PhD research is the highest level of academic research conducted in universities and institutes throughout the World. The methods used to conduct Ph.D. research are sound and the data that results from the research must be unique. There are many techniques has been created such as scientific methodologies have been created to ensure the soundness and uniqueness of doctoral research and to secure continuity in research results. The most often employed methodologies are quantitative methods, qualitative methods, comparative methods and clinical trials.

The world is producing a large number of PhD holders and their number is increasing every year. Therefore in this era how can a normal PhD holder can survive? World’s most population countries India and China producing more than 40,000 PhD every year. Most of the PhD has completed their research in a short duration which results in the quality of the graduates is not consistent. And Most of the PhD remains unemployed.

In our research whenever we think of doing the research we always think ofdoing something innovativewhich can change the world but when we come for real implementation of the idea we found only few things have done in our PhD research. The few things which done usually during PhD seems to be unique. What should we do? And, how to do? It depends on one’s circumstances but the research should contribute something new in the specific domain.

Silicon Mentor is a place where many new researchers are working together to make something innovative and unique. These young researchers help the research students to pursue their research with some of the unique methodologies. Silicon Mentor have Expertise in different Domain such as Computer vision , Biomedical Research, Digital Signal Processing, Machine Learning , Low Power VLSI , Mixed Signal VLSI , FPGA implementation.

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Advanced Driver Assistance Systems are an integral part of vehicles today. They can be passive, as in merely alerting the driver in case of emergencies, or actively respond by taking over vehicle controls during emergency scenarios. Such systems are expected to reach full autonomy during the next decade. The two major fields of interests in the problem are: road and lane perception, and obstacle perception. The former involves finding out road and lane markers, to ensure that vehicle position is correct, and to prevent any departures. Obstacle detection is necessary to prevent collisions with other traffic, or real-life artifacts like streetlights, stray animals, pedestrians, etc.

Road and lane perception include detecting the extent of the road, the number and position of lanes, merging and splitting lanes, over different scenarios like urban, highway or cross-country. While the problem seems trivial given recent advancements in image processing and feature detection algorithms, the problem is complicated by the presence of several challenges, such as:

• Case diversity: Due a verity of real-world parameters, the system has to be tolerant of a huge diversity of incoming parameters. These include:

• High reliability demands: In order to be useful and acceptable, the assistance system should achieve very low error rates. A high rate of false positives will lead to driver irritation and rejection, while false negatives will cause system compromise and low reliability.

The state-of-the-art research and commercial systems are looking at several perception modalities s sensors. A quick view at their operation and pros-cons is presented here:

1. Vision: Perhaps the most intuitive approach is to use vision based systems, as lane and road markers are already optimized for human vision detection. Use of front-mounted cameras is nearly standard approach in almost all systems, and it can be argued that since most of the signature of lane marks is in the visual domain, no detection system can totally ignore the vision modality. However, it must be stressed that the robustness of the current state-of-the-art processing algorithms is far from satisfactory, and they also lack the adaptive power of a human driver.

2. LIDAR: The most emerging technology is the use of Light Detection and Ranging sensors, which can produce a 3D structure of the vehicle surrounding, thereby increasing robustness as obstacles are more easily detected in 3D. In addition, LIDARs are active sources- thus they are more illuminance adaptive. The LIDAR sensors are however very expensive.

3. Stereo-vision: Stereo-vision uses two cameras to obtain the 3D information, which is much cheaper in terms of hardware, but requires significant software overhead. It also has poorer accuracy, and leads to more probability error.

4. Geographic Information Systems: The use of prior geographic database together with known host-vehicle position can in effect replace the on-board processing requirement and enable worldwide ‘blind’ autonomous driving. However, the system needs very accurate positioning in terms of resolution of the vehicle position, as well as updating the geographic database in real-time with changing traffic dynamics and obstacle positions, either by satellite imagery or GPS measurements. The uncertainty in obtaining and updating highly accurate map information over large terrains has constrained it as a complementary tool to on-board processing.

5. Vehicle Dynamics: The presence of sensors like Inertial Measurement Units (IMUs) provides insight into the motion parameters of the vehicle such as speed, yaw rate and acceleration. This information is used in the temporal integration module, to relate data across several time-frames.

The road and lane detection problem can be broken into the following functional modules. The implementation of said modules uses different approaches across different research and commercially available systems, but the ‘generic system’ presented here is present as the holistic skeleton for them.

1. Image Cleaning: A pre-filer is applied to the image to remove most of the noise and clutter, arising from obstacles, shadows, over and under exposure, lens flare and vehicle artifacts. If training data is available or data from previous frames is harnessed, a suitable region of interest can be extracted from the image to reduce processing.

2. Feature Extraction: Based on the required subtask low-level features such as road texture, lane marker color and gradient, etc. are extracted.

3. Model Fitting: Based on the evidence gathered, a road-lane model is fitted to the data.

4. Temporal Integration: The model so obtained is reconciled with the model of the previous frames, or the GPS data if available for the region. The new hypothesis is accepted if the difference is explainable based on the vehicle dynamics.

5. Post Processing: After computation of the model, this step involves translation from image to ground coordinates, and data gathering for use in processing of subsequent frames.

In concluding remarks, we can stress that road and lane segmentation are fundamental problems of Driver Assistance Systems. The extent of complexity can range from passive Lane Departure Warning systems to fully autonomous ‘blind’ drivers. The next step forward is to extend the scope of current detection techniques into new domains, and to improve its reliability. The first requires a better understanding and development of new road-scene models that can capture multiple lanes, non-linear topographies and other non-idealities successfully. The reliability challenge is harder, especially for closed-loop systems, where even small error rates may propagate. It might become essential to include modalities other than vision, and incorporate machine learning to train algorithms better.

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Extracting some useful information from images is considered as Computer Vision. These images can be of any form from Visual to Infrared to X-rays in the whole electromagnetic spectrum. The basic idea is to duplicate human visual perception in images to extract the same information.

Some major applications of Computer vision consists tasks including Object detection, Object tracking, segmentation, Image inpainting and 3d modelling from images.

A lot of research work is carried out all over the globe in all of the above mentioned fields. So in this article we are going to discuss some of the very interesting yet strange fields for new comers.

Image Inpainting:

Image inpainting is a process to recover some useful information from deteriorated images or give some artistic look to images by removing unwanted objects still maintaining smooth background. Such a task is a daily part of human life as imagine a person at some distinct place or removing a particular object from a scene but in computers, this is trivial.

Method:

First a binary map is created on the basis of which part of the image is to be removed. Now that part of the image is filled in a manner to minimize energy. This is often done using a very simple operator called Laplacian operator. This is essentially second order derivative of image. Second order derivatives are used because its direction is similar to the direction of edges rather than perpendicular to it as in the case of first order derivative. Thus we find to minimize this function as this would perfectly reflect the second order derivative.

Image Segmentation:

Segmentation is a process of image processing to segment out one or more objects from an image. The goal is to find out a boundary of pixels that can perfectly differentiate between two objects based on colour or shape or both. Applications for image segmentation includes Object detection, Face detection and in medical imaging. Tumour detection, Surgery planning and diagnosis of anatomical structures are some of the major applications of segmentation in medical imaging.

Motion Analysis:

Motion analysis is in the simplest case to find out a moving object from a sequence of images. This work can be extended to find the direction of movement, velocity and displacement calculation and object tracking. The basic idea is find out a static region (background) and a moving region with substantial displacement. One very popular method for this is finding the Optical flow. Motion analysis is extremely important in Surveillance and video object tracking. Tracking with a moving camera increases the complexity a lot due to the relative motion between camera and the object. Tracking with multiple cameras with overlapping or non-overlapping regions are current research issues in Object tracking.

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Digital image processing is done to improve the quality of images. The images are processed and enhanced in order to obtain the required information from it. It is an ever expanding field with a number of applications in medicine, space, art, meteorology, etc. Hardware implementation of digital image processing techniques is very commonly done using Verilog-HDL or VHDL gives a logical explanation of any circuit which can be further developed and tested. The main advantage of using HDLs is that any logical input can be simulated on FPGA (Hardware implementation).Application specific hardware implementation offers much greater speed than a software implementation.

Advancements in VLSI technology have made this hardware implementation an attractive, feasible and less time consuming task to undergo. FPGA is one of the best technologies available for hardware design because their structure is able to exploit spatial and temporal parallelism .FPGAs are ideal in many embedded systems applications because of their small size, low power consumption ,a number of input-outputports and logic blocks. Also FPGAs are reprogrammable chips, hence are very versatile to be used.

Algorithms can be implemented in Verilog HDL using Xilinx ISE, MATLAB and MODEL SIM. Hardware implementation of Verilog codes provides us with the ability to verify the logical codes simulated using software. Hardware implementation helps in the co-simulation of the already simulated logic. It helps the designer to reformulate the algorithm.

It provides us an overlook of the way the respective logical circuit will work when brought to real life applications. Also, an idea of the market need of the circuit and how it will actually work, is provided by hardware implementation. The use of reconfigurable hardware to implement algorithms for image processing minimizes the time-to-market cost, provides prototyping and debugging.

Therefore, the reconfigurable devices like FPGA seem to be the ideal choice for implementation of image processing algorithms. SiliconMentor is a team with expertise in the fields both software implementation and hardware prototyping. The team focuses to provide a shared platform for the researchers and innovators by providing guidance in the specified areas.

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Image Processing

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