How Capital Structure Adjusts Dynamically during Financial Crisis

The availability of a unique data set of financially distressed firms enabled this study to apply the dynamic capital structure adjustment model to a study of capital structure. In addition, the factors driving capital structure adjustment of financially distressed and of healthy firms were estimated. The results identified 13 significant variables, which included many macroeconomic variables previously not studied, thus evidence is produced of the impact of macroeconomic factors on capital structure for the first time. We also estimated the adjustment parameters using a new dynamic adjustment model applied to an unbalanced panel data set of distressed and healthy firms. It is found that the adjustment parameters are different in the short term and long term. These new findings add to the capital structure literature.

Cross match-CHMM fusion for speaker adaptation of voice biometric

The most significant factor affecting automatic voice biometric performance is the variation in the signal characteristics, due to speaker-based variability, conversation-based variability and technology variability. These variations give great challenge in accurately modeling and verifying a speaker. To solve this variability effects, the cross match (CM) technique is proposed to provide a speaker model that can adapt to variability over periods of time. Using limited amount of enrollment utterances, a client barcode is generated and can be updated by cross matching the client barcode with new data. Furthermore, CM adds the dimension of multimodality at the fusion-level when the similarity score from CM can be fused with the score from the default speaker modeling. The scores need to be normalized before the fusion takes place. By fusing the CM with continuous Hidden Markov Model (CHMM), the new adapted model gave significant improvement in identification and verification task, where the equal error rate (EER) decreased from 6.51% to 1.23% in speaker identification and from 5.87% to 1.04% in speaker verification. EER also decreased over time (across five sessions) when the CM is applied. The best combination of normalization and fusion technique methods is piecewise-linear method and weighted sum

Modeling of occupant's head movement behavior in motion sickness study via time delay neural network

Passengers are more susceptible to experiencing motion sickness (MS) than drivers. The difference in the severity of MS is due to their different head movement behavior during curve driving. When negotiating a curve, the passengers tilt their heads towards the lateral acceleration direction while the drivers tilt their heads against it. Thus, to reduce the passengers’ level of MS, they need to reduce their head’s tilting angle towards the lateral acceleration direction. Designing MS minimization strategies is easier if the correlation between the head movement and lateral acceleration is known mathematically. Therefore, this paper proposes the utilization of a time delay neural network (TDNN) to model the correlation of the occupant’s head movement and lateral acceleration. An experiment was conducted to gather real-time data for the modeling process. The results show that TDNN manages to model the correlation by producing a similar output response to the actual response. Thus, it is expected that the correlation model could be used as an occupant’s head movement predictor tool in future studies of MS

Path Tracking on Autonomous Vehicle for Severe Maneuvre

Autonomous vehicle consists self-learning process consists recognizing environment, real time localization, path planning and motion tracking control. Path tracking is an important aspect on autonomous vehicle. The main purpose path tracking is the autonomous vehicle have an ability to follow the predefined path with zero steady state error. The non-linearity of the vehicle dynamic cause some difficulties in path tracking problems. This paper proposes a path tracking control for autonomous vehicle. The controller consists of a relationship between lateral error, longitudinal velocity, the heading error and the reference yaw rate. In addition, the yaw rate controller developed based on the vehicle and tyre model. The effectiveness of the proposed controller is demonstrated by a simulation

Cartographer slam method for optimization with an adaptive multi-distance scan scheduler

This paper presents the use of Google's simultaneous localization and mapping (SLAM) technique, namely Cartographer, and adaptive multistage distance scheduler (AMDS) to improve the processing speed. This approach optimizes the processing speed of SLAM which is known to have performance degradation as the map grows due to a larger scan matcher. In this proposed work, the adaptive method was successfully tested in an actual vehicle to map roads in real time. The AMDS performs a local pose correction by controlling the LiDAR sensor scan range and scan matcher search window with the help ofscheduling algorithms. The scheduling algorithms manage the SLAM that swaps between short and long distances during map data collection. As a result, the algorithms efficiently improved performance speed similar to short distance LiDAR scanswhile maintaining the accuracy of the full distance of LiDAR. By swapping the scan distance of the sensor, and adaptively limiting the search size of the scan matcher to handle difference scan sizes, the pose's generation performance time is improved by approximately 16% as compared with a fixed scan distance, while maintaining similar accuracy

Fully convolutional neural network for Malaysian road lane detection

Recently, a deep learning, Fully Convolutional Neural Network (FCN) has been widely studied because it can demonstrate promising results in the application of detection of objects in an image or video. Hence, the FCN approach has been proposed as one of the solution methods in mitigating the issues pertinent to Malaysia’s road lane detection. Previously, FCN model for lane detection has not been tested in Malaysian road conditions. Therefore, this study investigates the further performance of this model in the Malaysia. The network model is trained and validated using the datasets obtained from Machine Learning NanoDegree. In addition, the real-time data collection has been conducted to collect the data sets for the testing at the highway and urban areas in Malaysia. Then, the collected data is used to test the performance of the FCN network in detecting the lane markings on Malaysia road. The results demonstrated that the FCN method is achieving 99% of the training and validation accuracy

A Study of Sukuk Bond market-making at the London Stock Exchange, 2011-20

This paper has the objective of studying the Islamic bond market in the London Stock Exchange (LSE) which surprisingly is a latecomer, 21 years after the first Islamic bond that was listed in 1990. London took time to assess the market debut although it is the Bank of England that paved the way for the level-playing-field regulations to create Islamic banking. The Shariah-compliant Islamic bonds known as sukuk are traded in 23 plus markets and there are 2,340 such faith-based issues as at 2021. The total funds raised to-date in such exchange-traded Islamic bond exchanges over 31 years is worth US$526 billion with an average issue size of US$220 million each globally. London has raised US$50 billion with some 125 issues during its ten-year history. With perhaps the fastest growth rate among all exchanges, LSE chalked a growth rate of 56% per year, an extraordinary achievement with an average issue size 2 times bigger than the world average. In contrast, the global sukuk market growth rate is about 8% to 12% per year. The growth rate of the much older and larger mainstream bond market in some 137 market places is in the range of 4% to 5% per year. That makes it interesting to study the London market to understand its institutional structure, market-making efforts, liquidity, issuance effect and other pertinent aspect

Circular Microstrip Patch Antenna for UHF RFID Reader

This paper presents an analysis of Circular shape patch antenna for Ultra High-Frequency Identification (UHF) Radio Frequency Identification (RFID) Reader Applications. The fabricated antenna has lightweight, simple structure, low profile and easy for fabrication due to the used of FR-4 materials with loss tangent 0.019, the dielectric constant of 4.7 and thickness of 1.6 mm. It can be operated for UHF RFID system in Malaysia with the frequency assigned from 919 MHz to 923 MHz. The antenna simulation was analysed by using CST Studio Suite 2016. From the results, the antenna has the reflection coefficient (S11) less than -10dB together with the bandwidth of 90 MHz. Other results of antenna parameter such as voltage standing wave ratio (VSWR), circular polarized radiation pattern, return loss and gain were also discussed. The complete size of the proposed antenna is 120 mm x 120 mm x 1.6 mm. Thus, it is suitable for RFID portable reader applications

An Exploration on New Product Development Process of Malaysian Small-Sized Automaker

This paper focused on the identification and description of a new product development (NPD) approach adopted by one of the small-sized car producers in Malaysia. The NPD processes for European, Japanese and American auto makers have been studied and discussed in literature. However, the business strategy of NPD approach of small-sized car makers remains unidentified and less understood. This research involved semi-structured face-to-face interview sessions at several occasions with senior project managers and development team members, a senior product planning manager together with a selected first tier vendor. The information obtained through literature on the NPD process was used as secondary data to correlate with the data obtained from the primary source (interview). Results derived from both sources later were used to completely identify and describe the NPD process of this car maker. The results indicated that the NPD process of the automaker was not that distinct as compared with the generic product development of others. In addition, the findings also showed the automaker has adopted the concurrent engineering practices in the product development process. This paper also highlighted the importance of a formal NPD with regard to the frequency of the new product introduction and managing risks and uncertainty

A Study of hotel and property sectors in Malaysia: Developers’ views and selected analyses of price drivers

This paper reports an assessment of supply-demand side economics of hotel and property sectors, while it also examines the pricing behavior of the real estate sector in Malaysia. First economic fact about the hotel sector is that it has been carefully developed over several decades, and forms an important destination point for international travelers attracting about 26 million visitors with demands ranging from simple hotel services to the most exotic private chalets. This sector has gone through a decline since 2017 and has been made worse by the Covid Pandemic. Analyses of the property sector provide interesting, unexpected finding that price increases in residential units have kept pace with inflation in the country, and that further localizing production of input materials, supply of more land regulatory consolidation would help the industry. Nonetheless, the supply of built units in recent years falls short of national policy aim to provide affordable housing because supply is 22 percent facing a demand closer to 40 percent at costs less than RM250,000. Some insights are gained from examining the broader price drivers other than inflation to understand how the economics of the property sector is shaping up the real estate economy. There is a body of relevant findings to be refined further for policy consideration to address affordable housing issue, for more l to be made available (as is done in Melbourne Australia, as an example),a nd finally to build local capacity to produce quality items that are currently imported