Surface reconstruction for incremental forming

In spite of extensive efforts being made with regard to virtual process optimization technology, the production of prototype parts is still a necessity. With respect to the production of sheet metal parts in low quantities, incremental sheet metal forming (ISMF) is a highly interesting process. ISMF allows the production of complex parts with drastically reduced costs in tooling and machinery compared to conventional processes like deep drawing. However, ISMF, with it’s incremental nature, introduces the need for generating a tool path considering both final geometry and process-induced deviations or constraints. Consequently, for the generation of the tool path a (tool path) surface, with an adequate offset, is necessary. That is why, within the scope of extensive research work at the Institute of Forming Technology and Lightweight Construction (IUL), a special correction module has been developed, determining this offset e.g. depending on the workpiece geometry. This paper presents the algorithm, the application, and the effect on the produced parts. Furthermore, a concept for an extension regarding further constraints like elastic workpiece behavior is presented.     

Investigation of micro-EDM material removal characteristics using single RC-pulse discharges

Similar to EDM, in micro-EDM, intense heat is generated between the workpiece and tool electrode by the discharge through a dielectric medium to result in the formation of a microcrater that is much smaller in size. In this study, a single-spark generator has been developed to study the erosion characteristics from the microcrater size. Using a simple heat transfer model, the efficiency at different discharge condition is also deduced. It is found that at lower-energy (<50 μJ) discharges, the energy required to remove the unit volume of material, defined as the specific energy, is found to be much less than that at higher-energy discharges. Additionally, the ratio of the standard deviation to the measured microcrater size is found to be lower at lower discharge energy, indicating greater consistency in shape and size when the discharge occurs at lower energy. The fundamental erosion mechanism of material is discussed by considering melting and evaporation phenomena using theoretical modeling. The average efficiency of erosion, when estimated to be due primarily to melting or evaporation alone, is found to be up to an order of magnitude higher at lower-energy discharges than that at higher-energy discharges.     

Determination of Active Slip/Twinning Modes in AZ31 Mg Alloy Near Room Temperature

In order to determine the deformation modes in AZ31 magnesium alloy at room temperature, computer simulations of deformation texture development and calculation of formability have been carried out. The simulation results were compared with the measured texture results. Based on agreement between the experiments and simulations the active deformation modes were determined. A Visco Plastic Self Consistent model was employed for the simulation of plastic deformation. Simulations and experiments were performed for different initial textures. The goal of the study was to develop the understanding of deformation texture evolution and its effects on mechanical properties of magnesium, with an ultimate goal of improving room temperature formability of magnesium alloys. This article was presented at Materials Science & Technology 2006, Innovations in Metal Forming symposium held in Cincinnati, OH, October 15-19, 2006.     

Dual polarized triple band hybrid MIMO cylindrical dielectric resonator antenna for LTE2500/WLAN/WiMAX applications

In this communication, triple band hybrid multi‐input–multi‐output (MIMO) cylindrical dielectric resonator antenna (CDRA) with high isolation is examined. The proposed MIMO antenna includes two symmetric folded microstrip line feeding structures along with CDRA at two different ends of substrate. Two inverted L‐shaped strips on the ground plane are used to enhance the isolation (S₁₂ < ?15 dB) as well as to generates 2.7 GHz frequency band. Metallic strip on the ground plane act as an electromagnetic reflector and also enhance the isolation between two antennas (S₁₂ < ?20 dB). Archetype of proposed MIMO antenna design has been fabricated and tested to validate the simulated results. The proposed antenna operates at three different frequency bands 2.24–2.38 GHz, 2.5–3.26 GHz, and 4.88–7.0 GHz (S₁₁ < ?6 dB) with the fractional bandwidth 6.06%, 26.4%, and 35.7%, respectively. Folded microstrip lines generate path delay between the electric field lines and originate circular polarization characteristics in the frequency range 5.55–5.75 GHz with the fractional bandwidth of 3.55%. In order to satisfy the different performance requirement of MIMO antenna such as envelop correlation coefficient, mean effective gain, effective diversity gain, peak gain are also examined. The proposed antenna is found suitable for LTE2500, WLAN, and WiMAX applications. © 2016 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2016.     

Pyramid coding based rate control for constant bit rate video streaming

In this paper, a novel pyramid coding based rate control scheme is proposed for video streaming applications constrained by a constant channel bandwidth. To achieve the target bit rate with the best quality, the initial quantization parameter (QP) is determined by the average spatio-temporal complexity of the sequence, its resolution and the target bit rate. Simple linear estimation models are then used to predict the number of bits that would be necessary to encode a frame for a given complexity and QP. The experimental results demonstrate that the proposed rate control scheme significantly outperforms the existing rate control scheme in the Joint Model (JM) reference software in terms of Peak Signal to Noise Ratio (PSNR) and consistent perceptual visual quality while achieving the target bit rate. Finally, the proposed scheme is validated through experimental evaluation over a miniature test-bed.

     

Developing a limb repositioning robotic interface for persons with severe physical disabilities

Limb repositioning is necessary for individuals with severe physical disabilities to sustain muscle strength and prevent pressure sores. As robotic technologies become ubiquitous, these tools offer promise to support the repositioning process. However, research has yet to focus on ways in which individuals with severe physical disabilities can control robots for these tasks. This paper presents a study that examines the needs and attitudes of potential users with physical disabilities to control a robotic aid for limb repositioning. Subjects expressed interest in using brain–computer interface (BCI) and speech recognition technologies for purposes of executing robotic tasks. The performance of four subjects controlling arm movements on an avatar through the keyboard, mouse, BCI, and Dragon NaturallySpeaking speech recognition was evaluated. Although BCI and speech technologies may limit physical fatigue, more challenges were faced using BCI and speech conditions compared to the keyboard and mouse. This research promotes accessibility into mainstream robotic technologies and represents the first step in the development of a robotic prototype using a BCI and speech recognition technologies for limb repositioning.     

Bi-directional LSTM based channel estimation in 5G massive MIMO OFDM systems over TDL-C model with Rayleigh fading distribution

In this work, a deep learning (DL)-based massive multiple-input multiple-output (mMIMO) orthogonal frequency division multiplexing (OFDM) system is investigated over the tapped delay line type C (TDL-C) model with a Rayleigh fading distribution at frequencies ranging from 0.5 to 100 GHz. The proposed bi-directional long short-term memory (Bi-LSTM) channel state information (CSI) estimator uses online learning during training and offline learning during the practical implementation phase. The design of the estimator takes into account situations in which prior knowledge of channel statistics is limited and targets excellent performance, even with limited pilot symbols (PS). Three separate loss functions (mean square logarithmic error [MSLE], Huber, and Kullback–Leibler Distance [KLD]) are assessed in three classification layers. The symbol error rate (SER) and outage probability performance of the proposed estimator are evaluated using a number of optimization techniques, such as stochastic gradient descent (SGD), momentum, and the adaptive gradient (AdaGrad) algorithm. The Bi-LSTM-based CSI estimator is trained considering a specific number of PS. It can be readily seen that by incorporating a cyclic prefix (CP), the system becomes more resilient to channel impairments, resulting in a lower SER. Simulations show that the SGD optimization approach and Huber loss function-trained Bi-LSTM-based CSI estimator have the lowest SER and very high estimation accuracy. By using deep neural networks (DNNs), the Bi-LSTM method for CSI estimation achieves a superior channel capacity (in bps/Hz) at 10 dB than long short-term memory (LSTM) and other conventional CSI estimators, such as minimum mean square error (MMSE) and least squares (LS). The simulation results validate the analytical results in the study.     

Energy optimization for CAN bus and media controls in electric vehicles using deep learning algorithms

The Journal of Supercomputing - This study offers a neural network-based deep learning method for energy optimization modeling in electric vehicles (EV). The pre-processed driving cycle is...