An Interaction-Based Bayesian Network Framework for Surgical Workflow Segmentation

Recognizing and segmenting surgical workflow is important for assessing surgical skills as well as hospital effectiveness, and plays a crucial role in maintaining and improving surgical and healthcare systems. Most evidence supporting this remains signal-, video-, and/or image-based. Furthermore, casual evidence of the interaction between surgical staff remains challenging to gather and is largely absent. Here, we collected the real-time movement data of the surgical staff during a neurosurgery to explore cooperation networks among different surgical roles, namely surgeon, assistant nurse, scrub nurse, and anesthetist, and to segment surgical workflows to further assess surgical effectiveness. We installed a zone position system (ZPS) in an operating room (OR) to effectively record high-frequency high-resolution movements of all surgical staff. Measuring individual interactions in a closed, small area is difficult, and surgical workflow classification has uncertainties associated with the surgical staff in terms of their varied training and operation skills, patients in terms of their initial states and biological differences, and surgical procedures in terms of their complexities. We proposed an interaction-based framework to recognize the surgical workflow and integrated a Bayesian network (BN) to solve the uncertainty issues. Our results suggest that the proposed BN method demonstrates good performance with a high accuracy of 70%. Furthermore, it semantically explains the interaction and cooperation among surgical staff.

PENGARUH PENSAKLARAN VIDEO OTOMATIS (Video Automatic Switch Effect)

Rangkaian pengendali kamera pengawas ini berdasarkan prinsip kerja rangkaian yang tersusun atas IC NE 555 dan IC 7401. IC ini akan dirangkai menjadi sebuah rangkaian pengendali yang berfungsi sebagai saklar pengatur isyarat masukan dari beberapa kamera digital multivideo (CMOS Color Digital Camera). Isyarat masukan yang berupa isyarat gambar (Signal Video) inilah yang akan diatur dari beberapa kamera yang dioperasikan secara bergantian.Rangkaian pensaklaran gambar otomatis merupakan rangkaian utama pada rangkaian pensaklaran gambar yang tersusun atas IC NE 555 sebagai pewaktu (Timer) dan IC 4017 sebagai pencatat pengkodean (Decoder Counter) yang merupakan otak pada penggerak utama (Device Driver), yang dapat mengendalikan beberapa keluaran sesuai dengan masukan. IC 4017 akan membagi keluaran dengan sepuluh bagian yang sama besar untuk melakukan proses pensaklaran. Kesepuluh keluaran IC 4017 ini berbentuk isyarat digital sebagai bias basis transisitor switch, sehingga tegangan dan arus akan melewati emiter ke kolektor.Rangkaian pencacah yang ada tersusun atas komponen IC digital yang dalam operasinya membutuhkan waktu tunda dari cacahan pertama ke cacahan berikutnya. Waktu tunda untuk CMOS biasanya cukup lama karena impendans keluarannya yang tinggi. Kata Kunci : Pensaklaran Video, Isyarat Digital, Waktu Tunda

The Design of Embedded System Based on DaVinci DSP6467

For application requirements of smart multimedia system, the paper design and implements system application framework and describes the function of each software module in the whole system. After description of the overall framework,paper analyze detailed implementation of several key software modules,that is supports SD and HD video signal, video capture module, object intrusion alarm motion detection module, audio and video multimedia processing synchronization mechanism, and web-based remote control module. And the article describes the main features of the DaVinci technology and DaVinci technology platform development, elaborated the DSP6467 DaVinci platform hardware structure which used in this paper.

A Video Semantic Mining Scheme

In this paper, we present a method for video semantic mining. Speech signal, video caption text and video frame images are all key factors for a person to understand the video content. Through above observation, we bring forward a method which integrating continuous speech recognition, video caption text recognition and object recognition. The video is firstly segmented to a serial of shots by shot detection. Then the caption text and speech recognition results are treated as two paragraphs of text. The object recognition results are presented by bag of words. The above three aspects of texts are processed by part of speech and stemming. Then only the noun words are kept. At last a video is represented by three bags of words. The words are further depicted as a graph. The graph vertices stand for the words and the edges denote the semantic distance between two neighboring words. In the last step, we apply the dense sub graph finding method to mine the video semantic meaning. Experiments show that our video semantic mining method is efficient.

Erratum

There was an error in Mr Richard Trim's paper, ‘Some causes of problems in the observation of standard racon marine beacons when observed by means of standard marine navigation radars’, which was published in the May 1993 issue of the Journal of Navigation. Section 3, paragraph 4 of page 276 should read:‘A third and very important cause of radar received-signal differentiation arises if a widely used form of automatic anti-sea-clutter processing is employed, since part of this processing is to differentiate the radar-received video so as to remove the d.c. term in the sea clutter echoes as part of the Constant False Alarm Rate (CFAR) processing. When such automatic sea clutter supression facilities are in operation, the gain level applied to the radar receiver video amplifier has an adaptive signal superimposed upon it which, while slow acting, generally follows the shape of the clutter returns on the received signal video, while being largely unaffected by the wanted echo returns such as those from ships, navigation marks, coastlines, etc. This effect may be reduced in the case of the very latest radar designs’.