Lowpass and bandpass transadmittance filter using operational amplifier pole

A new topology simultaneously implementing lowpass (LP) and bandpass (BP) transadmittance filtering signals using a single operational amplifier (OA), one capacitor, and two resistors is presented. The input impedance is very high which is essential for cascading without employment of buffers. The circuit uses absolute minimum number of active and passive components. The filter employs pole-model of OA and as such has acquired suitability for extended frequency operation. The circuit permits separate adjustment of natural frequency (ω₀) and quality factor (Q) in an orthogonal manner. The circuit has low sensitivity figures unlike the reported single amplifier biquads. The PSPICE simulation results are included.     

Measurement of Inlet Drop Size Distribution of Secondary Dispersions by Laser Particle Size Analyzer

Knowledge of the Oil drop size distribution, in addition to the mean drop size, is necessary in order to characterize secondary dispersions. Furthermore, the capture efficiency for any drop flowing through a packed bed depends upon its Oil inlet drop diameter. The Oil/Water secondary dispersions produced by the centrifugal pump were analyzed at regular intervals during an experiment to ascertain the drop size distribution and to check that the feed to the coalescence bed was consistent. New techniques that were developed for measurement of drop size distribution of secondary dispersions using Laser Particle Size Analyzer, which consisted of (He/Ne) laser emitter and laser receiver and lenses. The Laser Particle Size Analyzer was fixed directly to the experimental equipment by using the special designed circular cell. The measurement of drop size distribution was done by computer system with application software package. The new mean drop diameter (i.e., d₂₁) equation has been derived theoretically and the results that predicted from this equation exhibited a maximum error of ±15% from the experimental data.     

A scheduling decision aid in glass fiber manufacturing

A decision aid for scheduling production in glass fiber manufacturing industry is described. The methodology combines a linear programming (LP) optimization model with a heuristic model. The LP model determines production goals; the heuristic model then uses the LP output to incorporate system-specific constraints in developing processing sequences.     

Feature extraction and ML techniques for static gesture recognition

The main objective of this study is to explore the utility of a neural network-based approach in hand gesture recognition. The proposed system presents two recognition algorithms to recognize a set of six specific static hand gestures, namely open, close, cut, paste, maximize, and minimize. The hand gesture image is passed through three stages: preprocessing, feature extraction, and classification. In the first method, the hand contour is used as a feature that treats scaling and translation of problems (in some cases). However, the complex moment algorithm is used to describe the hand gesture and to treat the rotation problem in addition to scaling and translation. The back-propagation learning algorithm is employed in the multilayer neural network classifier. The second method proposed in this article achieves better recognition rate than the first method.     

Electric field assisted solid-state interfacial joining of TaC-HfC ceramics without filler

The application of ultra-high-temperature ceramics (UHTCs) demands effective ways of joining in overcoming the problems associated with the fabrication of complex-shaped components. In this study, we choose to investigate a new method of rapidly joining pre-sintered TaC and HfC ceramics without any filler material using the spark plasma sintering (SPS) technique. A well-bonded TaC–HfC interface was observed with no apparent cracking and porosity at the joint. The joining mechanisms were predominantly driven by solid-state diffusion and localized plastic deformation. The nanomechanical properties of the TaC-HfC joint are better than the HfC while comparable to that of the TaC. High-load indentation (up to 200 N) results suggest that the TaC–HfC interface is stronger than the parent UHTCs with no crack propagating at the interface. Upon comparison with the parent UHTCs, the damaged area and the average crack length at the interface, reduced up to ~94% and ~56%, respectively. This study shows that the SPS technique can also apply to joining other UHTCs without any filler, resulting in the new field of developing complex components for the thermal protection system (TPS).     

Human-centered design of a wearable kinesthetic haptic device for surgical teleoperation

Artificial Life and Robotics - This paper describes the design and performance evaluation of a flexible wearable haptic device that aims to realize full kinesthetic haptic feedback for application...     

An efficient implicit regularized Lagrangian twin support vector regression

Twin support vector regression (TSVR) and Lagrangian TSVR (LTSVR) satisfy only empirical risk minimization principle. Moreover, the matrices in their formulations are always positive semi-definite. To overcome these problems, we propose an efficient implicit Lagrangian formulation for the dual regularized twin support vector regression, called IRLTSVR for short. By introducing a regularization term to each objective function, the optimization problems in our IRLTSVR are positive definite and implement the structural risk minimization principle. Moreover, the 1-norm of the vector of slack variable is replaced with 2-norm to make the objective functions strongly convex. Our IRLTSVR solves two systems of linear equations instead of solving two quadratic programming problems (QPPs) in TSVR and one large QPP in SVR, which makes the learning speed of IRLTSVR faster than TSVR and SVR. Particularly, we compare three implementations of IRLTSVR with existing approaches. Computational results on several synthetic and real-world benchmark datasets clearly indicate the effectiveness and applicability of the IRLTSVR in comparison to SVR, TSVR and LTSVR.     

Optimization Algorithm for Selection and on Site Location of Mobile Cranes

Mobile crane manufacturers provide operators and practitioners with tabulated lift-capacity charts. These charts are structured based on predetermined crane configurations, which consist of boom/jib length, lifting radius, lifting height, main boom angles to ground, and jib angle to ground or its offset to its main boom centerline. Practitioners, however, are often required to lift on a partially extended hydraulic section and/or lifting radius other than those listed in the manufacturers’ lift-capacity charts. This paper presents a newly developed optimization algorithm (referred to in this paper as Algorithm-2) for selecting and locating mobile cranes on construction sites based on their minimum boom length and/or minimum radius, accordingly higher lifting capacities. Algorithm-2 includes an optimization procedure that avoids lifting capacity violations. In addition to the practical use of Algorithm 2, the mathematical representations of the crane’s geometry are also useful for research in the automation and robotic field of operations involving cranes. The algorithm is incorporated into a computer system that integrates a selection module and a database. Data pertinent to crane lift configurations are retrieved from the database and are then processed to determine the optimum, geometrical selection of the crane configuration. A case example is described in order to demonstrate the use of the developed algorithm and to illustrate its essential features.     

Biometric authentication based on PCG and ECG signals: present status and future directions

Due to the great advances in biomedical digital signal processing, new biometric traits have showed noticeable improvements in authentication systems. Recently, the ElectroCardioGram (ECG) and the PhonoCardioGraph (PCG) have been proposed as novel biometrics. This paper aims to review the previous studies related to the usage of the ECG and PCG signals in human recognition. In addition, we discuss briefly the most important techniques and methodologies used by researchers in the preprocessing, feature extraction and classification of the ECG and PCG signals. At the end, we introduce some future considerations that can be applied in this topic such as: the fusion between different techniques previously used, use both ECG and PCG signals in a multimodal biometric authentication system and building a prototype system for real-time authentication.