Vehicular Ad Hoc Network (VANET) has materialized as a major research area for the last decade. Since the vehicular network does not have stable infrastructure the main focus remains on networking part, which is divided into clusters using clustering techniques to increase scalability of the network. This paper is a survey on heterogeneous approaches for a cluster based routing protocol that is used to make stable topology of unsteady vehicular networks.
Vehicular Ad hoc Network (VANET) is a new way of communication which includes communication betweenvehicles moving at high speeds on the roads. VANET developed several new applications like traffic engineering, traffic management, provides emergency information to avoid accidents and other user applications. VANETs are similar to Mobile Ad hoc Networks (MANETs) but with different characteristics like, movement at high speeds, mobility, sufficient storage and processing power, unpredictable node density and difficult communication environment with short link lifetime etc. So for testing of any protocol for VANET needs realistic enviorment. In this paper, we create realistic environment for the performance analysis of proactive and reactive routing protocol in city scenarios is presented. The protocols evaluated are Ad hoc On demand Distance Vector (AODV), Ad-hoc On-demand Multipath Distance Vector Routing (AOMDV) and Destination Sequence Distance Vector (DSDV). In simulations, maps are used to model topology using traffic simulator and then vehicles with different parameter like length, maximum speed and acceleration etc. run on these topologies. The performance evaluation matrices used in this paper are packet delivery ratio and End to End Delay.
Depression is a most common severe mental disturbance health disorder causing high societal costs. In clinical practice rating for depression depends almost on self questionnaires and clinical patient history report opinion. In recent years, the awareness has generated for automatic detection of depression from the speech signal. Some queries are raised that which features are more responsible for depression from speech and which classifiers gives good results. By identifying proper features from speech signal system even one can save the life of a patient. In this paper, a survey of speech signal features which relates for depression analysis is presented. Specially focused on adolescence speech. After surveying it is hypothesized that many speech features are there which are responsible for depression like linear features Prosodic, cepstral, spectral and glottal features and non-linear feature Teager energy operator (TEO). Some classification methods for depression analysis from previous studies are summarized.
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