A Noninvasive Assistant System in Diagnosis of Lumbar Disc Herniation

The purpose of this study is the application of pressure sensors in diagnostics and evaluation of the accuracy diagnostics of lumbar disc herniation at levels L4/L5 and L5/S1 using the aforementioned platform. The motivation behind the idea to apply the pressure measurement platform is the fact that the motor weakness of plantar and dorsal flexia of the feet is one of the absolute indications for the operative treatment of patients with lumbar disc herniation at the indicated levels. In patients, MRI diagnosis of the lumbosacral spine served as the ground truth in the diagnosis of herniation at L4/L5 and L5/S1 levels. The inclusive criteria for the study were the proven muscle weakness based on manual muscle tests performed prior to surgery, after seven days of surgery and after physical therapy. The results obtained with the manual muscular test were compared with the results obtained using our platform. The study included 33 patients who met the inclusion criteria. The results of the measurements indicate that the application of our platform with pressure sensors has the same sensitivity diagnostics as a manual muscle test, when done preoperatively and postoperatively. After physical therapy, pressure sensors show statistically significantly better sensitivity compared to the clinical manual muscle test. The obtained results are encouraging in the sense that the pressure platform can be an additional diagnostic method for lumbar disc herniation detection and can indicate the effectiveness of operative treatment and physical therapy after operation. The main advantage of the system is the cost; the whole system with platform and sensors is not expensive

Prepoznavanje deforestacije digitalnom obradom slike

Deforestacija je proces krčenja ili uklanjanja šuma ili drveća sa određenog područja. Često uključuje seču drveća kako bi se napravio prostor za poljoprivredu, urbanizaciju ili komercijalne svrhe i ima značajne ekološke posledice, uključujući gubitak biodiverziteta, narušavanje ekosistema i povećane emisije gasova sa efektom staklene bašte. U radu će biti predstavljeno kako pomoću satelitskih snimaka prepoznati proces deforestacije sa ciljem da se ukaže na važnost ovog problema. Python programski jezik danas predstavlja osnovu mnogih automatizovanih kompjuterskih procesa. Kao alat, Python može da posluži i za obradu slike u funkciji zaštite životne sredine. Ovakav pristup zahteva kvalitetne izvore podataka i poznavanje materije u oblasti u okviru koje se koristi, kao i poznavanje osnova programiranja. Biće korišćene različite Python biblioteke. Pomoću biblioteka moguće je istaći konkretan prostor gde je povećana deforestacija, a ceo proces se zasniva na prepoznavanju boje piksela na satelitskim snimcima. Snimci se učitavaju u program i kroz kod se proverava da li su zadovoljeni postavljeni uslovi (npr. boja piksela, rezolucija i dr.). Koristeći svestranost i prilagodljivost programskog jezika moguće je sprovesti efi kasno istraživanje uz maksimalnu uštedu vremena i fokus na najvažnije elemente

Implementation of wireless sensor system in rehabilitation after back spine surgery

In this paper, we present a method for determining the mobility of the spinal column using a network of sensors. The sensors consist of accelerometers and gyroscopes, and mutual communication is accomplished using a I2C bus. The main sensor node collects data from all the sensors and sends them to a computer using Bluetooth communication. The collected data is then filtered and converted to the values of the angles that are of interest to quantify the movement. The experimental part of this work method is applied to determine the range of motion of patients in the Clinical Center in Kragujevac

Prepoznavanje deforestacije digitalnom obradom slike

Deforestacija je proces krčenja ili uklanjanja šuma ili drveća sa određenog područja. Često uključuje seču drveća kako bi se napravio prostor za poljoprivredu, urbanizaciju ili komercijalne svrhe i ima značajne ekološke posledice, uključujući gubitak biodiverziteta, narušavanje ekosistema i povećane emisije gasova sa efektom staklene bašte. U radu će biti predstavljeno kako pomoću satelitskih snimaka prepoznati proces deforestacije sa ciljem da se ukaže na važnost ovog problema. Python programski jezik danas predstavlja osnovu mnogih automatizovanih kompjuterskih procesa. Kao alat, Python može da posluži i za obradu slike u funkciji zaštite životne sredine. Ovakav pristup zahteva kvalitetne izvore podataka i poznavanje materije u oblasti u okviru koje se koristi, kao i poznavanje osnova programiranja. Biće korišćene različite Python biblioteke. Pomoću biblioteka moguće je istaći konkretan prostor gde je povećana deforestacija, a ceo proces se zasniva na prepoznavanju boje piksela na satelitskim snimcima. Snimci se učitavaju u program i kroz kod se proverava da li su zadovoljeni postavljeni uslovi (npr. boja piksela, rezolucija i dr.). Koristeći svestranost i prilagodljivost programskog jezika moguće je sprovesti efi kasno istraživanje uz maksimalnu uštedu vremena i fokus na najvažnije elemente

Defectoscopy of direct laser sintered metals by low transmission ultrasonic frequencies

This paper focuses on the improvement of ultrasonic defectoscopy used for machine elements produced by direct laser metal sintering. The direct laser metal sintering process introduces the mixed metal powder and performs its subsequent laser consolidation in a single production step. Mechanical elements manufactured by laser sintering often contain many hollow cells due to weight reduction. The popular pulse echo defectoscopy method employing very high frequencies of several GHz is not successful on these samples. The aim of this paper is to present quadraphonic transmission ultrasound defectoscopy which uses low range frequencies of few tens of kHz. Therefore, the advantage of this method is that it enables defectoscopy for honeycombed materials manufactured by direct laser sintering. This paper presents the results of testing performed on AlSi12 sample. [Projekat Ministarstva nauke Republike Srbije, br. OI 172057

Ubrzanje algoritama veštačke inteligencije sa primenom u prostornom planiranju korišćenjem Huawei ASCEND 310 arhitekture

Kada je reč o veštačkoj inteligenciji (VI) i prostornom planiranju, Huawei Ascend 310 procesor može biti korišćen za ubrzavanje različitih aspekata procesa prostornog planiranja. Korišćenjem VI algoritama, moguće je identifi kovati obrasce, prepoznati objekte, izvršiti klasifi kaciju terena i analizirati prostorne karakteristike.Veštačka inteligencija može se koristiti za predviđanje kretanja i modeliranje različitih elemenata u prostoru. Na primer, može se primeniti za predviđanje saobraćajnog toka, kretanja ljudi ili identifi kaciju potencijalnih lokacija za izgradnju infrastrukture. Ascend 310 procesor pruža mogućnost ubrzane obrade ovih algoritama, čime se omogućava brže i efi kasnije planiranje. U prostornom planiranju, VI može se koristiti za optimizaciju korišćenja resursa poput vode, energije ili zemljišta. Huawei Ascend 310 procesor može ubrzati analizu i optimizaciju resursa, što omogućava bolje upravljanje i racionalno korišćenje prostornih resursa. VI može biti korisna i za simulaciju scenarija i vizualizaciju planiranih prostornih intervencija. Korišćenjem Ascend 310 procesora za ubrzavanje obrade simulacija i vizualizacija, može se dobiti realističan prikaz predloženih planova, što pomaže donosiocima odluka da bolje razumeju i procene implikacije planiranih intervencija. Huawei Ascend 310 je specijalizovani procesor (ASIC) za ubrzanje veštačke inteligencije koji je razvijen od strane kompanije Huawei. ASCEND je arhitektura za ubrzavanje VI koja koristi nisku snagu i visoku propusnost

Object detection in order to determine locations for wildlife crossings

The intensive construction of road infrastructure due to urbanization and industrialization around the world carries with it negative environmental impacts, primarily due to increased emissions of gases, but also due to the separation of natural habitats and ecosystems. In order to overcome this problem, without affecting the mobility of the population, it is necessary to allow wild animals to cross over or below the roads, i.e. to create wildlife crossings, which requires knowledge of the locations where the corridors of animal movements intersect with existing or planned roads. This paper analysis the establishment of a camera system and the application of a deep learning methodology for the automatic identification of animals by species and number, in order to determine locations for the construction of crossings for large wildlife. Also, the paper presents the possibility of using geographic information systems to analyze information obtained by monitoring built wildlife crossings

Acceleration of Image Segmentation Algorithm for (Breast) Mammogram Images Using High-Performance Reconfigurable Dataflow Computers

Image segmentation is one of the most common procedures in medical imaging applications. It is also a very important task in breast cancer detection. Breast cancer detection procedure based on mammography can be divided into several stages. The first stage is the extraction of the region of interest from a breast image, followed by the identification of suspicious mass regions, their classification, and comparison with the existing image database. It is often the case that already existing image databases have large sets of data whose processing requires a lot of time, and thus the acceleration of each of the processing stages in breast cancer detection is a very important issue. In this paper, the implementation of the already existing algorithm for region-of-interest based image segmentation for mammogram images on High-Performance Reconfigurable Dataflow Computers (HPRDCs) is proposed. As a dataflow engine (DFE) of such HPRDC, Maxeler's acceleration card is used. The experiments for examining the acceleration of that algorithm on the Reconfigurable Dataflow Computers (RDCs) are performed with two types of mammogram images with different resolutions. There were, also, several DFE configurations and each of them gave a different acceleration value of algorithm execution. Those acceleration values are presented and experimental results showed good acceleration

Object detection in order to determine locations for wildlife crossings

The intensive construction of road infrastructure due to urbanization and industrialization around the world carries with it negative environmental impacts, primarily due to increased emissions of gases, but also due to the separation of natural habitats and ecosystems. In order to overcome this problem, without affecting the mobility of the population, it is necessary to allow wild animals to cross over or below the roads, i.e. to create wildlife crossings, which requires knowledge of the locations where the corridors of animal movements intersect with existing or planned roads. This paper analysis the establishment of a camera system and the application of a deep learning methodology for the automatic identification of animals by species and number, in order to determine locations for the construction of crossings for large wildlife. Also, the paper presents the possibility of using geographic information systems to analyze information obtained by monitoring built wildlife crossings

Ubrzanje algoritama veštačke inteligencije sa primenom u prostornom planiranju korišćenjem Huawei ASCEND 310 arhitekture

Kada je reč o veštačkoj inteligenciji (VI) i prostornom planiranju, Huawei Ascend 310 procesor može biti korišćen za ubrzavanje različitih aspekata procesa prostornog planiranja. Korišćenjem VI algoritama, moguće je identifi kovati obrasce, prepoznati objekte, izvršiti klasifi kaciju terena i analizirati prostorne karakteristike.Veštačka inteligencija može se koristiti za predviđanje kretanja i modeliranje različitih elemenata u prostoru. Na primer, može se primeniti za predviđanje saobraćajnog toka, kretanja ljudi ili identifi kaciju potencijalnih lokacija za izgradnju infrastrukture. Ascend 310 procesor pruža mogućnost ubrzane obrade ovih algoritama, čime se omogućava brže i efi kasnije planiranje. U prostornom planiranju, VI može se koristiti za optimizaciju korišćenja resursa poput vode, energije ili zemljišta. Huawei Ascend 310 procesor može ubrzati analizu i optimizaciju resursa, što omogućava bolje upravljanje i racionalno korišćenje prostornih resursa. VI može biti korisna i za simulaciju scenarija i vizualizaciju planiranih prostornih intervencija. Korišćenjem Ascend 310 procesora za ubrzavanje obrade simulacija i vizualizacija, može se dobiti realističan prikaz predloženih planova, što pomaže donosiocima odluka da bolje razumeju i procene implikacije planiranih intervencija. Huawei Ascend 310 je specijalizovani procesor (ASIC) za ubrzanje veštačke inteligencije koji je razvijen od strane kompanije Huawei. ASCEND je arhitektura za ubrzavanje VI koja koristi nisku snagu i visoku propusnost