Middle Cranial Fossa Approach: The Incudomalleolar Joint as a Reliable Landmark

Introduction The middle cranial fossa approach is performed by fewer neurotologists owing to a reduced number of indications. Consistent landmarks are mandatory to guide the surgeon in a narrow field. Objectives We have evaluated the incus and malleus head and the incudomalleal joint (IMJ) as a key landmark for identifying the superior semicircular canal (SSC) and to get oriented along the floor of the middle cranial fossa. Methods A combination of 20 temporal bone dissections and CT imaging were utilized to test and describe these landmarks. Results The blue line of the SSC is consistently identified along the prolongation of a virtual line through the IMJ and the angulation toward the root of zygoma. The mean distance from the zygoma toward the IMJ ranged from 1.60 to 1.90cm. Once the IMJ was identified, the blue line of the SSC was consistently found along the virtual line through the IMJ within 5 to 9mm. Conclusions The IMJ is a safe and consistent anatomical marker in the surgical approach to the middle cranial fossa floor. Opening the tegmen 1.5 to 2cm medial to the root of the zygoma and identifying the joint allows to trace a virtual line toward the SSC within 5 to 9mm. Knowledge of the close relationship between the direction of the IMJ and the superior canal can be used in all transtemporal approaches, thus orienting the surgeon in a rather narrow field with limited retraction of the dura and brain.

Virtual Line Shafting-Based Total-Amount Coordinated Control of Multi-Motor Traction Power

This paper investigates a virtual line shafting-based total-amount coordinated control method of multi-motor traction power to solve the traffic safety problem caused by train traction power loss. This method considers the total amount instead of the synchronous control amongst single motors in a multi-motor control system. Firstly, a block diagram of the proposed method is built. Secondly, on the basis of this diagram, an accurate system model with parameter perturbations is constructed. Thirdly, a virtual controller is designed to quickly adjust the output torque of the virtual motor and to realise a tracking control of the reference torque. A total-amount coordinated control strategy based on the integral sliding mode is also designed to keep the total traction power of the multi-motor system constant under uncertain and unknown disturbances. Lyapunov stability theory is used to prove the system stability. The simulation and experiment results verify the effectiveness of the virtual controller and the total-amount coordinated control strategy in guaranteeing system robustness under disturbances and parameter perturbations.

VOLUME TENSOR ESTIMATION USING A VIRTUAL LINE GRID: STUDY OF A DEVELOPING PHEASANT BRAIN

The volume tensor provides a robust estimate of the shape and orientation of an object in space. In this paper, we introduce Fakir method for estimating the tensor of an object in 3D data set based on the intersections of objects boundary with virtual lines. We calculate the precision of shape estimates by predicting the variance of estimators of integrals based on systematic sampling. To demonstrate the ability of the Fakir method, we measure changes in shape and orientation of compartments in the pheasant brain during development.

Sistem Deteksi Jumlah, Jenis dan Kecepatan Kendaraan Menggunakan Analisa Blob Berbasis Raspberry Pi

<p class="Abstrak">Penghitungan kondisi lalu lintas guna analisa kualitas jalan raya umumnya dilakukan secara manual. Hal ini tentunya membutuhkan biaya dan SDM yang tinggi serta tidak dapat dianalisa secara langsung. Dalam penelitian ini telah dikembangkan metode pengenalan jenis, jumlah dan kecepatan kendaraan secara otomatis menggunakan pengolahan citra digital. Metode yang berdasarkan analisa terhadap <em>BLOB (Binary Large OBject) </em>tersebut ditanamkan pada sistem berbasis <em>Raspberry Pi. </em>Setiap blob merupakan <em>connected-component </em>yang diperoleh dari proses <em>thresholding</em> terhadap perubahan nilai pixel dari sebuah frame dan frame rujukan dalam metode <em>background subtraction</em>. Jenis kendaraan ditentukan oleh jumlah piksel dalam <em>bounding-box</em> setiap <em>blob</em>. Jumlah kendaraan yang melaju dihitung dengan memberikan garis virtual dimana jumlahnya akan bertambah jika <em>centroid</em> dari setiap <em>bounding-box</em> kendaraan melewatinya. Kecepatan kendaraan dihitung dengan membagi jarak sebenarnya dari koordinat awal hingga garis virtual sepanjang 12 meter yang dibagi dengan waktu <em>centroid </em>tersebut untuk menempuhnya. Algoritma tersebut diimplementasikan pada sistem berbasis <em>Raspberry Pi</em> dengan input kamera yang terhubung dengan <em>serial monitor</em> untuk menampilkan output penghitungan. Pengujian akurasi deteksi jenis kendaraan yakni sepeda motor, kendaraan ringan dan berat menghasilkan akurasi 93,39%. Pengujian jumlah kendaraan menghasilkan rata-rata akurasi 93,48% untuk semua jenis kendaraan. Pengujian laju kendaraan yang dideteksi dengan dibandingkan kecepatan pada spedometer kendaraan menunjukkan akurasi 93,9%.</p><p class="Abstrak"> </p><p class="Judul2"><em><strong>Abstract</strong></em></p><p class="Abstract">An analysis on traffic condition usually carried out manually by visual observation. This method demands high human resource and cannot be analysed immediately. This paper present an algorithm to analyse type, number and speed of vehicles that passing by a road automatically using BLOB (Binary Large Object) analysis. Each blob is a connected-component as a result of thresholding after background subtration process. Type of vehicles was determined by measuring pixel number of blob’s bounding box. Number of vehicles was determined by drawing virtual line where the number was increased once a centroid of bounding box passed it. Speed of vehicles was determined using basic speed formula where 12 meters of actual distance between the beginning coordinate and virtual line was divided by time to travel it. The algorithm was embedded in Raspberry Pi where videos were acquired using attached web camera. The analysis result was shown in connected serial monitor. Testing on vehicles’ type detection (motorcycle, light vehicle, heavy vehicle) result accuracy of 93.9%, number of vehicles result accuracy of 93.48%, whilst speed of vehicles result accuracy of 93.9%.</p>