June 12th, 2015 by admin | No Comments | Filed in 1000 Projects, 10000 Engineering Project Ideas, ELECTRONICS PROJECTS


In this project we propose an efficient video searching and video retrieval of human actions using spatio-temporal localization algorithm. Content-based video retrieval(CBVR) which is an extension of content-based image retrieval(CBIR).Highly efficient localization model that first performs temporal localization based on histograms of evenly spaced time-slices, then spatial localization based on histograms of a 2D- spatial grid. In the existing method they used dollar detector. In this project we used Histogram of Gradient (HOG) descriptor and SVM classifier for feature detection. We also show the relevance feedback can be applied to our localization and ranking algorithm. It gives high performance and accuracy. As a result, the presented system is more applicable to real-world problems than any prior contentbased video retrieval. It can be used for surveillance actions and also in many restricted areas for recognizing human actions.


In existing method a video surveillance system in the environment of a stationary camera that can extract moving targets from a video stream in real time and classify them into predefined categories according to their spatiotemporal properties. Targets are detected by computing the pixel-wise difference between consecutive frames, and then classified with a temporally boosted classifier and “spatiotemporal-oriented energy” analysis. The proposed classifier can successfully recognize five types of objects: a person, a bicycle, a motorcycle, a vehicle, and a person with an umbrella. In addition, we process targets that do not match any of the AdaBoost-based classifier’s categories by using a secondary classification module that categorizes such targets as crowds of individuals or noncrowds. We show that the above classification task can be performed effectively by analyzing a target’s spatiotemporal-oriented energies, which provide a rich description of the target’s spatial and dynamic features.

Moving target recognition involves two major steps: feature extraction and classification. The feature extraction process derives a set of features from the video stream. The second step analyzes the extracted features in order to make an appropriate classification decision. A variety of machine learning classification techniques have been investigated for surveillance tasks, e.g., support vector machines naïve Bayes classification and AdaBoost .

People are usually the main objects of interest in surveillance tasks. AdaBoost-based classifier is very effective at identifying individuals, but it is difficult to design and train it to recognize groups or crowds of people due to their
different shapes. In this work, define a “crowd” as two or more people in a small 11 spatial region. Although crowd (group) recognition has received some attention in recent years, most research that has focused on determining the number of people in a small spatial region has been cast within the people counting and tracking paradigms. Since occlusion and projective effects are two of the major challenges associated with crowd detection, many existing systems use 3-D positions of humans and require camera calibration. Human trackers that do not require camera
models have also been proposed.

Information to detect crowds without employing explicit tracking techniques. One notable exception is the approach in which uses space-time slices to detect crowds in urban road environments. As with AdaBoost-based classifier, the use of temporal information can greatly improve the performance of crowd detection systems. However, to avoid the complex tracking process and the detection and segmentation tasks that are often involved, analyze spatiotemporaloriented energies because they encapsulate spatial and dynamic information and do not require specific motion computations.

The framework that can detect and classify moving targets in video streams based on the targets’ spatiotemporal properties. Targets are detected by computing the pixel-wise difference between consecutive frames, and then classified with a temporally boosted classifier and “spatiotemporal- oriented energy” analysis. The classifier improves weak classifiers by allowing them to make use of previous information when evaluating a frame. In addition, a method for processing targets that do not match any of the AdaBoost-based classifier’s categories. Such targets are categorized as crowds of individuals or non-crowds. It is shown that moving crowd recognition can be performed effectively by using spatiotemporal-oriented energies. The proposed framework was tested on an extensive dataset. The detection rates demonstrate that the proposed system is extremely effective at recognizing all the predefined object classes.


Histogram of oriented gradients is a feature descriptor used to detect objects in computer vision and image processing. The HOG descriptor technique counts occurrences of gradient orientation in localized portions of an image – detection window or region of interest.

Implementation of the HOG descriptor algorithm is as follows:

  1. Divide the image into small connected regions called cells and for each cell compute a histogram of gradient directions or edge orientations for the pixels within the cell.
  2. Discretize each cell into angular bins according to the gradient orientation.
  3. Each cell’s pixel contributes weighted gradient to its corresponding angular bin.
  4. Group of adjacent cells are considered as spatial regions called blocks. The grouping of cells into a block is the basis for grouping and normalization of histogram.
  5. Normalized group of histograms represents the block histogram. The set of these block histogram represent the descriptor.


We first prepared each dataset for the retrieval experiments. The datasets were scaled uniformly to 240 pixels in height (maintaining aspect ratio) and 15 frames per second, so the feature extraction procedure was identical for both. We extracted features from each dataset at an average rate of 180 features per second, detecting features with multiscale Dollar and describing them with HOG3-D. The resulting features were clustered into 1000 code words after PCA was performed to capture 95% of the features’ variance. Time-slice histograms were generated over the whole dataset in batch before the main retrieval experiments; as these preprocessing steps can be performed before a retrieval search is performed, they are not included in the performance statistics. Each time slice was 10 frames in length, and the 2-D spatial grid was divided into 10 by 10 pixel blocks. These parameters were chosen based on observations of the minimum length and size of the actions within the dataset.

Electronics Engineering mini Projects

Video Frame


• High performance and accuracy.
• Time consumption.


• Used in surveillance actions.
• It can be used in CC TV action in banks, home security systems and also in colleges schools.


Efficient content-based search systems, such as the model presented here, are becoming increasingly relevant in today’s . Effect on the accuracy of various spatial localization methods, as well as temporal localization alone. UT. UT Query Time Costs world, as sophisticated searches are increasingly necessary to navigate the huge amounts of data.
Through theoretical discussion and experimental results, we have demonstrated basic practical applicability of our system to this task of real-world video search. In designing our algorithm, we have taken an efficiency-first approach this has resulted in the creation of a fast permissive temporal-then-spatial localization technique, followed by a more orthodox histogram ranking step, both of which can be assisted by relevance feedback.



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June 12th, 2015 by admin | No Comments | Filed in 1000 Projects, ELECTRONICS PROJECTS, ENGINEERING PROJECTS


This project presents an implementation of delayed mean square adaptive filter (DLMS) for the application of EEG. The noised EEG signals are filtered to obtain a clean EEG with the help of reference signal.By using delayed mean square adaptive filter (DLMS) the critical path and its architecture for fast and low-complexity implementation is also analyzed.The proposed structure of transpose form of DLMS adaptive filter provide much faster convergence and lower register complexity when compared to existing structure of direct form LMS adaptive filter.Based on the experimental results of proposed structure it is clear that one adaption delay method is best one for low area complexity.one of the structure of DLMS adaptive filter,one adaption delay method is used in application of EEG to obtain clean EEG signal. One adaption delay is 44% less in area and 29% less in delay when compared to zero adaption delay and it is 66% less in area and 22% less in delay when compared to two adaption delay.The proposed transpose form of DLMS adaptive filter are implemented for filtering the EEG signal by using Verilog HDL. These designs are simulated by using Modelsim 6.4c and Synthesized by Xilinx 9.1 to implement it in Spartan 3E FPGA Kit.


The least mean squares (LMS) algorithm adjust the filter coefficients to minimize the cost function.The LMS algorithms do not involve any matrix operations. Therefore, the LMS algorithms require fewer computational resources and memory. The eigen value spread of the input correlation matrix, or the correlation matrix of the input signal, might affect the convergence speed  of the resulting adaptive filter.

There are six different types of LMS algorithm they are Standard LMS, Normalized LMS, Leaky LMS, Normalized Leaky LMS, Sign LMS, Fast Block LMS. Among six fast block LMS is best one because the fast block LMS algorithm uses the fast Fourier transform (FFT) to transform the input signal x(n) to the frequency domain. This algorithm also updates the filter coefficients in the frequency domain. Updating the filter coefficients in the frequency domain can save computational resources.

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Fig Block diagram of fast block LMS

These are the following steps to calculate the output and error signals by using fast block LMS algorithm.

  1. Concatenates the current input signal block to the previous blocks.
  2. Performs an FFT to transform the input signal blocks from the time domain to the frequency domain.
  3. Multiplies the input signal blocks by the filter coefficients vector .
  4. Performs an inverse FFT (IFFT) on the multiplication result.
  5. Retrieves the last block from the result as the output signal vector .
  6. Calculates the error signal vector by comparing the input signal vector  with .


A few popular applications for FIR filters are listed below:

  • Echo cancellation
  • Telecommunications
  • Data communications
  • Wireless communications
  • Video processing
  • Speech synthesis
  • Filtering
  • High-speed modems


Based on a precise critical-path analysis, we have derived low-complexity architectures for the LMS adaptive filter. We have shown that the direct-form and transpose-form LMS adaptive filters have nearly the same critical-path delay. The direct-from LMS adaptive filter, however, involves less register complexity and provides much faster convergence than its transpose-form counterpart since the latter inherently performs delayed weight adaptation. We have proposed three different structures of direct-form LMS adaptive filter with i) zero adaptation delay ii) one adaptation delay and iii) two adaptation delays. Proposed Design 1 does not involve any adaptation delay. It has the minimum of MUF among all the structures, but that is adequate to support the highest data rate in current communication systems. Among all the three Design 2 is considered to be better. Finally the LMS Adaptive FIR Filter is implemented using Verilog language and dumped into FPGA Spartan Series Device.


We can modify the proposed system by further reducing the area and delay of the design in future by implementing it in digital receiver. We can also use this for other filtering applications.



Electronics Projects

Snapshot :Zero adaptation delay FIR filter






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Mechanical Engineering Projects – Sprocket side-stand retrieve System

September 6th, 2014 by JJ | 27 Comments | Filed in 1000 Projects, AUTOMOBILE ENGINEERING, MECHANICAL ENGINEERING PROJECTS

Introduction to Mechanical Engineering Projects

Thanks for visiting www.engineeringminiprojects.com. We know that you are busy looking for Mechanical engineering projects for your academics. The aim of this venture of ours is to help you  for your Mechanical Engineering Projects by providing you different project ideas and reference materials so as to reduce your strain during the final semester curriculum and give you enough time to relax and prepare  your Mechanical engineering projects . We hope that this humble venture of us contributes lot to your academics.

Please let us know your feed backs on different topics and use the comment facility well.

Wish you Best of Luck with your Mechanical Engineering projects.

Mechanical Engineering Project Abstracts

This is a Mechanical engineering Projects under Design and fabrication. The total estimated cost of this Mechanical Engineering project is about rs.1500/-. In modern developing world, automobile plays important role especially two-wheeler i.e (motorcycles& bikes) plays a major role. Even though they are helpful there are some sad events like accidents due to careless of rider. Major accidents occur due to forgetting of lifting side stand. To rectify this problem many advance measure have taken, but they are useless. so as a  by considering that it should be implemented practically in all types bikes .the new system “SPROCKET SIDE-STAND RETRIEVE SYSTEM” is designed based on  the working principle of bikes. Since all bike transmit power from engine to rear wheel by means of chain drive. Since designed setup is kept in between chain drive, setup rotates and side stand get retrieves automatically.

Mechanical Engineering Projects for final years

Mechanical engineering Projects the fabricated sysem-bikes side stand


In existing mechanical Engineering projects many ideas had been found to lift the side-stand automatically.

  1. One small flat rod is kept attached and pivoted between the gear actuator lever and the side stand of the bike. when the gear is actuated the side stand get lifted automatically.
  2. Small stepper motor is connected  between the side stand and the engine ,when engine is started the stepper motor gains the source of power and retrieve side stand  automatically

These are some methods to retrieve side stand automatically when the vehicle moves but it is not implemented  in practical use due to its drawback.


  1. ECU methods are implemented only in costlier bikes but it does not implemented in normal domestic bikes due to their cost.
  2. When we come across those mechanical engineering projects we could note some drawbacks like wear out of gears, making injuries in legs while actuating gears. Major drawback is it cannot use in all type of two-wheelers. So, in order to solve this we thought and designed “SPROCKET-SIDE STAND RETRIEVE SYSTEM” this system can be attached in all type of two-wheelers (mopeds, geared, non-geared, hand geared bikes).


Based on the working principle of two-wheeler ( i.e the power is generated in the engine and it transmits  power to the pinion and makes it to rotate. The pinion transmits power to the rear wheel pinion and makes the vehicle to move). This is the basic principle followed in all type of two-wheelers, based on this “sprocket-side stand retrieve system” is designed because this system works by getting power from chain drive. This sprocket system consists of four components, which is assembled as two set up which would be explained briefly in construction and working part of this paper.


In Sprocket side stand retrieve system, the mechanical engineering projects, retrieves the side stand automatically if the rider forgets to lift the side stand while moving the bike. It works based on the working principle of the two-wheelers .every bikes transmit power from engine’s pinion to the rear wheel i.e. rotary motion of the pinion makes the linear motion of the chain. that linear motion of the chain is absorbed by rear wheel’s sprocket and converted into rotary motion. That rotary motion of the rear wheel makes the bikes to move. Based on this Sprocket side stand retrieve system is designed.

If Sprocket is kept between the chain drive, it make the sprocket to rotate so, using the sprocket as the major component this system works. It gains the power from the chain and make specially designed component (lifting lever) to rotate. This rotation incites engaged pushing lever to push the side stand to retrieve. When chain rotates anti-clockwise direction the inciter assemblies sprocket absorbs the power and rotates in clockwise direction.The working of “Sprocket-Side Stand Retrieve System is explained below in both (resting & riding condition of two-wheeler)

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Mechanical Projects -Vertical Lift Mechanism the Kattwyk Bridge

May 1st, 2012 by admin | 17 Comments | Filed in 1000 Projects, CIVIL PROJECTS, MECHANICAL ENGINEERING PROJECTS

Mechanical Projects Abstracts

 This Mechanical projects presentation is  a model and a  simplified version of  Kattwyk  Bridge and has   been  made  under the constraints of space, time and resources  available  in  the  lab.The  Bridge mechanism has been divided  in three sections.The central  section  lifts  above,while the two side parts remain intact with the supporting   angles. The lift mechanism used for the bridge is based on the  pulley  system  involving three pulleys. A string  is passed through the two pulleys on either  side of  middle section and the pulley at  the center synchronizes the   motion  of  two pulleys  and makes  the motion centralized.

Mechanical Engineering Projects
Mechanical projects on The Kattwyk Bridge demo

The  angles are used  to restrict  the  motion  of central  section  allowing  only  the   motion  in  vertical  direction  to occur  and provide the stability  to the   bridge   structure.  A chain  is  also  used  to   stop   the   central  section  at  any   desired  height depending  on  the height of ship assuming a real  case   situation  thus saving  the  power. The mechanism  used  to  lift  the central section  is  due  to  the constraint of not using any electric motor or gears. In real case  situation, the  power can be  given  by  electric motors and cables  can  be  used.  The   four  bridge  supporting  angles  that connect  the  three sections of  the  bridge will  actually  go  till  the  bottom  surface of  the   river.

Motivation behind the Mechanical Projects

 The motivation behind this Mechanical projects Presentation Topics is the KATTWYK BRIDGE, HAMSBERG,  GERMANY.   The bridge was built in 1972.  The bridge has a unique  feature  that  it based on the “Vertical Lif t Mechanism”,  its middle (movable)part lifts to give way to the ship passing  by thus  maintaining  waterways transport. Till  now  no bridge such has been built  in India.

Estimated Cost for the Mechanical Projects

  • Labor   charges:   Unskilled:  Rs.800  Skilled: Rs.40
  • Overall cost  of  the  Mechanical projects :  Rs.1484
  • Overhead  cost: Rs 135
  • Total  cost  of   materials  used: Rs. 433
  • Electricity  charges:  Rs. 76  (for  1  Hr.)

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