From MEMS to NEMS： Smart Chips with Senses and Muscles

The last half-century was transformed by the electronic revolution that essentially reproduced the human brain and its computing capacity on a chip. But over time, scientists have realized that something was missing to give life, so to speak, to the small chip with a brain： One needed to awaken its senses and develop its muscles! This challenge was solved through MEMS （micro electro mechanical systems）. Indeed, MEMS today are equipped with the sense of sight, smell, hearing, taste and touch through microsensors. They are also capable of physical exertion through small muscles called microactuators. These new capabilities open wide fields of imagination and important specific applications.

Identification of Probable Type of Bituminous Mix Depending on Higher Combined Index of Stone Aggregates

The stone aggregates constitute over 90% of the mass of a pavement and hence its properties play a very vital role in design of satisfactory unbound as well as bituminous mixes. Also since the aggregates availability is continuously becoming scarce, attempts have to be made to identify use of some marginal as well as inferior aggregates without sacrificing the desirable qualities of resulting mixes. One of the most important properties therefore is the combined index of aggregates which deals with aggregates shape and is the sum of flakiness and elongation indices of aggregates measured related to their thickness and length respectively. The Ministry of Road Transport and Highways （MoRTH） specifies an upper limit of 30% combined index for different types of bituminous mixes. In the field, many times the aggregates supplied have combined index of more than 30%. A pre determined mix such as bituminous concrete, dense or semi dense bituminous macadam etc. has to be designed using such aggregates. It may be easy to design the mix but to get the aggregates with desirable combined index is often very difficult. In order to overcome this, in many projects, secondary crushers are used to crush the aggregates for the second time just to bring down the combined index. This obviously increases the project cost. It is quite possible that the aggregates with higher combined index may be suitable for a particular type of mix. It is therefore appropriate to study what type of a bituminous mix can be satisfactorily prepared depending on the availability of aggregates with generally higher values of combined index. Hence, it was attempted to determine the usual Marshall test properties of two most commonly used types of bituminous mixes namely bituminous concrete and dense bituminous macadam using aggregates with combined index 30%, 40% and 50%. It was found that aggregates with higher combined index are more suitable to design dense bituminous macadam mix than bituminous concrete.

Detection of engineering vehicles in high-resolution monitoring images

This paper presents a novel formulation for detecting objects with articulated rigid bodies from highresolution monitoring images, particularly engineering vehicles. There are many pixels in high-resolution monitoring images, and most of them represent the background. Our method first detects ob ject patches from monitoring images using a coarse detection process. In this phase, we build a descriptor based on histograms of oriented gradient, which contain color frequency information. Then we use a linear support vector machine to rapidly detect many image patches that may contain ob ject parts, with a low false negative rate and a high false positive rate. In the second phase, we apply a refinement classification to determine the patches that actually contain ob jects. In this stage, we increase the size of the image patches so that they include the complete ob ject using models of the ob ject parts.Then an accelerated and improved salient mask is used to improve the performance of the dense scale-invariant feature transform descriptor. The detection process returns the absolute position of positive ob jects in the original images. We have applied our methods to three datasets to demonstrate their effectiveness.