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This paper is about Consultative Committee for Space Data System for Lossless Multispectral and Hyperspectral Image Compression (CCSDS-MHC) algorithm that is implemented on Raspberry Pi 3 Model B+ system using Open Multi-Processing (OpenMP). CCSDS-MHC algorithm along with the full prediction mode is opted due to its best compression ratio (CR) performance. The issue of Hyperspectral Image Compression is the loss of data when compress, thus CCSDS algorithm is used in this research. Besides, with current technologies that require low-power but high-performance devices, Raspberry Pi was chosen to be tested in terms of its performances while being compared to other platforms. AVIRIS (airborne) and Hyperion (spaceborne) are used to test the performance of the system. OpenMP is introduced to simplify the computational operation through parallelization to take advantage of the multi-core architecture of the hardware system. In term of execution time, CCSDS-MHC algorithm when parallelize using OpenMP gave the best performances about 69.4% for AVIRIS 1997 dataset, 69.3% for AVIRIS 2006 dataset and 67.7% for Hyperion dataset. The execution time of performance CCSDS-MHC in Raspberry Pi 3 Model B+ comparing with other different multicore platform is validate and the mean parallelize is measured. The result of comparison proves that faster performing task gives the best result due to higher speed of CPU-cores performances. From this research, it is proven that Raspberry Pi which is a low-power embedded platform able to compress the hyperspectral images at optimized speed with the implementation of OpenMP through CCSDS algorithm. Therefore, this research will be useful for further studies in lossless of hyperspectral images.

Hyperspectral sensors acquire images in many, very narrow, contiguous spectral bands throughout the visible, near-infrared IR, mid-IR and thermal IR portions of the spectrum, thus requiring large data storage on board the satellite and high bandwidth of the downlink transmission channel to ground stations. Image compression techniques are required to compensate for the limitations in terms of on-board storage and communication link bandwidth. In most remote-sensing applications, preservation of the original information is important and urges studies on lossless compression techniques for on-board implementation. This article first reviews hyperspectral spaceborne missions and compression techniques for hyperspectral images used on board satellites. The rest of the article investigates the suitability of the integer Karhunen–Loeve transform KLT for lossless inter-band compression in spaceborne hyperspectral imaging payloads. Clustering and tiling strategies are employed to reduce the computational complexity of the algorithm. The integer KLT performance is evaluated through a comprehensive numerical experimentation using four airborne and four spaceborne hyperspectral datasets. In addition, an implementation of the integer KLT algorithm is ported to an embedded platform including a digital signal processor DSP. The DSP performance results are reported and compared with the desktop implementation. The effects of clustering and tiling techniques on the compression ratio and latency are assessed for both desktop and the DSP implementation.

Clinical glass-slide capturing system is becoming an important part of telemedicine, medical database and diagnostic system that involves of microscope and image acquisition device which is digital camera. One of the areas of interest is slide capturing system for cervical cancer pre-screening program. An automated microscope with FireWire digital camera will be used to acquire the image and store in digital lossless format. The capturing procedure involves of 100X and 400 X magnifications that are necessary for further diagnosis actions. The system that comes with software and hardware parts also provides image retrieval or displaying facilities for ease of user who are pathologist and cytologist to perform the screening program.

Aggregates are one of the main ingredients for producing concrete. In order to produce ‘high performance’ concrete, the properties of aggregates matter most. In particular, shape and surface texture of aggregates immensely influence the strength and structure of the resulting concrete. Often, low quality shape aggregates reduce the durability and strength of the concrete with much more requirement of sand, cement and water. Because the strength of concrete depends on the ratio of well-shaped aggregates to poor shape ones contained in the concrete mixture, the classification process for aggregates into well shaped and poor shaped is an essential step toward high quality concrete. Traditional technique requires aggregate to pass a stringent series of mechanical, chemical and physical test in order to demonstrate that they will perform satisfactorily, and meet or exceed specifications. Such stringent tests, often perform manually, tends to be slow, highly subjective and laborious. Therefore, an intelligent system that can separate the aggregates automatically could help to overcome these problems. This paper discusses an implementation of an intelligent classification system for the aggregate particles using neural network. The features obtained with image analysis, which are area, perimeter, Hu’s moments, Zernike’s moments of the aggregate’s area and perimeter were used as input data for the neural network. The hybrid multilayered perceptron network trained using modified recursive prediction error algorithm is employed to perform the classification. Performance analysis of the hybrid multilayered perceptron network is compared with the standard multilayered perceptron network trained using four different training algorithms, i.e. recursive prediction error, Bayesian Regulazation, Levenberg–Marquardt and scale conjugate gradient. The experimental results show that the performance for hybrid multilayered perceptron and multilayered perceptron networks are significantly better using the moments extracted based on object’s perimeter compared to object’s area. Model comparison confirms that the selected features combined with hybrid multilayered perceptron network attains the best performance in aggregates recognition.

The Integer KLT algorithm is an approximation of the Karhunen-Loeve Transform that can be used as a lossless spectral decorrelator. This paper addresses the application of the Integer KLT to lossless compression of hyperspectral satellite imagery. Design space exploration is carried out to investigate the impact of tiling and clustering techniques on the compression ratio and execution time of Integer KLT. AVIRIS hyperspectral images are used as test image data and the spatial compression is carried out with JPEG2000. The results show that clustering canspeed up the execution process and can increase the compression performance.

The Ziehl-Neelsen stain, also known as the acid-fast stain, was first described by two German doctors; Franz Ziehl (1859 to 1926), a bacteriologist and Friedrich Neelsen (1854 to 1894), a pathologist.It is a special bacteriological stain used to identify acid-fast Mycobacteria. The preparation of Ziehl-Neelsen slides require several procedures and the slide should be examined under oil immersion by using the 100X magnification. There are some factors that may degrade the image quality such as under expose, over expose, under stain and thick samples. Thus, the images should be enhanced to improve the image quality in term of contrast and intensity. This paper proposes a contrast enhancement technique based on bright stretching and dark stretching algorithms for color images. Although the adopted image processing technique is quite simple, the results indicate that our method may have some potential to be used for improving the quality of Ziehl-Neelsen slide images.