Enerzi Microwave Systems: Growth Strategies in Emerging Market

In April 2018, Enerzi Microwave Systems Pvt. Ltd. (EMSPL), a company established by Dr Prakash, was selected as one of the best companies in the medium, small and micro enterprise category. Dr Prakash was very proud of his company’s achievements, but at the same time, he was anxious about its growth in the future. EMSPL, which started in a 200-sq ft space in Bengaluru, is now one of the leading industrial microwave heating systems suppliers. However, Dr Prakash is now facing a challenging situation—to find a path that can provide rapid growth to the company. There are multiple questions in front of Dr Prakash. Should EMSPL expand into different market segments or remain focussed on the current segment? Should EMSPL invest in new product development? His team has suggested four options that can help Dr Prakash make a decision. Dr Prakash needs to find the best possible option, which can provide rapid growth to the company.

A Dynamic Bayesian Network-Based Real-Time Crash Prediction Model for Urban Elevated Expressway

Traffic crash is a complex phenomenon that involves coupling interdependency among multiple influencing factors. Considering that interdependency is critical for predicting crash risk accurately and contributes to revealing the underlying mechanism of crash occurrence as well, the present study attempts to build a Real-Time Crash Prediction Model (RTCPM) for urban elevated expressway accounting for the dynamicity and coupling interdependency among traffic flow characteristics before crash occurrence and identify the most probable risk propagation path and the most significant contributors to crash risk. In this study, Dynamic Bayesian Network (DBN) was the framework of the RTCPM. Random Forest (RF) method was employed to identify the most important variables, which were used to build DBN-based RTCPMs. The PC algorithm combined with expert experience was further applied to investigate the coupling interdependency among traffic flow characteristics in the DBN model. A comparative analysis among the improved DBN-based RTCPM considering the interdependency, the original DBN-based RTCPM without considering the interdependency, and Multilayer Perceptron (MLP) was conducted. Besides, the sensitivity and strength of influences analyses were utilized to identify the most probable risk propagation path and the most significant contributors to crash risk. The results showed that the improved DBN-based RTCPM had better prediction performance than the original DBN-based RTCPM and the MLP based RTCPM. The most probable risk influencing path was identified as follows: speed on current segment (V) (time slice 2)⟶V (time slice 1)⟶speed on upstream segment (U_V) (time slice 1)⟶Traffic Performance Index (TPI) (time slice 1)⟶crash risk on current segment. The most sensitive contributor to crash risk in this path was V (time slice 2), followed by TPI (time slice 1), V (time slice 1), and U_V (time slice 1). These results indicate that the improved DBN-based RTCPM has the potential to predict crashes in real time for urban elevated expressway. Besides, it contributes to revealing the underlying mechanism of crash and formulating the real-time risk control measures.

A Detailed Description on Unsupervised Heterogeneous Anomaly Based Intrusion Detection Framework

Observing network traffic flow for anomalies is a common method in Intrusion Detection. More effort has been taken in utilizing the data mining and machine learning algorithms to construct anomaly based intrusion detection systems, but the dependency on the learned models that were built based on earlier network behaviour still exists, which restricts those methods in detecting new or unknown intrusions. Consequently, this investigation proposes a structure to identify an extensive variety of abnormalities by analysing heterogeneous logs, without utilizing either a prepared model of system transactions or the attributes of anomalies. To accomplish this, a current segment (clustering) has been used and a few new parts (filtering, aggregating and feature analysis) have been presented. Several logs from multiple sources are used as input and this data are processed by all the modules of the framework. As each segment is instrumented for a particular undertaking towards a definitive objective, the commitment of each segment towards abnormality recognition is estimated with various execution measurements. Ultimately, the framework is able to detect a broad range of intrusions exist in the logs without using either the attack knowledge or the traffic behavioural models. The result achieved shows the direction or pathway to design anomaly detectors that can utilize raw traffic logs collected from heterogeneous sources on the network monitored and correlate the events across the logs to detect intrusions.

Velocity Connection Algorithm of Arbitrary Multi-Axis Linkage

In this paper, a velocity connection algorithm of arbitrary multi-axis linkage is presented with regard to the problem of velocity connection efficiency of multiple straight-line paths in the motion control system. For this algorithm, with the velocity, acceleration and displacement of the current segment and the previous segment as constraint conditions, the acceleration is not set as a constant but the intersegmental velocity variable is set independently. The initial velocity and final velocity of each segment is solved on this basis, improving the movement efficiency and simplifying the calculation. Therefore, this algorithm is particularly suitable for the high-speed movement mechanism driven by a stepper motor. As for the actual motion track of the rapid prototyping machine, a comparison was made between this algorithm and the traditional velocity connection algorithm to verify the effectiveness of this algorithm.

Brest Union in its prehistory and achievements for Ukrainians

At the current segment of the life story, we are throwing more and more new problems, without which solutions can not be advanced on the way to a dysfunctional rule of law, with the attributive, integral features of which will be national prosperity and democracy, humanity and economic prosperity. Unfortunately, these problems are not diminished, and one of the reasons is that we often try to solve them from the standpoint of ignorance - not rising above desire and emotions, interest and strength

Inflection Points Processing of Continuous Tiny Line Segments for CNC System High-Speed Machining

Overcut or speed impact exists in high-speed interpolation of consecutive tiny segments. Because the remaining distance of the current segment does not exactly equal to an interpolation cycle required distance. Taking processing accuracy as a precondition, a new inflection point processing method is proposed to solve this problem. With different solutions of different angles, this method realizes the tiny line continuous processing with high processing quality. And this method needs a small amount of calculation with strong real-time. Error analysis is done and it can meet machining accuracy requirements. Now this method has been applied to engineering practice.

Image Segmentation

This chapter introduces image segment techniques. These techniques, including pixel- based approaches, region-based approaches, classification techniques, deformable model algorithms, artificial neural network approaches and texture-based algorithms are detailed. Color image segmentations and 3D image segment methods are briefly introduced. With developments of computer and medical imaging techniques, there is an increase in demand of new segment algorithms for developing or upgrading medical image systems. Therefore, the author hopes that this chapter not only details the current segment techniques, but also assists researchers in quickly selecting their research directions under their applications, imaging modality, image features and other factors.