Image processing algorithm requireshigh computational power. Optimizing the algorithm to be run on an embedded platform is very critical as the platform provides limited computational resources. This research focused on optimizing and implementing a vision-based Around View Monitoring (AVM) system running on two embedded boards of Cortex-A7 quad and Cortex-A15 quad-core, and desktop platform of Intel i7 core. This paper presented a study on several techniques of software optimization that is removing code redundancy and multi-threading. The two methods improve the total processing time of the AVM system by 45% on ARM Cortex-A15 and 47% on ARM Cortex-A7

Reliability of lead-free solders, used in harsh conditions such as in automotive, is still a serious concern. Simultaneous additions of multiple alloying elements like Bismuth (Bi), Antimony (Sb), Nickel (Ni) etc. to conventional Sn-Ag-Cu (SAC) based solders have recently been investigated to improve their reliability. The additional elements can bring about the improvements through solid solution and precipitation strengthening. In this study, the effects of simultaneous additions of Bi, Sb and Ni to SAC 305 solder on the evolution of microstructure and mechanical properties of the solder under thermal aging at 125 °C for up to 1008 h are investigated. Superior microstructural stability and improved mechanical strength of the multicomponent alloy at long aging time and its fracture behavior are discussed.

The continuous advancement in innovative electronic applications leads to closer interconnection spacing and higher electric field density, thus increasing the risk of electrochemical migration (ECM)-related failures. The ECM of tin (Sn) attracts great interest due to the wide use of Sn on the surface of the printed circuit board assembly. In this work, we investigated the effects of adipic acid (1 ppm—saturated concentration) on the ECM of Sn using the water drop test (WDT) at 5 V. In situ observation and ex situ characterization of ECM products were carried out using optical and electrochemical techniques. Results show that the ECM failure probability is higher at intermediate adipic acid concentrations (10 ppm, 100 ppm and 1000 ppm). The major ECM reactions include anodic corrosion and the formation of dendrites, precipitates and gas bubbles. ECM failure does not occur at higher adipic acid concentrations (≥ 5000 ppm) although the anodic corrosion becomes more severe. The complexation of Sn with adipic acid to form Sn adipate complex is suggested as the main factor suppressing ECM failure at higher concentrations (≥ 5000 ppm) by retarding ion transport. The electrochemical parameters (Ecorr and Icorr) do not correlate with the ECM failure probability. They affect the anodic dissolution stage, but not the subsequent stages in the ECM mechanism. In this study, the ion transport stage plays a more significant role in determining the ECM failure probability.

Vision-based Forward Collision Warning System (FCWS) is a promising assist feature in a car to alleviate road accidents and make roads safer. In practice, it is exceptionally hard to accurately and efficiently develop an algorithm for FCWS application due to the complexity of steps involved in FCWS. For FCWS application, multiple steps are involved namely vehicle detection, target vehicle verification and time-to-collision (TTC). These involve an elaborated FCWS pipeline using classical computer vision methods which limits the robustness of the overall system and limits the scalability of the algorithm. Deep neural network (DNN) has shown unprecedented performance for the task of vision-based object detection which opens the possibility to be explored as an effective perceptive tool for automotive application. In this paper, a DNN based single-shot vehicle detection and ego-lane estimation architecture is presented. This architecture allows simultaneous detection of vehicles and estimation of ego-lanes in a single-shot. SSD-MobileNetv2 architecture was used as a backbone network to achieve this. Traffic ego- lanes in this paper were defined as semantic regression points. We collected and labelled 59,068 images of ego-lane datasets and trained the feature extractor architecture MobileNetv2 to estimate where the ego- lanes are in an image. Once the feature extractor is trained for ego-lane estimation the meta-architecture single-shot detector (SSD) was then trained to detect vehicles. Our experimental results show that this method achieves real-time performance with test results of 88% total precision on the CULane dataset and 91% on our dataset for ego-lane estimation. Moreover, we achieve a 63.7% mAP for vehicle detection on our dataset. The proposed architecture shows that an elaborate pipeline of multiple steps to develop an algorithm for the FCWS application is eliminated. The proposed method achieves real-time at 60 fps performance on standard PC running on Nvidia GTX1080 proving its potential to run on an embedded device for FCWS.

The miniaturization of electronic devices and the consequent decrease in the distance between conductive lines have increased the risk of short circuit failure due to electrochemical migration (ECM). The presence of ionic contaminants affects the ECM process. This work systematically investigates the ECM of tin (Sn) in the presence of bromide ions (Br−) in the range of 10−6 M to 1.0 M. Water drop test (WDT) was conducted in the two-probe semiconductor characterization system under an optical microscope as an in-situ observation. Polarization test was carried out to study the correlation between the corrosion properties of Sn and its ECM behaviour. The products of ECM were characterized by scanning electron microscope coupled with an energy dispersive X-rays spectrometer (SEM/EDX) and X-ray photoelectron spectrometer (XPS). The results confirm that the rate of anodic dissolution of Sn monotonously increases with the Br− concentration. However, the probability of ECM failure follows a normal distribution initially, but later increases with the Br− concentration. The main products of the ECM reactions are identified as Sn dendrites and tin hydroxide precipitates. The mechanisms of the ECM process of Sn in the presence of Br− are also suggested.

This study investigates the chemistry and performance of two commercial no-clean fluxes (NCFs) ii.e., NC-1 and NC-2. One water-soluble (WS) flux was also studied for comparison. Chemistry of these fluxes and their residues was studied using the Fourier transmission infrared (FTIR) spectroscopy. Thermal behavior of the flux was evaluated using differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA). Hygroscopicity of the residue was determined using water contact angle on residue-contaminated substrates. These fluxes were used to prepare fine pitch (

Stretchable conductive ink has been widely investigated to be used in the fabrication of stretchable electrical devices. Experimentation methods to test the mechanical and electrical behaviors of the stretchable conductive ink composite are widely applied, however, not much of computational method has been used to further validate the results. In this paper, finite element analysis method has been employed to investigate the relationship between the stress distribution of the stretchable conductive ink with the highest strain obtained. This research validates the past experimentation works of different patterns of stretchable conductive ink for its stretchability and electrical performance. The average stress distribution of the stretchable conductive ink played a significant role in the determination of its electrical performance, rather than the localization of high Von Mises stress (VMS) at certain locations within the stretchable conductive ink pattern. The lower average stress distribution contributed to a better stretchability which is indicated by a higher strain rate prior to electrical conductivity.

Conductive Polymer Composites (CPC) are widely studied due to their functionality in flexible electronics. The conductive filler, which contributes to its conductive characteristic, has many effects on the characteristics of the CPC. So, in this study, we will study the effect of Graphene Nanoplatelets filler size, weight percentage, and hybridization on the electrical and mechanical characterization of the CPC. The result shows that an increase in size and amount of fillers will result in a decrease in electrical resistivity. While for mechanical characteristics, increasing the filler size will reduce the hardness of CPC and increasing the amount of filler will increase the hardness of CPC