Distributed Beamforming with Wirelessly Powered Relay Nodes

This paper studies a system where a set of $N$ relay nodes harvest energy from the signal received from a source to later utilize it when forwarding the source's data to a destination node via distributed beamforming. To this end, we derive (approximate) analytical expressions for the mean SNR at destination node when relays employ: i) time-switching based energy harvesting policy, ii) power-splitting based energy harvesting policy. The obtained results facilitate the study of the interplay between the energy harvesting parameters and the synchronization error, and their combined impact on mean SNR. Simulation results indicate that i) the derived approximate expressions are very accurate even for small $N$ (e.g., $N=15$), ii) time-switching policy by the relays outperforms power-splitting policy by at least $3$ dB.Comment: 4 pages, 3 figures, accepted for presentation at IEEE VTC 2017 Spring conferenc

Exploiting Lack of Hardware Reciprocity for Sender-Node Authentication at the PHY Layer

This paper proposes to exploit the so-called reciprocity parameters (modelling non-reciprocal communication hardware) to use them as decision metric for binary hypothesis testing based authentication framework at a receiver node Bob. Specifically, Bob first learns the reciprocity parameters of the legitimate sender Alice via initial training. Then, during the test phase, Bob first obtains a measurement of reciprocity parameters of channel occupier (Alice, or, the intruder Eve). Then, with ground truth and current measurement both in hand, Bob carries out the hypothesis testing to automatically accept (reject) the packets sent by Alice (Eve). For the proposed scheme, we provide its success rate (the detection probability of Eve), and its performance comparison with other schemes

Transfer Learning with Deep Convolutional Neural Network (CNN) for Pneumonia Detection using Chest X-ray

Pneumonia is a life-threatening disease, which occurs in the lungs caused by either bacterial or viral infection. It can be life-endangering if not acted upon in the right time and thus an early diagnosis of pneumonia is vital. The aim of this paper is to automatically detect bacterial and viral pneumonia using digital x-ray images. It provides a detailed report on advances made in making accurate detection of pneumonia and then presents the methodology adopted by the authors. Four different pre-trained deep Convolutional Neural Network (CNN)- AlexNet, ResNet18, DenseNet201, and SqueezeNet were used for transfer learning. 5247 Bacterial, viral and normal chest x-rays images underwent preprocessing techniques and the modified images were trained for the transfer learning based classification task. In this work, the authors have reported three schemes of classifications: normal vs pneumonia, bacterial vs viral pneumonia and normal, bacterial and viral pneumonia. The classification accuracy of normal and pneumonia images, bacterial and viral pneumonia images, and normal, bacterial and viral pneumonia were 98%, 95%, and 93.3% respectively. This is the highest accuracy in any scheme than the accuracies reported in the literature. Therefore, the proposed study can be useful in faster-diagnosing pneumonia by the radiologist and can help in the fast airport screening of pneumonia patients.Comment: 13 Figures, 5 tables. arXiv admin note: text overlap with arXiv:2003.1314

Forecasting wind speed data by using a combination of ARIMA model with single exponential smoothing

Wind serves as natural resources as the solution to minimize global warming and has been commonly used to produce electricity. Because of their uncontrollable wind characteristics, wind speed forecasting is considered one of the best challenges in developing power generation. The Autoregressive Integrated Moving Average (ARIMA), Simple Exponential Smoothing (SES) and a hybrid model combination of ARIMA and SES will be used in this study to predict the wind speed. The mean absolute percentage error (MAPE) and the root mean square error (RMSE) are used as measurement of efficiency. The hybrid model provides a positive outcome for predicting wind speed compare to the single model of ARIMA and SES

Efficacy of Protein, Symbiotic and Probiotic Supplementation on Body Performance and Organs Weight in Molted Layers

Two hundred White Leg Horn Layers (70 week age) were arranged and brought to the poultry research station, Department of Physiology and Pharmacology, University of Agriculture, Faisalabad. Four groups were made (n=50 each) into keeping G1 as control (CP 16%, No other supplement), G2 (CP 18% diet), G3 (CP16% diet; symbiotic @ 85 mg L-1/day) and G4 (CP 16% diet; probiotic @ 85 mg L-1/day). The body and organs (heart, liver, spleen, kidney, brain and pituitary) weight from fifteen birds in each group at 5% (5P), peak (PP) and end (EP) of post molt production stage were determined. The overall mean heart weight in G2 and pituitary weight in G2 and G3 reduced (P≤0.05) as compared to G1. The mean kidney weight found increased (P≤0.05) in G3 and G4 as compared to G1. The results show metabolic relation of protein and probiotics with body organs

Modulation mode detection and classification for in-vivo nano-scale communication systems operating in terahertz band

This paper initiates the efforts to design an intelligent/cognitive nano receiver operating in terahertz band. Specifically, we investigate two essential ingredients of an intelligent nano receiver—modulation mode detection (to differentiate between pulse-based modulation and carrier-based modulation) and modulation classification (to identify the exact modulation scheme in use). To implement modulation mode detection, we construct a binary hypothesis test in nano-receiver’s passband and provide closed-form expressions for the two error probabilities. As for modulation classification, we aim to represent the received signal of interest by a Gaussian mixture model (GMM). This necessitates the explicit estimation of the THz channel impulse response and its subsequent compensation (via deconvolution). We then learn the GMM parameters via expectation–maximization algorithm. We then do Gaussian approximation of each mixture density to compute symmetric Kullback–Leibler divergence in order to differentiate between various modulation schemes (i.e., ${M}$ -ary phase shift keying and ${M}$ -ary quadrature amplitude modulation). The simulation results on mode detection indicate that there exists a unique Pareto-optimal point (for both SNR and the decision threshold), where both error probabilities are minimized. The main takeaway message by the simulation results on modulation classification is that for a pre-specified probability of correct classification, higher SNR is required to correctly identify a higher order modulation scheme. On a broader note, this paper should trigger the interest of the community in the design of intelligent/cognitive nano receivers (capable of performing various intelligent tasks, e.g., modulation prediction, and so on)

Analisis Kapasitas Ruas Sungai Ciliwung Hilir (Gunung Sahari) Terhadap Debit Banjir Serta Penanggulangannya Pada DAS Marina DKI Jakarta

Provinsi DKI Jakarta memiliki luas daerah ± 661,52 Km2, kota DKI Jakarta merupakan dataran rendah yang dialiri oleh 13 sungai yang bermuara ke utara pulau jawa, aliran air di DKI Jakarta sebagian dibuang ke laut dengan sistem gravitasi dan sebagian lagi dengan sistem pompanisasi. Banjir yang setiap tahun yang terjadi di DKI Jakarta tidak lepas dari pengaruh sungai-sungai yang melintasinya. Sungai-sungai besar berhulu di bagian selatan DKI Jakarta yaitu daerah Bogor yang mempunyai ketinggian lebih dari 200 m dpl dan curah hujan tinggi, sehingga DKI Jakarta secara alamiah menjadi daerah tempat berakumulasi air dari hulu sungainya. Kawasan yang rentan terhadap banjir dan genangan adalah Jakarta Utara, khususnya untuk sungai ciliwung gunung sahari karena sungai ciliwung gunung sahari ini melewati beberapa kawasan penting seperti stasiun gambir, Istana Negara, Monas, Balaikota DKI Jakarta, Masjid Istiqlal. Oleh sebab itu di wilayah ini harus mempunyai penanganan khusus, sektor ini sangat vital untuk DKI Jakarta. Pintu Air Hai Lai Marina yang merupakan pintu air pengontrol drainase aliran Sungai Cilliwung serta sebagai pintu pasang surut (Tidal Gate) terletak di hilir aliran kali Cilliwung Gunung Sahari.Pintu Air ini merupakan pemisah antara Sungai Cilliwung dan Laut Utara Jakarta. Sistem kerja Pintu Air Hai Lai Marina ini tergantung perbedaan antara ketinggian muka air laut dengan muka air Sungai Cilliwung Gunung Sahari. Saat muka air laut tinggi pintu air ini ditutup agar air laut tidak masuk ke dalam aliran Sungai Cilliwung gunung sahari dan jika muka air laut lebih rendah maka pintu air dibuka agar aliran dari Sungai Cilliwung bisa masuk ke laut. Hasil pemodelan untuk kala ulang 5 tahun diperlukan pompa dengan kapasitas total 50m3/dtk untuk menurunkan muka air sungai agar tidak meluap, pemodelan untuk kala ulang 10 tahun diperlukan pompa dengan kapasitas total 60m3/dtk untuk menurunkan muka air sungai agar tidak meluap, pemodelan untuk kala ulang 25 tahun diperlukan pompa dengan kapasitas total 70m3/dtk untuk menurunkan muka air sungai agar tidak meluap

THE DEVELOPMENT OF BRYOPHYTA TEACHING BOOK FOR INCREASING THE STUDENTS’ UNDERSTANDING OF THE CONCEPT

One of the determinants of learning success is the achievement of cognitive abilities or, commonly called, concept understanding. In fact, mastery of concepts based on the results of prior research at various levels of education is still relatively low. The results of observations on the subject of Low Plant Botany at the Biology Education Department FKIP Universitas Sulawesi Barat also showed that students' conceptual understanding was still low. This problem certainly requires a solution such as the implementation of a Low Plant Botany textbook that has been developed by the research team. The purpose of this study was to find out the stages of developing teaching materials based on the results of research in the first year of research, knowing the results of validation test by design experts, material experts, results of individual trials, trial results of readability of teaching materials, and to determine the effectiveness of development results textbooks for understanding student concepts. The method of developing teaching books in this study using the ADDIE Model which according to Branch (2009) consists of 5 stages, namely analysis (analysis), design (design), development (development), implementation (product implementation/ testing), and evaluation (evaluation). The results of this development study revealed that: (1) developed textbooks fulfilled valid criteria by the validators so that it was feasible to be implemented into the learning process, and (2) textbooks that had been developed by the research team proved effective in improving understanding the concepts of students.  Article visualizations