make the histograms of corrected images in three channels get closer to normal distribution. Among them, the maximum isolated degree removal method can obtain higher richness of color, and the brightest - the darkest isolated pixels removal method can make the corrected image has higher similarity with the original image. Furthermore, the methods suggested in this paper can better reflect the transformation of surface features in the images.

This paper presents the optimal simulation and design results for arrayed waveguide gratings (AWGs) devices with channel spacing of 0.4 nm and 0.8 nm, which are suitable for the Dense Wavelength Division Multiplexing (DWDM) and Coarse Wavelength Division Multiplexing (CWDM) applications.

This paper reviewed the evolution of the Passive Optical Network (PON) and discussed the viable way of implementation of the next generation optical network, i.e., wavelength-division-multiplexing passive optical networks (WDM-PON). It also presented the designs of the optical components for the WDM-PON.

A new color image restoration algorithm is put forward based on LAB and RBF neural network. Firstly, gray image is obtained by the LAB transform method in color image. Image restoration model is got by the operating of the window roaming and training of RBF neural network and the establishment of corresponding relation between motion blurred image and clear image, which is concentrated on the motion blurred image. Finally, the prepared image is recovered according to the model. The motion blurred image can be recovered well with the new algorithm, and color image is got by the clear image according to LAB, which can keep the color information. The recovery effect is more advantaged than the classical algorithms such as Wiener filtering and with small computation.

Multi-layer feed-forward network has a good ability of function approximation, but the usual training algorithm, BP algorithm may easily fall into local minimum and it has weak generalization ability. While the space contraction particle swarm optimization (SCPSO) algorithm has a good capability of global search, the training algorithm for multi-layer feed-forward network is constructed on the basis of the SCPSO. Considering the nonlinear feature of the investment issue, a multi-layer feed-forward network model is established. The SCPSO algorithm as learning algorithm is applied to training of multi-layer feed-ward network and then a simulated prediction is made. The comparison of the prediction result between the network based on SCPSO and BP network indicates that the former has high prediction accuracy.

With developments of anonymous communication, traditional mix systems have to face more challenges such as scalability, which creates an opportunity for P2P network. In order to solve problems in P2P based anonymous communication, this paper presents a new model introducing both peer's trust and distrust, which represent different points of view in reputation degree. It also achieves anonymity with layered encryption and improves reliability with multiple peer choice.

This paper presents a design for a label free Si photonic wire "lab on a chip" arrayed biosensor with 8 sensing and 8 reference channels. It provides an advanced means for the label free detection of target molecules, for affinity binding analysis and for the time domain real-time monitoring and parallel detection of solutions, gases, or reactions near the sensing channel surface. The sensitivity of detection of the device is at the highest level among all the other methods, which is at least 10-100 times more sensitive than that of currently commercial available biosensor devices and instruments. The fabrication of the device is compatible with well-established semiconductor fabrication technologies. The device is able to be monolithically integrated with CMOS electronics, and combined with microfluidics.