Analog eller digital summering vid mixning av ljud : Blir det någon skillnad i slutresultatet?

I denna uppsats undersöks huruvida en lyssnare kan skilja på två ljudmixar som är mixade på samma sätt men summerade på olika sätt. Den ena summeras analogt och den andra digitalt. Om så är fallet, hur tycker lyssnaren att dessa skillnader yttrar sig? Som metod fick en testpanel i ett blindtest lyssna på två olika mixar, en rocklåt och en jazzlåt, som båda fanns i både analogt och digitalt summerade versioner. Dessa fick testsubjekten sedan besvara några frågor kring och tycka till om dessa olika versioner i en kvalitativ enkätundersökning Det visade sig att skillnader uppfattades av merparten av lyssnarna. Den vanligast förekommande beskrivningen var att det var skillnad på stereobredd, djup och dynamik i mixarna beroende på hur de summerats. Även ord som klarhet, värme och tydlighet förekom. Testpanelen var överlag även väldigt bra på att gissa vilken version som var summerad på vilket sätt. Dessa gissningar verkar i på många fall vara baserad på den förutfattade meningen att analogt borde vara bättre. Det är ganska tydligt att detta forskningsarbete i mångt och mycket är en fallstudie för just de variabler som förekom (märke på den analoga summeraren, ljudkvalité på de inspelade ljudet, programmaterial med mera). Fler tester och undersökningar behövs för att kunna dra generella slutsatser.

Use Multilevel Graph Partitioning Scheme to solve traveling salesman problem

The traveling salesman problem is although looking very simple problem but it is an important combinatorial problem. In this thesis I have tried to find the shortest distance tour in which each city is visited exactly one time and return to the starting city. I have tried to solve traveling salesman problem using multilevel graph partitioning approach.Although traveling salesman problem itself very difficult as this problem is belong to the NP-Complete problems but I have tried my best to solve this problem using multilevel graph partitioning it also belong to the NP-Complete problems. I have solved this thesis by using the k-mean partitioning algorithm which divides the problem into multiple partitions and solving each partition separately and its solution is used to improve the overall tour by applying Lin Kernighan algorithm on it. Through all this I got optimal solution which proofs that solving traveling salesman problem through graph partition scheme is good for this NP-Problem and through this we can solved this intractable problem within few minutes.Keywords: Graph Partitioning Scheme, Traveling Salesman Problem.

Real time video segmentation for recognising paint marks on bad wooden railway sleepers

Wooden railway sleeper inspections in Sweden are currently performed manually by a human operator; such inspections are based on visual analysis. Machine vision based approach has been done to emulate the visual abilities of human operator to enable automation of the process. Through this process bad sleepers are identified, and a spot is marked on it with specific color (blue in the current case) on the rail so that the maintenance operators are able to identify the spot and replace the sleeper. The motive of this thesis is to help the operators to identify those sleepers which are marked by color (spots), using an “Intelligent Vehicle” which is capable of running on the track. Capturing video while running on the track and segmenting the object of interest (spot) through this vehicle; we can automate this work and minimize the human intuitions. The video acquisition process depends on camera position and source light to obtain fine brightness in acquisition, we have tested 4 different types of combinations (camera position and source light) here to record the video and test the validity of proposed method. A sequence of real time rail frames are extracted from these videos and further processing (depending upon the data acquisition process) is done to identify the spots. After identification of spot each frame is divided in to 9 regions to know the particular region where the spot lies to avoid overlapping with noise, and so on. The proposed method will generate the information regarding in which region the spot lies, based on nine regions in each frame. From the generated results we have made some classification regarding data collection techniques, efficiency, time and speed. In this report, extensive experiments using image sequences from particular camera are reported and the experiments were done using intelligent vehicle as well as test vehicle and the results shows that we have achieved 95% success in identifying the spots when we use video as it is, in other method were we can skip some frames in pre-processing to increase the speed of video but the segmentation results we reduced to 85% and the time was very less compared to previous one. This shows the validity of proposed method in identification of spots lying on wooden railway sleepers where we can compromise between time and efficiency to get the desired result.