Hybrid wideband transceiver design for achievable rate maximization in millimeter-wave MIMO systems

Telecommunication Systems - This paper proposes two algorithms for hybrid (Analog–Digital) beamforming in a single-user millimeter-wave (mm-wave) multi-input multi-output (MIMO) systems under...     

A Robust Wearable Point-of-Care CNT-Based Strain Sensor for Wirelessly Monitoring Throat-Related Illnesses

Point-of-care testing (POC) has the ability to detect chronic and infectious diseases early or at the time of occurrence and provide a state-of-the-art personalized healthcare system. Recently, wearable and flexible sensors have been employed to analyze sweat, glucose, blood, and human skin conditions. However, a flexible sensing system that allows for the real-time monitoring of throat-related illnesses, such as salivary parotid gland swelling caused by flu and mumps, is necessary. Here, for the first time, a wearable, highly flexible, and stretchable piezoresistive sensing patch based on carbon nanotubes (CNTs) is reported, which can record muscle expansion or relaxation in real-time, and thus act as a next-generation POC sensor. The patch offers an excellent gauge factor for in-plane stretching and spatial expansion with low hysteresis. The actual extent of muscle expansion is calculated and the gauge factor for applications entailing volumetric deformations is redefined. Additionally, a bluetooth-low-energy system that tracks muscle activity in real-time and transmits the output signals wirelessly to a smartphone app is utilized. Numerical calculations verify that the low stress and strain lead to excellent mechanical reliability and repeatability. Finally, a dummy muscle is inflated using a pneumatic-based actuator to demonstrate the application of the affixed wearable next-generation POC sensor.     

Malware Detection Using Decision Tree Based SVM Classifier for IoT

The development in Information and Communication Technology has led to the evolution of new computing and communication environment. Technological revolution with Internet of Things (IoTs) has developed various applications in almost all domains from health care, education to entertainment with sensors and smart devices. One of the subsets of IoT is Internet of Medical things (IoMT) which connects medical devices, hardware and software applications through internet. IoMT enables secure wireless communication over the Internet to allow efficient analysis of medical data. With these smart advancements and exploitation of smart IoT devices in health care technology there increases threat and malware attacks during transmission of highly confidential medical data. This work proposes a scheme by integrating machine learning approach and block chain technology to detect malware during data transmission in IoMT. The proposed Machine Learning based Block Chain Technology malware detection scheme (MLBCT-Mdetect) is implemented in three steps namely: feature extraction, Classification and blockchain. Feature extraction is performed by calculating the weight of each feature and reduces the features with less weight. Support Vector Machine classifier is employed in the second step to classify the malware and benign nodes. Furthermore, third step uses blockchain to store details of the selected features which eventually improves the detection of malware with significant improvement in speed and accuracy. ML-BCT-Mdetect achieves higher accuracy with low false positive rate and higher True positive rate.     

3-Ethyl-4-(p-chlorobenzylidenamino-4,5-dihydro-1H-1,2,4-triazol-5-one (EPHBAT) as precipitant for carrier element free coprecipitation and speciation of chromium(III) and chromium(VI)

A method for the separation and speciative determination of Cr(VI) and Cr(III) has been developed. The procedure is based on coprecipitation of Cr(III) on 3-ethyl-4-(p-chlorobenzylidenamino-4,5-dihydro-1H-1,2,4-triazol-5-one (EPHBAT) without carrier element. The Cr(III) can be selectively precipitated on EPHBAT in the pH range of 8.0–9.0, while Cr(VI) cannot be retained. Total chromium was determined after the reduction of Cr(VI) to Cr(III) with 0.5 mL of concentrated H₂SO₄ and 0.5 mL of ethanol. Cr(VI) concentrations were obtained as the respective differences between total chromium and Cr(III). Experiments were performed to optimize conditions, such as pH, amounts of EPHBAT, sample volume, etc. A preconcentration factor of 50-fold was achieved for Cr(III). The detection limit of the method for Cr(III) was 1.0 μg L⁻¹. To validate the developed method, the certified reference materials (NIST SRM 1573a and GBW 0703) were analyzed. The method was applied for the speciation of chromium in spiked natural water samples with satisfactory results.     

A preconcentration system for determination of copper and nickel in water and food samples employing flame atomic absorption spectrometry

A separation/preconcentration procedure using solid phase extraction has been proposed for the flame atomic absorption spectrometric determination of copper and nickel at trace level in food samples. The solid phase is Dowex Optipore SD-2 resin contained on a minicolumn, where analyte ions are sorbed as 5-methyl-4-(2-thiazolylazo) resorcinol chelates. After elution using 1 mol L(-1) nitric acid solution, the analytes are determinate employing flame atomic absorption spectrometry. The optimization step was performed using a full two-level factorial design and the variables studied were: pH, reagent concentration (RC) and amount of resin on the column (AR). Under the experimental conditions established in the optimization step, the procedure allows the determination of copper and nickel with limit of detection of 1.03 and 1.90 microg L(-1), respectively and precision of 7 and 8%, for concentrations of copper and nickel of 200 microg L(-1). The effect of matrix ions was also evaluated. The accuracy was confirmed by analyzing of the followings certified reference materials: NIST SRM 1515 Apple leaves and GBW 07603 Aquatic and Terrestrial Biological Products. The developed method was successfully applied for the determination of copper and nickel in real samples including human hair, chicken meat, black tea and canned fish.     

Maximization of heat transfer density from a vertical array of flat tubes in cross flow under fixed pressure drop using constructal design

The density of heat transfer rate from a vertical array of flat tubes in cross flow is maximized under fixed pressure drop using constructal design. With the constructal design, the tube arrangement is found such that the heat currents from the tubes to the coolant flow easily. The constraint in the present constructal design is the volume where the tubes are arranged inside it. The two degrees of freedom available inside the volume are the tube‐to‐tube spacing and the length of the flat part of the tubes (tube flatness). The tubes are heated with constant surface temperature. The equations of continuity, momentums, and energy for steady, two‐dimensional, and laminar forced convection are solved by means of a finite‐volume method. The ranges of the present study are Bejan number (dimensionless pressure drop) (10³ ≤ Be ≤ 10⁵) and tube flatness (dimensionless length of the tube flat part) (0 ≤ F ≤ 0.8). The coolant used is air with Prandtl number (Pr = 0.72). The results reveal that the maximum heat transfer density decreases when the tube flatness decreases at constant Bejan number. At constant tube flatness, the heat transfer density increases as the dimensionless pressure drop (Bejan number) increases. Also, the optimal tube‐to‐tube spacing is constant, irrespective of the tube flatness at constant Bejan number.