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Abstract Malware attacks on the Android platform are rapidly increasing due to the high consumer adoption of Android smartphones. Advanced technologies have motivated cyber-criminals to actively create and disseminate a wide range of malware on Android smartphones. The researchers have conducted numerous studies on the detection of Android malware, but the majority of the works are based on the detection of generic Android malware. The detection based on malware categories will provide more insights about the malicious patterns of the malware. Therefore, this paper presents a detection solution for different Android malware categories, including adware, banking, SMS malware, and riskware. In this paper, a novel Huffman encoding-based feature vector generation technique is proposed. The experiments have proved that this novel approach significantly improves the efficiency of the detection model. This method makes use of system call frequencies as features to extract malware’s dynamic behavior patterns. The proposed model was evaluated using machine learning and deep learning methods. The results show that the proposed model with the Random Forest classifier outperforms some existing methodologies with a detection accuracy of 98.70%.

The photoacoustic signal generated by an optically absorbing target is determined by the spatial profile of absorbed optical energy within the target. The analysis of the time profile and frequency content of the signal enables the recovery of the geometry of the object, as well as information about the optical properties. The photoacoustic response of spheres with a homogeneous absorbed optical energy profile is well described, and it is known that the width of the photoacoustic pulse is determined by the diameter of the sphere and its sound speed. In practice, the optical attenuation coefficients within the sphere will result in an inwardly decaying fluence profile leading to a similarly decaying absorbed optical energy profile. Further, the optical attenuation coefficients may be inhomogeneously distributed in the sphere. The implication for both cases is that the existing model for spheres does not fully apply. In this work, we developed analytical expressions for the photoacoustic time traces and amplitude spectra generated by a sphere with absorbed optical energy only in a spherical shell, and by a sphere with an inwardly decaying optical energy profile. Numerical simulations and experiments were conducted on these two imperfect sphere types. Fitting our model to the simulated or measured spectra allowed us to test our model’s ability to extract the sphere size and optical properties. We found that the radii can be recovered with high accuracy, even when the frequency response of the detector recording the photoacoustic pulse is not precisely known. The model was found to be less sensitive in recovering the optical attenuation coefficient, but it is feasible when the detector’s frequency response is well known.

Clinical measurements on breast cancer patients were performed with a three-dimensional tomographic photoacoustic prototype imager (PAM 2). Patients with a suspicious lesion, visiting the center for breast care of a local hospital, were included in the study. The acquired photoacoustic images were compared to conventional clinical images. Of 30 scanned patients, 19 were diagnosed with one or more malignancies, of which a subset of four patients was selected for detailed analysis. Reconstructed images were processed to enhance image quality and the visibility of blood vessels. Processed photoacoustic images were compared to contrast-enhanced magnetic resonance images where available, which aided in localizing the expected tumoral region. In two cases, spotty high-intensity photoacoustic signals could be seen in the tumoral region, attributable to the tumor. One of these cases also displayed a relatively high image entropy at the tumor site, likely related to the chaotic vascular networks associated with malignancies. For the other two cases, it was not possible to identify features indicative of malignancy, because of limitations in the illumination scheme and difficulties in locating the region of interest in the photoacoustic image.

Abstract Background Two-dimensional (2D) ultrasound is well established for thyroid nodule assessment and treatment guidance. However, it is hampered by a limited field of view and observer variability that may lead to inaccurate nodule classification and treatment. To cope with these limitations, we investigated the use of real-time three-dimensional (3D) ultrasound to improve the accuracy of volume estimation and needle placement during radiofrequency ablation. We assess a new 3D matrix transducer for nodule volume estimation and image-guided radiofrequency ablation. Methods Thirty thyroid nodule phantoms with thermochromic dye underwent volume estimation and ablation guided by a 2D linear and 3D mechanically-swept array and a 3D matrix transducer. Results The 3D matrix nodule volume estimations had a lower median difference with the ground truth (0.4 mL) compared to the standard 2D approach (2.2 mL, p < 0.001) and mechanically swept 3D transducer (2.0 mL, p = 0.016). The 3D matrix-guided ablation resulted in a similar nodule ablation coverage when compared to 2D-guidance (76.7% versus 80.8%, p = 0.542). The 3D mechanically swept transducer performed worse (60.1%, p = 0.015). However, 3D matrix and 2D guidance ablations lead to a larger ablated volume outside the nodule than 3D mechanically swept (5.1 mL, 4.2 mL (p = 0.274), 0.5 mL (p < 0.001), respectively). The 3D matrix and mechanically swept approaches were faster with 80 and 72.5 s/mL ablated than 2D with 105.5 s/mL ablated. Conclusions The 3D matrix transducer estimates volumes more accurately and can facilitate accurate needle placement while reducing procedure time.

We present a protocol for the design, fabrication and characterisation of laser-induced ultrasound transmitters with a specific, user-defined frequency response for the purpose of ultrasound tomography of large-volume biomedical samples. Using an analytic solution to the photoacoustic equation and measurements of the optical and acoustic properties of the materials used in the transmitters, we arrive at a required mixture of carbon black and polydimethylsiloxane to achieve the desired frequency response. After an in-depth explanation of the fabrication and characterisation approaches we show the performance of the fabricated transmitter, which has a centre frequency of 0.9 MHz, 200% bandwidth and 45.8∘opening angle, multi-kPa pressures over a large depth range in water.

We present a new methodology, which we call Single Pair of Observations Technique with Eddy Covariance (SPOT-EC), to estimate regional-scale surface fluxes of 222Rn from tower-based observations of 222Rn activity concentration, CO2 mole fractions and direct CO2 flux measurements from eddy covariance. For specific events, the regional (222Rn) surface flux is calculated from short-term changes in ambient (222Rn) activity concentration scaled by the ratio of the mean CO2 surface flux for the specific event to the change in its observed mole fraction. The resulting 222Rn surface emissions are integrated in time (between the moment of observation and the last prior background levels) and space (i.e. over the footprint of the observations). The measurement uncertainty obtained is about ±15 % for diurnal events and about ±10 % for longer-term (e.g. seasonal or annual) means. The method does not provide continuous observations, but reliable daily averages can be obtained. We applied our method to in situ observations from two sites in the Netherlands: Cabauw station (CBW) and Lutjewad station (LUT). For LUT, which is an intensive agricultural site, we estimated a mean 222Rn surface flux of (0.29 ± 0.02) atoms cm−2 s−1 with values > 0.5 atoms cm−2 s−1 to the south and south-east. For CBW we estimated a mean 222Rn surface flux of (0.63 ± 0.04) atoms cm−2 s−1. The highest values were observed to the south-west, where the soil type is mainly river clay. For both stations good agreement was found between our results and those from measurements with soil chambers and two recently published 222Rn soil flux maps for Europe. At both sites, large spatial and temporal variability of 222Rn surface fluxes were observed which would be impractical to measure with a soil chamber. SPOT-EC, therefore, offers an important new tool for estimating regional-scale 222Rn surface fluxes. Practical applications furthermore include calibration of process-based 222Rn soil flux models, validation of atmospheric transport models and performing regional-scale inversions, e.g. of greenhouse gases via the SPOT 222Rn-tracer method.

Electric vehicles are showing some promises in the automotive industry and can be the answer for mitigating carbon footprint. In the process of upgrading electric vehicles to the customer demands, battery performance serves a crucial part in deciding the performance of the electric vehicles. So, Battery Management System becomes the brains behind monitoring and controlling the battery. Real-time sensing of the battery parameters, decision-making capability to choose the type of charging, and which cell to be charged are all the functionalities of BMS. All these criteria can be assessed precisely and efficiently via processors like Raspberry pi, along with IoT and cloud computing technologies. These approaches can be used for remote accessing of the battery's performance, which will help the customer and the company to analyse the vehicle's condition. They also help prevent battery degradation. Since IoT and cloud computing technologies are being used, if an adverse state occurs in the battery, the customer can be notified directly via their mobile. In this article, a combined technology of locally hosted processor and cloud-based decision making has been discussed to improve the battery intern Electric Vehicle's performance. [ABSTRACT FROM AUTHOR]

Power grids transport electricity from the point of generation to the market. Power conversion from HV to LV and vice versa occurs in grids, also known as substations. These substations or power grids can be accessible or situated in remote locations. Transformers are used to convert power; they are an essential part of transmission and distribution networks. The method of grid monitoring and maintenance is essentially a very monotonous one. Monitoring the health of the transformers to maintain an uninterrupted power supply to the customers is difficult in such circumstances. Overvoltage, load currents, oil temperature, transformer oil level, and other parameters are monitored. The condition of the distribution transformer's is evaluated in this article using real-time data from the transformer and specific sensors connected to Raspberry pi and artificial neural networks, are used to analyse the situation and make decisions regarding the health of the transformer. A model has been proposed for continuous monitoring consistent vigilance and swift actions against any faulty situations. [ABSTRACT FROM AUTHOR]