Design of a Commercial Hybrid VTOL UAV System

For the last four decades Unmanned Air Vehicles (UAVs) have been extensively used for military operations that include tracking, surveillance, active engagement with weapons and airborne data acquisition. UAVs are also in demand commercially due to their advantages in comparison to manned vehicles. These advantages include lower manufacturing and operating costs, flexibility in configuration depending on customer request and not risking the pilot on demanding missions. Even though civilian UAVs currently constitute 3 % of the UAV market, it is estimated that their numbers will reach up to 10 % of the UAV market within the next 5 years. Most of the civilian UAV applications require UAVs that are capable of doing a wide range of different and complementary operations within a composite mission. These operations include taking off and landing from limited runway space, while traversing the operation region in considerable cruise speed for mobile tracking applications. This is in addition to being able traverse in low cruise speeds or being able to hover for stationary measurement and tracking. All of these complementary and but different operational capabilities point to a hybrid unmanned vehicle concept, namely the Vertical Take-Off and Landing (VTOL) UAVs. In addition, the desired UAV system needs to be cost-efficient while providing easy payload conversion for different civilian applications. In this paper, we review the preliminary design process of such a capable civilian UAV system, namely the TURAC VTOL UAV. TURAC UAV is aimed to have both vertical take-off and landing and Conventional Take-off and Landing (CTOL) capability. TURAC interchangeable payload pod and detachable wing (with potential different size variants) provides capability to perform different mission types, including long endurance and high cruise speed operations. In addition, the TURAC concept is to have two different variants. The TURAC A variant is an eco-friendly and low-noise fully electrical platform which includes 2 tilt electric motors in the front, and a fixed electric motor and ducted fan in the rear, where as the TURAC B variant is envisioned to use high energy density fuel cells for extended hovering time. In this paper, we provide the TURAC UAV’s iterative design and trade-off studies which also include detailed aerodynamic and structural configuration analysis. For the aerodynamic analysis, an in-house software including graphical user interface has been developed to calculate the aerodynamic forces and moments by using the Vortex Lattice Method (VLM). Computational Fluid Dynamics (CFD) studies are performed to determine the aerodynamic effects for various configurations For structural analysis, a Finite Element Model (FEM) of the TURAC has been prepared and its modal analysis is carried out. Maximum displacements and maximal principal stresses are calculated and used for streamlining a weight efficient fuselage design. Prototypes have been built to show success of the design at both hover and forward flight regime. In this paper, we also provide the flight management and autopilot architecture of the TURAC. The testing of the controller performance has been initiated with the prototype of TURAC. Current work focuses on the building of the full fight test prototype of the TURAC UAV and aerodynamic modeling of the transition flight.     

Performance analysis of support vector machines classifiers in breast cancer mammography recognition

Support vector machine (SVM) is a supervised machine learning approach that was recognized as a statistical learning apotheosis for the small-sample database. SVM has shown its excellent learning and generalization ability and has been extensively employed in many areas. This paper presents a performance analysis of six types of SVMs for the diagnosis of the classical Wisconsin breast cancer problem from a statistical point of view. The classification performance of standard SVM (St-SVM) is analyzed and compared with those of the other modified classifiers such as proximal support vector machine (PSVM) classifiers, Lagrangian support vector machines (LSVM), finite Newton method for Lagrangian support vector machine (NSVM), Linear programming support vector machines (LPSVM), and smooth support vector machine (SSVM). The experimental results reveal that these SVM classifiers achieve very fast, simple, and efficient breast cancer diagnosis. The training results indicated that LSVM has the lowest accuracy of 95.6107 %, while St-SVM performed better than other methods for all performance indices (accuracy = 97.71 %) and is closely followed by LPSVM (accuracy = 97.3282). However, in the validation phase, the overall accuracies of LPSVM achieved 97.1429 %, which was superior to LSVM (95.4286 %), SSVM (96.5714 %), PSVM (96 %), NSVM (96.5714 %), and St-SVM (94.86 %). Value of ROC and MCC for LPSVM achieved 0.9938 and 0.9369, respectively, which outperformed other classifiers. The results strongly suggest that LPSVM can aid in the diagnosis of breast cancer.     

Dynamic watermarking scheme for quantum images based on Hadamard transform

In this paper, a novel watermarking scheme for quantum images based on Hadamard transform is proposed. In the new scheme, a unitary transform controlled by a classical binary key is implemented on quantum image. Then, we utilize a dynamic vector, instead of a fixed parameter as in other previous schemes, to control the embedding process. The dynamic embedding vector is decided by both the carrier quantum image and the watermark image, which is only known by the authorized owner. The proposed scheme is analyzed from visual quality, computational complexity, and payload capacity. Analysis and results show that the proposed scheme has better visual quality under a higher embedding capacity and lower complexity compared with other schemes proposed recently.     

Piezoresistivity in Polymer-Ceramic Composites

Composite materials composed of randomly dispersed semiconducting ceramic particles in an insulating polymer matrix show a pronounced change in resistivity with pressure. Different amounts of iron oxide (Fe₃O₄) powder and antimony-doped tin oxide (SnO₂:Sb) powder were dispersed in an epoxy polymer matrix to form pressure-sensitive composites. In each family of materials, an insulator-to-semiconductor transition is observed in agreement with percolation theory. Composites within a certain range of filler content showed substantial piezoresistive effect under both uniaxial and hydrostatic pressure in which sensitivity is controlled by the choice of filler material and the volume fraction. The effect of temperature on the piezoresistance effect was also examined. Piezoresistors made from Fe₃O₄ composites showed larger temperature changes than those filled with Sb-doped SnO₂.     

Variations in fat content and lipid class composition in ten different mango varieties

The kernels of 10 different mango varieties were extracted. The physico-chemical characteristics and lipid class composition of fats were studied. The fat content of mango kernels grown under the soil and climatic conditions of Bangladesh varied from 7.1% to 10%, depending on the variety. The total lipid extracts were fractionated into lipid classes by a combination of column and thin layer chromatography (TLC). The hydrocarbon and sterol esters varied from 0.3% to 0.7%, triglycerides from 55.6% to 91.5%, partial glycerides from 2.3% to 4% and free sterol from 0.3% to 0.6%. Free fatty acids amounted to 3.0–37% as oleic; glycolipids were 0.6–1.2% and phospholipids 0.11–0.8%. The fatty acid composition of triglyceride (TG) fractions was analyzed by gas liquid chromatography (GLC). Palmitic acid varied from 7.9 molar % to 10.0 molar %, stearic from 38.2% to 40.2%, oleic from 41.1% to 43.8%, linoleic from 6.0% to 7.6%, linolenic from 0.6% to 1.0% and arachidic acid from 1.7% to 2.6%. TLC revealed the presence of lyso-phosphatidylcholine, phosphatidylcholine, phosphatidylinositol, phosphatidylethanolamine and phosphatidic acid in the phospholipid fraction.     

Predictive and content-aware load balancing algorithm for peer-service area based IPTV networks

Load balancing is a crucial factor in IPTV delivery networks. Load balancing aims at utilizing the resources efficiently, maximizing the throughput, and minimizing the request rejection rate. The peer-service area is the recent architecture for IPTV delivery networks that overcomes the flaws of the previous architectures. However, it still suffers from the load imbalance problem. This paper investigates the load imbalance problem, and tries to augment the peer-service area architecture to overcome this problem. To achieve the load balancing over the proposed architecture, we suggest a new load-balancing algorithm that considers both the expected and the current load of both contents and servers. The proposed load-balancing algorithm consists of two stages. The first stage is the contents replication according to their expected load, while the second stage is the content-aware request distribution. To test the effectiveness of the proposed algorithm, we have compared it with both the traditional Round Robin algorithm and Cho algorithm. The experimental results depict that the proposed algorithm outperforms the two other algorithms in terms of load balance, throughput, and request rejection rate.     

Electrochemical intercalation into graphite seen as an electrocapillary process

The electrochemical intercalation of H₂SO₄ into graphite leads to the well known graphite salts C⁺_n HSO⁻₄. χH₂SO₄ (with n → 20), χ ≈ 2.5). The potential—charge relations observed during intercalation agree well with a model involving the extended graphene layer-intercalate interfacial capacitance. The components of this capacitance are studied, including the effect of Fermi level lowering. A computed electrocapillary curve is compared to the separation work of graphene layers.