Reinforcing the role of the conventional C-arm - a novel method for simplified distal interlocking

<p>Abstract</p> <p>Background</p> <p>The common practice for insertion of distal locking screws of intramedullary nails is a freehand technique under fluoroscopic control. The process is technically demanding, time-consuming and afflicted to considerable radiation exposure of the patient and the surgical personnel. A new concept is introduced utilizing information from within conventional radiographic images to help accurately guide the surgeon to place the interlocking bolt into the interlocking hole. The newly developed technique was compared to conventional freehand in an operating room (OR) like setting on human cadaveric lower legs in terms of operating time and radiation exposure.</p> <p>Methods</p> <p>The proposed concept (guided freehand), generally based on the freehand gold standard, additionally guides the surgeon by means of visible landmarks projected into the C-arm image. A computer program plans the correct drilling trajectory by processing the lens-shaped hole projections of the interlocking holes from a single image. Holes can be drilled by visually aligning the drill to the planned trajectory. Besides a conventional C-arm, no additional tracking or navigation equipment is required.</p> <p>Ten fresh frozen human below-knee specimens were instrumented with an Expert Tibial Nail (Synthes GmbH, Switzerland). The implants were distally locked by performing the newly proposed technique as well as the conventional freehand technique on each specimen. An orthopedic resident surgeon inserted four distal screws per procedure. Operating time, number of images and radiation time were recorded and statistically compared between interlocking techniques using non-parametric tests.</p> <p>Results</p> <p>A 58% reduction in number of taken images per screw was found for the guided freehand technique (7.4 ± 3.4) (mean ± SD) compared to the freehand technique (17.6 ± 10.3) (<it>p </it>< 0.001). Total radiation time (all 4 screws) was 55% lower for the guided freehand technique compared to conventional freehand (<it>p </it>= 0.001). Operating time per screw (from first shot to screw tightened) was on average 22% reduced by guided freehand (<it>p </it>= 0.018).</p> <p>Conclusions</p> <p>In an experimental setting, the newly developed guided freehand technique for distal interlocking has proven to markedly reduce radiation exposure when compared to the conventional freehand technique. The method utilizes established clinical workflows and does not require cost intensive add-on devices or extensive training. The underlying principle carries potential to assist implant positioning in numerous other applications within orthopedics and trauma from screw insertions to placement of plates, nails or prostheses.</p

A novel framework for intelligent surveillance system based on abnormal human activity detection in academic environments

Abnormal activity detection plays a crucial role in surveillance applications, and a surveillance system thatcan perform robustly in an academic environment has become an urgent need. In this paper, we propose a novel framework for an automatic real-time video-based surveillance system which can simultaneously perform the tracking, semantic scene learning, and abnormality detection in an academic environment. To develop our system, we have divided the work into three phases: preprocessing phase, abnormal human activity detection phase, and content-based image retrieval phase. For motion object detection, we used the temporal-differencing algorithm and then located the motions region using the Gaussian function.Furthermore, the shape model based on OMEGA equation was used as a filter for the detected objects (i.e.,human and non-human). For object activities analysis, we evaluated and analyzed the human activities of the detected objects. We classified the human activities into two groups:normal activities and abnormal activities based on the support vector machine. The machine then provides an automatic warning in case of abnormal human activities. It also embeds a method to retrieve the detected object from the database for object recognition and identification using content-based image retrieval.Finally,a software-based simulation using MATLAB was performed and the results of the conducted experiments showed an excellent surveillance system that can simultaneously perform the tracking, semantic scene learning, and abnormality detection in an academic environment with no human intervention

LoRa-LiSK: a lightweight shared secret key generation scheme for LoRa Networks

Physical layer security (PLS) schemes use the randomness of the channel parameters, namely, channel state information (CSI) and received signal strength indicator (RSSI), to generate the secret keys. There has been limited work in PLS schemes in long-range (LoRa) wide area networks (Lo- RaWANs), hindering their widespread application. Limitations observed in existing studies include the requirement of having a high correlation between channel parameter measurements and the evaluation in either fully indoor or outdoor environments. The real-world wireless sensor networks (WSNs) and LoRa use cases might not meet both requirements, thus making the current PLS schemes inappropriate for these systems. This paper proposes LoRA-LiSK, a practical and efficient shared secret key generation scheme for LoRa networks to address the limitations of existing PLS schemes. Our proposed LoRa-LiSK scheme consists of several preprocessing techniques (timestamp matching, two sample Kolmogorov Smirnov tests, and a Savitzky- Golay filter), multi-level quantization, information reconciliation using Bose-Chaudhuri-Hocquenghem (BCH) codes, and finally, privacy amplification using secure hash algorithm SHA-2. The LoRa-LiSK scheme is extensively evaluated on real WSN/IoT devices in practical application scenarios: 1) indoor to outdoor and 2) long range static and mobile outdoor links. It outperforms existing schemes by generating keys with channel parameter measurements of low correlation values (0:2 to 0:6) while still achieving high key generation rates and low key disagreement rates (10%–20%). The scheme updates a key in one hour approximately using an application profile with a high transmission rate compared to three hours reported by existing works while still respecting the duty cycle regulation. It also incurs less communication overhead compared to the existing works

Action-02MCF : A robust space-time correlation filter for action recognition in clutter and adverse lighting conditions

Human actions are spatio-temporal visual events and recognizing human actions in different conditions is still a challenging computer vision problem. In this paper, we introduce a robust feature based space-time correlation filter, called Action-02MCF (0’zero-aliasing’ 2M’ Maximum Margin’) for recognizing human actions in video sequences. This filter combines (i) the sparsity of spatio-temporal feature space, (ii) generalization of maximum margin criteria, (iii) enhanced aliasing free localization performance of correlation filtering using (iv) rich context of maximally stable space-time interest points into a single classifier. Its rich multi-objective function provides robustness, generalization and recognition as a single package. Action-02MCF can simultaneously localize and classify actions of interest even in clutter and adverse imaging conditions. We evaluate the performance of our proposed filter for challenging human action datasets. Experimental results verify the performance potential of our action-filter compared to other correlation filtering based action recognition approaches. © Springer International Publishing AG 2016.Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics