Prefect Transfer of Quantum States on Spin Chain with Dzyaloshinskii- Moriya interaction in inhomogeneous Magnetic field

In this work, we use the Hamiltonian of a modified Dzyaloshinskii-Moriya model and investigate the perfect transfer of the quantum state on the spin networks. In this paper, we calculate fidelity in which fidelity depends on magnetic field and another parameters. Then, by using the numerical analysis we show that the fidelity of the transferred state is determined by magnetic field $B$, exchange coupling $J$ and the Dzyaloshinskii- Moriya interaction $D$. We also found that the perfect transfer of the quantum state is possible with condition $B \gg \Gamma^2\omega^{N/2}$ where $\Gamma =((J+iD)/2)$ and $\omega=\Gamma^*/ \Gamma$.Comment: 8 pages, 2 figure

Huygens principle based UWB microwave imaging method for skin cancer detection

In recent years, Ultra Wideband (UWB) technology has emerged as a promising alternative for use in a wide range of applications. One of the potential applications of UWB is in healthcare and imaging, motivated by its non-ionizing signals, low cost, low complexity, and its ability to penetrate through mediums. Moreover, the large bandwidth covered by UWB signals permits the very high resolution required in imaging experiments. In this paper, a recently introduced UWB microwave imaging technique based on the Huygens principle (HP), has been applied to multilayered skin model with an inclusion representing a tumor. The methodology of HP permits the capture of contrast such that different material properties within the region of interest can be discriminated in the final image, and its simplicity removes the need to solve inverse problems when forward propagating the waves. Therefore the procedure can identify and localize significant scatterers inside a multilayered volume. Validation of the technique through simulations on multilayered cylindrical model of the skin with inclusion representing the tumor has been performed

New Reconstructed Database for Cost Reduction in Indoor Fingerprinting Localization

Location fingerprinting is a technique widely suggested for challenging indoor positioning. Despite the significant benefits of this technique, it needs a considerable amount of time and energy to measure the Received Signal Strength (RSS) at Reference Points (RPs) and build a fingerprinting database to achieve an appropriate localization accuracy. Reducing the number of RPs can reduce this cost, but it noticeably degrades the accuracy of positioning. In order to alleviate this problem, this paper takes the interior architecture of the indoor area and signal propagation effects into account and proposes two novel recovery methods for creating the reconstructed database instead of the measured one. They only need a few numbers of RPs to reconstruct the database and even are able to produce a denser database. The first method is a new zone-based path-loss propagation model which employs fingerprints of different zones separately and the second one is a new interpolation method, zone-based Weighted Ring-based (WRB). The proposed methods are compared with the conventional path-loss model and six interpolation functions. Two different test environments along with a benchmarking testbed, and various RPs configurations are also utilized to verify the proposed recovery methods, based on the reconstruction errors and the localization accuracies they provide. The results indicate that by taking only 11% of the initial RPs, the new zone-based path-loss model decreases the localization error up to 26% compared to the conventional path-loss model and the proposed zone-based WRB method outperforms all the other interpolation methods and improves the accuracy by 40%

Occupancy Based Household Energy Disaggregation using Ultra Wideband Radar and Electrical Signature Profiles

Human behaviour and occupancy accounts for a substantial proportion of variation in the energy efficiency pro le of domestic buildings. Yet while people often claim that they would like to reduce their energy bills, rhetoric frequently fails to match action due to the effort involved in understand- ing and changing deeply engrained energy consumption habits. Here, we present and, through dedicated experiments, test in-house developed soft-ware to remotely identify appliance energy usage within buildings, using energy equipment which could be placed at the electricity meter location. Furthermore, we monitor and compare the occupancy of the location under study through Ultra-Wideband (UWB) radar technology and compare the resulting data with those received from the power monitoring software, via time synchronization. These signals when mapped together can potentially provide both occupancy and speci c appliances power consumption, which could enable energy usage segregation on a yet impossible scale as well as usage attributable to occupancy behaviour. Such knowledge forms the basis for the implementation of automated energy saving actions based on a households unique energy profi le

Experimental Investigation of the Transient Flow in Roots Blower

Rotary positive displacement machines are common method to pump flow in various process industries. Their performance highly depends on the operational clearances. It is widely believed that computational fluid dynamics (CFD) can help understanding and reducing internal leakage flows. However, Developments of grid generating tools for use of CFD in rotary positive displacement machines have not yet been fully validated. Thereby arising a need to validate these models that help in better understanding of the leakage flows. Roots blower is a good representative of positive displacement machines and as such is convenient for optical access to analyse flows in in such machines. This paper describes the setup of the experimental test rig with the optical Roots blower in the Centre for Compressor Technology at City, University of London and the first results obtained using three different flow visualization methods. These are namely i) the high-speed camera (HC), ii) the continuous time resolved PIV (CPIV) and iii) the instantaneous PIV obtained with double pulse PIV laser and double shutter camera (IPIV). Test results from these three tests are compared and discussed in the paper. The CPIV test shows the movement of the vortex and the general shape of the flow field clearly but is not sufficient to calculate velocity vectors of high-velocity particles due to the limitation of the laser and camera. The IPIV test can produce quantitative velocity vector images of the internal flow but needs improvement to look into the leakage flow. The work described in this paper is a part of the large project set to evaluate characteristics of the internal flow in rotary positive displacement machines and to characterize leakage flows. The objective is to enable further improvements in 3D CFD analysis of leakage flows in rotary positive displacement machines and ultimately lead to the improvement in the performance of rotary positive displacement machines

Improving Indoor Localization Using Mobile UWB Sensor and Deep Neural Networks

Accurate localization in indoor environments with ultra-wideband (UWB) technology has long attracted much attention. However, due to the presence of multipath components or non-line of sight (NLOS) propagation of the radio signals, it has been converted to a critical challenge. Existing solutions use many fixed anchors in the indoor environment. Particularly, large areas require many anchor points and in the case of unexpected events that lead to the destruction of existing infrastructures, the fixed anchor points cannot be used. In this paper, a novel localization framework based on the transmitting signal from a mobile UWB sensor on the outside of the building and its received signal regarding the modified Saleh Valenzuela (SV) channel model is presented. After preprocessing the received signals, two new procedures to reduce the ranging error caused by multipath components are proposed. In the first procedure, two machine learning algorithms including multi-layer perceptron (MLP) and support vector machine (SVM) using the extracted features from the received UWB signal time and power vectors are implemented. Moreover, in the second procedure, two deep learning algorithms including MLP and convolutional neural networks (CNNs) using the received UWB signal time and power vectors are implemented to improve the performance of the indoor localization system. The simulation results show that the architecture designed for the convolutional neural network based on the hybrid dataset (the combination of the dataset related to received UWB signal time and power vectors) provides a mean absolute error (MAE) of about 3 cm

Improving time–frequency domain sleep EEG classification via singular spectrum analysis

Background: Manual sleep scoring is deemed to be tedious and time consuming. Even among automatic methods such as Time-Frequency (T-F) representations, there is still room for more improvement. New method: To optimise the efficiency of T-F domain analysis of sleep electroencephalography (EEG) a novel approach for automatically identifying the brain waves, sleep spindles, and K-complexes from the sleep EEG signals is proposed. The proposed method is based on singular spectrum analysis (SSA). The single-channel EEG signal (C3-A2) is initially decomposed and then the desired components are automatically separated. In addition, the noise is removed to enhance the discrimination ability of features. The obtained T-F features after preprocessing stage are classified using a multi-class support vector machines (SVM) and used for the identification of four sleep stages over three sleep types. Furthermore, to emphasize on the usefulness of the proposed method the automatically-determined spindles are parameterised to discriminate three sleep types. Result: The four sleep stages are classified through SVM twice: with and without preprocessing stage. The mean accuracy, sensitivity, and specificity for before the preprocessing stage are: 71.5 ± 0.11%, 56.1 ± 0.09% and 86.8 ± 0.04% respectively. However, these values increase significantly to 83.6 ± 0.07%, 70.6 ± 0.14% and 90.8 ± 0.03% after applying SSA. Comparison with existing method: The new T-F representation has been compared with the existing benchmarks. Our results prove that, the proposed method well outperforms the previous methods in terms of identification and representation of sleep stages. Conclusion: Experimental results confirm the performance improvement in terms of classification rate and also representative T-F domain

Free space operating microwave imaging device for bone lesion detection: a phantom investigation

In this letter, a phantom validation of a low complexity microwave imaging device operating in free space in the 1-6.5 GHz frequency band is presented. The device, initially constructed for breast cancer detection, measures the scattered signals in a multi-bistatic fashion and employs an imaging procedure based on Huygens principle. Detection has been achieved in both bone fracture lesion and bone marrow lesion scenarios using the superimposition of five doublet transmitting positions, after applying the rotation subtraction artefact removal method. A resolution of 5 mm and a signal to clutter ratio (3.35 in linear scale) are achieved confirming the advantage of employing multiple transmitting positions on increased detection capability

Numerical and Experimental Analysis of Transient Flow in Roots Blower

The performance of rotary positive displacement machines highly depends on the operational clearances. It is widely believed that computational fluid dynamics (CFD) can help understanding internal leakage flows. Developments of grid generating tools for analysis of leakage flows by CFD in rotary positive displacement machines have not yet been fully validated. Roots blower is a good representative of positive displacement machines and as such is convenient for optical access in order to analyse internal flows. The experimental investigation of flow in optical roots blower by phase-locked PIV (Particle Image Velocimetry) performed in the Centre for Compressor Technology at City, University of London provided the velocity field suitable for validation of the simulation model. This paper shows the results of the three-dimensional CFD transient simulation model of a Roots blower with the dynamic numerical grids generated by SCORG and flow solution solved in ANSYS CFX flow solver to obtain internal flow patterns. The velocity fields obtained by simulation agree qualitatively with the experimental results and show the correct main flow features in the working chamber. There are some differences in the velocity magnitude and vortex distribution. The flow field in roots blower is highly turbulent and three-dimensional. The axial clearances should be included, and the axial grids should be refined in the simulation method. The paper outlines some directions for future simulation and experimental work. The work described in this paper is a part of the large project set to evaluate characteristics of the internal flow in rotary positive displacement machines and to characterize leakage flow