Almost periodic solutions of periodic linear partial functional differential equations

We study conditions for the abstract periodic linear functional differential equation $\dot{x}=Ax+F(t)x_t+f(t)$ to have almost periodic with the same structure of frequencies as $f$. The main conditions are stated in terms of the spectrum of the monodromy operator associated with the equation and the frequencies of the forcing term $f$. The obtained results extend recent results on the subject. A discussion on how the results could be extended to the case when $A$ depends on $t$ is given.Comment: 17 page

Indoor assistance for visually impaired people using a RGB-D camera

In this paper a navigational aid for visually impaired people is presented. The system uses a RGB-D camera to perceive the environment and implements self-localization, obstacle detection and obstacle classification. The novelty of this work is threefold. First, self-localization is performed by means of a novel camera tracking approach that uses both depth and color information. Second, to provide the user with semantic information, obstacles are classified as walls, doors, steps and a residual class that covers isolated objects and bumpy parts on the floor. Third, in order to guarantee real time performance, the system is accelerated by offloading parallel operations to the GPU. Experiments demonstrate that the whole system is running at 9 Hz

Deep Learning-Based Signal Detection for Dual-Mode Index Modulation 3D-OFDM

In this paper, we propose a deep learning-based signal detector called DuaIM-3DNet for dual-mode index modulation-based three-dimensional (3D) orthogonal frequency division multiplexing (DM-IM-3D-OFDM). Herein, DM-IM-3D- OFDM is a subcarrier index modulation scheme which conveys data bits via both dual-mode 3D constellation symbols and indices of active subcarriers. Thus, this scheme obtains better error performance than the existing IM schemes when using the conventional maximum likelihood (ML) detector, which, however, suffers from high computational complexity, especially when the system parameters increase. In order to address this fundamental issue, we propose the usage of a deep neural network (DNN) at the receiver to jointly and reliably detect both symbols and index bits of DM-IM-3D-OFDM under Rayleigh fading channels in a data-driven manner. Simulation results demonstrate that our proposed DNN detector achieves near-optimal performance at significantly lower runtime complexity compared to the ML detector

On asymptotic periodic solutions of fractional differential equations and applications

In this paper we study the asymptotic behavior of solutions of fractional differential equations of the form $D^{\alpha}_Cu(t)=Au(t)+f(t), u(0)=x, 0<\alpha\le1, ( *)$ where $D^{\alpha}_Cu(t)$ is the derivative of the function $u$ in the Caputo's sense, $A$ is a linear operator in a Banach space \X that may be unbounded and $f$ satisfies the property that $\lim_{t\to \infty} (f(t+1)-f(t))=0$ which we will call asymptotic $1$-periodicity. By using the spectral theory of functions on the half line we derive analogs of Katznelson-Tzafriri and Massera Theorems. Namely, we give sufficient conditions in terms of spectral properties of the operator $A$ for all asymptotic mild solutions of Eq. (*) to be asymptotic $1$-periodic, or there exists an asymptotic mild solution that is asymptotic $1$-periodic.Comment: 13 pages. arXiv admin note: text overlap with arXiv:1910.0860

Dynamic Analysis of Mindlin Plates Resting on a Viscoelastic Foundation Subjected to Moving Loads During Abrupt Braking using Moving Element Method

The paper proposes a new computational approach using the moving element method (MEM) for simulating the dynamic responses of Mindlin plate resting on a viscoelastic foundation subjected to moving loads during abrupt braking. In this approach, the governing equations as well as the plate element mass, damping and stiffness matrices are formulated in a convected coordinate in which the origin is attached to the applied point of the moving load. Thus, the proposed method simply treats the moving loads as ‘stationary’ at the nodes of the plate to avoid updating the locations of moving loads due to the change of the contact points on the plate. The interaction between the moving load and the plate during abrupt braking is accounted for through the vertical force and tangential wheel-pavement friction force. The effects of wheel sliding, load deceleration magnitude, friction coefficient, and plate thickness on the dynamic responses of plate are investigated

Deep Neural Network-Based Detector for Single-Carrier Index Modulation NOMA

In this paper, a deep neural network (DNN)-based detector for an uplink single-carrier index modulation nonorthogonal multiple access (SC-IM-NOMA) system is proposed, where SC-IM-NOMA allows users to use the same set of subcarriers for transmitting their data modulated by the sub-carrier index modulation technique. More particularly, users of SC-IMNOMA simultaneously transmit their SC-IM data at different power levels which are then exploited by their receivers to perform successive interference cancellation (SIC) multi-user detection. The existing detectors designed for SC-IM-NOMA, such as the joint maximum-likelihood (JML) detector and the maximum likelihood SIC-based (ML-SIC) detector, suffer from high computational complexity. To address this issue, we propose a DNN-based detector whose structure relies on the model-based SIC for jointly detecting both M-ary symbols and index bits of all users after trained with sufficient simulated data. The simulation results demonstrate that the proposed DNN-based detector attains near-optimal error performance and significantly reduced runtime complexity in comparison with the existing hand-crafted detectors

NGHIÊN CỨU KHẢ NĂNG HẤP THỤ CACBON CỦA RỪNG NGẬP MẶN VEN BIỂN HẢI PHÒNG

Mangrove is known as a big carbon sink in coastal areas. It is an important organic carbon source which provides for coastal ecosystems. The assessment of the carbon sequestration potential of mangrove contributes to making a scientific base for mangrove conservation and rehabilitation. In this study, the carbon sequestration of mangrove in the Hai Phong coastal areas was measured at three dominant species of mangrove Rhizophora stylosa Griff; Kandelia obovata Sheue, Liu Yong and Sonneratia caseolaris (L.) Engl. The result of the assessment was described by the net canopy photosynthesis (PN­), above and below ground biomass (AGB and BGB), and organic carbon content in sediment. The result showed that the PN ranged from 31.94 ± 1.59 tC.ha-1.yr-1 to 34.83 ± 1.95 tC.ha-1.yr-1 with the R. stylosa community being highest. Above and below ground biomass C stock ranged from 4.03 ± 0.31 t.ha-1 to 294.43 ± 24.67 t.ha-1 and from 2.38 ± 0.16 t.ha-1 to 114.16 ± 8.9 t.ha-1, respectively. S. caseolaris community had the highest biomass and R. stylosa community had the lowest biomass. The measurements of C stock in mangrove biomass for three species were R. stylosa (2.69 ± 0.19 t.ha-1); K. obovata (6.72 ± 0.34 t.ha-1) and S. caseolaris (171.61 ± 14.1 t.ha-1). The organic carbon content of sedimentscores at 10 cm depth ranged from 685.63 milligram.kg-1 of se. dry to 2676.64 milligram.kg-1 of se. dry and at 40 cm depth ranged from 937.38 milligram.kg-1 of se. dry to 2557.55 milligram.kg-1 of se. dry. The total organic carbon was stored highest in the R. stylosa community.Rừng ngập mặn là một bể chứa cacbon lớn khu vực ven biển, là một nguồn cung cấp cacbon hữu cơ quan trọng cho hệ sinh thái ven biển. Việc đánh giá khả năng hấp thụ và lưu giữ cacbon của rừng ngập mặn góp phần tạo cơ sở khoa học cho việc bảo tồn và phát triển rừng ngập mặn. Bài báo trình bày kết quả nghiên cứu khả lưu giữ cacbon của rừng ngập mặn ven biển Hải Phòng tại ba kiểu rừng đặc trưng: Đước vòi (Rhizophora stylosa Griff.); Trang (Kandelia obovata Sheue, Liu Yong) và Bần chua (Sonneratia caseolaris (L.) Engl.). Qua đó đánh giá mức độ lưu trữ cac bon qua quá trình quang hợp tán lá, sinh khối cây và trong trầm tích của ba kiểu rừng nói trên. Kết quả nghiên cứu cho thấy: lượng cacbon tích lũy qua quá trình quang hợp từ 31,94 ±          1,59 tC/ha/năm đến 34,83 ± 1,95 tC/ha/năm, trong đó cao nhất là quần xã Đước vòi (R. stylosa). Sinh khối trên (AGB) và sinh khối dưới (BGB) nằm trong khoảng tương ứng là 4,03 ± 0,31 t/ha đến 294,43 ± 24,67 t/ha và 2,38 ± 0,16 t/ha đến 114,16 ± 8,9 t/ha, Bần chua (S. caseolaris) có trữ lượng lớn nhất và thấp nhất là Đước vòi (R. stylosa). Hàm lượng cacbon hữu cơ trong trầm tích ở độ sâu 10 cm từ 685,63 mg/kg khô đến 2676,64 mg/kg khô; ở độ sâu 40 cm từ 937,38 mg/kg khô đến 2557,55 mg/kg khô, trong đó khả năng lưu trữ cacbon trong trầm tích của rừng Đước vòi (R. stylosa) là cao nhất

A homogeneous-heterogeneous ensemble of classifiers.

In this study, we introduce an ensemble system by combining homogeneous ensemble and heterogeneous ensemble into a single framework. Based on the observation that the projected data is significantly different from the original data as well as each other after using random projections, we construct the homogeneous module by applying random projections on the training data to obtain the new training sets. In the heterogeneous module, several learning algorithms will train on the new training sets to generate the base classifiers. We propose four combining algorithms based on Sum Rule and Majority Vote Rule for the proposed ensemble. Experiments on some popular datasets confirm that the proposed ensemble method is better than several well-known benchmark algorithms proposed framework has great flexibility when applied to real-world applications. The proposed framework has great flexibility when applied to real-world applications by using any techniques that make rich training data for the homogeneous module, as well as using any set of learning algorithms for the heterogeneous module